MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202641115488 A) filed by Dr. Mamatha C M; G Yashaswini; and Cambridge Institute Of Technology North Campus on September 26, 2026, for Foodzie – Smart Food Safety & Nutrition Companion..
Inventors include Dr. Mamatha C M; G Yashaswini; and Cambridge Institute Of Technology North Campus.
The application for the patent was published on October 02, 2026, under issue no. 40/2026.
Abstract: The growing consumption of packaged and processed foods has made food safety and nutrition awareness increasingly important for consumers. However, understanding food labels is not always easy. Ingredient lists can be lengthy and difficult to interpret, allergen information may vary between products, and nutritional claims on packaging can sometimes be confusing or difficult for consumers to verify. This becomes an even greater challenge for people with food allergies, dietary restrictions, or specific health concerns, who need to make careful decisions about the products they consume. Although several mobile and web applications are available for food analysis, most address only specific aspects of the problem. Some applications focus mainly on calorie tracking, while others provide barcode-based nutrition scores. Few platforms bring together barcode scanning, optical character recognition (OCR), ingredient analysis, allergen identification, and personalized health guidance in one integrated system. To address this gap, Foodzie is proposed as a Smart Food Safety and Nutrition Companion, an AI-powered web application designed to help users understand packaged food products quickly and easily. Users can either scan a product barcode or capture an image of its food label. The system analyzes the available product information and presents an easy-to-understand summary of ingredients, potential allergens, nutritional composition, certifications, and possible health considerations. Based on the user's health profile and dietary preferences, the system can also provide personalized guidance and suggest comparatively healthier alternatives. Foodzie uses React.js, Tailwind CSS, and Framer Motion to provide an interactive user interface, while FastAPI (Python) serves as the backend application layer and PostgreSQL manages structured product and user data. QuaggaJS is used for barcode recognition, and Tesseract.js performs OCR to extract text from food labels. Google Gemini and OpenAI APIs support ingredient interpretation, allergen risk analysis, multilingual translation, and personalized recommendations. Firebase Authentication is used to provide secure user account management. The application is organized into five core modules: Home, Scanner, OCR & Translator, Dashboard, and Health Profile. Together, these modules provide a centralized platform that helps users understand food products more clearly and make more informed food choices based on their individual dietary and health-related needs.
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