US 11,748,796 B2
Automatic clustering and mapping of user generated content with curated content
Sourabh Gupta, Noida (IN); Mrinal Kumar Sharma, Hazaribagh (IN); and Gourav Singhal, Delhi (IN)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Mar. 19, 2020, as Appl. No. 16/824,180.
Prior Publication US 2021/0295423 A1, Sep. 23, 2021
Int. Cl. G06Q 30/0601 (2023.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06F 16/583 (2019.01); G06V 20/20 (2022.01); G06F 18/23 (2023.01); G06F 18/214 (2023.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06F 3/0482 (2013.01); G06V 10/22 (2022.01); G06F 3/04842 (2022.01); G06N 3/088 (2023.01); G06F 16/54 (2019.01); G06F 3/0488 (2022.01); G06F 3/04845 (2022.01)
CPC G06Q 30/0623 (2013.01) [G06F 16/583 (2019.01); G06F 18/2148 (2023.01); G06F 18/23 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 30/0643 (2013.01); G06V 10/454 (2022.01); G06V 10/763 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/20 (2022.01); G06F 3/0482 (2013.01); G06F 3/0488 (2013.01); G06F 3/04842 (2013.01); G06F 3/04845 (2013.01); G06F 16/54 (2019.01); G06F 2203/04806 (2013.01); G06N 3/088 (2013.01); G06V 10/235 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium for presenting clustered images, the non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to:
present, via a graphical user interface at a user client device, curated images displaying a product;
extract, utilizing a machine learning algorithm, one or more frequency domain descriptors from the curated images;
compile, utilizing the machine learning algorithm, the one or more frequency domain descriptors into feature vectors from the curated images;
extract, utilizing the machine learning algorithm, one or more frequency domain descriptors from a plurality of user-submitted images displaying the product;
compile, utilizing the machine learning algorithm, the one or more frequency domain descriptors from the plurality of user-submitted images displaying the product into feature vectors from the plurality of user-submitted images displaying the product;
determine a sub-set of the plurality of user-submitted images for each curated image that show a view of the product positioned at an angle within a threshold distance to a positioned angle of the product within a given curated image by comparing the feature vectors of the plurality of user-submitted images with the feature vectors of the curated images;
determine an additional sub-set of user-submitted images that show the view of the product positioned at one or more additional angles outside of the threshold distance of the positioned angle of the product within the curated images by comparing the feature vectors of the plurality of user-submitted images with the feature vectors of the curated images;
receive, via the graphical user interface, a user selection of a curated image;
present, via the graphical user interface and based on the user selection of the curated image, the sub-set of the user-submitted images that show the view of the product positioned at the angle within the threshold distance to the positioned angle of the product within the curated image;
receive, via the graphical user interface, a user selection of an additional views element; and
present, via the graphical user interface and based on the user selection of the additional views element, one or more of the user-submitted images in the additional sub-set of user-submitted images that show the view of the product positioned at the one or more additional angles outside of the threshold distance of the positioned angle of the product within the curated images.