Queue Management Using ML Proximity Prediction
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Solution Overview
Problem
Current queue management systems fail to optimize waiting times effectively due to the inability to dynamically configure devices or resources in real-time based on user proximity and behavior, leading to delayed task performance.
Innovation Solution
A computer-implemented queue management system using machine learning to determine the probability of user engagement with configurable nodes, dynamically adjusting thresholds and altering node states based on proximity and historical data, allowing for real-time configuration of resources such as elevators or teller systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If queue management systems use traditional methods to manage waiting users, then the system structure remains simple, but waiting times are not optimized effectively
Solution Approach 1:
The system performs preliminary actions by predicting user engagement probability and dynamically configuring nodes before users actually need them. The machine learning engine forecasts which nodes users will engage with and prepares the system state in advance, reducing waiting time without requiring complete system reconfiguration.
Solution Approach 2:
The system implements dynamics by continuously adjusting node states based on real-time proximity data and predicted user behavior. The queue management system dynamically reconfigures nodes as users move through the space, adapting the system state to anticipated needs rather than following static or reactive configurations.
2Loss of time
If the system dynamically configures devices in real-time based on user proximity, then waiting times are reduced, but the computational complexity and data processing requirements increase
Solution Approach 1:
The machine learning engine operates autonomously, continuously analyzing proximity data and historical patterns to predict user engagement without requiring manual intervention. The system serves itself by automatically adjusting node configurations based on real-time inputs, reducing the need for complex external control mechanisms.
Solution Approach 2:
The system implements feedback loops where proximity measurements and user engagement data continuously inform predictions and configuration decisions. Historical data feeds into the machine learning model, which adjusts its predictions based on actual user behavior patterns, creating a self-optimizing system that reduces computational overhead over time.
3Productivity
If the system pre-configures resources in anticipation of user needs, then task performance efficiency is enhanced, but the accuracy of predicting user behavior must be maintained
Solution Approach 1:
The system performs preliminary configuration actions based on predicted user engagement probability. By calculating the likelihood that a user will engage with a specific node and preparing the system state in advance, the system enhances task performance efficiency while maintaining prediction accuracy through continuous model refinement.
Solution Approach 2:
The machine learning engine changes parameters such as node state configurations based on predicted user behavior. The system adjusts operational parameters dynamically, changing the state of configurable nodes to match anticipated user needs, thereby improving productivity while maintaining accurate predictions through parameter optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces waiting times by ensuring that resources are pre-configured in anticipation of user needs, enhancing the efficiency of task performance across various scenarios like transportation, banking, and healthcare.
Implementation Method 1
the proximity is determined using mobile signal strength
Data Source
AI summary
A computer-implemented process using a queue management system includes the following operations. A user and a plurality of configurable nodes are identified in a scope of the queue management system. A proximity between the user and a particular one of the plurality of configurable nodes is dynamically determined in real-time. Using a machine learning engine and based upon the proximity and historical data associated with the scope of the queue management system, a probability measure that the user will engage with the particular one of the plurality of configurable nodes is determined. Based upon the probability measure exceeding a threshold, the user is added to a queue managed by the queue management system. Based upon the user being added to the queue, a state of the one of the plurality of plurality of configuration nodes is altered by the queue management system.