| US 11,720,942 B1 | ||
| Interactive retrieval using visual semantic matching | ||
| Loris Bazzani, Berlin (DE); and Yanbei Chen, London (GB) | ||
| Assigned to AMAZON TECHNOLOGIES, INC., Seattle, WA (US) | ||
| Filed by Amazon Technologies, Inc., Seattle, WA (US) | ||
| Filed on Jun. 29, 2020, as Appl. No. 16/915,361. | ||
| Claims priority of provisional application 62/934,440, filed on Nov. 12, 2019. | ||
| Int. Cl. G06Q 30/06 (2023.01); G06N 3/02 (2006.01); G06F 16/535 (2019.01); G06Q 30/0601 (2023.01); G06T 7/00 (2017.01) | ||
| CPC G06Q 30/0613 (2013.01) [G06F 16/535 (2019.01); G06N 3/02 (2013.01); G06T 7/00 (2013.01)] | 20 Claims |
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1. A method of interactive shopping assistance, said method comprising:
training a machine learning product prediction model based at least in part on:
determining, using at least one processor, a predicted output vector based on an encoding of a reference image input and an encoding of a modification text input describing a modification to the reference image input that results in a target image output;
determining, using the at least one processor, a target output vector based at least in part on an encoding of the target image output; and
determining, using the at least one processor, a compositional matching loss based at least in part on a difference between the predicted output vector and the target output vector;
receiving image data from a user, the image data representing an image of an article of clothing; receiving a modification input from the user, the modification input describing a desired modification to the article of clothing; and processing the image data and the modification input with the machine learning product prediction model to identify a target product corresponding to the desired modification to the article of clothing; and sending image data of the target product to the user. |