TRIGGERING IMAGE ANALYSIS IN RETAIL STORES IN RESPONSE TO POINT OF SALE DATA

1 - 140 . (canceled)

141 . A non-transitory computer-readable medium including instructions that when executed by at least one processor cause the at least one processor to perform a method for triggering actions in response to point of sales data, the method comprising:

obtaining point of sale data from a retail store;

analyzing the point of sale data to identify at least one anomalous transaction;

in response to the identified at least one anomalous transaction, providing information configured to cause capturing of image data from the retail store;

analyzing the image data relating to the at least one anomalous transaction to determine at least one condition associated with the at least one anomalous transaction in the retail store; and

based on the analyzed image data relating to the at least one anomalous transaction, generating an indicator associated with the at least one condition.

142 . The non-transitory computer-readable medium of claim 141 , wherein the at least one anomalous transaction includes a detection of a customer purchasing a first product type rather than a second product type, wherein the first product type and the second product type are included in a common product category.

143 . The non-transitory computer-readable medium of claim 142 , wherein a first promotion applies to the first product type and a second promotion applies to the second product type, and wherein the first promotion differs from the second promotion in at least one aspect.

144 . The non-transitory computer-readable medium of claim 142 , wherein the customer is expected to prefer products of the second product type over the first product type based on an analysis of historic shopping activities of the customer.

145 . The non-transitory computer-readable medium of claim 142 , wherein the customer is expected to prefer products of the second product type over the first product type based on purchases of related products.

146 . The non-transitory computer-readable medium of claim 142 , wherein the at least one condition includes an out-of-stock inventory status for products of the second product type.

147 . The non-transitory computer-readable medium of claim 142 , wherein the at least one condition includes a low-stock inventory status for products of the second product type.

148 . The non-transitory computer-readable medium of claim 142 , wherein the at least one condition includes a promotional offer associated with the first product type.

149 . The non-transitory computer-readable medium of claim 142 , wherein the at least one condition includes non-compliance with at least one planogram associated with the second product type.

150 . The non-transitory computer-readable medium of claim 142 , wherein the at least one condition includes a relative shelf location between the second product type and the first product type.

151 . The non-transitory computer-readable medium of claim 142 , wherein the image data includes representations of products of both the first product type and the second product type.

152 . The non-transitory computer-readable medium of claim 142 , wherein the image data includes a representation of a text conveying a promotion associated with products of the first type or products of the second type.

153 . The non-transitory computer-readable medium of claim 141 , wherein the at least one anomalous transaction is identified based on analysis of information relating to the at least one of historic shopping activities of a customer or demographic information associated with the customer.

154 . The non-transitory computer-readable medium of claim 141 , wherein the identified at least one anomalous transaction corresponds to a selection of a first product type for purchase, wherein the selection of the first product type occurs after an idle period, since a previous selection of the first product type for purchase, of greater than a predetermined threshold.

155 . The non-transitory computer-readable medium of claim 141 , wherein the identified at least one anomalous transaction includes a selection of a first product type for purchase at a rate less frequent than predicted.

156 . The non-transitory computer-readable medium of claim 141 , wherein the indicator of the at least one condition is configured to prompt initiation of an action to address the at least one condition.

157 . The non-transitory computer-readable medium of claim 141 , wherein analyzing the image data includes using a machine learning model trained using training examples to analyze the image data and determine the at least one condition associated with the at least one anomalous transaction in the retail store.

158 . The non-transitory computer-readable medium of claim 141 , wherein analyzing the image data includes:

calculating a convolution of at least part of the image data thereby obtain a result value of the calculated convolution;

in response to the result value of the calculated convolution being a first value, including a first condition in the determined at least one condition; and

in response to the result value of the calculated convolution being a second value, including a second condition in the determined at least one condition, the second condition differs from the first condition.

159 . A method for triggering actions in response to sales information, the method comprising:

obtaining point of sale data from a retail store;

analyzing the point of sale data to identify at least one anomalous transaction;

in response to the identified at least one anomalous transaction, providing information configured to cause capturing of image data from the retail store; and

analyzing the image data relating to the at least one anomalous transaction to determine at least one condition associated with the at least one anomalous transaction, generating an indicator associated with the at least one condition.

160 . A system for monitoring actions associated with a retail store or retail space, the system comprising:

at least one processor configured to execute instructions for performing a method, the method comprising:

obtaining point of sale data from a retail store;

analyzing the point of sale data to identify at least one anomalous transaction;

in response to the identified at least one anomalous transaction, providing information configured to cause capturing of image data from the retail store;

analyzing the image data relating to the at least one anomalous transaction to determine at least one condition associated with the at least one anomalous transaction in the retail store; and

based on the analyzed image data relating to the at least one anomalous transaction, generating an indicator associated with the at least one condition.

161 - 180 . (canceled)