Social Activity Reward System for Player Engagement
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Solution Overview
Problem
Conventional online games lack effective mechanisms to incentivize players based on the activity levels of their associated players, leading to suboptimal engagement and retention.
Innovation Solution
A reward system that calculates activity metrics for associated players, such as daily active users, and provides incentives like virtual items or currency based on these metrics, encouraging friends and other users to play the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional online games use traditional incentive mechanisms (rewarding only direct game performance), then implementation simplicity is maintained, but player engagement and retention are suboptimal
Solution Approach 1:
The patent changes the parameters of the incentive system by introducing social activity metrics (number of active friends, daily active users) as new dimensions for reward calculation. This transforms the traditional single-dimension performance-based rewards into multi-dimensional rewards that consider both individual game performance and social network activity, thereby improving player engagement without significantly complicating the implementation.
Solution Approach 2:
The reward system is designed to serve multiple functions simultaneously: it rewards individual player performance, encourages social networking, increases daily active users, and promotes game invitations. By making the incentive system multi-functional, the patent addresses various player motivations and game growth objectives within a unified framework, improving overall effectiveness without requiring separate incentive mechanisms for each goal.
2Productivity
If the game provider implements a reward system based on associated players' activity, then player engagement and retention improve, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where players receive real-time or periodic notifications about their eligibility for rewards based on their friends' activity. The system continuously monitors social network activity, calculates reward thresholds, and provides feedback to players about their progress toward reward qualification. This automated feedback loop manages system complexity by using algorithmic calculations rather than manual assessment, enabling sophisticated reward logic without proportionally increasing operational complexity.
Solution Approach 2:
The reward system operates autonomously by automatically tracking friends' activity levels, calculating DAU metrics, determining reward eligibility, and distributing rewards without requiring manual intervention. The system self-manages the complex tasks of monitoring social graphs, computing activity metrics, and enforcing reward rules, thereby reducing the operational burden on game providers despite the sophisticated logic involved.
3Productivity
If the game monitors and rewards based on social network activity, then new player acquisition increases, but data processing requirements increase
Solution Approach 1:
The patent applies partial monitoring by focusing on specific, high-impact metrics rather than comprehensively analyzing all social interactions. Instead of tracking every friend activity in detail, the system monitors key indicators such as daily active status and basic interaction types. This selective monitoring approach captures sufficient social network dynamics to drive player acquisition while significantly reducing data processing requirements compared to exhaustive tracking of all social behaviors.
Data Source
AI summary
A computer-implemented method and system are described for providing rewards, awards, and other incentives to players based on the activities of associated players is described. In some examples, the technology determines an activity level for players associated with a distinguished player, such as friends and other players within a social or game network, and rewards the distinguished player based on their activity levels.