Avatar Pattern Detection in Virtual Environments
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Solution Overview
Problem
Existing virtual online environments face challenges in detecting and preventing 'formation bots' that create prohibited patterns or shapes, which can evade safety filters and compromise online safety and content appropriateness.
Innovation Solution
A system and method that utilize pattern recognition to identify groups of avatars controlled by automated scripts forming prohibited patterns, selecting subgroups based on attributes like IP address or account creation time, and performing remedial actions such as disconnection or pattern disruption to prevent the formation of objectionable shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If safety filters process and filter user-to-user chat messages by removing swear words and slurs, then online safety is improved, but formation bots can evade these filters by appearing similar to prohibited words or shapes
Solution Approach 1:
The patent transitions from analyzing one-dimensional chat text to analyzing two-dimensional spatial formations of avatars. By detecting patterns in the spatial arrangement and movement of multiple avatars rather than just text content, the system identifies formation bots that evade traditional chat filters. This dimensional shift enables detection of visual patterns formed by avatar positions and movements.
Solution Approach 2:
The system introduces an intermediary detection layer between user avatars and the online environment. This intermediary monitoring system tracks avatar positions, movements, and formations, acting as a mediator that identifies and disrupts prohibited patterns before they can damage the online environment's reputation or expose players to inappropriate content.
2Measurement precision
If the system monitors all avatar groups for pattern formation, then detection accuracy improves, but false positives increase due to legitimate groups forming similar patterns
Solution Approach 1:
The patent segments the monitoring system to focus on specific subgroups of avatars with common attributes (e.g., same IP address, account creation time, or other identifying characteristics). Rather than monitoring all avatars uniformly, the system divides them into segments and applies pattern detection to relevant segments, reducing false positives from unrelated avatar groups while maintaining detection accuracy for bot-controlled formations.
3Object-affected harmful factors
If remedial actions are taken to disrupt avatar formations, then prohibited patterns are prevented, but legitimate user interactions may be disrupted
Solution Approach 1:
The system implements feedback mechanisms where detected patterns are analyzed to determine whether remedial disruption actions are necessary. The feedback loop allows the system to distinguish between malicious formation bots and legitimate user groups, applying disruption only when prohibited patterns are confirmed. This feedback-based approach prevents unnecessary interference with legitimate user interactions while maintaining protection against harmful formations.
Data Source
AI summary
Techniques are described for detecting script-controlled avatars in a virtual environment engaging in certain pattern formation behaviors. In particular, a virtual environment server may select a group of avatars and determine a pattern formed by the selected group. The virtual environment system may further search for a match of the pattern from a library of prohibited words or symbols. In the event the pattern formed by the selected avatars matches one of the prohibited patterns, some form of remedial action may be performed to disrupt the pattern.