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.

How It Could Work

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:

• Longer delays during reading or video playback
• Shorter delays when no input is detected for extended periods
• Manual overrides to refine predictions (e.g., "I'm still here" prompts)

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.

Why It Stands Out

Unlike existing solutions, this approach doesn't require manual toggles or hardware dependencies. For instance:

• Unlike apps like Caffeine, it automates adjustments based on actual activity.
• Compared to Windows Dynamic Lock, it detects inactivity more precisely without needing a paired device.

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.