Neural Network Preflight System for PDF Error Detection
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Solution Overview
Problem
Current preflight systems in print shops are limited in detecting subtle problems in PDF files, relying on deterministic checks that may not catch all issues, 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 problem detection and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic preflight checks are used, then the system is easy to operate and implement, but it cannot detect subtle problems that affect productivity and RIP performance
Solution Approach 1:
The patent replaces traditional deterministic rule-based preflight checks with a machine learning neural network system. The neural network processes normalized print job data through multiple layers (input layer, hidden layers with neural nodes that multiply inputs by weights and sum them, and output layer) to detect subtle problems in PDF files that traditional checks cannot identify. This substitution enables detection of issues affecting RIP performance and productivity while maintaining system operability.
2Measurement precision
If comprehensive preflight checks are performed on every job, then problem detection accuracy improves, but processing time and productivity decrease
Solution Approach 1:
The patent implements a normalization process that prepares print job data in advance by converting various PDF structures into a standardized format suitable for neural network processing. This preliminary action organizes the data efficiently, allowing the neural network to process jobs quickly without sacrificing detection accuracy. The normalization occurs before the actual preflight analysis, enabling comprehensive checks to be performed more efficiently.
3Reliability
If traditional preflight checks are used, then the system structure is simple, but it cannot identify problems that only RIP vendors can detect
Solution Approach 1:
The patent replaces simple deterministic checklists with a sophisticated neural network architecture that includes input layers for receiving normalized print job data, hidden layers with neural nodes that perform weighted multiplication and summation operations, and output layers that generate detection results. This complex structure enables the system to identify subtle PDF issues and RIP performance problems that traditional checks cannot detect, achieving high reliability in problem detection.
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.