PROVIDING LOW-STOCK ALERTS BASED ON PRODUCT FACING EVENTS

1 - 181 . (canceled)

182 . A system for processing images captured in a retail store and automatically generating low-stock alerts, the system comprising:

at least one processor configured to:

receive image data associated with images captured during a first period of time by one or more image capturing devices mounted in the retail store, wherein the captured data depicts inventory changes in at least a portion of a retail shelving unit dedicated to products from a specific product type;

analyze the image data to identify a restocking event associated with the at least a portion of the retail shelving unit;

analyze the image data to identify at least one facing event associated with the at least a portion of the retail shelving unit, wherein the at least one facing event occurs after the restocking event;

analyze the image data to identify a low-stock event associated with the at least a portion of the retail shelving unit, wherein the low-stock event occurs after the at least one facing event;

determine a demand pattern for products from the specific product type based on the identified restocking event, the at least one facing event, and the low-stock event;

receive additional image data associated with images captured during a second period of time, subsequent to the low-stock event, by the plurality of image capturing devices, wherein the additional image data depicts additional inventory changes in the at least a portion of the retail shelving unit;

based on the additional image data and the determined demand pattern, predict when a next low-stock event will occur; and

generate an alert associated with the predicted next low-stock event.

183 . The system of claim 182 , wherein the at least one processor is further configured to determine the demand pattern based on a first time duration between the restocking event and the at least one facing event, and a second time duration between the at least one facing event and the low-stock event.

184 . The system of claim 182 , wherein the at least one facing event includes a plurality of facing events, and at least one processor is further configured to determine the demand pattern based on at least one time duration between the plurality of facing events.

185 . The system of claim 182 , wherein the at least one facing event includes a plurality of facing events, and at least one processor is further configured to determine the demand pattern based on a number of facing events that precedes the low-stock event.

186 . A computer program product for processing images captured in a retail store and automatically generating low-stock alerts, the computer program product embodied in a non-transitory computer-readable medium and including instructions for causing at least one processor to execute a method comprising:

receiving image data associated with images captured during a first period of time by one or more image capturing devices mounted in the retail store, wherein the captured data depicts inventory changes in at least a portion of a retail shelving unit dedicated to products from a specific product type;

analyzing the image data to identify a restocking event associated with the at least a portion of the retail shelving unit;

analyzing the image data to identify at least one facing event associated with the at least a portion of the retail shelving unit, wherein the at least one facing event occurs after the restocking event;

analyzing the image data to identify a low-stock event associated with the at least a portion of the retail shelving unit, wherein the low-stock event occurs after the at least one facing event;

determining a demand pattern for products from the specific product type based on the identified restocking event, the at least one facing event, and the low-stock event;

receiving additional image data associated with images captured during a second period of time subsequent to the low-stock event by the plurality of image capturing devices, wherein the additional image data depicts additional inventory changes in the at least a portion of the retail shelving unit;

based on the additional image data and the determined demand pattern, predicting when a next low-stock event will occur; and

generating an alert associated with the predicted next low-stock event.

187 . The computer program product of claim 186 , wherein the first time period is longer than a week, and the determined pattern accounts for certain days of the week with higher demand for the specific product type.

188 . The computer program product of claim 186 , wherein identifying the restocking event includes detecting a store employee replenishing the at least a portion of the retail shelving unit with additional products of the specific product type, and the method includes:

identifying a time of day that the store employee replenished the at least a portion of the retail shelving unit; and

determining the demand pattern based on the identified time of day.

189 . The computer program product of claim 186 , wherein identifying the at least one facing event includes detecting a store employee reorganizing products from the specific product type such that more products out of the products already placed on the at least a portion of the retail shelving unit will be placed in a front row of the at least a portion of the retail shelving unit, and the method includes:

assigning a facing level for the at least one facing event; and

determining the demand pattern based on the facing level for the at least one facing event.

190 . The computer program product of claim 186 , wherein identifying the low-stock event includes detecting that a number of products from the specific product type in the at least a portion of the retail shelving unit is less than threshold, and the method includes:

determining a typical time duration from the identification of the low-stock event of products from the specific product type until the at least a portion of the retail shelving unit is replenished; and

determining a time for issuing the alert based on the determined time duration.

191 . The computer program product of claim 186 , wherein the method further includes:

obtaining statistical data indicative of a number of consumers in the retail store during different hours of a day; and

determining the demand pattern based on the statistical data.

192 . The computer program product of claim 186 , wherein the method further includes:

analyzing the image data to determine a time duration between the restocking event and the low-stock event;

analyzing the additional image data to identify a second restocking event and group of facing events; and

based on the group of facing events, predicting that a time duration between the second restocking event and the predicted next low-stock event will be greater than the time duration between the restocking event and the low-stock event.

193 . The computer program product of claim 186 , wherein the method further includes:

analyzing the image data to determine a time duration between the restocking event and the low-stock event;

analyzing the additional image data to identify a second restocking event and group of facing events; and

based on the group of facing events, predicting that a time duration between the second restocking event and the predicted next low-stock event will be less than the time duration between the restocking event and the low-stock event.

194 . The computer program product of claim 186 , wherein the method further includes:

analyzing the additional image data associated with images captured during the second period to detect a second low-stock event that happened earlier than a predicted time of the predicted low-stock event; and

using a machine learning module to update the demand pattern with the second low-stock event.

195 . The computer program product of claim 186 , wherein the method further includes:

estimating a number of products picked up from the at least a portion of the retail shelving unit during the second period of time to improve a certainty level for the predicted next low-stock event.

196 . The computer program product of claim 186 , wherein the method further includes:

determining a confidence level associated with the predicted next low-stock event;

when the confidence level is lower than a threshold, withholding the alert associated with the predicted next low-stock event; and

initiating an action to increase the confidence level.

197 . The computer program product of claim 196 , wherein the action to improve the confidence level includes instructing a store employee to count a current number of products from the specific product type located on the at least a portion of the retail shelving unit.

198 . The computer program product of claim 186 , wherein the method further includes:

obtaining inventory data associated with the specific product type;

based on the inventory data, determining to withhold the alert associated with the predicted next low-stock event; and

initiating an alternate action to change the arrangement of products on the retail shelving unit.

199 . The computer program product of claim 198 , wherein the alternate action includes instructing a store employee to place products from a differing product type on the at least a portion of a retail shelving unit.

200 . The computer program product of claim 186 , wherein the method further includes:

obtaining schedule data associated with scheduled restocking events;

based on the schedule data, determining to forgo providing the alert associated with the predicted next low-stock event.

201 . A method for processing images captured in a retail store and automatically generating low-stock alerts, the method comprising:

receiving image data associated with images captured during a first period of time by one or more image capturing devices mounted in the retail store, wherein the captured data depicts inventory changes in at least a portion of a retail shelving unit dedicated to products from a specific product type;

analyzing the image data to identify a restocking event associated with the at least a portion of the retail shelving unit;

analyzing the image data to identify at least one facing event associated with the at least a portion of the retail shelving unit, wherein the at least one facing event occurs after the restocking event;

analyzing the image data to identify a low-stock event associated with the at least a portion of the retail shelving unit, wherein the low-stock event occurs after the at least one facing event;

determining a demand pattern for products from the specific product type based on the identified restocking event, the at least one facing event, and the low-stock event;

receiving additional image data associated with images captured during a second period of time subsequent to the low-stock event by the plurality of image capturing devices, wherein the additional image data depicts additional inventory changes in the at least a portion of the retail shelving unit;

based on the additional image data and the determined demand pattern, predicting when a next low-stock event will occur; and

generating an alert associated with the predicted next low-stock event.

202 - 221 . (canceled)