Customized presentation of items on electronic visual displays in retail stores based on availability of products

1. A non-transitory computer-readable medium including instructions that when executed by a processor cause the processor to perform a method for customized presentation of items on electronic visual displays in retail stores, the method comprising:

obtaining a plurality of images of products in a retail store captured using at least one image sensor, the plurality of images comprises at least a first image corresponding to a first point in time and a second image corresponding to a second point in time, the first point in time is earlier than the second point in time;

analyzing, using a machine learning model trained using training example images and product type availabilities associated with the training example images, the first image to determine whether products of a particular product type are available at the first point in time;

analyzing, using the machine learning model, the second image to determine whether products of the particular product type are available at the second point in time;

analyzing the second image to determine at least one position associated with the particular product type;

using the determined at least one position associated with the particular product type to select a region of an electronic visual display in the retail store;

based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, selecting at least one display parameter for a particular item; and

using the selected at least one display parameter to display the particular item on the selected region of the electronic visual display in the retail store.

2. The non-transitory computer-readable medium of claim 1 , wherein the at least one display parameter includes a display size for the particular item.

3. The non-transitory computer-readable medium of claim 1 , wherein the at least one display parameter includes a motion pattern for the particular item.

4. The non-transitory computer-readable medium of claim 1 , wherein the at least one display parameter includes a display position on the electronic visual display for the particular item.

5. The non-transitory computer-readable medium of claim 1 , wherein the at least one display parameter includes a color scheme for the particular item.

6. The non-transitory computer-readable medium of claim 1 , wherein the plurality of images comprises a preceding image corresponding to a preceding point in time, the preceding point in time is earlier than the first point in time, and the method further comprises:

analyzing the preceding image to determine whether products of the particular product type are available at the preceding point in time; and

further basing the selection of the at least one display parameter for the particular item on the determination of whether products of the particular product type are available at the preceding point in time.

7. The non-transitory computer-readable medium of claim 1 , wherein the selection of the at least one display parameter for the particular item is further based on an elapsed time between the first point in time and the second point in time.

8. The non-transitory computer-readable medium of claim 1 , wherein the selection of the at least one display parameter for the particular item is further based on an elapsed time since the second point in time.

9. The non-transitory computer-readable medium of claim 1 , wherein the selection of the at least one display parameter for the particular item is further based on information related to a person in a vicinity of the electronic visual display.

10. The non-transitory computer-readable medium of claim 1 , wherein the selection of the at least one display parameter for the particular item is further based on a time of day.

11. The non-transitory computer-readable medium of claim 1 , wherein the electronic visual display is connected to a shelf in the retail store.

12. The non-transitory computer-readable medium of claim 1 , wherein the electronic visual display is connected to a door of a retail storage container in the retail store.

13. The non-transitory computer-readable medium of claim 1 , wherein the electronic visual display is part of a personal device of a store associate.

14. The non-transitory computer-readable medium of claim 1 , wherein the electronic visual display is part of a personal device of a customer.

15. The non-transitory computer-readable medium of claim 1 , wherein the method further comprises:

obtaining data captured at the first point in time using a plurality of sensors positioned on a shelf in the retail store and configured to be positioned between the shelf and products positioned on the shelf;

obtaining data captured at the second point in time using the plurality of sensors;

basing the determination of whether products of the particular product type are available at the first point in time on an analysis of the data captured at the first point in time using the plurality of sensors; and

basing the determination of whether products of the particular product type are available at the second point in time on an analysis of the data captured at the second point in time using the plurality of sensors.

16. The non-transitory computer-readable medium of claim 1 , wherein the method further comprises:

obtaining weight data captured at the first point in time using a weight sensor corresponding to at least part of a shelf in the retail store;

obtaining weight data captured at the second point in time using the weight sensor;

basing the determination of whether products of the particular product type are available at the first point in time on an analysis of the weight data captured at the first point in time using the weight sensor; and

basing the determination of whether products of the particular product type are available at the second point in time on an analysis of the weight data captured at the second point in time using the weight sensor.

17. The non-transitory computer-readable medium of claim 1 , wherein the selection of the region of the electronic visual display is further based on a position of a face of a person facing a retail storage container.

18. A method for customized presentation of items on electronic visual displays in retail stores, the method comprising:

obtaining a plurality of images of products in a retail store captured using at least one image sensor, the plurality of images comprises at least a first image corresponding to a first point in time and a second image corresponding to a second point in time, the first point in time is earlier than the second point in time;

analyzing, using a machine learning model trained using training example images and product type availabilities associated with the training example images, the first image to determine whether products of a particular product type are available at the first point in time;

analyzing, using the machine learning model, the second image to determine whether products of the particular product type are available at the second point in time;

analyzing the second image to determine at least one position associated with the particular product type;

using the determined at least one position associated with the particular product type to select a region of an electronic visual display in the retail store;

based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, selecting at least one display parameter for a particular item; and

using the selected at least one display parameter to display the particular item on the selected region of the electronic visual display in the retail store.

19. A system for customized presentation of items on electronic visual displays in retail stores, the system comprising:

at least one processor configured to:

obtain a plurality of images of products in a retail store captured using at least one image sensor, the plurality of images comprises at least a first image corresponding to a first point in time and a second image corresponding to a second point in time, the first point in time is earlier than the second point in time;

analyze, using a machine learning model trained using training example images and product type availabilities associated with the training example images, the first image to determine whether products of a particular product type are available at the first point in time;

analyze, using the machine learning model, the second image to determine whether products of the particular product type are available at the second point in time;

analyze the second image to determine at least one position associated with the particular product type;

use the determined at least one position associated with the particular product type to select a region of an electronic visual display in the retail store;

based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, select at least one display parameter for a particular item; and

use the selected at least one display parameter to display the particular item on the selected region of the electronic visual display in the retail store.