US 12,112,252 B1Granted

Enhanced brand matching using multi-layer machine learning

AssigneeAmazon Technologies, Inc.

InventorsXianshun Chen, Archiman Dutta

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Abstract

Devices, systems, and methods are provided for brand matching using multi-layer machine learning. A method may include generating, based on a first embedding vector and a second embedding vector as inputs to a twin neural network, a third embedding vector and a fourth embedding vector; generating, based on the first embedding vector and the second embedding vector as inputs to a difference neural network, a difference vector indicative of a difference between the first embedding vector and the second embedding vector; generating a concatenated vector by concatenating the third embedding vector with the fourth embedding vector and the difference vector; generating, based on the concatenated vector as an input to a feedforward neural network (FFN), a score between zero and one, the score indicative of a relationship between a first entity and a second entity.

Claims20

Dependent claims

A method comprising:

generating, based on a first universal image embedding and a second universal image embedding as inputs to a twin neural network, a first image vector associated with a first brand and a second image vector associated with a second brand, the first universal image embedding associated with the first brand and the second universal image embedding associated with the second brand;
generating, based on a first universal text embedding and a second universal text embedding as inputs to the twin neural network, a first text vector associated with the first brand and a second text vector associated with the second brand, the first universal text embedding associated with the first brand and the second universal text embedding associated with the second brand;
generating, based on a first universal name vector of a first product and a second universal name vector of a second product as inputs to the twin neural network, a third text vector associated with the first brand and a fourth text vector associated with the second brand, the first universal name vector associated with the first brand and the second universal name vector associated with the second brand;
generating, based on the first universal image embedding and the second universal image embedding as inputs to a difference neural network, a first difference vector indicative of a difference between the first universal image embedding and the second universal image embedding;
generating, based on the first universal text embedding and the second universal text embedding as inputs to the difference neural network, a second difference vector indicative of a difference between the first universal text embedding and the second universal text embedding;
generating, based on the first universal name vector and the second universal name vector as inputs to the difference neural network, a third difference vector indicative of a difference between the first universal name vector and the second universal name vector;
generating a concatenated vector by concatenating the first image vector to the second image vector, the first text vector, the second text vector, the first name vector, the second name vector, the first difference vector, the second difference vector, and the third difference vector;
generating, based on the concatenated vector as an input to a feedforward neural network (FFN), a score between zero and one, the score indicative of a relationship between the first brand and the second brand.

5 more elements

A method comprising:

generating, based on a first universal embedding vector and a second universal embedding vector as inputs to a twin neural network, a third universal embedding vector and a fourth universal embedding vector;
generating, based on the first universal embedding vector and the second universal embedding vector as inputs to a difference neural network, a difference vector indicative of a difference between the first universal embedding vector and the second universal embedding vector;
generating a concatenated vector by concatenating the third universal embedding vector with the fourth universal embedding vector and the difference vector;
generating, based on the concatenated vector as an input to a feedforward neural network (FFN), a score between zero and one, the score indicative of a relationship between a first entity and a second entity, the first entity associated with the first universal embedding vector and the second entity associated with the second universal embedding vector.

1 more element

A system comprising:

a twin neural network that receives a first universal embedding vector and a second universal embedding vector as inputs, and that generates a third universal embedding vector and a fourth universal embedding vector as outputs;
a difference neural network that receives the first universal embedding vector and the second universal embedding vector as inputs, and that generates a difference vector indicative of a difference between the first universal embedding vector and the second universal embedding vector;
and
a feedforward neural network (FFN) that generates a concatenated vector by concatenating the third universal embedding vector with the fourth universal embedding vector and the difference vector, and that generates, based on the concatenated vector, a score between zero and one, the score indicative of a relationship between a first brand and a second brand, the first brand associated with the first universal embedding vector and the second brand associated with the second universal embedding vector.

1 more element

Dependencies are inferred from the claim text.