Eye Movement Distraction Factor in Web A/B Testing
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Solution Overview
Problem
Current A/B testing methods for web applications do not effectively account for user distraction, which is crucial in determining the effectiveness of user interface modifications and advertisements, as they lack comprehensive analysis of eye movements and emotional states.
Innovation Solution
A computing system that provides first and second variants of a web application, utilizing a camera to record eye movements and facial expressions, and an eye movement analyzer to determine a distraction factor, which compares the recorded eye movements to assess the effectiveness of UI components and advertisements by calculating a distraction factor based on eye movement ratios or differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional A/B testing methods are used to compare web application variants, then basic user interaction metrics can be collected, but user distraction and emotional states cannot be effectively measured
Solution Approach 1:
The patent combines multiple measurement functions into a single integrated system: the camera captures both eye movement data and facial expression data, the eye movement analyzer processes gaze patterns, and the facial recognition analyzer processes emotional states. This merging of previously separate functions into one unified system enables comprehensive user distraction measurement without proportionally increasing system complexity
Solution Approach 2:
The camera device serves multiple functions: it records eye movements for distraction analysis, captures facial expressions for emotional state detection, and provides both datasets to the web server. This multi-functionality allows a single device to replace what would traditionally require multiple separate measurement tools, improving measurement precision while controlling complexity
2Measurement precision
If eye movement tracking is added to A/B testing to measure distraction, then assessment accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent divides the analysis process into distinct modular components: the eye movement analyzer specifically processes gaze data to determine distraction factors, while the facial recognition analyzer separately processes facial expression data for emotional state detection. The web server then integrates these separate analysis results. This segmentation allows each component to specialize in one type of data processing, improving overall assessment accuracy while making the complex processing pipeline more manageable and efficient
3Measurement precision
If facial recognition is added to analyze emotional states, then advertisement effectiveness measurement improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by capturing facial expression data continuously during user interaction, so that when advertisement effectiveness needs to be measured, the data is already available for immediate analysis. The facial recognition analyzer processes this pre-captured data to determine emotional states, eliminating the need for additional processing time when measurement is required. This aligns with the patent's approach of collecting comprehensive data during normal interaction rather than adding separate measurement sessions
Data Source
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AI summary
A computing system includes a web server to provide first and second variants of a web application for A/B testing, and at least one client computing device operated by at least one user. The at least one client computing device includes a web browser and a camera. The web browser is for accessing the first variant of the web application, and for accessing the second variant of the web application. The camera is to record eye movements of the at least one user when viewing the displayed web page from the first variant of the web application, and to record eye movements of the at least one user when viewing the displayed web page from the second variant of the web application. The web server includes an eye movement analyzer to compare the recorded eye movements of the at least one user to determine a distraction factor.