UI Image Validation Using AI ROI Analysis for Deviation Insights
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Solution Overview
Problem
Existing image comparison tools provide limited insights for testers, making it challenging to understand the nature and type of differences between UI designs, requiring significant manual effort to analyze tool outputs.
Innovation Solution
A method and system utilizing a similarity check algorithm, cognizance ROI algorithm based on AI, and predictive models to identify and generate detailed observations of deviations between production and baseline images, creating Regions of Interest (ROIs) with position coordinates and generating insightful reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image comparison tools are used to identify differences between UI designs, then the differences can be highlighted, but the insights provided are limited and require significant manual analysis time
Solution Approach 1:
The patent introduces an intermediary AI-based analysis system that processes the raw difference data between images. This intermediary layer (the predictive model system) transforms basic pixel-level differences into meaningful observations about UI elements, text changes, and functional impacts, thereby reducing the manual analysis time while maintaining detection precision.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated AI-based system. Instead of testers manually examining highlighted differences, the system uses predictive models to automatically interpret and categorize differences, substituting human cognitive effort with machine intelligence to reduce time loss.
2Loss of information
If manual validation of UI design differences is performed, then detailed understanding of each difference can be achieved, but the process requires significant time and effort
Solution Approach 1:
The system performs self-service by automatically generating comprehensive validation reports without requiring manual intervention. The AI models autonomously analyze differences, categorize them by type (text, UI element, layout), and provide actionable insights, enabling the system to serve its own validation function without human assistance while maintaining detailed understanding.
Solution Approach 2:
The patent introduces an intermediary layer of predictive models that bridge the gap between raw image differences and meaningful validation insights. This intermediary system translates pixel-level variations into structured observations about UI changes, preserving information about the nature of differences while eliminating the need for manual analysis time.
3Loss of information
If comprehensive image validation with detailed observations is implemented, then the understanding of UI changes is enhanced, but the system complexity increases
Solution Approach 1:
The patent segments the validation system into distinct functional modules: image comparison module, difference detection module, predictive modeling module, and report generation module. Each module handles a specific aspect of the validation process, reducing overall system complexity through modular design while maintaining comprehensive validation capabilities.
Solution Approach 2:
The patent creates a universal validation system that handles multiple types of UI changes (text, images, layout, colors) through a single integrated platform. The predictive models are designed to be multi-functional, capable of analyzing various deviation types without requiring separate specialized systems, thereby reducing complexity while enhancing validation insights.
Data Source
AI summary
A method for validating images with observations is disclosed. The method includes receiving production image and baseline image from user device. The method includes identifying a plurality of deviations in the production image from the baseline image using a similarity check algorithm. The method includes extracting first output corresponding to the plurality of deviations. The method includes creating, using cognizance Region of Interest (ROI) algorithm, one or more ROIs based on the position coordinates of each of the plurality of deviations. Each of the one or more ROIs comprises at least one deviation of the plurality of deviations. The cognizance ROI algorithm is based on Artificial Intelligence (AI). The method includes, for each ROI, generating, a second output corresponding to the ROI using one or more predictive models. The second output includes observations corresponding to each of the at least one deviation in the ROI.