US 12,093,305 B2
Machine learning multiple features of depicted item
Oren Barkan, Rishon Lezion (IL); Noam Razin, Jerusalem (IL); Noam Koenigstein, Tel Aviv (IL); Roy Hirsch, Ramat Yishai (IL); and Nir Nice, Salit (IL)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Jun. 19, 2023, as Appl. No. 18/211,352.
Application 18/211,352 is a continuation of application No. 17/836,779, filed on Jun. 9, 2022, granted, now 11,720,622.
Application 17/836,779 is a continuation of application No. 16/725,652, filed on Dec. 23, 2019, granted, now 11,373,095.
Prior Publication US 2023/0334085 A1, Oct. 19, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/532 (2019.01); G06F 17/18 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06T 7/00 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06F 16/532 (2019.01) [G06F 17/18 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06T 7/0002 (2013.01); G06T 7/97 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing system that trains a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, said computing system comprising:
at least one processor; and
at least one hardware storage device that stores instructions that are executable by the at least one processor to cause the computing system to:
access a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item;
machine train on the plurality of images using a neural network to identify a plurality of features of the item;
generate a plurality of vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of vectors includes an identity embedding vector that provides a supposed identity for the item; and
use at least one vector included in the plurality of vectors to facilitate a search for one or more different items that are determined to meet a similarity requirement with regard to the item in the plurality of images, wherein:
a search definition for the search includes accessing a latent vector that describes a user-specified change to at least one feature included in the plurality of features, and
the search is based on a combination of said at least one vector and the latent vector such that the search involves searching for different items that are identified as having the changed at least one feature.