Real-Time Congestion Avoidance In A Retail Environment

• US 20160063610A1
• Filed: 08/29/2014
• Published: 03/03/2016
• Est. Priority Date: 08/29/2014
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Wal-Mart Stores Texas LLC (WalMart Inc.)
• Inventors: Argue, Stuart, Marcar, Anthony Emile
• Granted Patent: US 9,928,542 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G06Q 10/04 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 Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: G06Q 30/0639 Item locations
• CPC Class Codes: G06Q 30/0641 Shopping interfaces

Abstract

A shopping list may be ordered in order to guide a shopper through a store in an efficient manner. Congestion in the store may be identified, such as using security camera footage. The shopping list may be reordered in order to avoid congested areas in the store. Expected areas of congestion may be determined based on velocities and maneuverability of other shoppers. Maneuverability of other shoppers may be estimated by analyzing video data and determining attributes of the other shoppers, such as whether they are pushing a cart or have children. The shopping list may be presented with directions to the locations of items within the store and may be periodically reordered throughout the shopper'"'"'s visit responsive to detected congestion.

Claims

1. A method comprising:

receiving, by a server system, a shopping list of a user;

retrieving, by the server system, locations of items in the shopping list in a floor plan of a store;

ordering, by the server system, the items of the shopping list according to the locations of the items;

evaluating, by the server system, video data of the store;

determining, by the server system, using the video data, locations and velocities of shoppers in the store;

(a) identifying, by the server system, at least one of currently congested locations and expected congested locations according to the locations and velocities of the shoppers in the store; and

(b) re-ordering, by the server system, the items of the shopping list such that a path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations.

2. The method of claim 1, wherein identifying the at least one of the currently congested locations and the expected congested locations according to the locations and velocities of the shoppers in the store further comprises, for each shopper:

determining from the video data whether the each shopper is or is not pushing a shopping cart; and

characterizing the each shopper'"'"'s maneuverability according to whether the each shopper is or is not pushing a shopping cart.

3. The method of claim 2, further comprising, for each shopper

determining an expected route of the each shopper according to the each shopper'"'"'s maneuverability and the location and velocity of the each shopper

identifying as the expected congested locations those locations at which the expected routes of two or more shoppers meet a threshold physical proximity condition at an expected time.

4. The method of claim 3, wherein re-ordering the items of the shopping list such that the path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations comprises:

determining an ordering of the items such that at a traveling speed of the user the user will not be at the expected congestion locations at the expected time of the expected congestion location when retrieving the items of the shopping list according to the re-ordering.

5. The method of claim 1, wherein re-ordering the items of the shopping list such that the path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations comprises:

identifying a plurality of orderings of the lists;

scoring the orderings such that a score of each ordering of the plurality of orderings increases with similarity to an original ordering of the list, decreases with length, and decreases with temporal and positional proximity to the at least one of the currently congested locations and expected congestion locations;

selecting a selected ordering of the plurality of orderings having a highest score.

6. The method of claim 1, wherein re-ordering the items of the shopping list such that the path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations comprises:

determining the re-ordering of the items in combination with the re-ordering of items of a plurality of other shopping lists of other users in the store such that congestion in the store is reduced.

7. The method of claim 1, further comprising transmitting the re-ordering of the items of the shopping list to a mobile device of the user.

8. The method of claim 7, further comprising:

receiving, by the server system, notification of checking off of items of the shopping list from the mobile device of the user.

9. The method of claim 8, further comprising:

inferring, by the server system, a location of the mobile device of the user from the location in the floor plan of a most-recent item referenced in a most recent notification of checking off of the most-recent item.

10. The method of claim 8, further comprising:

inferring, by the server system, a velocity of the mobile device of the user from a difference in locations in the floor plan of two or more most-recent items referenced in two or more most recent notifications of checking off of the most-recent item and times at which the two or more most recent notifications were received.

11. The method of claim 1, further comprising periodically repeating (a) and (b) while the user remains in the store.

12. A system comprising one or more processors and one or more memory devices operably coupled to the one or more processors and storing executable and operational data effective to cause the one or more processors to:

receive a shopping list of a user;

retrieve locations of items in the shopping list in a floor plan of a store;

order the items of the shopping list according to the locations of the items;

evaluate video data of the store;

determining, by the server system, using the video data, locations and velocities of shoppers in the store;

(a) identifying, by the server system, at least one of currently congested locations and expected congested locations according to the locations and velocities of the shoppers in the store; and

(b) re-ordering, by the server system, the items of the shopping list such that a path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations.

13. The system of claim 12, wherein the executable and operational data are further effective to cause the one or more processors to identify the at least one of the currently congested locations and the expected congested locations according to the locations and velocities of the shoppers in the store by, for each shopper:

determining from the video data whether the each shopper is or is not pushing a shopping cart; and

characterizing the each shopper'"'"'s maneuverability according to whether the each shopper is or is not pushing a shopping cart.

14. The system of claim 13, wherein the executable and operational data are further effective to cause the one or more processors to:

for each shopper, determine an expected route of the each shopper according to the each shopper'"'"'s maneuverability and the location and velocity of the each shopper;

identify as the expected congested locations those locations at which the expected routes of two or more shoppers meet a threshold physical proximity condition at an expected time.

15. The system of claim 14, wherein the executable and operational data are further effective to cause the one or more processors to re-order the items of the shopping list such that the path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations by:

determining an ordering of the items such that at a traveling speed of the user the user will not be at the expected congestion locations at the expected time of the expected congestion location when retrieving the items of the shopping list according to the re-ordering.

16. The system of claim 12, wherein the executable and operational data are further effective to cause the one or more processors to re-order the items of the shopping list such that the path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations by:

identifying a plurality of orderings of the lists;

scoring the orderings such that a score of each ordering of the plurality of orderings increases with similarity to an original ordering of the list, decreases with length, and decreases with temporal and positional proximity to the at least one of the currently congested locations and expected congestion locations;

selecting a selected ordering of the plurality of orderings having a highest score.

17. The system of claim 12, wherein the executable and operational data are further effective to cause the one or more processors to re-order the items of the shopping list such that the path passing the locations of the items of the shopping list avoids the at least one of the currently congested locations and expected congested locations by:

determining the re-ordering of the items in combination with the re-ordering of items of a plurality of other shopping lists of other users in the store such that congestion in the store is reduced.

18. The system of claim 12, wherein the executable and operational data are further effective to cause the one or more processors to transmit the re-ordering of the items of the shopping list to a mobile device of the user.

19. The system of claim 18, wherein the executable and operational data are further effective to cause the one or more processors to:

receive notification of checking off of items of the shopping list from the mobile device of the user; and

infer a location of the mobile device of the user from the location in the floor plan of a most-recent item referenced in a most recent notification of checking off of the most-recent item.

20. The system of claim 12, wherein the executable and operational data are further effective to cause the one or more processors to periodically repeat (a) and (b) while the user remains in the store.