Print Volume Classification via Statistical Segmentation
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Solution Overview
Problem
Modern printing environments face challenges in managing and forecasting print volumes across multiple printing assets, as existing methods struggle to provide quick and accurate interpretations of large volumes of data, making it difficult for managers to understand and predict future needs.
Innovation Solution
A method and system that involve querying computer-readable media for print volume data, performing statistical analysis, generating print volume pseudocode, and encoding classifications for storage and retrieval, allowing for structured queries and efficient data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional data management methods are used for print volume tracking, then data storage is simple, but data analysis speed and accuracy deteriorate
Solution Approach 1:
The patent segments print volume data into multiple time horizons (e.g., weekly, monthly, yearly) and creates separate data structures for each time period. This segmentation allows the system to quickly retrieve and analyze only the relevant time period data without processing the entire dataset, thereby improving analysis speed while maintaining manageable system complexity through organized data division.
Solution Approach 2:
The patent introduces a time horizon dimension to organize print volume data, transforming flat data storage into a multi-dimensional structure. By adding this temporal dimension, the system can efficiently query and analyze data across different time scales simultaneously, improving both analysis speed and the depth of insights without proportionally increasing system complexity.
2Measurement precision
If detailed print volume data is collected for accurate forecasting, then forecast accuracy improves, but data processing time increases
Solution Approach 1:
The patent performs preliminary aggregation and organization of print volume data into standardized time horizon categories during data collection. By pre-processing and structuring the data in advance into meaningful time-based groups, the system eliminates the need for time-consuming processing during analysis, thus maintaining high measurement precision while reducing data processing time when forecasts are generated.
3Reliability
If multiple time horizon data is analyzed for comprehensive insights, then forecasting accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides comprehensive print volume analysis into segmented time horizon components (short-term, medium-term, long-term). Each segment is analyzed independently using appropriate methods, then integrated to form the complete forecast. This segmentation improves forecast reliability by applying tailored analysis to each time period while preventing system complexity from becoming unmanageable through modular processing.
Solution Approach 2:
The patent creates a universal data structure and analysis framework that handles multiple time horizons simultaneously. This multi-functional system can process weekly, monthly, and yearly data through a unified approach, improving forecast reliability across all time scales without requiring separate complex systems for each time period, thus managing overall system complexity effectively.
Data Source
AI summary
Methods, systems, and storage media for characterizing print volume information for a printing asset are disclosed. Exemplary implementations may: query a first computer-readable medium having stored thereon a first print volume data; query a second computer-readable medium having stored thereon a second print volume data; perform a statistical analysis based at least on the first and second print volume data to generate a print volume statistic; code a print volume classification for storage on a data storage device; and store the print volume classification on the data storage device such that the print volume classification is accessible via a structured query.