Digital Twin Models for Dynamic Pricing and User Engagement
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Solution Overview
Problem
Incentivized engagement systems face challenges in harmonizing the divergent interests of providers and users within open and dynamic online environments, particularly in regulating the creation of earned digital currency and managing pricing and availability of digital goods to ensure fairness and impartiality.
Innovation Solution
An adaptive adversarial system using machine learning models to manage pricing, placement, and post-interaction of digital offers, balancing competing interests through ensemble models configured to meet individual needs of providers and users, with digital twins simulating user behavior and supply-demand prediction models generating optimal product placements and prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If incentivized engagement systems are implemented to reward user participation, then user engagement and interaction are enhanced, but harmonizing divergent interests of providers and users becomes more challenging
Solution Approach 1:
The patent implements dynamic pricing and reward mechanisms that automatically adjust based on real-time supply and demand conditions. The system continuously adapts engagement incentives to balance provider revenues and user participation levels, transforming static reward structures into responsive, condition-based systems that harmonize divergent interests through automated adjustment.
Solution Approach 2:
The system changes key parameters such as reward amounts, pricing levels, and incentive structures based on observed engagement patterns and market conditions. By dynamically modifying these parameters, the system optimizes the balance between provider interests (revenue generation) and user interests (engagement motivation), resolving the contradiction through continuous parameter optimization.
2Reliability
If digital currency creation and digital goods pricing are regulated to ensure fairness, then marketplace impartiality is improved, but system complexity increases
Solution Approach 1:
The patent implements self-regulating mechanisms where the system automatically monitors and adjusts pricing, rewards, and resource allocation based on predefined fairness criteria and real-time data. This autonomous self-service approach ensures marketplace impartiality without requiring complex external oversight structures, as the system independently maintains fairness through automated decision-making algorithms.
Solution Approach 2:
The system incorporates continuous feedback loops that monitor marketplace transactions, user engagement metrics, and provider performance. This feedback mechanism enables automatic detection and correction of fairness issues, maintaining marketplace impartiality through real-time adjustments while avoiding the need for complex manual regulation structures.
3Productivity
If machine learning models are used to personalize user experiences and optimize pricing, then user satisfaction and revenue are enhanced, but computational resources and processing time increase
Solution Approach 1:
The patent implements pre-computation and caching strategies where machine learning models generate predictions and optimize pricing strategies in advance based on historical data and anticipated conditions. By performing computations preliminarily and storing results for rapid retrieval, the system reduces real-time computational resource requirements while maintaining revenue optimization capabilities.
Data Source
AI summary
Data describing how multiple users interact with an online system is gathered. For each user, a digital twin model is trained using reinforcement learning on their data to predict their interactions in various virtual environment variants. This model is then run in several candidate virtual environment variants to simulate how the user might interact with each. A score is assigned to each environment based on the likelihood of the user interacting in a specific way. The virtual environment variant with the best score is selected and displayed to the user.