MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123122 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy Engineering College For Womens; Malla Reddy College Of Engineering And Technology; and Dr. J. Anitha, Medchal-Malkajgiri, Telangana, on Dec. 6, 2025, for 'ai powered smart notification filtering system based on user behavior.'
Inventor(s) include Dr. J. Anitha; Mr. Valle Shyam Kumar; Dr. Ekta Maini; Dr. Nalli Vinaya Kumari; Bala Veeravatnam; and K. Narsimhulu.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "Digital devices come in a constantly-alert state and many of our devices interrupt important things from getting done and lower the quality of our attention overall. Manual notification settings are often inadequate because user preferences vary due to differences in activity, location and the time of day. The system proposed herein proposed an adaptive notification filtering framework which analyses the individual behaviour pattern to determine which alerts need to be given more attention immediately, and which alerts can be deferred without affecting the raw's goals. The framework is aware of factors such as how a user most often interacts with various applications such as when and how long they can interact with an application, how the user will respond to different categories of notification, and how their daily routine changes. Contextual elements such as ambient environment, currently active tasks, connected device status and scheduled events are also guiding the system's decisions. When a new notification arrives, an internal scoring model analyzes how relevant it is based upon the relevance of current conditions, in comparison with those in the past. Notifications with a higher importance are delivered instantly but low priority alerts can be grouped, muted, delayed, or silently archived. The system is therefore constantly adjusting the boundaries of its filtering, based on the course of behavior, so that it remains true even if habits change. This allows for less unnecessary interruptions and makes certain that important alerts are getting to the user without delay. The approach is suitable for mobile platforms, desktop systems, wearable devices, and IoT-based communication environments and presented a practical solution for management of digital distractions in professional, academic and everyday settings."
Disclaimer: Curated by HT Syndication.
