Print Performance Prediction Model Using Metadata Simulation
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Solution Overview
Problem
High-speed production printers require lengthy testing to determine optimal hardware configurations, leading to inefficiencies and customer dissatisfaction due to high turnaround times and economic constraints, as well as miscommunication across teams regarding printer configurations.
Innovation Solution
A prediction model is trained using print job metadata and configuration criteria to simulate and estimate processing performance measurements, eliminating the need for actual printer testing and reducing the requirement for domain-specific knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual printer testing is conducted to determine performance, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent creates a virtual copy of the printing system through a simulation environment that replicates the behavior and performance characteristics of actual hardware. This virtual model allows performance testing without physical printer testing, thereby maintaining measurement precision while dramatically reducing testing time from days to minutes.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models using historical printing data and system configurations. This preliminary training enables the simulation to predict performance outcomes without requiring actual physical testing, thus achieving accurate performance measurements in advance of actual deployment.
2Productivity
If more hardware resources are acquired to reduce turnaround time, then productivity is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical physical testing system with a computational simulation system. Instead of acquiring more physical printers and hardware resources to increase testing throughput, the system uses software-based simulation and machine learning models to evaluate multiple configurations virtually, thereby improving productivity without increasing physical device complexity.
Solution Approach 2:
The simulation environment serves multiple functions: it can evaluate different printer configurations, test various document types, predict performance metrics, and generate recommendations all within a single virtual platform. This multi-functionality allows the system to handle diverse testing scenarios without requiring separate physical hardware for each test case.
3Measurement precision
If domain experts conduct performance testing, then measurement precision is improved, but loss of time increases due to scheduling constraints
Solution Approach 1:
The system enables self-service performance evaluation where customers can submit their document samples and receive performance predictions automatically without requiring scheduling with domain experts. The machine learning model autonomously processes the documents, runs simulations, and generates performance reports, eliminating the need for expert intervention while maintaining measurement quality.
Solution Approach 2:
The patent introduces an intermediary automated evaluation system that acts as a bridge between customer document submissions and performance results. This intermediary uses machine learning models and simulation environments to translate document characteristics into performance predictions, eliminating the need for direct expert-customer interaction and reducing wait times from months to minutes.
4Measurement precision
If configuration communication is improved across teams, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges the configuration specification, simulation execution, and performance evaluation functions into a single integrated platform. By combining these previously separate processes into one unified system, the patent improves configuration communication and measurement accuracy without proportionally increasing complexity, as the merged system handles multiple tasks through coordinated software modules rather than requiring complex inter-team communication protocols.
Data Source
AI summary
A printing system is described. The printing system includes a memory to store print performance prediction logic and a processor to execute the print performance prediction logic to train a prediction model, receive print job metadata, receive configuration criteria of a printing system and predict first processing performance measurements of the printing system using the prediction model to simulate the printing system based on the print job metadata and the configuration criteria.