MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118909 A) filed by Mlr Institute Of Technology; and Marri Laxman Reddy Institute Of Technology And Management on October 05, 2026, for A System And Method For Mood-Based Personalised Tourism Recommendation Using Artificial Intelligence.
Inventors include Mrs. Tahneyath Ahmed; Ms. Kamble Navyasri; Mr. Redammagari Purushotham Reddy; and Mr. Tamtam Avanindra.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: The present invention relates to an artificial intelligence-based system and method for generating personalised tourism recommendations based on a user's mood, geographical location, and contextual travel parameters. The system receives user input comprising an explicitly specified mood or determines a mood from user- provided behavioural and textual information using artificial intelligence and sentiment analysis techniques. Based on the determined mood, the system identifies corresponding tourism categories and retrieves geographically relevant points of interest using real-time geolocation data and digital mapping services, including OpenStreetMap. The system processes the retrieved points of interest based on one or more parameters comprising geographical distance, estimated travel time, accessibility, operating conditions, weather conditions, time of day, and crowd conditions. An artificial intelligence-based recommendation module ranks the identified points of interest and generates a personalised itinerary comprising selected destinations, route information, and time allocation. The system further receives user interactions and feedback and updates recommendation parameters for subsequent recommendations. A user interface implemented through a mobile application or web application provides the recommended destinations, itinerary, interactive map information, and navigation-related information. The invention thereby provides an automated mechanism for generating context-aware tourism recommendations using mood, location, and dynamic travel-related parameters.
Disclaimer: Curated by HT Syndication.
