One-Way Privacy Shutter for Camera Lens Automation
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Solution Overview
Problem
Users of computing devices with cameras face privacy concerns due to the camera's visibility and accessibility, allowing unauthorized access and control, which existing solutions fail to adequately address through automatic and proactive measures.
Innovation Solution
A one-way privacy shutter electronically controlled to obscure the camera lens, requiring manual intervention to expose it again, combined with machine learning that analyzes video streams and sensor data to automate privacy triggering, ensuring protection against software hacks and user forgetfulness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the camera is mounted on an exterior portion of the computing device to promote capture of electronic images, then the camera's accessibility and visibility for image capture is improved, but the camera becomes vulnerable to unauthorized access and control
Solution Approach 1:
The shutter is positioned in advance to cover the camera lens before unauthorized access can occur. The physical shutter provides preliminary protection by blocking the camera view, while machine learning models analyze video streams proactively to detect sensitive situations before they lead to privacy breaches.
Solution Approach 2:
A physical shutter mechanism is introduced as an intermediary between the camera lens and the external environment. This mechanical barrier mediates access to the camera, requiring physical manipulation to open, thereby preventing direct unauthorized electronic access to the camera while maintaining operational accessibility when needed.
2Object-affected harmful factors
If a privacy shutter is implemented to cover the camera lens, then protection against unauthorized access is improved, but the shutter requires manual intervention to expose the lens again
Solution Approach 1:
The patent replaces purely mechanical manual shutter operation with an intelligent system combining machine learning analysis and automatic triggering. The machine learning models process video streams and sensor data to automatically determine when privacy protection is needed, substituting manual mechanical operation with automated intelligent control while maintaining the physical shutter mechanism for robust protection.
Solution Approach 2:
The system performs self-service by automatically monitoring video streams through machine learning models and autonomously triggering the shutter when sensitive situations are detected. The camera system serves itself by implementing privacy protection without requiring user awareness or manual intervention, addressing the limitation of user forgetfulness.
3Extent of automation
If machine learning is used to automate privacy triggering by analyzing video streams, then proactive privacy protection is improved, but the system complexity increases
Solution Approach 1:
The system applies partial automation by using machine learning selectively for specific privacy scenarios rather than complete system automation. The machine learning models analyze only relevant portions of video streams to detect sensitive situations, providing sufficient automation for privacy protection without requiring excessive complexity in the entire system architecture.
Data Source
AI summary
Examples disclosed herein provide a computing device. As an example, the computing device includes a camera comprising a lens, and a housing comprising an opening extending through the housing, wherein the opening is to accommodate the lens. The housing further includes a shutter to selectively obscure the opening, and a mechanical trigger, upon receiving an electrical signal, to move the shutter to obscure the opening. Upon the mechanical trigger engaging the shutter to obscure the opening, the housing further includes a mechanical feature to block the mechanical trigger from moving the shutter to expose the opening