Print Control Device Job Rescheduling for Failed Material

The patent segments the job queue into two categories: jobs affected by the failure cause and jobs not affected. When a failure is detected, only the affected jobs are suspended while unaffected jobs continue to execute. This segmentation allows the system to maintain quality control for problematic jobs while preserving productivity by continuing processing of unaffected jobs.

The patent implements dynamic job execution control where the printing machine's operation state changes based on real-time failure analysis. Instead of a static stop-all-approach, the system dynamically adjusts execution by identifying and suspending only specific affected jobs while allowing unaffected jobs to proceed, thereby adapting the production flow to actual conditions.

The patent segments the job queue into affected and unaffected jobs based on failure cause analysis. By suspending only affected jobs while continuing unaffected jobs, the system minimizes downtime while still allowing the operator to address the root cause of failures. This selective suspension reduces the time loss compared to stopping all jobs.

The patent performs preliminary analysis of the failure cause and its potential impact on subsequent jobs before suspending execution. By identifying which jobs are actually affected by the failure cause (e.g., same paper tray, same print head), the system avoids unnecessary suspensions and reduces downtime while still preventing potential quality issues.

The patent implements a feedback mechanism where inspection results of printed materials are continuously monitored and fed back to the control system. When failures are detected, the system analyzes the cause and automatically determines which subsequent jobs are affected, then suspends only those specific jobs. This feedback-driven approach automates the control logic, managing the complexity through intelligent algorithms rather than manual intervention.

The patent enables the printing system to automatically analyze failure causes, identify affected jobs, and adjust execution without requiring manual intervention. The control system performs self-diagnosis and self-adjustment by comparing failure patterns with job characteristics, thereby managing control complexity through automated decision-making rather than human operators.