Automatic initialization of customer assistance based on computer vision analysis
Techniques for automatically initializing customer assistance in a retail store based on video analysis are provided. An exemplary method includes retrieving video data from a database and extracting one or more features from the video data. The method also includes identifying a product is missing on a store shelf based on the one or more features. The method includes training an artificial intelligence model using the one or more features to identify a customer needs assistance, and applying the trained artificial intelligence model to predict a customer needs assistance at a particular location in the store. The method includes sending a notification to a computing device of a customer service representative, wherein the notification identifies the customer, the customer's location, and information about the missing product.
1. A method for automatically initializing customer assistance in a retail store based on video analysis, the method comprising:
retrieving, by one or more processors, video data captured by one or more cameras;
extracting, by the one or more processors, one or more features from the video data;
receiving, by the one or more processors, an indication that a product is out of stock;
executing, by the one or more processors, an image processing algorithm using object recognition techniques or masking techniques to identify a human customer;
training, by the one or more processors, an artificial intelligence model using the one or more features to identify a customer needs assistance;
applying, by the one or more processors, the trained artificial intelligence model to predict the customer needs assistance at a particular location in the store; and
sending, by the one or more processors, a notification to a computing device of a customer service representative, wherein the notification identifies the customer, the customer's location, and information about the out of stock product.
2. The method of claim 1 , wherein the video data comprises near real-time video data and historical video data stored in a database.
3. The method of claim 2 , wherein the near real-time video data is continuously captured.
4. The method of claim 1 , wherein the one or more cameras comprise a 3D scanner, a laser scanner, or a LIDAR system.
5. The method of claim 4 , wherein the 3D scanner and laser scanner are configured to collect laser-derived images and/or videos.
6. The method of claim 1 , wherein the one or more features further comprise a pattern.
7. The method of claim 6 , wherein the pattern comprises both customer behavior and customer movement in the retail store.
8. The method of claim 1 , wherein executing the image processing algorithm comprises using the masking techniques to identify the customer.
9. The method of claim 1 , further comprising training the artificial intelligence model using the one or more features to identify a customer demographic.
10. The method of claim 1 , further comprising training the artificial intelligence model using the one or more features to identify store conditions.
11. The method of claim 10 , wherein the store conditions comprise one or more problems, wherein the one or more problems comprise a liquid spill within an aisle, spilled product on a floor, electrical problems, or plumbing problems.
12. The method of claim 1 , wherein the notification comprises sales or coupons associated with the product.
13. The method of claim 1 , wherein the notification further comprises a location of the customer in the store including a specific aisle.
14. The method of claim 1 , wherein the notification further comprises information of the out of stock product and provides information about an alternative product.
15. The method of claim 14 , wherein the notification further comprises providing comparisons of products, identifying similar products, providing product descriptions and price comparisons.
16. The method of claim 1 , wherein the one or more features comprises at least one of a customer's eye movement and a customer's facial expression.
17. The method of claim 16 , wherein the customer's eye movement is tracked for a threshold period of time.
18. A computer system for automatically initializing customer assistance in a retail store based on video analysis, the system comprising:
one or more processors;
one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to:
retrieve video data captured by one or more cameras;
extract one or more features from the video data;
receive an indication that a product is out of stock;
execute an image processing algorithm using object recognition techniques or masking techniques to identify a human customer;
train an artificial intelligence model using the one or more features to identify a customer needs assistance;
apply the trained artificial intelligence model to predict the customer needs assistance at a particular location in the store; and
send a notification to a computing device of a customer service representative, wherein the notification identifies the customer, the customer's location, and information about the out of stock product.
19. The method of claim 1 , wherein training the artificial intelligence model using the one or more features comprises using the one or more features as training data to train the artificial intelligence model using a suitable training method.
20. The method of claim 19 , wherein the suitable training method is at least one of:
a gradient-based algorithm,
supervised learning,
unsupervised learning, or
reinforcement learning.