Web Conference Privacy Control via Real-Time Content Analysis
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Solution Overview
Problem
Current web conferencing systems lack effective privacy controls to prevent the dissemination of unauthorized content, such as confidential, offensive, or illicit material, during web conference sessions.
Innovation Solution
A system and method utilizing machine learning models for privacy control, which includes determining the presence of authorized attendees and detecting unauthorized content in real-time data exchanges between client devices, and taking remedial actions to terminate the dissemination of such content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If web conferencing systems allow free data exchange between client devices, then communication efficiency and ease of operation are improved, but privacy security and control over unauthorized content deteriorate
Solution Approach 1:
The patent introduces an intermediary content analysis system that mediates between client devices and the web conferencing network. This intermediary automatically analyzes shared content in real-time using machine learning models to detect unauthorized material (confidential, offensive, or illicit content) while allowing legitimate data exchange to proceed freely. The intermediary acts as a transparent filter that maintains communication efficiency while enforcing privacy security controls.
Solution Approach 2:
The system implements real-time feedback loops where shared content is continuously monitored and analyzed. When unauthorized content is detected, the system immediately provides feedback by blocking or removing the problematic content while allowing the rest of the communication to continue. This feedback mechanism enables dynamic content control without disrupting overall communication flow.
2Reliability
If automated content monitoring is implemented to detect unauthorized material, then privacy security is improved, but system complexity and processing requirements worsen
Solution Approach 1:
The patent replaces manual content review mechanisms with automated machine learning-based analysis systems. Instead of relying on human moderators to review shared content, the system uses trained machine learning models to automatically detect unauthorized content types (confidential, offensive, illicit material). This substitution reduces operational complexity while enhancing security monitoring capabilities.
Solution Approach 2:
The system creates copies of shared content for analysis purposes without disrupting the original data flow. By analyzing copies of the content rather than intercepting and blocking the original stream, the system can perform comprehensive security checks while maintaining communication efficiency and minimizing system complexity.
3Measurement precision
If real-time content analysis is performed on all shared data, then detection precision of unauthorized content is improved, but processing time and energy consumption worsen
Solution Approach 1:
The patent implements partial content analysis by focusing computational resources on detecting specific types of unauthorized content (confidential, offensive, illicit material) rather than analyzing every byte of shared data equally. The machine learning models are trained to identify characteristic patterns of unauthorized content, allowing the system to achieve high detection precision while reducing overall processing energy requirements by not performing exhaustive analysis on all content types.
Data Source
AI summary
A method may include determining an absence of an authorized attendee and/or a presence of an unauthorized attendee at a first client device engaged in a web conference session with a second client device. Data from the first client device may also be analyzed to determine a presence of unauthorized content. Remedial actions may be performed in response to determining the absence of an authorized attendee, the presence of an unauthorized attendee, and/or the presence of the unauthorized content. The remedial actions may include terminating, at the first client device, the capture and/or uploading of audio and/or video data. The remedial actions may also include terminating, at the second client device, the downloading and/or display of data from the first client device. The remedial actions may further include terminating, at a web conference server, the sending of data from the first client device to the second client device.