Real-Time Scam Detection Overlays for Interactive Communication
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Solution Overview
Problem
Existing virtual reality and interactive communication environments are vulnerable to scams and social engineering attacks, as users are susceptible to providing sensitive information due to immersive interactions, lacking real-time threat detection and prevention mechanisms.
Innovation Solution
A system utilizing machine learning and natural language processing to analyze user communications, detect potential threats, and display overlays or alerts to users, temporarily disabling functions to prevent the sharing of sensitive information, while providing metadata about the attacker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If real-time communication functionality is provided in virtual reality environments, then user interaction and immersion are improved, but vulnerability to scams and social engineering attacks increases
Solution Approach 1:
The patent introduces an intermediary system that sits between users in virtual reality communications. This system includes a machine learning model that analyzes communication content, user behavior patterns, and contextual data to detect potential scams. The intermediary displays warnings, metadata about communicating parties, and prevents harmful interactions without disrupting the immersive VR experience, thus maintaining ease of operation while reducing scam vulnerability.
2Reliability
If machine learning analysis is applied to detect threats, then scam prevention capability is improved, but system complexity and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with extensive scam data before deployment. The system pre-establishes threat detection thresholds, user profile baselines, and communication pattern norms. This allows the system to perform rapid real-time analysis without requiring complex runtime decision-making, thereby improving threat detection accuracy while managing system complexity through upfront preparation.
Solution Approach 2:
The machine learning model operates autonomously to analyze communications, detect threats, and generate warnings without requiring manual intervention. The system self-adjusts by continuously learning from new data patterns, automatically updating threat assessments and user profiles. This self-service capability improves reliability while reducing the operational complexity burden on system administrators.
3Reliability
If overlays and warnings are displayed to alert users, then awareness of threats is improved, but user experience and immersion are disrupted
Solution Approach 1:
The patent applies local quality by making warning overlays context-dependent and location-specific within the VR environment. Instead of uniform full-screen warnings, the system displays targeted alerts in specific VR spaces, adjusts opacity and prominence based on threat severity, and positions warnings strategically without completely blocking the user's view. This maintains threat awareness while preserving immersion and user experience for non-threatening interactions.
4Speed
If communication monitoring is performed in real-time, then scam detection speed is improved, but processing resources and energy consumption increase
Solution Approach 1:
The patent implements partial action by applying different levels of monitoring intensity based on risk assessment. Low-risk communications receive minimal processing with quick validation checks, while high-risk interactions trigger full machine learning analysis. The system monitors communication metadata and basic patterns continuously at low energy cost, reserving intensive processing only when necessary. This approach maintains fast detection speed for critical threats while significantly reducing overall energy consumption compared to uniform high-intensity monitoring of all communications.
Data Source
AI summary
Embodiments relate to automatically preventing scams in real time in an interactive communication environment. A machine learning model determines that at least one communication from a first user to a second user in an interactive communication environment includes a potential threat, the interactive communication environment providing real time communications between the first user of a first computer system to the second user of a second computer system. The potential threat of the at least one communication is determined to above a threshold. An overlay is displayed to the second user on the second computer system in which the overlay displays metadata about the first user and informs the second user to avoid providing any sensitive information in the interactive communication environment.