Media Feed Privacy via Digital Avatar Overlay
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Solution Overview
Problem
Monitoring and surveillance systems face challenges in protecting privacy while maintaining the ability to detect and respond to emergencies, as conventional video processing and redaction methods often obscure important details and fail to account for various data types beyond audio and video, such as LIDAR, sonar, and geolocation data.
Innovation Solution
A system and method for privatizing media feeds that uses machine learning and video processing to detect and modify vulnerable segments in media data, including audio, video, LIDAR, sonar, and geolocation data, by overlaying digital avatars that emulate user actions and environment, ensuring privacy without compromising emergency detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional video processing and redaction methods are used to protect privacy, then privacy protection is improved, but the ability to detect and respond to emergencies deteriorates because important details are obscured
Solution Approach 1:
The patent segments the media feed into multiple data types (video, audio, LIDAR, sonar, geolocation) and processes each segment differently. Privacy-sensitive segments are redacted while emergency-detection segments are preserved, allowing simultaneous privacy protection and emergency detection without mutual interference
Solution Approach 2:
The patent applies different processing qualities to different portions of the media feed. Video portions containing personal information receive high-level redaction for privacy protection, while portions containing emergency indicators maintain full quality for detection purposes, achieving local optimization of both privacy and safety
2Reliability
If comprehensive media data is collected for monitoring, then monitoring effectiveness is improved, but privacy protection becomes more difficult due to the increased amount of sensitive information
Solution Approach 1:
The patent extracts and removes sensitive personal information from the comprehensive media data stream while preserving the essential monitoring content. Machine learning models identify and extract personally identifiable information, facial features, and voice patterns for redaction, leaving the core monitoring data intact for effective surveillance
Solution Approach 2:
The patent introduces machine learning models and augmented reality overlays as intermediary layers between the comprehensive media data and the end user. These intermediaries automatically identify, classify, and redact sensitive information, mediating between the need for comprehensive monitoring and the need for privacy protection
3Speed
If real-time processing is applied to all media data, then response time is improved, but computational resources are excessively consumed
Solution Approach 1:
The patent applies partial real-time processing by prioritizing analysis of specific data types (audio for screams, LIDAR for distance changes, sonar for impact detection) over others. This selective partial processing achieves rapid emergency detection without the excessive computational cost of processing all media data at full speed
Solution Approach 2:
The patent implements a multi-pass processing approach where critical emergency indicators are detected quickly in the first pass, allowing the system to rush through the most important detection tasks immediately while deferring less critical privacy redaction tasks to subsequent processing passes
Data Source
AI summary
Techniques are described with respect to a system, method, and computer product for privatizing media feeds. An associated method includes receiving a plurality of media data. The method further includes monitoring the plurality of media data, determining at least one vulnerable segment of the plurality of media data, and modifying the at least one vulnerable segment based on the determination.