MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202641106027 A) filed by Saveetha Institute Of Medical And Technical Sciences on September 03, 2026, for Smart Mood Based Music Recommendation System.
Inventors include Arshath Ali A; Dr. J. Mohemed Yasin; Dr. S. Jothi Arunachalam; and Dr Ramya Mohan.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: In the modern lifestyle, many people face difficulties in selecting suitable music due to busy schedules, lack of awareness about music choices, and the overwhelming availability of songs across different platforms. Music often fails to match the listener's emotional state not because of poor quality, but due to improper selection, mood mismatch, and lack of personalized recommendations. To overcome these challenges, the Smart Mood-Based Music Recommendation System is proposed as an intelligent music support system that uses artificial intelligence and machine learning techniques to analyze user emotions and provide music suggestions in real time. The system aims to replace traditional manual music selection methods with a smart, efficient, and personalized recommendation approach. The Smart Mood-Based Music Recommendation System works by collecting and analyzing important user inputs such as facial expressions, voice tone, and interaction behavior to determine the current emotional state of the user. These inputs are processed using machine learning algorithms to classify emotions like happy, sad, relaxed, or stressed. The processed data is then used to recommend suitable songs from a music database. The recommendations are delivered through a mobile or web application, allowing users to instantly access music that matches their mood. Based on these suggestions, users can enjoy a more personalized and engaging listening experience. The system continuously monitors user preferences and provides timely updates, ensuring better accuracy even when the user's mood changes. Unlike conventional music selection methods that depend on manual searching and predefined playlists, this system provides accurate real-time recommendations and automated suggestions, reducing user effort and improving listening experience. It helps users discover relevant songs easily, avoids mood mismatch, and enhances emotional satisfaction. By promoting personalized music recommendations and reducing the risk of irrelevant song selection, the Smart Mood-Based Music Recommendation System improves user engagement and supports intelligent music listening practices. This project highlights the role of AI-based technologies in transforming traditional music listening into a more reliable, efficient, and user-friendly experience.
Disclaimer: Curated by HT Syndication.
