Systems and methods for predicting and identifying retail shrinkage activity

• US 10,380,855 B2
• Filed: 07/19/2018
• Issued: 08/13/2019
• Est. Priority Date: 07/19/2017
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Inventors: Lobo, Charles, Zhang, Jinzhi, Moore, Trisha, Tyagi, Aman
• Primary Examiner(s): Akki, Munear T
• Application Number:
  US16/040,066
• Publication Number:
  US 20190027003A1
• Time in Patent Office: 390 Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/0639 Item locations
• CPC Class Codes: G08B 13/19602 Image analysis to detect motion of the intruder, e.g. by frame subtraction
• CPC Class Codes: G08B 13/19613 Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
• CPC Class Codes: G08B 13/19645 Multiple cameras, each having view on one of a plurality of scenes, e.g. multiple cameras for multi-room surveillance or for tracking an object by view hand-over
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

A retail shrinkage activity prediction and identification system that includes: a sensor control system, a first shrinkage database, a second shrinkage database, an analytics engine, and a machine learning engine. The sensor control system is communicatively coupled with a plurality of sensors arranged in a retail environment. The sensor control system is configured to control a setting of each of the plurality of sensors. The first shrinkage database includes retail shrinkage data for at least the retail environment. The retail shrinkage data includes at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity. The second shrinkage database includes external data related to shrinkage in a geographic area of the retail environment. The analytics engine is communicatively coupled with the first shrinkage database, the second shrinkage database, and the sensor control system.

Claims

1. A retail shrinkage activity prediction and identification system comprising:

a sensor control system communicatively coupled with a plurality of sensors arranged in a retail environment, the sensor control system configured to control a setting of each of the plurality of sensors;

a first shrinkage database comprising retail shrinkage data for at least the retail environment, the retail shrinkage data comprising at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity;

a second shrinkage database comprising external data related to shrinkage in a geographic area of the retail environment;

an analytics engine communicatively coupled with the first shrinkage database to access the retail shrinkage data, the second shrinkage database to access the external data, and the sensor control system to receive real-time sensor data from the plurality of sensors, the analytics engine configured to;

compare the real-time sensor data with the external data to identify a high shrinkage risk situation, and if a high shrinkage risk situation is identified, issue an alert, cause the sensor control system to alter a setting of at least one of the plurality of sensors, and update at least one of the first shrinkage database or the second shrinkage database; and

a machine learning engine communicatively coupled with the first shrinkage database, the second shrinkage database, and the analytics engine to use the retail shrinkage data, the external data, and the issuance of an alert to conduct predictive modeling and cause the analytics engine to issue an alert if the predictive modeling determines that a high shrinkage risk situation is likely to occur.

2. The system of claim 1, wherein the setting of each of the plurality of sensors comprises at least one of an activation, a direction, a location or a sensing area.

3. The system of claim 1, wherein the plurality of sensors comprise any of an optical sensor, a motion detection sensor, a temperature sensor, an infrared sensor, a microphone, or a pressure sensor.

4. The system of claim 1, wherein the external data comprises at least one of criminal report data or public safety notice data.

5. The system of claim 1, wherein comparing the real-time sensor data with the external data to identify a high shrinkage risk situation comprises matching an image in the real-time sensor data with an image in the external data.

6. The system of claim 1, wherein issuing an alert by the analytics engine comprises notifying at least one of asset protection personnel in the retail environment, asset protection personnel at a home office of a retailer, or a partner of the retailer.

7. The system of claim 6, wherein the partner of the retailer is another retail environment of the retailer, another retailer, or an enforcement agency.

8. The system of claim 1, wherein the predictive modeling comprises identifying trends in any combination of the retail shrinkage data, the external data, the issuance of an alert, and the real-time sensor data that relate to high shrinkage risk situations.

9. The system of claim 1, wherein the analytics engine is configured to cause the sensor control system to alter the setting of at least one of the plurality of sensors if the predictive modeling determines that a high shrinkage risk situation is likely to occur.

10. A method of predicting or identifying retail shrinkage activity comprising:

accessing retail shrinkage data comprising at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity in a retail environment;

accessing external data related to shrinkage in a geographic area of the retail environment;

receiving real-time sensor data from a plurality of sensors arranged in the retail environment;

comparing the real-time sensor data with the external data to identify a high shrinkage risk situation;

if a high shrinkage risk situation is identified;

issuing an alert,

causing a sensor control system to alter a setting of at least one of the plurality of sensors, and

updating at least one of the retail shrinkage data or the external data;

conducting predictive modeling using the retail shrinkage data, the external data, and the issuance of an alert; and

issuing an alert if the predictive modeling determines that a high shrinkage risk situation is likely to occur.

11. The method of claim 10, wherein the setting of at least one of the plurality of sensors comprises at least one of an activation, a direction, a location or a sensing area.

12. The method of claim 10, wherein the plurality of sensors comprise any of an optical sensor, a motion detection sensor, a temperature sensor, an infrared sensor, a microphone, or a pressure sensor.

13. The method of claim 10, wherein accessing external data comprises accessing at least one of criminal report data or public safety notice data.

14. The method of claim 10, wherein comparing the real-time sensor data with the external data to identify a high shrinkage risk situation comprises matching an image in the real-time sensor data with an image in the external data.

15. The method of claim 10, wherein issuing an alert if a high shrinkage risk situation is identified comprises notifying at least one of asset protection personnel in the retail environment, asset protection personnel at a home office of a retailer, or a partner of the retailer.

16. The method of claim 15, wherein the partner of the retailer is another retail environment of the retailer, another retailer, or an enforcement agency.

17. The method of claim 10, wherein conducting predictive modeling comprises identifying trends in any combination of the retail shrinkage data, the external data, the issuance of an alert, and the real-time sensor data that relate to high shrinkage risk situations.

18. The method of claim 10, further comprising causing a sensor control system to alter a setting of at least one of the plurality of sensors if the predictive modeling determines that a high shrinkage risk situation is likely to occur.