Dynamic Queue Management via Urgency-Based Priority Scoring
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Solution Overview
Problem
Existing queue management systems fail to dynamically assign positions within a queue based on the urgency of a user's call and other factors, leading to inefficient customer service and potential loss of customers due to long wait times.
Innovation Solution
A queue management system that utilizes one or more processors and memory to determine a dynamic priority score for users based on their intent, urgency data such as battery life, location, and recent account activity, and assigns a user-specific position within a queue that differs from a default position using a machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional queue management systems place all customers in line based on order of calling, then the system structure remains simple and easy to operate, but urgent calls are not prioritized leading to longer wait times and potential customer loss
Solution Approach 1:
The patent changes the queue position assignment from a static first-come-first-served parameter to a dynamic parameter based on multiple factors including battery level, call urgency, and customer history. This allows urgent calls to be prioritized while maintaining system operability through automated scoring.
Solution Approach 2:
The system automatically calculates priority scores and assigns queue positions without requiring manual intervention from operators. The machine learning model self-adjusts based on input data, reducing operational complexity while improving service efficiency.
2Reliability
If the system dynamically determines queue positions based on multiple factors including battery level, then customer service quality improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system collects and analyzes multiple data points (battery level, call history, urgency indicators) before assigning queue position. This preliminary analysis ensures reliable prioritization decisions are made based on comprehensive information rather than simple queue order.
Solution Approach 2:
The machine learning model acts as an intermediary that processes multiple complex inputs (battery data, call records, urgency signals) and converts them into a simple priority score that determines queue position, managing system complexity through abstraction.
3Ease of operation
If urgent calls are placed at the end of the queue like non-urgent calls, then the queue management process remains simple, but customer satisfaction decreases due to long wait times for urgent issues
Solution Approach 1:
The patent applies different queue position assignment rules to different types of calls based on their urgency and customer characteristics. Urgent calls with low battery levels receive priority positioning, while routine calls follow standard queue order, optimizing wait times for critical situations.
Solution Approach 2:
The queue position is made dynamic rather than static, allowing automatic repositioning of customers based on changing conditions such as battery level depletion. This enables the system to adapt to urgency changes without manual intervention while maintaining operational simplicity.
Data Source
AI summary
Disclosed embodiments may include a queue management system. The system may receive one or more utterances comprising a customer intent from a user device, determine a first queue from a plurality of queues in which to place the user based on the user intent, and receive first urgency data comprising battery indication data from the user device. The system may then determine, using a machine learning model, a first dynamic priority score for the user based on the user intent and the first urgency data including battery indication data associated with the user device. Based on the first dynamic priority score for the user, the system may assign an initial user-specific position within the first queue to the user that differs from a default initial position in the first queue. Based on updated urgency data, the system may dynamically update the user's position to a second user-specific position.