Approaches to crowdsourced-based wait time estimates

Patent metadata

Field	Value
Publication number	US-9671230-B2
Application number	US-201514737399-A
Country	US
Kind code	B2
Filing date	Jun 11, 2015
Priority date	Jun 11, 2015
Publication date	Jun 6, 2017
Grant date	Jun 6, 2017

Abstract

In one embodiment of the present invention, a wait time client enables prediction of wait times (e.g., time required to checkout at a grocery store) based on crowdsourced wait data. In operation, the wait time application downloads predicted wait data from a server. The predicted wait data reflects measured wait times for one or more location, such as the ticket line at a movie theater. The wait time client then selects a wait time data point that corresponds to a location of a point-of-interest. Based on the selected wait time data point, the wait time client determines a predicted wait time at the point-of-interest. Advantageously, by leveraging crowdsourced, deterministically measured wait times, the wait time clients enables the incorporation of realistic and up-to-date predicted wait times into the trip planning process.

First claim

What is claimed is:

1. A method implemented by a computing device for predicting a wait time at a point-of-interest, the method comprising:
receiving, via a processor, sensor data from a first sensor, wherein the sensor data is associated with movements of a mobile device;
analyzing, via the processor, the sensor data to determine a duration of time for which a path of the mobile device satisfies one or more queue pattern constraints; and
generating a predicted wait time for the point-of-interest based on the duration of time.

2. The method of claim 1 , further comprising, prior to receiving the sensor data, determining that a location of the mobile device is associated with the point-of-interest, wherein the predicted wait time is generated based on a start time and an end time associated with the mobile device.

3. The method of claim 2 , wherein determining at least one of the start time and the end time comprises receiving a message, and analyzing the message to determine a transaction time associated with the first point-of-interest.

4. The method of claim 3 , wherein the message is received from a proximity beacon.

5. The method of claim 3 , wherein determining the end time comprises receiving a message that corresponds to a financial transaction, and analyzing the message to determine a timestamp associated with the message.

6. The method of claim 3 , wherein determining the start time comprises:
performing one or more backtracking operations on the sensor data to determine the path from a first location associated with the transaction time to second location associated with a previous time specified in the sensor data, wherein the mobile device moving between the first location and the second location satisfies the one or more queue pattern constraints; and
setting the start time to the previous time.

7. The method of claim 1 , further comprising performing one or more search operations based on a location of the mobile device to identify the point-of-interest.

8. A non-transitory computer-readable storage medium including instructions that, when executed by a processor, cause the processor to predict wait times at points-of-interest by performing the steps of:
receiving sensor data from a first sensor, wherein the sensor data is associated with movements of a mobile device;
analyzing the sensor data to determine a duration of time for which a path of the mobile device satisfies one or more queue pattern constraints; and
generating a predicted wait time for the point-of-interest based on the duration of time.

9. The non-transitory computer-readable storage medium of claim 8 , further comprising, prior to receiving the sensor data determining that a location of the mobile device is associated with the point-of-interest, wherein the predicted wait time is generated based on a start time and an end time associated with the mobile device.

10. The non-transitory computer-readable storage medium of claim 9 , wherein determining the start time comprises:
receiving a message, and analyzing the message to determine a transaction time associated with the first point-of-interest;
performing one or more backtracking operations on the sensor data to determine the path from a first location associated with the transaction time to a second location associated with a previous time specified in the sensor data, wherein the mobile device moving between the first location and the second location satisfies the one or more queue pattern constraints; and
setting the start time to the previous time.

11. The non-transitory computer-readable storage medium of claim 10 , wherein the one or more queue pattern constraints include at least one of a maximum change in direction and a maximum change in velocity.

12. The non-transitory computer-readable storage medium of claim 10 , wherein at least one of the one or more backtracking operations comprises a step-detection operation.

13. The non-transitory computer-readable storage medium of claim 10 , wherein at least one of the one or more backtracking operations comprises a turn-detection operation.

14. The non-transitory computer-readable storage medium of claim 8 , further comprising estimating a travel time to the first point-of-interest and calculating a total time based on the predicted wait time and the travel time.

15. The non-transitory computer-readable storage medium of claim 8 , wherein analyzing the sensor data comprises detecting one or more turns that satisfy the one or more queue pattern constraints.

16. The non-transitory computer-readable storage medium of claim 8 , wherein analyzing the sensor data comprises detecting a rate of steps that satisfy the one or more queue pattern constraints.

17. The non-transitory computer-readable storage medium of claim 8 , wherein the sensor data comprises at least one of accelerometer data, gyroscope data, and magnetometer data.

18. A system configured to predict wait times, the system comprising:
a processor; and
a memory coupled to a processor and including a wait time calculator, wherein, when executed by the processor, the wait time calculator is configured to:
receive sensor data from a first sensor, wherein the sensor data is associated with movements of a mobile device;
analyze the sensor data to determine a duration of time for which a path of the mobile device satisfies one or more queue pattern constraints; and
generate a predicted wait time for the point-of-interest based on the duration of time.

Assignees

• Harman Int Ind

Inventors

• Sahay Pratyush
• Biswal Rajesh

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G01C21/1654

  with electromagnetic compass · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G01C22/006

  Pedometers · CPC title

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title