Abstract

This disclosure presents a system and method for managing print queues as a persistent, hierarchical structure of folders, subfolders, and job records within production printing environments. Unlike conventional flat-list queue models, the disclosed approach preserves semantic relationships among related print jobs throughout the entire execution lifecycle, enabling group-level operations—such as print, pause, resume, cancel, and prioritize—to be applied coherently to collections of jobs without requiring individual job manipulation.

A central queue manager maintains a hierarchical model composed of folder nodes, subfolder nodes, and job nodes, each carrying metadata such as priority, state, sequencing policy, media requirements, and device constraints. A classification component assigns incoming print jobs to appropriate folders automatically or with operator assistance, leveraging metadata, job ticket fields, and optional artificial intelligence or machine learning inference to recognize batch relationships and recommend folder assignments with confidence scoring.

A command translation layer converts folder-level directives into standard job-level commands, ensuring full compatibility with legacy spoolers, raster image processors, and print engines. Runtime reorganization logic monitors queue conditions and may dynamically move, split, merge, hold, or redirect folders in response to events such as urgent job arrivals, media unavailability, device failures, or deadline changes—all without requiring job re-ingestion. A tree-based operator interface exposes the hierarchy with aggregate status indicators and supports both hierarchical and flat views over the same underlying job records.

The disclosed system establishes prior art for structured, folder-based print queue management that combines hierarchical job organization, AI-assisted classification, real-time runtime reorganization, and legacy compatibility through command translation.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

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