Boost Button Updates Using Server-Driven Engagement Prediction

The patent applies color changes by modifying the visual appearance of buttons (boost and comment buttons) based on predicted user engagement. The server sends update instructions that change button colors, icons, or other visual properties to make them more appealing when the user is likely to engage, thereby encouraging actions without using disruptive banners or pop-ups.

The patent implements dynamics by making the button appearance adaptive and responsive to predicted user behavior. Instead of static buttons, the system dynamically updates button properties (such as color, icon, or highlighting) based on real-time predictions from machine learning models about whether the user is likely to boost or comment on the content.

The patent uses an intermediary approach by introducing a server component that acts as a mediator between the content display system and the user interaction system. The server receives content information, predicts user engagement using machine learning models, and sends update instructions to modify button appearances, thereby centralizing the complexity in a manageable server layer.

The patent applies preliminary action by using machine learning models to predict user engagement likelihood before the user actually interacts with the content. The system performs these predictions in advance and pre-preares button update instructions, allowing the system to respond proactively to likely user behaviors rather than reacting passively to actual interactions.