Video Chat Abuse Detection via External Context Analysis
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Solution Overview
Problem
Video chat sessions face challenges in detecting abusive interactions due to the worldwide reach of the internet, where inappropriate content and participant behavior can occur, and conventional screening techniques infringe on user privacy by monitoring internal context.
Innovation Solution
A computer-implemented technique that utilizes external context parameters such as participant numbers, geo-location, age, and audio/video muted status to calculate an abuse score, triggering human review when the score exceeds a threshold, and modifies participant trust scores and profiles based on abuse determination, while maintaining user privacy by focusing on external context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional screening techniques monitor internal context of video chat sessions, then detection accuracy of abusive interactions is improved, but user privacy is infringed
Solution Approach 1:
The patent segments the detection approach into two distinct parts: external context parameters (metadata, participant information, session characteristics) and internal context parameters (actual conversation content, video content). By focusing only on external context segmentation, the system achieves detection capability while preserving user privacy as internal context remains unmonitored.
Solution Approach 2:
The patent extracts and utilizes only the external context parameters from the video chat session, separating them from the internal context. This extraction principle allows the system to obtain detection-relevant information (participant ages, session duration, geographic locations) without accessing or monitoring the actual private communications, thus resolving the contradiction between detection accuracy and privacy protection.
2Reliability
If the system calculates abuse scores based on external context parameters and triggers human review frequently, then detection reliability is improved, but system complexity and operational costs increase
Solution Approach 1:
The patent implements partial action by using an abuse score threshold mechanism. Instead of reviewing all video chat sessions, the system calculates abuse scores for all sessions but only triggers human review when scores exceed the threshold. This partial approach maintains high detection reliability for suspicious sessions while avoiding the excessive complexity and costs of universal human review.
Solution Approach 2:
The abuse score calculation acts as an intermediary mechanism between automatic detection and human review. This intermediary layer processes external context parameters to generate scores, which then determine whether human review is necessary. This intermediary approach reduces system complexity by filtering out low-risk sessions automatically while maintaining reliability through human review of high-risk cases.
3Reliability
If the system modifies trust scores and restricts participant access based on abuse determination, then prevention of future abusive sessions is improved, but false positives may occur
Solution Approach 1:
The patent implements preliminary action by modifying trust scores and restricting access proactively after abuse is detected. Instead of waiting for future abusive behavior to occur, the system takes preventive measures by adjusting participant trust scores and limiting their access to video chat sessions. This preliminary prevention approach improves reliability by stopping potential abuse before it happens, while the threshold-based detection helps minimize false positives.
Data Source
AI summary
A server-implemented technique can include obtaining external context parameters indicative of an external context of a video chat session, calculating an abuse score based on the external context parameters, the abuse score being indicative of a likelihood the video chat session is abusive, and comparing the abuse score to an abuse score threshold. When the abuse score exceeds an abuse score threshold, the server can transmit, to a reviewer computing device, a request for a human reviewer to review a recorded portion of the video chat session for abuse. When the reviewer computing device returns a response indicating that the human reviewer deemed the video chat session to be abusive, the server can modify a profile of a participant in the abusive video chat session to obtain a modified profile, and generate an output based on the modified profile.