Screen timeout settings often disrupt productivity by activating too soon during focused tasks or wasting energy by staying on when users are idle. A fixed timer fails to account for the nuances of user behavior, such as reading long articles or stepping away briefly. This inefficiency affects remote workers, students, and energy-conscious users alike.
One way to address this is by creating a smart system that dynamically adjusts screen timeouts based on real-time activity. Instead of relying on a fixed timer, it could monitor inputs like keystrokes, mouse movement, and application context to predict true idleness. For example:
An MVP could start as a browser extension that overrides default screensavers during active reading, then expand to OS-level integration with machine learning for personalization.
Unlike existing solutions, this approach doesn't require manual toggles or hardware dependencies. For instance:
Potential monetization could include freemium features (e.g., per-app rules) or enterprise licensing for energy savings. Privacy concerns could be addressed by processing data locally and offering opt-in transparency.
By combining automation with behavioral insights, this idea could bridge the gap between rigid power management and real-world usage patterns.
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Digital Product