Dynamic Wait Time Estimation Using Mobile Device Counting
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Solution Overview
Problem
Existing point-of-sale (POS) systems fail to dynamically and accurately update expected wait times for customers based on real-time changes in customer presence and environmental factors, leading to inaccurate estimates and customer dissatisfaction.
Innovation Solution
Implementing a POS device that uses location sensors and other technologies to count mobile electronic devices within a threshold distance, comparing this number to a baseline to adjust wait times dynamically, and considering additional factors like weather and events to provide real-time updates to customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed wait time is provided to customers, then the system is simple to operate, but the accuracy of the wait time estimate deteriorates when business conditions change
Solution Approach 1:
The patent implements dynamic wait time estimation by continuously monitoring the number of mobile devices at the merchant location and adjusting the wait time accordingly. The system transitions from a static fixed wait time to a dynamic estimate that automatically updates based on real-time customer presence data, thereby improving measurement precision without requiring complex manual adjustments
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring the number of mobile devices at the merchant location and using this information to adjust the wait time estimate. The updated wait time is then communicated back to customers, creating a closed-loop system that automatically responds to changing business conditions, improving accuracy while maintaining operational simplicity
2Measurement precision
If real-time monitoring of mobile devices is implemented, then the wait time estimate accuracy is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system implements periodic monitoring of mobile devices at the merchant location rather than continuous monitoring. By checking the number of devices at regular intervals and updating the wait time estimate accordingly, the system maintains accurate real-time estimates while significantly reducing energy consumption and computational resource usage compared to continuous monitoring
3Measurement precision
If multiple factors like weather and events are considered, then the accuracy of wait time prediction is improved, but the device complexity increases
Solution Approach 1:
The patent implements a multi-functional system that monitors not only the number of mobile devices at the merchant location but also integrates additional factors such as weather conditions and local events. This universal approach allows a single system to account for multiple influences on wait times, improving prediction accuracy while consolidating functionality rather than adding separate systems for each factor
Data Source
AI summary
Systems and methods for determining and displaying an average or median count of mobile devices near an entity location is described. The systems and methods comprise receiving location data from a plurality of mobile devices to determine their locations over a period of time. An average or median count of mobile devices of the plurality of mobile devices that are within a threshold distance from an entity during individual hours of a plurality of hours of a day is determined. The highest average or median count of mobile devices during the plurality hours of a day is set as a benchmark count. A user interface is presented that includes a plurality of graphical representations individually corresponding to respective hours of the plurality of hours and a visual indication of the average or median count for the respective hour relative to the benchmark count.