Context-Sensitive Help System Using OCR Screen Capture
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Solution Overview
Problem
Users of software applications often face difficulties in accessing relevant help information, especially when offline, and community-driven help forums require users to accurately identify context-sensitive information, which can be challenging without proper tools.
Innovation Solution
A computer-implemented method and system that receives a message requesting help information from a user application, forwards a query to a help repository, and sends a notification with relevant resource data to a mobile device, utilizing image analysis and OCR to extract context-specific information from screen snapshots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users access community-driven help forums to locate relevant help information, then additional help-related information becomes available, but users must accurately identify pertinent context-sensitive information and describe errors while minimizing extraneous information
Solution Approach 1:
The system enables self-service by automatically capturing screen snapshots and extracting context information without requiring users to manually describe their problems. The error detection module and context extraction module work autonomously to prepare help requests, reducing the burden on users while maintaining access to community-driven help resources.
Solution Approach 2:
The patent replaces the manual mechanical process of users typing and describing errors with an automated image processing and text extraction system. OCR technology and screen snapshot analysis substitute for manual information entry, automatically identifying context-sensitive information and converting it into structured help requests.
2Quantity of substance
If users manually search through extensive help information, then comprehensive help resources are available, but time and effort are required to locate relevant context-sensitive information
Solution Approach 1:
The system extracts only the relevant context-sensitive information from screen snapshots using OCR and image analysis, separating essential error details from extraneous content. This extraction process identifies specific error messages, context indicators, and relevant parameters, delivering only the necessary information to help forums without requiring users to search through entire help databases.
Solution Approach 2:
The patent transforms unstructured screen images into structured text parameters through OCR and natural language processing. By converting visual information into searchable text parameters, the system enables efficient querying of help resources based on extracted error messages and context, significantly reducing search time while maintaining access to comprehensive help materials.
3Ease of operation
If built-in help information is provided in software applications, then immediate help is available, but users may still be confused or uncertain how to proceed
Solution Approach 1:
The system introduces community-driven help forums as an intermediary between built-in help resources and users. When built-in help proves insufficient, the automated system captures screen context and queries community forums for additional assistance, combining immediate built-in help with broader community knowledge to improve overall help effectiveness and user understanding.
Data Source
AI summary
Providing help information includes receiving a message, wherein contents of the message comprise a request for help information related to a user application executing on a separate computer; and forwarding a query to a help repository, the query based on the contents of the message. In response, receipt from the repository of an identification of a resource within the repository occurs that is relevant to the query. Ultimately a notification message is sent to a mobile device associated with a user that is utilizing the user application, wherein contents of the notification message comprise data related to the identification of the resource.