Print-Queue Based Wait Time Estimator Using ML Fallback
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Solution Overview
Problem
Current systems lack an efficient method to accurately predict wait times for orders in production locations, such as stores, which can lead to resource overburden and inaccurate estimates, especially when relying solely on local computing resources or failing machine-learning models.
Innovation Solution
A system that utilizes a combination of machine-learning models and print-queue data to estimate wait times, where a trained machine-learning model generates accurate wait times when available, and falls back to a less computationally intensive print-queue approach if the model fails, ensuring timely and resource-efficient estimates are provided to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine-learning model is used to predict wait times, then prediction accuracy is improved, but computational burden increases
Solution Approach 1:
The patent introduces print-queue data as an intermediary indicator that correlates with wait times but requires minimal computational resources to collect and process. Instead of directly using complex machine-learning models on local devices, the system uses print-queue metrics (number of jobs, priority levels, estimated processing times) as a proxy to predict wait times, thereby maintaining accuracy while reducing computational burden.
Solution Approach 2:
The patent creates a simplified copy or representation of the wait time prediction problem through print-queue metrics. Rather than running full machine-learning inference locally, the system collects print-queue data that mirrors the state of the production system and uses this copied information to estimate wait times, reducing the need for heavy local computation.
2Reliability
If machine-learning models are deployed locally, then prediction capability is improved, but resource overburden worsens
Solution Approach 1:
The patent extracts the heavy machine-learning model from the local production device and relocates it to a remote server with sufficient computational resources. The local device retains only the lightweight functionality of collecting print-queue data and displaying predictions, thereby maintaining prediction capability while eliminating resource overburden on local systems.
Solution Approach 2:
The patent shifts the computational workload from the local device dimension to the remote server dimension by implementing a client-server architecture. The local device focuses on data collection and display, while the remote server handles the computationally intensive machine-learning inference, effectively distributing the system across different dimensional layers.
3Productivity
If simple estimation methods are used, then computational efficiency is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by collecting and analyzing print-queue data in advance to train the machine-learning model on a remote server. The model is pre-trained to recognize patterns in print-queue metrics that correlate with wait times, enabling the system to make accurate predictions using simple, efficient queries to the trained model rather than complex real-time analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the machine-learning model continuously learns from actual wait time data and adjusts its predictions accordingly. The system compares predicted wait times with actual completion times and uses this feedback to refine the model, progressively improving accuracy while maintaining computational efficiency through the use of trained patterns rather than raw data processing.
Data Source
AI summary
In one example, a system can receive an order from a client device. The system can also receive data about a set of print queues at a set of printers via a network. The system can then determine a set of queue times corresponding to the set of print queues. Each respective queue time can be determined based on a corresponding print queue. The system can generate an estimated wait time for the order based on the set of queue times. The system can then transmit the estimated wait time to the client device for display in a graphical user interface to a user associated with the order.