ML Preflight System for Print Job Error Detection
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Solution Overview
Problem
Current preflight systems in print shops are limited in detecting subtle issues in PDF files, relying on deterministic checks that may not catch all problems, particularly those affecting productivity, and cannot preprocess every job efficiently.
Innovation Solution
A machine learning-based preflight system using neural networks and generative adversarial networks to normalize data, process it through hidden layers, and determine potential issues in print jobs, including RIP performance and fidelity data, enabling more comprehensive detection and correction of problematic files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic preflight checks are used, then the system is simple to operate and implement, but it cannot detect subtle problems that affect productivity and RIP performance
Solution Approach 1:
The patent replaces traditional deterministic preflight check mechanisms with a machine learning-based neural network system. The neural network processes normalized print job data to detect subtle issues that rule-based systems miss, such as RIP performance problems and fidelity issues. This substitution enables detection of previously undetectable patterns while maintaining system usability through automated analysis.
Solution Approach 2:
The system transforms print job data into normalized representations that can be processed by the neural network. By changing the parameter representation from raw PDF structures to normalized feature vectors, the system enables the neural network to analyze subtle relationships and detect problems that traditional checks cannot identify.
2Measurement precision
If comprehensive preflight checks are performed on every job, then detection accuracy improves, but processing time and productivity decrease
Solution Approach 1:
The system performs normalization of print job data as a preliminary action before neural network analysis. This preprocessing step organizes data in advance, enabling the neural network to process jobs efficiently. The normalization is done once and reused, avoiding redundant processing while maintaining high detection accuracy.
Solution Approach 2:
The system creates normalized copies of print job data that can be processed by the neural network without modifying the original files. This copying approach allows comprehensive analysis while preserving the ability to process multiple representations of the same job data efficiently.
3Productivity
If traditional preflight checks are used, then the system is fast to process jobs, but it misses subtle problems that impact print fidelity and RIP performance
Solution Approach 1:
The patent replaces traditional rule-based preflight checking with neural network-based analysis. The neural network learns patterns from training data that correlate with RIP performance and print fidelity issues, enabling detection of subtle problems that deterministic rules miss. This substitution maintains processing efficiency while significantly improving reliability and quality detection.
Solution Approach 2:
The system uses feedback from RIP performance data and print fidelity measurements to continuously improve the neural network's detection capabilities. By incorporating actual performance outcomes into the analysis, the system learns to identify patterns that reliably indicate quality issues, improving both detection accuracy and processing efficiency over time.
Data Source
AI summary
A printing system includes a printing device. The printing system also includes a preflight system that checks incoming print jobs for possible errors or issues before commencing printing operations. The preflight system implements a generative adversarial network to facilitate the identification of possible problems with printing. The generative adversarial network includes a generative neural network and a discriminatory neural network. The generative neural network introduces errors into input data to train the discriminatory neural network in identifying problems with print jobs. The discriminatory neural network backpropagates its output to train the generative neural network.