Bad Actor Detection via Game Interaction Metrics
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Solution Overview
Problem
Existing anti-spam techniques are ineffective in protecting individuals from scamming within videogame platform online networks, as scammers typically engage in direct interactions rather than mass communication.
Innovation Solution
A bad actor detection system and method that monitors user interactions within a videogame platform network, using metrics such as interaction counts, churn rates, interaction durations, game-context based chat metrics, and location-based metrics to identify suspicious behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing anti-spam techniques are used in videogame platform networks, then mass communication spam can be detected, but direct interaction scamming cannot be detected
Solution Approach 1:
The patent changes the detection parameters from email-based metrics (message volume, spam keywords) to interaction-based metrics (interaction frequency, chat patterns, location data, game behavior). This allows the system to adapt from detecting mass communication spam to detecting direct interaction scamming by measuring different behavioral parameters specific to videogame platform interactions.
Solution Approach 2:
Instead of detecting spam through content analysis of messages (traditional approach), the patent inverts the approach by detecting scamming through analysis of interaction patterns and behavioral metrics. The system looks for abnormal patterns in how users interact rather than what they say, reversing the traditional spam detection methodology to suit the direct interaction scamming context.
2Reliability
If direct interaction monitoring is implemented to detect scamming, then scamming detection capability improves, but system complexity increases
Solution Approach 1:
The patent makes the monitoring system multi-functional by using the same data collection infrastructure to gather multiple types of metrics (interaction frequency, chat content, location data, game behavior) simultaneously. This universal approach allows the system to detect various scamming techniques through a single integrated monitoring framework, reducing overall system complexity compared to separate specialized systems.
Solution Approach 2:
The system automatically collects and analyzes interaction data without requiring manual intervention. The monitoring framework self-services by continuously gathering metrics from platform interactions and automatically comparing them against established patterns to identify potential scammers, reducing the operational complexity of the detection system.
3Measurement precision
If multiple interaction metrics are collected and analyzed, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent establishes baseline metrics and detection thresholds in advance before actual scamming occurs. By pre-configuring what constitutes normal versus abnormal interaction patterns across multiple metrics, the system can quickly compare real-time data against these pre-established standards, reducing processing time while maintaining high detection accuracy through multi-metric analysis.
Data Source
AI summary
A method is disclosed of detecting whether a first user is a bad actor within a platform network, in which the platform network enables interactions between individuals within video games, the method comprising the steps of measuring for the first user one or more selected from the list consisting of at least a first a game-agnostic behaviour metric that measures an investment of effort by the first user in interactions with other individuals; and at least a first a game-dependent behaviour metric that measures in-game patterns of interaction with other individuals by the first user, comparing the or each metric with an average or reference metric for a typical individual, and where a difference in the comparison or a combination of the comparisons meets a first criterion, treating this as indicative that the first user may be a bad actor.