EVENT-TRIGGERED CAPTURE OF ITEM IMAGE DATA AND GENERATION AND STORAGE OF ENHANCED ITEM IDENTIFICATION DATA

1 . A method of event-triggered capture of an image of an item, the method comprising:

detecting an event at a self-checkout (SCO) machine;

determining that the event is an item-identifying event;

triggering capture of image data of the item identified by the item-identifying event; and

generating enhanced item identification data that associates the captured image data with identifying information of the item.

2 . The method of claim 1 , wherein the image data is captured responsive to detecting the event.

3 . The method of claim 1 , wherein the image data is captured responsive to determining that the event is an item-identifying event

4 . The method of claim 1 , wherein the enhanced item identification data is stored in a local datastore in a retail environment in which the SCO machine is located.

5 . The method of claim 1 , wherein the enhanced item identification data is stored in a remote datastore provided in a cloud-based environment.

6 . The method of claim 1 , wherein the image data is captured by a camera embedded in a scanner of the SCO machine.

7 . The method of claim 1 , further comprising annotating the enhanced item identification data.

8 . A method of item identification in connection with a self-checkout transaction, the method comprising:

determining whether an item can be identified using a first learning methodology; and

identifying the item using a second alternative learning methodology responsive to determining that the item cannot be identified using the first learning methodology.

9 . The method of claim 8 , wherein the first learning methodology is a computer vision machine learning model (CVMLM).

10 . The method of claim 9 , wherein determining whether the item can be identified using the first learning methodology comprises:

providing image data as input to the CVMLM;

receiving item identification data as output from the CVMLM; and

determining whether the item can be identified based on the item identification data.

11 . The method of claim 10 , wherein the item identification data comprises a candidate item identifier of the item and a confidence value associated with the candidate item identifier.

12 . The method of claim 11 , wherein determining whether the item can be identified based on the item identification data comprises:

determining that the confidence value fails to satisfy a first threshold value; and

determining, based on predetermined transaction logic, that the candidate item identifier cannot be accepted as a true identifier of the item for the self-checkout transaction due to the confidence value failing to satisfy the first threshold value.

13 . The method of claim 12 , further comprising:

determining that additional learning input is needed to determine the true identifier for the item;

obtaining the additional learning input;

determining the true identifier for the item based on the additional learning input; and

generating enhanced item identification data based on the additional learning input.

14 . The method of claim 13 , wherein the enhanced item identification data associates the true identifier with at least a portion of the image data that includes the item.

15 . The method of claim 14 , wherein the enhanced item identification data associates the true identifier with coordinates of a bounding box around the item in the image data.

16 . The method of claim 13 , further comprising:

re-training the CVMLM based on the enhanced item identification data to improve an item identification accuracy of the CVMLM.

17 . The method of claim 12 , further comprising:

determining that the confidence value satisfies a second threshold confidence level less than the first threshold confidence level;

identifying a set of candidate items;

presenting a representation of the set of candidate items on a display of a self-checkout (SCO) machine; and

receiving a selection of a particular candidate item or other input indicative of an alternative item.

18 . The method of claim 17 , further comprising:

accessing item identifier data to determine an item identifier corresponding to the selected particular candidate item or corresponding to the alternative item; and

generating enhanced item identification data that associates the item identifier with at least a portion of the image data that includes the item.

19 . The method of claim 12 , further comprising:

determining that the confidence value fails to satisfy a second threshold confidence level less than the first threshold confidence level;

prompting a customer for scanner input;

receiving the scanner input at a self-checkout (SCO) machine, the scanner input obtained from a scan of machine-readable indicia associated with the item;

accessing item identifier data to determine an item identifier corresponding to the scanner input; and

generating enhanced item identification data that associates the item identifier with at least a portion of the image data that includes the item.

20 . A self-checkout (SCO) apparatus, comprising:

at least one memory storing computer-executable instructions; and

at least one processor configured to access the at least one memory and execute the computer-executable instructions to perform operations comprising:

determining whether an item can be identified using a first learning methodology; and

identifying the item using a second alternative learning methodology responsive to determining that the item cannot be identified using the first learning methodology.