Method, system, and non-transitory computer readable medium for providing product image recommendations

Patent metadata

Field	Value
Publication number	US-12002079-B2
Application number	US-202217836233-A
Country	US
Kind code	B2
Filing date	Jun 9, 2022
Priority date	Oct 24, 2019
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

For products that are sold online, the manner in which a product is displayed in an image can affect sales of the product. Embodiments of the present disclosure relate to computer-implemented systems and methods to provide a user with recommendations when generating an image of a product. A method includes obtaining a product image and determining parameters of the product image. A recommendation for modifying the product image is then generated using a model to relate these parameters to market success of the product image. The recommendation is displayed on the user device, and a user can potentially improve subsequent product images by following the recommendation.

First claim

The invention claimed is:

1. A computer-implemented method comprising:
obtaining a first product image captured by a user device, the first product image having a resolution less than a particular resolution achievable by the user device;
obtaining data indicating a capability of the user device, the data including an indication of the particular resolution achievable by the user device;
determining particular parameters of the first product image, wherein determining the particular parameters of the first product image includes determining a parameter of the first product image based on the particular resolution achievable by the user device that is higher than the resolution of the first product image;
generating, based on the particular parameters, a recommendation for capturing a further product image with the user device; and
instructing, based on the recommendation, the user device to display at least a portion of the recommendation in a viewfinder on the user device and automatically adjust a setting used for capturing the further product image by the user device.

2. The computer-implemented method of claim 1 , wherein the setting automatically adjusted includes at least one of: a sensitivity of a camera of the user device, an aperture size of the camera of the user device, an exposure time of the camera of the user device, a zoom setting of the camera of the user device, a filtering performed by the user device, and/or a flash setting of the camera of the user device.

3. The computer-implemented method of claim 1 , wherein the recommendation displayed is in the form of a user notification.

4. The computer-implemented method of claim 1 , further comprising instructing the user device to automatically take a picture to capture the further product image.

5. The computer-implemented method of claim 1 , wherein the particular resolution is the resolution at which a picture is taken by a camera of the user device.

6. The computer-implemented method of claim 5 , wherein the particular resolution is the true resolution of the camera.

7. The computer-implemented method of claim 1 , wherein the recommendation is generated based on both the particular parameters and a model that relates parameters of a product image to market success of the product image.

8. The computer-implemented method of claim 7 , wherein the model is implemented using a machine learning algorithm, and generating the recommendation comprises:
inputting the particular parameters into the machine learning algorithm; and
calculating, using the machine learning algorithm, a prediction of market success of the first product image, wherein the recommendation is associated with an improvement to the prediction of the market success of the first product image.

9. The computer-implemented method of claim 1 , wherein determining the particular parameters of the first product image comprises performing image analysis on the first product image.

10. A system comprising:
a processor; and
a memory storing processor-executable instructions that, when executed, cause the processor to:
obtain a first product image captured by a user device, the first product image having a resolution less than a particular resolution achievable by the user device;
obtain data indicating a capability of the user device, the data including an indication of the particular resolution achievable by the user device;
determine particular parameters of the first product image, wherein determining the particular parameters of the first product image includes determining a parameter of the first product image based on the particular resolution achievable by the user device that is higher than the resolution of the first product image;
generate, based on the particular parameters, a recommendation for capturing a further product image with the user device; and
instruct, based on the recommendation, the user device to display at least a portion of the recommendation in a viewfinder on the user device and automatically adjust a setting used for capturing the further product image by the user device.

11. The system of claim 10 , wherein the setting automatically adjusted includes at least one of: a sensitivity of a camera of the user device, an aperture size of the camera of the user device, an exposure time of the camera of the user device, a zoom setting of the camera of the user device, a filtering performed by the user device, and/or a flash setting of the camera of the user device.

12. The system of claim 10 , wherein the recommendation displayed is in the form of a user notification.

13. The system of claim 10 , wherein the processor is to instruct the user device to automatically take a picture to capture the further product image.

14. The system of claim 10 , wherein the particular resolution is the resolution at which a picture is taken by a camera of the user device.

15. The system of claim 10 , wherein the recommendation is generated based on both the particular parameters and a model that relates parameters of a product image to market success of the product image.

16. The system of claim 15 , wherein the model is implemented using a machine learning algorithm, and generating the recommendation comprises:
inputting the particular parameters into the machine learning algorithm; and
calculating, using the machine learning algorithm, a prediction of market success of the first product image, wherein the recommendation is associated with an improvement to the prediction of the market success of the first product image.

17. The system of claim 10 , wherein determining the particular parameters of the first product image comprises performing image analysis on the first product image.

18. A non-transitory computer readable medium having stored thereon computer-executable instructions that, when executed by a computer, cause the computer to perform operations comprising:
obtaining a first product image captured by a user device, the first product image having a resolution less than a particular resolution achievable by the user device;
obtaining data indicating a capability of the user device, the data including an indication of the particular resolution achievable by the user device;
determining particular parameters of the first product image, wherein determining the particular parameters of the first product image includes determining a parameter of the first product image based on the particular resolution achievable by the user device that is higher than the resolution of the first product image;
generating, based on the particular parameters, a recommendation for capturing a further product image with the user device; and
instructing, based on the recommendation, the user device to display at least a portion of the recommendation in a viewfinder on the user device and automatically adjust a setting used for capturing the further product image by the user device.

19. The non-transitory computer readable medium of claim 18 , wherein the setting automatically adjusted includes at least one of: a sensitivity of a camera of the user device, an aperture size of the camera of the user device, an exposure time of the camera of the user device, a zoom setting of the camera of the user device, a filtering performed by the user device, and/or a flash setting of the camera of the user device.

20. The non-transitory computer readable medium of claim 19 , wherein the recommendation displayed is in the form of a user notification.

Assignees

• Shopify Inc

Inventors

• D'Souza Franklyn
• Wade Jonathan
• Haapoja Juho Mikko

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08

  Learning methods · CPC title