Long lines at venues, retail stores, and service counters remain a persistent challenge for operators and customers alike. Concerts, stadiums, theme parks, and supermarkets frequently experience high visitor volumes that lead to congestion at points of service. Traditional approaches to estimating wait times rely on staff observations, averages, or basic counting methods, which often fail to capture real-time fluctuations caused by crowd movement, service speed, or individual behavior. As a result, customers face uncertainty, spend more time waiting, and may even abandon their purchase or activity, while operators lose potential revenue and struggle to manage resources effectively.
Advances in artificial intelligence and real-time analytics are beginning to change how wait times are managed. Systems now use data from overhead cameras, mobile devices, beacons, and other sensors to measure crowd density, predict line lengths, and generate individualized wait time estimates. These technologies provide operators with actionable insights to open additional service points, redirect guests, or adjust staffing in real time.
As these systems evolve, legal and implementation considerations are becoming increasingly important. In public-facing environments where AI is deployed at scale, privacy concerns are also becoming more prominent. Gerard Reinhardt from Reinhardt IP notes that the use of location tracking, camera feeds, and behavioral data raises issues under frameworks such as Fourth Amendment protections and state level biometric regulations like the Illinois Biometric Information Privacy Act. At the same time, real-world deployment challenges remain significant. Past failures in AI-driven order-taking systems, including the McDonald’s IBM powered drive thru rollout, highlight how accuracy limitations, operational complexity, and cost constraints can significantly impact adoption.
In this latest AI Patent Watch article, we showcase how AI is being applied across venues, retail locations, and service counters to streamline operations and enhance the customer experience.
Real-time queue tracking and wait time estimation system
This invention provides a computer-implemented system and method that uses overhead images to calculate real-time wait times for users in a line at a venue, allowing users to make informed decisions about whether to join a line or wait for a shorter queue.
Many venues such as entrances, concession stands, restrooms, and checkout areas experience long lines, yet users often have little visibility into how long they will need to wait. Wait times vary significantly depending on service speed, individual behavior, and fluctuations in line length, making them difficult to estimate. As a result, users must make decisions without reliable information and may wait longer than necessary or miss better opportunities to access services.
U.S. Patent No. 10,902,441 uses one or more overhead cameras positioned above a line to capture images of users. The computing system processes the images to identify a target user, typically the second user in the line, and tracks when this user reaches the front of the line and when they exit. The system calculates a period of time from the start to the end and multiplies it by the number of remaining users in the line to estimate a user wait time.
The system can combine multiple camera images to correct for distortion or obtain a broader view, and it can ignore users whose movement indicates they are not waiting. Calculated wait times are output to mobile devices, remote computing systems, or analytics platforms, allowing users to decide when to join a line or when to wait for a shorter queue. The system can also provide real-time density metrics for areas of a venue, indicating user concentration and enabling the management of crowd flow or capacity limits. It is adaptable for various venues, including entertainment arenas, theme parks, retail stores, and business locations.
The patent, titled “Techniques for automatic real-time calculation of user wait times”, was filed on June 25, 2020, and was granted on January 26, 2021 to WaitTime. The inventors listed are CEO Zachary Klima; Thomas Sterling, John Mars, JR., and Doyle Mosher. Jason Benedict from RMCK Law Group represented WaitTime in the patent filing.
Dynamic wait time estimation for high-traffic venues
This invention provides a system and method for estimating wait times at points of interest in a venue using data from mobile devices, cameras, beacons, and event schedules, enabling both current and predictive wait time estimates to be delivered to guests or staff.
Venues like theme parks, cruise ships, stadiums, concert halls, and universities often face high visitor volumes at rides, restaurants, restrooms, and other attractions. Wait times fluctuate with visitor density, time, and operational throughput, but traditional manual estimates are often inaccurate, reactive, and limited to major attractions. Smaller points of interest usually provide no wait information, and predicting future wait times is difficult because it requires integrating multiple data sources, including crowd movements, guest behavior, and environmental factors.
U.S. Patent No. 11,526,916 collects mobile device location data from guests, event schedule data for points of interest, and optionally additional inputs such as camera images, beacon proximity, network traffic, entitlement redemption, or weather data. These inputs are analyzed using algorithms to generate estimated wait times for specific points of interest, both for the present and for future periods.
The system can display wait times through front-end devices, including mobile devices for guests or staff devices, optionally using map-based interfaces that scale regions according to the venue layout. Multiple wait times can be predicted at predetermined intervals, and trends can be visualized through graphs. By integrating real-time tracking, predictive modeling, and venue-specific event schedules, the system provides accurate, dynamic, and predictive wait time estimates that improve guest experience and support operational decision-making.
The patent, titled “Intelligent prediction of queue wait times”, was filed on April 28, 2015, and was granted on December 13, 2022 to Blazer and Flip Flops, Inc (now under Accesso Technology Group PLC). The inventors listed are Benjamin Harry Ziskind, Joshua David Bass, Scott Sebastian Sahadi, and Benjamin Keeling Mathews. Ross Dannenberg from Banner & Witcoff represented the patent filing.
Smart queue management for faster checkouts
This disclosure relates to methods and systems for predicting wait times in service area queues, using visual cues, service item load, and operator efficiency to help customers make informed decisions and improve overall satisfaction.
In busy retail stores and supermarkets, customers often wait a long time at service counters, especially during weekends or peak hours. Traditional queue management methods usually estimate wait times by counting the number of people in line or tracking how quickly customers arrive and are served. Some systems use Bluetooth or Wi-Fi, but they need extra equipment and cannot accurately tell waiting time from service time. Short-term fixes, such as opening special counters for small purchases or electronic payments, may also create new inefficiencies instead of solving the problem.
U.S. Patent No, 11,636,498 describes a system that predicts how long customers will wait in a checkout line using camera images. The system analyzes each person in the queue and estimates simple details such as their age, mood, and the number of items in their cart. It combines this information with how fast the cashier usually works to estimate how long each customer will take to finish their transaction. By adding these estimated service times together, the system calculates the total waiting time for the entire line in real time. The predicted wait times can then be shown on displays so shoppers can choose the fastest queue.
This approach provides more accurate wait time estimates, helping customers reduce time spent in line while giving stores better visibility into queue performance and opportunities to improve staffing or manage busy periods more effectively.
The patent, titled “Methods and systems for predicting wait time of queues at service area”, was filed on September 21, 2020, and was granted on April 25, 2023 to Tata Consultancy Services. The inventors listed are Kunal Milind Patil, Kaushal Balkrishna Govil, and Kedar Narayan Phadke. Finnegan handled the patent prosecution process.
Real-time order wait time prediction system
This invention provides a system and method for predicting wait times for orders at stores or manufacturing facilities, by analyzing the status of multiple print queues and using machine learning to provide accurate real-time estimates to clients.
Production facilities often run many orders at once on multiple machines, but clients usually do not know how long their order will take. Current systems give rough estimates or only show results after the job is done. Some places share average wait times from past data, but these do not reflect what’s happening in real time. This makes it hard for clients to plan and can slow down operations. Problems include no real-time wait info, multiple machines making timing hard to predict, system outages, and methods that don’t consider order size or complexity.
U.S. Patent No. 12,026,413 presents a system that lets clients see how long their orders will take in real time. When a client submits an order, the system collects data from all relevant printers or production devices, calculates how long each queued item will take, and combines this information into an overall estimated wait time. This estimate is sent back to the client and shown in a simple interface.
The system updates dynamically based on real-time conditions, uses fallback methods if a machine-learning server fails, and improves predictions over time using historical data. It even shows the longest single-item wait time to give a conservative estimate. This helps clients plan their orders, reduces uncertainty, and improves the customer experience, while stores gain better visibility into operations, fewer bottlenecks, and improved resource planning.The patent, titled “Machine-learning system and print-queue based estimator for predicting wait times”, was filed on February 27, 2024 and was granted on July 2, 2024 to Starbucks. The patent lists Harsh Nigam, Michael J. Harlach, Zach A. Thieme, Shadi Hassani Goodarzi, Kelly L. Broad, Ross W. Marshall, John J. Schultz, Matthew A. Scheid, Chadwick C. Meyer, and Omobolaotan O. Agbonile as inventors. Zachary Kelton from Kilpatrick Townsend & Stockton represented Starbucks in the filing.