Screenshot Error Analysis via Machine Learning Image Segmentation
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Solution Overview
Problem
Information technology application support teams face challenges in describing application errors accurately, leading to delayed or incorrect resolutions due to a lack of application knowledge and varying error descriptions, which complicates the communication and resolution process.
Innovation Solution
A method using machine learning image analysis to determine application errors from screenshots by analyzing error regions, extracting text, and applying automated fixes, incorporating image classification, segmentation, computer vision, and OCR techniques to enhance error text recognition and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual error description is used by support staff, then human judgment and context understanding are applied, but time consumption and accuracy of error identification increase
Solution Approach 1:
The system enables self-service by automatically analyzing screenshots and generating error descriptions without requiring manual human intervention. The machine learning model processes the screenshot independently, extracting error information and formulating descriptions autonomously, thus eliminating the time-consuming manual analysis while maintaining high accuracy
Solution Approach 2:
The patent replaces the mechanical human analysis process with an automated machine learning system. The ML model substitutes human visual inspection and interpretation, using computational algorithms to identify error patterns, extract text, and generate descriptions, thereby achieving both speed and consistency without human time investment
2Productivity
If automated image analysis is used to extract error information, then processing speed and consistency improve, but system complexity and development effort increase
Solution Approach 1:
The machine learning model is designed with multi-functionality to handle various error types and screenshot formats within a single system. By creating a universal model that can process different error scenarios, the patent avoids the need for multiple specialized systems, thereby managing complexity while maintaining high processing speed and versatility
Solution Approach 2:
The error analysis process is segmented into distinct modular components: screenshot reception, error region identification, text extraction, and description generation. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity while enabling parallel processing that enhances productivity
3Measurement precision
If multiple image processing techniques are applied to enhance error text recognition, then text extraction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies preliminary image enhancement techniques such as contrast adjustment and noise reduction before text extraction. By pre-processing the screenshot to improve text visibility and reduce artifacts, the subsequent OCR and text extraction processes require fewer computational iterations and resources, achieving high accuracy without excessive energy consumption
Solution Approach 2:
The machine learning model applies different processing techniques to different regions of the screenshot based on local characteristics. Error-critical regions receive enhanced processing while other areas use standard processing, optimizing the balance between text extraction accuracy and computational resource usage by focusing resources where they are most needed
Data Source
AI summary
A method for determining an application error from a screenshot includes receiving, by a computing device, a computer application screenshot image indicating a computer error has occurred. The computing device analyzes the computer application screenshot image using a machine learning image analysis to determine one or more error regions in the computer application screenshot image. The computing device further processes the analyzed computer application screenshot to extract text from the one or more error regions in the computer application screenshot image. The computing device determines the application error based upon the extracted text. The computing device further automatically applies an automated error fix based upon the determined application error.