Queue Wait Time Prediction Using Visual Customer Cues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for predicting wait times in service queues at brick-and-mortar businesses are inaccurate due to limited consideration of customer factors, leading to suboptimal customer experience and operational inefficiencies.
Innovation Solution
A processor-implemented method and system that utilizes deep learning-based image analysis to extract customer demographics, emotional states, and shopping cart contents to predict wait times by calculating service times based on age, emotional state, and operator efficiency, thereby enhancing the accuracy of wait time predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use only customer count related factors (average arrival rate, average departure rate) to predict wait time, then the prediction system is simple to implement, but the wait time prediction accuracy is low
Solution Approach 1:
The patent segments the prediction system into multiple independent modules: image acquisition module, deep learning recognition module (further segmented into face recognition and cart recognition), factor extraction module, and wait time calculation module. This segmentation allows the system to handle complex predictions through modular components while maintaining implementability.
Solution Approach 2:
The patent introduces deep learning recognition models as intermediary components that bridge the gap between raw image data and prediction results. These models act as mediators that extract meaningful features (age, emotional state, cart contents) from images, enabling accurate predictions without directly complexifying the overall system architecture.
2Loss of time
If the service area introduces separate counters for customers with fewer items or who prefer electronic payments, then the wait time for those specific customers is reduced, but operational inefficiencies occur and customer experience deteriorates
Solution Approach 1:
The patent implements dynamic queue management where counter assignments are not fixed but dynamically adjusted based on real-time predictions. The system continuously monitors queue characteristics and customer attributes, dynamically routing customers to appropriate counters to optimize both wait time and operational efficiency, eliminating the need for static separate counters.
Solution Approach 2:
The system changes the parameters used for queue management from simple customer count or item count to composite parameters including demographic factors, emotional state, and cart load factors. This parameter transformation enables more nuanced and efficient customer routing decisions that balance wait time reduction with operational productivity.
3Loss of information
If customers choose queues based on visible factors (number of customers, number of items), then customers can make informed decisions, but the wait time prediction remains inaccurate due to insufficient consideration of customer-specific factors
Solution Approach 1:
The patent adds new dimensions to the prediction model by extracting multiple attributes from customer images: demographic factors (age), emotional state, and cart load factors. This dimensional expansion transforms the prediction from relying on single-dimensional queue length to multi-dimensional customer-specific factors, significantly improving information completeness and prediction accuracy.
Data Source
Figure 1
Figure 2
AI summary
This disclosure relates generally to methods and systems for predicting wait time of queues at service area such as market places including retail stores and super markets. The present methods and systems accurately predicts the wait times of the plurality of queues by utilizing various visual cues of the customers, along with the number of service items and the efficiency of the service operator. The visual cues including a demographic factor such as age, gender, ethnicity of the customer and a senti-motional factor such as sentiments including positive attitude or negative attitude and emotions of the customers including happy state, sad state, and irritation state of the customer. The customers may choose the queue having least predicted wait time and may take informed decision hoping for faster check-out, based on the predicted wait times of the queues. Hence, customer experience and customer satisfaction may be achieved.