Game Update Effect Analysis via Gamer Action Sequence Detection
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Solution Overview
Problem
Conventional methods for analyzing game update effects fail to provide detailed insights into the influence of updates on gamer behavior, relying on general key performance indicators rather than specific action sequence analysis.
Innovation Solution
An apparatus and method that collect and analyze gamer action information, including hunting, transaction, and character enhancement data, to identify changes in behavior patterns before and after a game service update, providing feedback on the update's effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If conventional KPI-based methods are used to analyze update effects, then analysis simplicity is maintained, but measurement precision of gamer behavior changes deteriorates
Solution Approach 1:
The patent segments gamer behavior into discrete action sequences (e.g., login→hunting→item purchase→logout) and analyzes changes in these sequential patterns before and after updates. This segmentation enables precise measurement of specific behavior changes while maintaining analytical feasibility through automated sequence detection and comparison algorithms.
2Measurement precision
If detailed action sequence analysis is implemented, then measurement precision of update effects improves, but device complexity increases
Solution Approach 1:
The analysis system is designed as a multi-functional integrated platform that performs data collection, action sequence detection, update effect analysis, and feedback generation within a single system architecture. This universal design consolidates multiple analytical functions into one cohesive system, reducing overall complexity while maintaining high measurement precision through standardized data processing pipelines.
3Measurement precision
If comprehensive gamer action data is collected, then analysis accuracy improves, but loss of information processing burden increases
Solution Approach 1:
The system extracts only the essential action sequence patterns from comprehensive gamer data, isolating key behavioral metrics (sequence frequency, duration, transition patterns) from raw data. This extraction process maintains analysis accuracy by focusing on critical behavior indicators while reducing processing burden through selective data retention and automated pattern recognition algorithms.
Data Source
AI summary
Disclosed is an apparatus for analyzing a game update effect according to a change in a gamer action sequence, the apparatus including: a gamer action information collector configured to collect action information of a gamer from a game operating server that provides a gamer terminal with a game and stores the action information of the gamer therein; a gamer action information sequence identifier configured to detect an action sequence of the gamer from the collected action information of the gamer; and an update result analyzer configured to analyze an consequence on behavior of the gamer with respect to an update of a game service by comparing action sequences of the gamer detected through the gamer action information sequence identifier on the basis of a time point of the update of the game service.