Business-Type Log Classification for MPS Facility Allocation
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Solution Overview
Problem
Current managed print services (MPS) struggle to accurately analyze job logs and optimize image forming apparatus arrangements based on usage, leading to inefficiencies in facility allocation and data relevance.
Innovation Solution
An information processing apparatus that collects and classifies job logs, image logs, and IoT information, aggregating them to provide customized business analysis and optimize resource allocation by installing business-specific plug-ins, such as the business analysis plug-in, which analyzes usage patterns and recommends improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If job logs and image logs are collected and analyzed to optimize image forming apparatus arrangements, then facility allocation efficiency is improved, but analysis accuracy deteriorates due to insufficient consideration of business-specific usage patterns
Solution Approach 1:
The patent applies local quality by classifying logs according to business types (e.g., finance, manufacturing, retail) and analyzing them with business-specific criteria. Different business types have different usage patterns, so the analysis method is customized locally for each business type rather than using a uniform approach, thereby improving analysis accuracy while maintaining facility allocation efficiency.
Solution Approach 2:
The patent changes the parameters of analysis by introducing business type as a classification parameter and using different analysis criteria for different business types. This parameter change allows the system to adapt its analysis approach to match specific business characteristics, resolving the contradiction between general efficiency and specific accuracy.
2Quantity of substance
If multiple types of data (job logs, image logs, IoT information) are collected comprehensively, then data completeness is improved, but data relevance deteriorates due to inclusion of unnecessary information
Solution Approach 1:
The patent extracts only the necessary log items and IoT information relevant to each business type through classification. Instead of uniformly collecting all possible data, the system selectively extracts data that is actually useful for each business type, thereby maintaining data completeness for relevant information while eliminating unnecessary data that would reduce overall relevance.
Solution Approach 2:
The patent segments data collection by business type, dividing the data collection process into business-specific segments. Each business type has its own set of relevant log items and IoT information to collect, which prevents the mixing of irrelevant data from different business contexts and maintains high data relevance while ensuring completeness within each segment.
3Device complexity
If generic analysis methods are used for all business types, then system complexity is reduced, but problem-solving capability deteriorates due to inability to address business-specific issues
Solution Approach 1:
The patent introduces dynamics by making the analysis method adaptable to different business types through classification. The system structure remains relatively simple, but the analysis behavior dynamically adjusts based on the classified business type, allowing the system to solve specific business problems effectively without requiring complex customizations for each business type.
Solution Approach 2:
The patent achieves universality by creating a classification framework that handles multiple business types through a unified system. The same basic infrastructure and analysis engine serve all business types, with business-specific logic activated through classification, thereby maintaining low system complexity while providing versatile problem-solving capability across different business contexts.
Data Source
AI summary
An information processing apparatus includes a collection unit that collects at least any two selected from a job log, an image log of a document, and information to be monitored through communication with a terminal present around the information processing apparatus; a classification unit that classifies at least two of the job log, the image log, and the information to be monitored collected by the collection unit by type; and an aggregation unit that aggregates results of use for combinations in which at least two types of the job log, the image log, and the information to be monitored classified by the classification unit are combined.