VERIFICATION OF FRAUDULENT ACTIVITIES AT A SELF-CHECKOUT TERMINAL

• US 20140176719A1
• Filed: 12/23/2013
• Published: 06/26/2014
• Est. Priority Date: 12/21/2012
• Status: Active Grant

• Current Assignee: NCR Voyix Corporation
• Original Assignee: Joshua Migdal, Malay Kundu, Matthew Farrow
• Inventors: Migdal, Joshua, Farrow, Matthew, Kundu, Malay
• Granted Patent: US 9,564,031 B2
• Time in Patent Office: Days
• US Class Current: 348/150
• CPC Class Codes: G06T 3/02 Affine transformations for image registration G06T3/147; for image mosaicing G06T3/4038
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/44 Event detection
• CPC Class Codes: G06V 20/48 Matching video sequences
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G07G 1/0054 with control of supplementary check-parameters, e.g. weight or number of articles
• CPC Class Codes: G07G 1/0072 with means for detecting the weight of the article of which the code is read, for the verification of the registration weighing combined with recording means, e.g. in weighing scale, G01G19/415
• CPC Class Codes: G07G 3/003 Anti-theft control theft alarms G08B13/00
• CPC Class Codes: G08B 13/196 using television cameras
• CPC Class Codes: G08B 13/19613 Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
• CPC Class Codes: G08B 13/19691 Signalling events for better perception by user, e.g. indicating alarms by making display brighter, adding text, creating a sound
• CPC Class Codes: G08B 29/188 Data fusion; cooperative systems, e.g. voting among different detectors
• CPC Class Codes: G11B 27/031 Electronic editing of digitised analogue information signals, e.g. audio or video signals

Abstract

A verification method of a fraudulent activity taking place at a self-checkout terminal is disclosed. The method verifies the fraudulent activity by confirming an incident of the fraudulent activity with multiple data generated by monitoring the transaction area and the bagging area of the self-checkout terminal. A human validation is optionally performed to verify machine-identified incidents.

Claims

1. A method of verifying a possible activity of interest at a self-checkout terminal, the method operated via a computer processing unit, comprising the steps of:

obtaining a video feed generated from a video source wherein the video feed is generated by monitoring an item during at least a portion of a transaction time, the item being located at a transaction area;

obtaining a data feed from a data source wherein the data feed is generated by monitoring the item during at least a portion of the transaction time, the item being located at the transaction area;

analyzing the video feed to identify the possible activity of interest;

analyzing the data feed to identify the possible activity of interest; and

issuing an alert if both the analyzed video feed and the analyzed data feed confirm the possible activity of interest.

2. The method of claim 1 wherein the possible activity of interest is a possible fraudulent activity.

3. The method of claim 2 further comprising the steps of:

modifying the video feed wherein the video feed is simplified;

transmitting the modified video feed to a human validation terminal to perform a human validation; and

receiving a human validation response from the human validation terminal to verify the alert.

4. The method of claim 2 wherein the step of obtaining a data feed from a data source comprises at least one of:

obtaining the data feed from a transaction data wherein the transaction data is generated from the self-checkout terminal; and

obtaining the data feed from a weight sensor wherein the weight sensor detects a weight of the item placed on a bagging area.

5. The method of claim 4 wherein the step of analyzing the data feed to identify the possible fraudulent activity by obtaining the data feed from a weight sensor comprises:

receiving an unexpected weight data of the item from the self-checkout terminal, the weight sensor communicatively coupled with the self-checkout terminal wherein the self-checkout terminal obtains the unexpected weight data of the item being placed on a bagging area; and

identifying the possible fraudulent activity based on the unexpected weight data, wherein the unexpected weight data is obtained to identify whether a weight of the item from the weight sensor correspond with an expected weight tolerance.

6. A method of verifying a possible fraudulent activity at a self-checkout terminal, the method operated via a computer processing unit, comprising the steps of:

receiving an unexpected weight data of an item from the self-checkout terminal, the weight sensor communicatively coupled with the self-checkout terminal wherein the self-checkout terminal obtains the unexpected weight data of the item being placed on a bagging area;

identifying the possible fraudulent activity based on the unexpected weight data, wherein the unexpected weight data is obtained to identify whether the item from the weight sensor correspond with an expected weight tolerance; and

identifying the possible fraudulent activity based on a data feed, wherein the data feed is generated from a data source monitoring the item at a transaction area, the data feed being generated during at least a portion of a transaction time.

7. The method of claim 6 further comprising the step of correlating the identified possible fraudulent activity based on the data feed with the identified possible fraudulent activity based on the unexpected weight data to verify the possible fraudulent activity.

8. The method of claim 6 further comprising the steps of:

modifying a video feed wherein the video feed is simplified, wherein the video feed is generated from a video source monitoring the item during at least a portion of the transaction time, the item being located at the transaction area;

transmitting the modified video feed to a human validation terminal to perform a human validation; and

receiving a human validation response from the human validation terminal to verify the possible fraudulent activity.

9. The method of claim 7 wherein the step of identifying the possible fraudulent activity based on a data feed comprises at least one of:

identifying the possible fraudulent activity based on a video analysis, the video analysis being performed by the computer processing unit analyzing a video feed, wherein the video feed is generated from a video source monitoring the item; and

identifying the possible fraudulent activity based on a transaction data wherein the transaction data is generated from the self-checkout terminal.

10. The method of claim 7 further comprising the step of:

issuing an alert if both the identified possible fraudulent activity based on the data feed and the identified possible fraudulent activity based on the unexpected weight data confirm the possible fraudulent activity.

11. The method of claim 7 further comprising the step of:

denying an alert if at least one of the identified possible fraudulent activity based on the data feed and the identified possible fraudulent activity based on the unexpected weight difference does not confirm the possible fraudulent activity.

12. The method of claim 8 further comprising the step of:

issuing an alert if the possible fraudulent activity is confirmed by at least one of;

the human validation response;

the identified possible fraudulent activity based on the data feed; and

the identified possible fraudulent activity based on the unexpected weight difference.

13. The method of claim 8 further comprising the step of issuing an alert if a combined analysis of the identified possible fraudulent activity based on the data feed, the unexpected weight difference, and the human validation response confirm the possible fraudulent activity.

14. A system for verifying a possible fraudulent activity at a self-checkout terminal comprising:

a computer processing unit operable to analyze a plurality of data feed generated during at least a portion of a transaction time, the plurality of data feeds being generated by monitoring an item being transacted at the self-checkout terminal, the computer processing unit being operable to correlate each of the plurality of data feeds with a preset data associating the item to identify the possible fraudulent activity, the computer processing unit configured to issue an alert when at least two of the plurality of data feeds confirm the identified possible fraudulent activity, wherein the plurality of data feeds comprises;

a video feed, a transaction data, and an unexpected weight data;

a video source communicatively coupled with the computer processing unit, the video source operable to generate the video feed of the item placed at a transaction area;

a weight sensor communicatively coupled with the self-checkout terminal, wherein the unexpected weight data is transmitted to the computer processing unit from the self-checkout terminal, the unexpected weight data being obtained to identify whether a weight of the item measured from the weight sensor correspond with an expected weight tolerance; and

the self-checkout terminal communicatively coupled to the computer processing unit.

15. The system of claim 14 further comprising:

a human validation terminal communicatively coupled with the computer processing unit wherein the human validation terminal utilizes at least a portion of the video feed to verify the possible fraudulent activity.

16. A method of identifying a loss incident at a self-checkout terminal, the method operated via a computer processing unit, comprising the steps of:

obtaining a video feed, wherein the video feed is generated from a video source monitoring an item at a transaction area, the video feed being generated during at least a portion of a transaction time;

identifying the loss incident based on the obtained video feed by a video analysis performed by the computer processing unit;

modifying the video feed wherein the video feed is simplified;

transmitting the modified video feed to a human validation terminal to perform a human validation;

receiving a human validation response from the human validation terminal;

identifying the loss incident based on the human validation; and

issuing an alert based on the video analysis and the human validation response.

17. The method of claim 16 wherein the step of obtaining a video feed comprises storing at least a portion of the video feed to the computer processing unit.

18. A method of verifying a possible fraudulent activity at a self-checkout terminal, the method operated via a computer processing unit, comprising the steps of:

obtaining a video feed wherein the video feed is generated from a video source monitoring an item during at least a portion of a transaction time, the item being located at a transaction area;

obtaining an unexpected weight data of the item from the self-checkout terminal, a weight sensor communicatively coupled with the self-checkout terminal wherein the self-checkout terminal obtains the unexpected weight data of the item being placed on a bagging area, the unexpected weight data being obtained to identify whether a weight of the item measured from the weight sensor correspond with an expected weight tolerance;

analyzing the video feed to identify the possible fraudulent activity by performing a video analysis;

modifying the video feed wherein the video feed is simplified;

transmitting the modified video feed to a human validation terminal to perform a human validation;

receiving a human validation response from the human validation terminal;

identifying the possible fraudulent activity based on the human validation; and

issuing an alert if all of the analyzed video feed, the unexpected weight data, and the human validation response confirm the possible fraudulent activity.

19. A method of verifying a possible fraudulent activity at a self-checkout terminal, the method operated via a computer processing unit, comprising the steps of:

receiving an unexpected weight data of an item from the self-checkout terminal, the weight sensor communicatively coupled with the self-checkout terminal wherein the self-checkout terminal obtains the unexpected weight data of the item being placed on a bagging area;

identifying the possible fraudulent activity based on the unexpected weight data, wherein the unexpected weight data is obtained to identify whether a weight of the item from the weight sensor correspond with an expected weight tolerance;

modifying a video feed wherein the video feed is simplified, wherein the video feed is generated from a video source monitoring the item during at least a portion of the transaction time, the item being located at the transaction area;

transmitting the modified video feed to a human validation terminal to perform a human validation;

receiving a human validation response from the human validation terminal; and

verifying the identified possible fraudulent activity based on the human validation response.