MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202621051642 A) filed by Dr. D. Y. Patil Institute Of Technology, Pimpri, Pune - on April 23, 2026, for Helm: A Data Driven Framework For Mitigating Digital Burnout Through Personalized Analytics On Student Wellbeing And Mobile Interaction.
Inventors include Ms. Shraddha Shingne; Aditya Rajput; Aditya Pantula; and Atharva Parab.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: It is not surprising that university students today are experiencing increased stress, lack of sleep, and excessive screen time. However, most digital tools designed to help are ineffective. These tools are inconsistent in their methods; some require the user to track daily, which is a habit that most users do not follow. Some just display a general number of device usage - data that hardly ever results in change. The idea for Helm came from the desire to solve this problem. In a nutshell, it is similar to a phone-powered assistant, which is created to provide personalized mental health feedback. For this, it merges three distinct inputs - automatically collected sensor data, short user surveys each day, or emotion tracking from the written input. The configuration employs two information streams. For example, Helm collects behavior patterns silently so that there is no interruption to the user. This includes very detailed information like the duration of screen use, the types of apps used, the number of times the device is unlocked, step count, or the number of sleep cycles calculated. What is more, to ensure that personal data is safe, every operation is done on the- device using SQLite storage. Instead of collecting more passively collected metrics, we have limited direct feedback from users for whom we pair it. Our method involves one evening survey - the "Daily Check-In" - along with a brief morning evaluation about rest. At those times, people quickly note emotions related to the psychological state, stress level, and concentration, energy, as well as overall rest. The two sources are combined to provide a detailed example not only of the person's actions but also their emotions. Collecting information, however, is not sufficient. To get better mood insights from the written logs, Helm employs a detailed process that evaluates the tone of four phases one after another. Then, a linking system searches for the patterns - aligning the automatic measures, like device usage, with emotional results, such as perceived pressure. This is the point where individual trends begin to emerge. As a matter of fact, one person might demonstrate that the use of screens late at night leads to disrupted sleep, which then results in feeling bad the next day. The components are based on Flutter and Firebase, with certain parts of the processing being done on the device.
Disclaimer: Curated by HT Syndication.
