Remote Proctoring Browser AI for Multi-Session Anomaly Detection
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Solution Overview
Problem
Existing remote proctoring systems lack sophisticated behavioral analysis tools, have limited accuracy and reliability, inefficient real-time monitoring, inadequate data management, and fail to leverage GPU capabilities for enhanced processing in browser settings, particularly in self-enrollment processes.
Innovation Solution
A web-based AI-driven proctoring system with advanced object detection and behavioral analysis models, utilizing convolutional neural networks and long-short term memory networks for real-time anomaly detection, integrated alert mechanisms, and decentralized processing to support multiple sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual monitoring by proctor agents through direct visual observation is used, then the system is simple to implement, but the productivity and accuracy of anomaly detection are insufficient
Solution Approach 1:
The patent replaces manual visual observation with automated machine learning models including convolutional neural networks for image abnormality detection and long-short term memory networks for behavioral abnormality detection. This substitution of mechanical human monitoring with automated AI systems directly resolves the contradiction by maintaining implementation feasibility while dramatically improving detection efficiency and productivity
Solution Approach 2:
The system enables self-enrollment processes with automated proctoring capabilities that monitor and detect abnormalities without requiring constant human intervention. The AI models autonomously perform monitoring, detection, alerting, and documentation functions, allowing the system to serve itself in the proctoring task while improving productivity
2Device complexity
If basic face recognition and object detection functionalities are used, then the device complexity is low, but the measurement precision and reliability of behavioral analysis are limited
Solution Approach 1:
The patent combines multiple detection modalities into a composite proctoring system: convolutional neural networks for image abnormality detection, long-short term memory networks for behavioral analysis, and integration with existing biometric enrollment data. This composite approach achieves high measurement precision by leveraging the strengths of multiple AI models working together
Solution Approach 2:
The system segments the proctoring task into distinct functional components: image abnormality detection handled by CNNs, behavioral abnormality detection handled by LSTMs, and multi-session monitoring capabilities. This segmentation allows each component to be optimized for its specific function while maintaining overall system manageability
3Ease of manufacture
If traditional processing methods are used, then the system is easy to deploy, but the speed of real-time analysis and processing is insufficient
Solution Approach 1:
The patent replaces traditional sequential processing with parallel AI model execution in the browser environment. Multiple machine learning models operate simultaneously on video feed data, enabling real-time analysis speed while maintaining deployment simplicity through web-based implementation
4Device complexity
If single-session proctoring is implemented, then the system complexity is low, but the productivity for handling multiple enrollments is limited
Solution Approach 1:
The patent implements a universal proctoring system architecture that can handle multiple enrollment sessions simultaneously through a single integrated platform. The AI models process multiple video feeds and detection streams in parallel, enabling one system to perform multiple proctoring functions across different sessions without proportionally increasing complexity
Data Source
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AI summary
A system and method for supervised remote proctoring includes an administrator device, a client device, a database, and an analysis module. During proctoring, a live video feed is captured from client device and sent to analysis module for processing. Analysis module performs behavioral analysis and object detection on received video footage and images. If an abnormality is detected by analysis module, an alert is generated and sent to administrator device to notify a proctor, and any information relating to the abnormality is sent to the database for storage and future reference.