Personalized UI Layout Using ML User Type Prediction

The patent segments users into different types (searchers and browsers) based on their interaction patterns with the online system. By dividing the user base into distinct segments with different characteristics, the system can provide tailored user interfaces for each segment, improving interface efficiency without overwhelming complexity through targeted personalization rather than universal customization.

The patent implements dynamic user interface adaptation by using machine learning models to predict user types in real-time and adjusting the user interface accordingly. The system dynamically switches between different interface configurations based on predicted user behavior, allowing the interface to adapt to user needs without requiring complex manual configuration or redesign.

The patent implements self-service personalization where the system automatically predicts user types and generates appropriate user interfaces without requiring manual intervention from users or system administrators. The machine learning model autonomously analyzes user behavior patterns and configures the interface accordingly, reducing the operational burden despite the increased personalization capability.

The patent changes key parameters of the user interface based on predicted user type, such as the arrangement and prominence of different interface elements. By adjusting these parameters dynamically based on user characteristics, the system achieves personalized interfaces through controlled parameter modification rather than complete interface redesign, managing complexity through parameter-based adaptation.

The patent performs preliminary action by pre-training machine learning models on user behavior data before actual user interactions. The models are prepared in advance to quickly predict user types during real-time operations, reducing the computational burden and time required during actual user sessions. The heavy lifting of pattern recognition is done beforehand, enabling fast real-time personalization.

The patent applies partial personalization by focusing on the most significant interface elements and user characteristics that have the greatest impact on user experience. Rather than personalizing every aspect of the interface, the system concentrates computational resources on the key parameters and interface elements that matter most, achieving effective personalization with reduced computational overhead.