ML Print Setting Assistance for Varying Image Forming States
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Solution Overview
Problem
Existing print setting technologies fail to provide optimal settings for image forming apparatuses with varying states, such as different photosensitive drum conditions, leading to suboptimal print quality.
Innovation Solution
A service providing system utilizing a learning model that integrates machine learning to determine print job settings based on device and job characteristics, leveraging state and job information from multiple image forming apparatuses to adapt settings dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If print settings are determined using fixed rules or statistical information, then the system is simple to operate, but the settings are not optimal for different apparatus states such as varying photosensitive drum conditions
Solution Approach 1:
The patent changes the parameter of setting determination from fixed rules to dynamic machine learning models. The learning model takes apparatus state parameters (such as photosensitive drum age, environmental conditions) and job parameters as inputs, and dynamically determines optimal print settings. This resolves the contradiction by using parameter changes to adapt settings to different apparatus states while maintaining automated operation.
Solution Approach 2:
The system performs self-service by automatically determining optimal print settings through machine learning without requiring user expertise. The learning model autonomously analyzes apparatus state and job characteristics to generate appropriate settings, eliminating the need for users to understand complex print parameters while ensuring high print quality.
2Adaptability or versatility
If a learning model using data from multiple apparatuses is used to determine settings, then optimal settings for varying apparatus states are achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent implements universality by creating a single learning model that can handle multiple apparatus types and states. The model is trained on aggregated data from multiple image forming apparatuses, enabling it to universally determine optimal settings across different apparatus configurations, photosensitive drum conditions, and environmental states, thereby achieving high adaptability without requiring separate models for each apparatus.
Solution Approach 2:
The learning model acts as an intermediary between raw apparatus state data and final print settings. It processes and interprets complex apparatus state information (such as drum age, environmental conditions) and translates it into optimal print parameters, simplifying the overall system architecture while maintaining high adaptability to varying conditions.
3Manufacturing precision
If detailed setting items are required for high image quality printing, then print quality is improved, but the ease of operation decreases as users need specialized knowledge
Solution Approach 1:
The system performs self-service by automatically determining all necessary print settings through the machine learning model without user intervention. The model comprehensively analyzes apparatus state and job characteristics to generate complete setting configurations, eliminating the need for users to manually adjust detailed parameters while ensuring high image quality output.
Solution Approach 2:
The learning model serves as an intermediary between the user's simple print request and the complex setting requirements. It automatically translates high-level job information and apparatus state into detailed optimal settings, shielding users from complexity while maintaining high print quality through comprehensive parameter optimization.
Data Source
AI summary
A service providing system includes a hardware processor, wherein the hardware processor acquires respective state information and job information as respective first state information and first job information, with the state information relating to a first image forming apparatus, and with the job information relating to a first print job to be executed by the first image forming apparatus, and outputs setting information for execution of the first print job by using a learning model, with the setting information being determined based on the first state information and the first job information, and with the learning model having executed machine learning using second state information that is the state information of each of a plurality of second image forming apparatuses and second job information corresponding to a second print job that has been executed in each of the plurality of second image forming apparatuses.