Machine Learning Model for Copying Apparatus Output Sheet Size Selection
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Solution Overview
Problem
When copying documents of varying sizes, existing technologies face challenges in ensuring that marks on the documents are identifiable on the output sheets, particularly when documents are copied at reduced magnification, leading to potential errors and increased costs due to repeated copying.
Innovation Solution
A method involving machine learning is developed to determine the appropriate output sheet size by collecting data on document images and their sizes, using this information to train a model that predicts whether marks are identifiable on the output sheet, allowing for optimal size selection during copying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If documents are copied at reduced magnification to fit smaller output sheets, then output sheet size is reduced and cost is decreased, but mark identifiability deteriorates
Solution Approach 1:
The system changes the parameter of output sheet size selection by using machine learning to predict appropriate sizes. Instead of always reducing to the smallest available sheet, the ML model predicts the optimal sheet size that maintains mark identifiability while minimizing waste, thus resolving the contradiction between size reduction and mark clarity.
2Adaptability or versatility
If documents of various sizes are mixedly loaded, then copying flexibility is improved, but determining appropriate output sheet size becomes more difficult
Solution Approach 1:
The system employs a machine learning model that automatically analyzes the loaded documents and self-determines the appropriate output sheet size without requiring manual user input or complex configuration. The model processes the mixed document sizes and autonomously selects the optimal sheet size, simplifying the determination process while maintaining flexibility.
3Measurement precision
If the user manually determines output sheet size, then mark identifiability can be ensured, but copying efficiency and productivity decrease
Solution Approach 1:
The system replaces the manual mechanical process of user judgment with an automated machine learning-based determination system. The ML model automatically evaluates document characteristics and predicts appropriate output sheet sizes, substituting human cognitive processes with computational algorithms that operate faster and more consistently, thereby maintaining mark identifiability while improving copying efficiency.
Data Source
AI summary
A method of producing a machine learning model includes collecting, as training data, data including an image of a document and a size of the document or a size of an actually copied output sheet from a copying apparatus that copies the document on the output sheet, and producing a model for determining whether or not a mark included in the image of the document is identifiable by a user on the document or the copied output sheet based on the training data.