POI Queue Request Timing Using Predicted Wait and Arrival Time
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Solution Overview
Problem
Existing methods for predicting waiting times at points of interest (POIs) are unreliable due to arbitrary input errors, leading to inaccurate information and user inconvenience, such as being redirected to the back of the queue or missing the opportunity to use the POI.
Innovation Solution
A method and device that utilize a processor and memory to generate an expected arrival time, estimate waiting times based on actual patterns, and request use of the POI at an optimal time, considering both travel and waiting times, using cumulative waiting information and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If arbitrary input method is used for waiting time, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system automatically collects waiting time data from actual customer experiences and uses this data to predict waiting times without requiring manual input from store staff. The electronic device autonomously gathers, processes, and updates waiting time information based on real-world observations, eliminating the need for arbitrary human input while maintaining high prediction accuracy.
2Ease of operation
If predicted waiting time is used for navigation, then user convenience is improved, but reliability deteriorates due to input errors
Solution Approach 1:
The system continuously collects actual waiting time data from customers who visit the POI and uses this feedback to update and refine its predictions. The waiting time information is dynamically adjusted based on real-world outcomes, ensuring that the predictions remain accurate and reliable while providing convenient navigation assistance to users.
3Extent of automation
If manual waiting time input is used, then automation extent is reduced, but measurement precision is improved
Solution Approach 1:
The system automatically collects waiting time data from actual customer experiences and uses this data to predict waiting times without requiring manual input from store staff. The electronic device autonomously gathers, processes, and updates waiting time information based on real-world observations, eliminating the need for arbitrary human input while maintaining high prediction accuracy.
4Productivity
If automated waiting time prediction is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system automatically collects waiting time data from actual customer experiences and uses this data to predict waiting times without requiring manual input from store staff. The electronic device autonomously gathers, processes, and updates waiting time information based on real-world observations, eliminating the need for arbitrary human input while maintaining high prediction accuracy.
Data Source
AI summary
Disclosed herein a method for requesting use of a point of interest using prediction of waiting time and device for implementing the same. The method includes, using an electronic device, comprising a processor and a memory, generating an expected arrival time for a destination designated by a user in response to determining that the destination is a spot available for a request, estimating a waiting time for using the destination based on waiting information comprising a waiting-for-use pattern of the spot; estimating an expected time of use for the spot based on the waiting time of the spot, requesting use of the spot to generate a request for use in response to the expected arrival time being the expected time of use or earlier, and providing a route to the spot in response to an approval for the request of use.