Job Flow Extraction Without Attribute Information
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Solution Overview
Problem
Existing information processing systems fail to effectively extract job flows when image data lacks attribute information, such as file names, leading to incomplete registration of related jobs like image reading and printing.
Innovation Solution
An information processing apparatus with an extraction unit that compares job log entries based on the relationship between common and different image areas, determining operational relevance to register job flows without relying on attribute information, using thresholds and image processing techniques to identify relevant operational sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attribute information (file names) is used to extract job flows, then extraction accuracy is improved, but the system fails when such information is missing from images
Solution Approach 1:
The patent introduces image comparison as an intermediary method to bridge the gap between jobs. Instead of directly using attribute information (which may be missing), the system compares image content between jobs to establish relationships. This intermediary approach allows the system to extract job flows even when traditional attribute-based methods fail.
Solution Approach 2:
The patent changes the extraction parameter from attribute information (file names, metadata) to image content parameters (visual similarity, common portions). By shifting the basis of extraction from textual/metadata parameters to visual parameters, the system gains the ability to handle images without attribute information while maintaining extraction capability.
2Adaptability or versatility
If image comparison is performed to extract job flows without attribute information, then adaptability is improved, but processing complexity increases
Solution Approach 1:
The patent extracts only the essential comparison elements from images - specifically identifying common portions between images rather than performing full image analysis. By taking out only the necessary comparison data (common areas), the system reduces processing complexity while maintaining the ability to handle images without attribute information.
Solution Approach 2:
The patent applies local quality comparison by focusing on specific regions (common portions) of images rather than analyzing entire images uniformly. This localized approach to image comparison reduces overall processing complexity by concentrating computational resources on relevant areas that indicate job relationships.
3Productivity
If traditional attribute-based extraction is used, then processing speed is maintained, but job flow extraction completeness deteriorates when attribute information is absent
Solution Approach 1:
The patent performs preliminary image comparison and identification of common portions before final job flow extraction. This preliminary action establishes the basis for reliable extraction even when attribute information is missing, ensuring completeness without significantly impacting overall processing speed through efficient pre-processing.
Data Source
AI summary
An information processing apparatus includes an extraction unit that extracts two job log entries on the basis of a relationship between a first area of a common portion and a second area of a different portion. The job log entries are associated with a single job flow, and are constituted by a certain job log entry and at least one different job log entry. The common portion and the different portion are specified between an image related to the certain job log entry and an image related to the at least one different job log entry.