Virtual Reality Chat Curation Using Segmented-Message Reconstruction
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Solution Overview
Problem
Existing virtual reality systems are vulnerable to security breaches due to 'Trolls' and bots that exploit current chat security features by sending consecutive segmented messages to bypass content filters, disrupting public chats with unwanted advertisements and solicitations.
Innovation Solution
A system utilizing machine learning and natural language processing to identify and combine consecutive segmented messages, determining if they form a complete message that violates security rules, and implementing security actions such as text obfuscation and user avatar removal to prevent display and visibility in virtual reality environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional chat security features are used in virtual reality systems, then basic content filtering is provided, but security breaches occur due to trolls and bots sending consecutive segmented messages to bypass filters
Solution Approach 1:
The system segments the chat monitoring process into multiple analysis stages: individual message evaluation, consecutive message pattern recognition, and complete message reconstruction. This multi-level segmentation allows the system to detect segmented messages that attempt to bypass single-message filters by analyzing the cumulative content across multiple messages.
Solution Approach 2:
The system performs preliminary analysis of message patterns and user behavior before security violations occur. By monitoring consecutive messages in real-time and predicting potential bypass attempts, the system can preemptively block segmented message sequences before they successfully evade content filters.
2Ease of operation
If consecutive segmented messages are allowed to pass through, then chat flow and user communication are maintained, but disruptive messages and unwanted advertisements are disseminated
Solution Approach 1:
The system introduces an intermediary analysis layer between message transmission and display. This intermediary component reconstructs consecutive segmented messages into complete messages for security evaluation, allowing legitimate chat flow to continue while blocking harmful content that emerges from segmented message sequences.
Solution Approach 2:
The system replaces traditional mechanical content filtering with machine learning-based natural language processing. This substitution enables the system to understand semantic meaning across consecutive messages, distinguish between legitimate multi-part communications and malicious segmented advertisements, and maintain chat flow while blocking harmful content.
3Measurement precision
If machine learning models analyze consecutive segmented messages to detect violations, then security accuracy is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system implements dynamic analysis that adapts to different chat contexts and message patterns. The machine learning model adjusts its analysis depth and reconstruction strategies based on real-time observations of user behavior, message frequency, and content patterns, optimizing security detection accuracy while managing system complexity through adaptive processing.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it analyzes individual messages, detects consecutive message patterns, reconstructs complete messages, evaluates security violations, and provides feedback for continuous improvement. This multi-functionality consolidates what could be separate complex systems into a single unified security framework.
Data Source
AI summary
Embodiments relate to providing automatic curation of chats in virtual reality. A technique includes determining, using a machine learning model, that consecutive segmented messages combine to form a complete message, the consecutive segmented messages being from a virtual environment. The technique includes determining that the complete message violates a security rule and performing a security action that affects a display of the consecutive segmented messages forming the complete message.