Intellectual Property India Publishes Patent Application for Ayurscan: An Ai-Based Multi-Feature Health Diagnosis And Personalized Ayurvedic Recommendation System Using Facial And Skin Image Analysis

MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641106658 A) filed by Mahalakshmi G; Dayananda Sagar Academy Of Technology And Management; Shivani Manjunath Gouda; Pooja Sunilkumar Kataraki; and Harika Gonuguntla on September 04, 2026, for Ayurscan: An Ai-Based Multi-Feature Health Diagnosis And Personalized Ayurvedic Recommendation System Using Facial And Skin Image Analysis.

Inventors include Mahalakshmi G; Dayananda Sagar Academy Of Technology And Management; Shivani Manjunath Gouda; Pooja Sunilkumar Kataraki; and Harika Gonuguntla.

The application for the patent was published on September 11, 2026, under issue no. 37/2026.

Abstract: AyurScan is an artificial-intelligence-based system for health diagnosis and personalized treatment recommendation using facial and skin images. It addresses the limitation of existing single-modality diagnostic systems that rely solely on deep-learning features and provide only a disease label without further guidance. The system employs a multi-branch feature-extraction architecture wherein a Convolutional Neural Network (CNN) extracts deep visual features, a Gray-Level Co- occurrence Matrix (GLCM) module extracts texture features, and an HSV colour-model module extracts colour features that account for variation in skin tone. The extracted features are combined through a fusion layer to produce a unified representation of the input image, which is passed to a classification module for disease prediction. Unlike conventional systems that stop at diagnosis, AyurScan further incorporates an Ayurvedic recommendation module that maps the diagnosed condition and detected skin characteristics to personalized traditional remedy suggestions. The architecture is designed to be extensible to additional anatomical regions, including the eyes, mouth, and nails, enabling future multi-region correlated diagnosis. The invention improves diagnostic accuracy through feature fusion, improves personalization through skin-tone-aware analysis, and improves real-world clinical usefulness by pairing diagnosis with actionable, traditional-medicine-based treatment guidance.

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