AR Security Assistant for Premises Vulnerability Assessment
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Solution Overview
Problem
Users self-installing security systems face challenges in accurately selecting and placing security components due to reliance on intuition or vendor recommendations, leading to potential over-purchasing and network overload, which can result in missed security alerts and suboptimal protection.
Innovation Solution
A system that uses a server computer and user device to analyze images of a premises, identify vulnerabilities, and generate augmented reality display data, including virtual objects and security options, to aid users in selecting and installing necessary security components, leveraging machine learning and deep learning for object detection and vulnerability assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users install more security components to ensure adequate protection, then security coverage is improved, but network load increases causing missed security alerts
Solution Approach 1:
The system performs preliminary vulnerability assessment and security analysis before users install security components. By using image processing and machine learning to identify actual vulnerabilities in advance, the system enables users to install only the necessary security components, preventing both over-installation and under-installation, thus optimizing network load while ensuring adequate security coverage.
2Ease of operation
If users rely on vendor recommendations to select security components, then selection process is simplified, but accuracy of security solution deteriorates due to vendor biases
Solution Approach 1:
The system replaces vendor-based recommendation mechanisms with an automated image processing and machine learning-based vulnerability assessment system. The system captures images of the premises, processes them through neural networks to identify vulnerabilities, and generates objective security recommendations, eliminating vendor biases while maintaining ease of use through automated analysis.
3Measurement precision
If users conduct thorough vulnerability assessment to select appropriate security components, then security solution accuracy is improved, but time and complexity of setup increases
Solution Approach 1:
The system replaces manual vulnerability assessment processes with automated image processing and machine learning algorithms. Users simply capture images of the premises, and the system's neural network automatically analyzes the images to identify vulnerabilities, generating accurate security recommendations without requiring user expertise or significant time investment.
Solution Approach 2:
The system creates a digital representation (image) of the physical premises and processes this copy to identify vulnerabilities. This allows thorough vulnerability assessment without physically inspecting each area, significantly reducing setup time while maintaining high accuracy through advanced image analysis capabilities.
Data Source
AI summary
Concepts and technologies are disclosed herein for identifying vulnerabilities associated with a premises and generating and/or presenting augmented reality display data to aid in selecting security components to protect the vulnerabilities identified. A processor can execute a security vulnerability assistant service. A request from a user device can be received. The request can comprise image data of a premises. An object captured by the image data can be identified, and a determination can be made whether the object is associated with a vulnerability. In response to determining that the object is associated with a vulnerability, augmented reality display data can be generated. The augmented reality display data can include a virtual object for overlaying on an image of the object presented by the user device.