Automated Job Basket Selection for Print Pricing Models
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Solution Overview
Problem
Current manual selection of jobs in reverse auctions for print market ports is cumbersome, inaccurate, and expensive, making it inefficient for determining optimal price models.
Innovation Solution
An automated system using a processor and computer-readable storage medium with programming instructions to partition job datasets, construct neural network models, predict costs, and iteratively remove jobs with high prediction errors until an optimal set is reached, thereby determining an optimal job basket for establishing baseline prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of jobs is used for determining price models, then accuracy can be maintained through expert judgment, but the process becomes cumbersome, time-consuming, and expensive
Solution Approach 1:
The patent replaces the manual mechanical process of job selection with an automated computer-based system that uses neural networks and iterative algorithms to select optimal job samples for price model determination, eliminating the need for manual expert judgment while maintaining or improving accuracy
Solution Approach 2:
The system performs self-service by automatically selecting jobs based on predetermined criteria and iterative error analysis without requiring external human intervention, allowing the computer system to autonomously determine the optimal job sample set for pricing models
2Reliability
If manual selection of jobs is used for determining price models, then expertise can guide the selection, but the process becomes expensive to implement
Solution Approach 1:
The patent substitutes expensive manual expert processes with an automated computer-based neural network system that performs job selection through iterative error analysis, significantly reducing the cost while maintaining or improving the reliability of job selection for price models
3Measurement precision
If iterative error analysis is performed on all jobs, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts and removes jobs with high prediction errors from the dataset after each iterative analysis cycle, progressively refining the job sample set to include only those jobs that meet predetermined error thresholds, thereby improving accuracy while managing computational complexity through selective elimination
Data Source
AI summary
A system for determining price models of a print market port including a processor and a computer-readable storage medium in communication with the processor, wherein the computer-readable storage medium comprises one or more programming instructions for: partitioning a job dataset into a plurality of categories, each of the plurality of categories having a pricing model; determining one or more factors within the job dataset that influence a price of each job; developing an input/output model for each job in the job dataset that influences the price of the job; performing an iteration to compute a prediction error for each job in the job dataset that influences the price of the job; removing one or more jobs from a subsequent iteration that include prediction errors that exceed a prediction error threshold; and performing a plurality of iterations on remaining jobs until a predetermined average error prediction is reached.