Automatic Player Profile Generation for Online Games
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Solution Overview
Problem
Online games face challenges in matching players effectively and providing detailed information for teammate and opponent evaluation, as current leaderboards are limited in usefulness and player-submitted biographies are often inaccurate.
Innovation Solution
An automatic player profile generation system that collects game activity data, calculates relevant statistics, and generates profiles including biographical information and style identifiers, allowing for balanced team matching and strategic evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If player-submitted biographies are used, then players can provide personal information, but the information is perceived as inaccurate representations of player skill
Solution Approach 1:
The system automatically generates player profiles using game activity data collected from players' own gameplay, eliminating the need for players to manually submit biographies. The profile is self-updated based on objective performance metrics
Solution Approach 2:
The system continuously monitors player performance and updates profile information based on feedback from game outcomes, ensuring the information remains accurate and current without requiring player intervention
2Loss of information
If detailed player statistics are collected and displayed, then player evaluation becomes more comprehensive, but the system complexity increases
Solution Approach 1:
The system extracts only the most relevant statistics from comprehensive game data, such as key performance indicators and achievement metrics, presenting them in a simplified profile format that is easy to read and interpret
Solution Approach 2:
Player profiles are divided into distinct sections or categories of statistics, allowing players to quickly scan and evaluate specific aspects of opponent or teammate performance without being overwhelmed by all available data
3Productivity
If automatic player matching is implemented, then player pairing efficiency improves, but the ability to create balanced teams based on detailed skill categories is reduced
Solution Approach 1:
The matching system considers specific local skill qualities in different game categories rather than treating all players uniformly, allowing for nuanced team balancing based on complementary strengths and weaknesses in various skill areas
Data Source
AI summary
Automatic player profile generation may be implemented for an interactive online game in which one or more players interact via one or more client devices connected to a server device via a network. Information relating to game activity may be collected for the one or more players during one or more game sessions with the server device. One or more player statistics relevant to the game activity may be calculated for each of the one or more players during the game session based on the information relating to game activity with the server device. A player profile may be generated for each of the one or more players with the server. Each player profile may include the one or more player statistics for a corresponding one of the one or more players.