Online Game Player Segment Comparison System
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Solution Overview
Problem
Conventional systems fail to effectively compare and display user-selected segments of players in online games to identify trends, predict player activity, and analyze correlations, as they lack the ability to differentiate players based on specific system variables and features.
Innovation Solution
A system configured with processors to execute modules for segmenting and comparing players based on user-selected system variables and features, including account creation date, participation in the game, and player type, to facilitate trend analysis and event response prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems display fixed set of data relating to player operation and performance, then the system structure is simple and easy to implement, but the system fails to enable comparison and display of user-selected segments of players differentiated by system variables
Solution Approach 1:
The patent applies segmentation by dividing the player population into distinct segments based on system variables (e.g., player type, account creation date, participation in game events). The segmentation module creates multiple player segments that can be independently selected and compared, allowing users to analyze specific groups of players with shared characteristics rather than treating all players as a single homogeneous group.
Solution Approach 2:
The system implements dynamics by allowing users to dynamically select and configure which player segments to compare and which features to analyze. The comparison features and player segments can be changed on-demand based on user needs, transforming the static fixed-data display into a dynamic customizable analysis tool that adapts to different analytical requirements.
2Loss of information
If the system enables comparison of player segments based on multiple system variables and features, then trend identification and prediction capabilities are improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The segmentation module divides the complex player population into manageable segments based on system variables, allowing trend analysis to be performed on homogeneous groups rather than the entire player base. This segmentation reduces the complexity of identifying trends by focusing analysis on specific player groups with shared characteristics.
Solution Approach 2:
The patent introduces an intermediary comparison module that acts as a mediator between the raw player data and the trend analysis functionality. This module handles the complex data processing by systematically comparing selected player segments across chosen features, managing the complexity of multi-variable analysis through structured comparison procedures.
3Loss of information
If the system provides customized player segment comparison with multiple features, then user analysis capability is enhanced, but the ease of operation decreases due to more configuration options
Solution Approach 1:
The comparison module is designed with universality by enabling users to select from multiple player segments and various comparison features through a unified interface. The same module handles different segmentation criteria (player type, account creation date, event participation) and multiple comparison features (monetization, activity, engagement), providing versatile analysis capability through a single configurable system rather than requiring separate tools for each analysis type.
4Loss of information
If conventional systems use fixed player groupings, then the system is easy to implement and maintain, but the system fails to enable identification of correlations and predictions related to player activity and monetization
Solution Approach 1:
The segmentation module creates distinct player segments based on system variables, enabling correlation analysis by comparing metrics across segments with different characteristics. This segmentation allows the system to identify correlations between player attributes (e.g., player type, account creation date) and outcomes (monetization, activity levels) that would be obscured in fixed homogeneous groupings.
Solution Approach 2:
The system implements dynamic segment creation and comparison, allowing users to adaptively select player segments and comparison features based on specific analytical questions. This dynamic approach enables correlation identification by flexibly configuring which player groups to compare and which features to analyze, transforming the static fixed-grouping system into an adaptive analysis tool.
Data Source
AI summary
A system and method is described for comparing and displaying user-selected segments of players of an online game to identify and predict trends related to player activity and monetization in the game, determine relative response to events that occur in the game, and/or analyze other correlations. The players may be segmented on or more system variables chosen by a user. A user may be a system administrator, a game developer, and/or other user interested in identifying and predicting trends, determining responses to events, and/or engaging in other analysis relating to the game. A comparison of the first segment of players and the second segment of players based on a first feature and a second feature may allow identification and prediction of trends related to player activity and monetization in the game, determine relative response to events that occur in the game, and/or analyze other correlations in an online game.