Image Diagnosis Results Tailored to User Expertise
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Solution Overview
Problem
Existing image forming apparatus diagnosis systems are difficult for users other than maintenance engineers to understand and utilize, especially when maintenance engineers are not present at the site, necessitating a user-friendly maintenance system.
Innovation Solution
An image diagnosis system that acquires user information, such as classification or login information, to tailor the diagnosis result display content based on the user's expertise, providing differentiated maintenance inspection information and necessary countermeasure methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the diagnosis result is displayed with detailed technical contents, then the diagnostic accuracy and completeness is improved, but the ease of understanding for non-maintenance engineers deteriorates
Solution Approach 1:
The diagnosis result is segmented into multiple display modes: a first display mode for maintenance engineers showing detailed technical contents, and a second display mode for other users showing simplified contents. This segmentation allows the same system to present appropriately detailed information to different user groups, resolving the contradiction between information completeness and ease of understanding.
Solution Approach 2:
The system dynamically switches between different display modes based on user identification. When a maintenance engineer logs in, the detailed technical display mode is activated; when other users log in, the simplified display mode is activated. This dynamic adaptation allows the system to optimize information presentation according to the user's expertise level, maintaining both information completeness for engineers and ease of understanding for other users.
2Measurement precision
If the diagnosis system is designed for maintenance engineers, then the diagnostic accuracy is improved, but the adaptability to different users deteriorates
Solution Approach 1:
The diagnosis system is designed with universal adaptability to serve multiple user types (maintenance engineers and other users) through a single system. The system maintains high diagnostic accuracy while adapting its output presentation to different user capabilities, eliminating the need for separate diagnosis systems for different user groups.
Solution Approach 2:
The system applies local quality by providing different levels of information detail to different user groups. Maintenance engineers receive comprehensive technical diagnosis results, while other users receive simplified results. This localized adaptation of information quality allows the system to maintain diagnostic accuracy while being adaptable to various user expertise levels.
3Ease of operation
If the diagnosis result is simplified for general users, then the ease of understanding is improved, but the loss of technical information increases
Solution Approach 1:
The information is segmented and selectively presented based on user type. For general users, only essential simplified information is displayed, while the complete technical information is preserved in the system and made available to maintenance engineers. This segmentation prevents information loss while ensuring ease of understanding for general users.
Solution Approach 2:
The system acts as an intermediary that preserves complete diagnosis information internally while presenting simplified versions to general users. When needed, maintenance engineers can access the full technical details. This intermediary function allows the system to protect technical information from being lost while still providing ease of understanding to general users through selective information presentation.
Data Source
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AI summary
An image diagnosis system includes: one or plural processors configured to: acquire diagnosis information used for diagnosing an image forming apparatus; acquire user information related to a user who uses the diagnosis from the diagnosis information; and output a diagnosis result for the user based on the acquired diagnosis information and the acquired user information.