Incentive Offer Search System for Dynamic Game Rewards
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Solution Overview
Problem
Online multiplayer games and merchant systems face challenges in incentivizing users to complete transactions, as existing methods lack effective mechanisms to offer personalized and dynamic in-game rewards that align with user preferences and game progress.
Innovation Solution
An incentive offer search system that determines and presents in-game rewards based on user game state data, item details, and transaction dynamics, allowing for static, dynamic, or mystery rewards, and offering varying incentives to encourage timely purchases or gameplay engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If in-game rewards are offered to incentivize online purchases, then transaction rates and user engagement increase, but the system complexity and difficulty of personalizing rewards increase
Solution Approach 1:
The patent introduces an incentive offer search system as an intermediary component that bridges the game platform and merchant systems. This mediator handles the complex tasks of selecting and personalizing rewards based on user profiles, game state, and purchase context, thereby resolving the contradiction by centralizing complexity in a dedicated subsystem rather than distributing it throughout the entire system
Solution Approach 2:
The system dynamically changes reward parameters (type, value, timing) based on multiple factors including user preferences, game progress state, and purchase characteristics. By making reward parameters adaptive rather than fixed, the system achieves personalized incentivization without requiring complete system redesign, thus improving transaction rates while managing complexity through parameter optimization
2Adaptability or versatility
If personalized in-game rewards are provided based on user game state data, then user engagement and relevance of rewards improve, but the computational requirements and data processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user profile data, game state information, and reward catalog data in accessible formats. User preferences and game progress are maintained in updated states before purchase events occur, enabling rapid reward selection without real-time computational burden during the critical purchase moment
Solution Approach 2:
The system implements feedback mechanisms where user responses to offered rewards (acceptance, rejection, engagement patterns) are fed back into the user profile and preference data. This continuous feedback loop allows the system to learn and adapt to user preferences over time, improving personalization accuracy while reducing the computational complexity of real-time decision-making by relying on learned patterns
3Productivity
If dynamic reward offers are presented during the purchasing process, then transaction conversion increases, but the integration complexity between game system and merchant system increases
Solution Approach 1:
The incentive offer search system is designed with universal interfaces that can interact with multiple different merchant systems and game platforms through standardized protocols. By creating a multi-functional intermediary layer with universal communication capabilities, the system enables dynamic reward integration across diverse systems without requiring complex custom integrations for each partnership
Solution Approach 2:
The patent positions the incentive offer search system as a mediator between game platforms and merchant systems, handling all complex integration tasks centrally. This intermediary absorbs the integration complexity within its own architecture, allowing game systems and merchant systems to connect through simplified standardized interfaces, thereby improving transaction conversion while managing integration complexity in a centralized manner
4Productivity
If in-game rewards are used to incentivize purchases, then user participation and gameplay engagement increase, but the cost of providing rewards and managing reward systems increases
Solution Approach 1:
The system dynamically adjusts reward parameters including type, quantity, and value based on multiple factors such as user preferences, purchase amount, game state, and business objectives. By optimizing reward parameters rather than providing fixed high-value rewards, the system achieves effective user engagement while controlling reward expenditure through data-driven parameter selection
Solution Approach 2:
The system applies different reward strategies to different user segments, purchase types, and game contexts based on local characteristics. Rather than using a uniform high-cost reward approach, the system tailors reward quality and value to match the specific context, providing higher-value rewards only where they generate the most engagement and conversion, thereby controlling overall reward cost while maintaining user participation
Data Source
AI summary
Example systems and methods related to providing rewards for an online game in response to web-based purchases are presented. In an example, an online catalog webpage including information describing a product available for purchase is generated. The online catalog webpage includes code instructing a device receiving the online catalog webpage to issue a request to an online gaming system to determine an in-game reward for a user based on the product. The online catalog webpage is transmitted to a client device of the user. An order for the product is received from the client device. The order indicates the in-game reward determined in the online gaming system. An order confirmation webpage is transmitted to the client device in response to the order, and the order confirmation webpage indicates the in-game reward.