Incremental Cost Prediction for User-Treatment Selection Gaps

The patent applies different treatments to different user segments based on their characteristics and predicted responses. Instead of uniform treatment application, the system identifies specific user groups (e.g., high responders vs. low responders) and applies treatments selectively to those most likely to benefit, thereby improving interaction rates while controlling costs.

The system dynamically adjusts treatment parameters such as treatment type, intensity, and timing based on user characteristics and environmental conditions. By optimizing these parameters for each user segment, the system maximizes interaction likelihood while minimizing unnecessary treatment costs.

The system applies treatments to only the necessary subset of users rather than all users. By identifying and treating only those users with high predicted response rates, the system achieves sufficient interaction levels without incurring excessive treatment costs associated with treating low-response users.

The system uses predictive models to automatically identify which users are most likely to respond to treatments, eliminating the need for manual selection. This self-service approach optimizes treatment allocation based on data-driven predictions of user response patterns.

The system pre-computes user response predictions and segments users into groups based on their likely treatment responses before treatment allocation. This preliminary segmentation simplifies the subsequent treatment selection process while maintaining high interaction rates by ensuring treatments are directed at appropriate user groups.

The patent introduces predictive modeling and user segmentation as intermediary steps between the simple act of applying treatments and the complex reality of varying user responses. These intermediaries translate complex user behavior patterns into actionable segmentation rules that simplify treatment allocation decisions.