ML-Assisted Printer Image Quality Adjustment for Consistent Color
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Solution Overview
Problem
Conventional color adjustment for printers requires high expertise and experience, leading to variable results when performed by low-skilled operators, and is common in general image quality adjustment including color and density unevenness.
Innovation Solution
An information processing system utilizing machine learning to infer candidate handling methods for image quality adjustment issues, based on job and history information, and providing assistance through a display device and notification system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If color adjustment is performed by low-skilled operators using conventional methods, then the operation is easier to perform, but the manufacturing precision of color quality varies
Solution Approach 1:
The system enables self-service by allowing the image forming apparatus to automatically perform color adjustment using embedded sensors and machine learning models, eliminating the need for skilled operators to manually adjust color settings while maintaining consistent quality results
Solution Approach 2:
The patent replaces manual mechanical color adjustment operations with an automated sensing and processing system that uses image sensors, machine learning inference, and automatic parameter modification to achieve color quality adjustment without human intervention
2Manufacturing precision
If color adjustment requires high expertise and experience, then the manufacturing precision of color quality is maintained, but the ease of operation deteriorates
Solution Approach 1:
The system enables self-service by allowing the image forming apparatus to automatically perform color adjustment using embedded sensors and machine learning models, eliminating the need for skilled operators to manually adjust color settings while maintaining consistent quality results
Solution Approach 2:
The system automatically changes color adjustment parameters based on sensor measurements and machine learning inference, dynamically modifying printing conditions such as ink concentrations and image processing parameters to achieve optimal color quality without requiring operator expertise
3Manufacturing precision
If automated machine learning inference is implemented, then the manufacturing precision of image quality adjustment is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary information processing system that acts as a mediator between the image forming apparatus and the machine learning model, handling data collection, preprocessing, and result application to manage system complexity while maintaining adjustment precision
Data Source
AI summary
An information processing system includes circuitry to acquire job information related to one or more image forming apparatuses on which image quality adjustment are performed and history information of an interaction between a user and a service provider of a service for solving a problem related to the image quality adjustment; obtain a candidate handling method for the problem in response to an assistance request for the problem according to a user operation by using the job information and the history information, the candidate handling method being obtained from a learning model to infer a candidate handling method for the problem; cause a display device of the service provider to display the candidate handling method on an assistance screen; and send a notification indicating a response made by the service provider in response to the assistance request based on the candidate handling method.