SYSTEMS AND METHODS FOR COMPUTER GENERATION OF A MODIFIABLE PRODUCT DESCRIPTION

    公开(公告)号:US20230259692A1

    公开(公告)日:2023-08-17

    申请号:US17670110

    申请日:2022-02-11

    Applicant: Shopify Inc.

    Inventor: Asher Wright

    CPC classification number: G06F40/166 G06F40/289 G06F40/40

    Abstract: Generative language models are able to generate a sequence of text that may closely mimic a native human speaker's own generated text. However, technical challenges exist when implementing a generative language model for generating product descriptions. The model may output certain inaccuracies due to the predictive nature of generating the output. Further, the model does not have the ability to identify words from the product description that a merchant may want to modify, nor the ability to provide meaningful alternatives to such words. In some embodiments, a natural language processor might be built and/or trained using classification data. The natural language processor may identify one or more words and/or phrases in a product description as a candidate for modification. The product description might then be displayed on a merchant-facing user interface with an indication that the candidate for modification may be modified.

    Method and system for generating images using generative adversarial networks (GANs)

    公开(公告)号:US12243133B2

    公开(公告)日:2025-03-04

    申请号:US17734938

    申请日:2022-05-02

    Applicant: SHOPIFY INC.

    Abstract: An image processing method and system that generates output images. The system receives a first input image depicting a first set of products and determines the first set of products and corresponding first product categories. The system then receives, on a user interface of a requestor device, a second input image depicting other products selected as being of interest having corresponding second product categories for the other products. In response to a match between one of the first product categories and the second product categories: the system applies the first input image and the second input image to generative adversarial networks (GANs). Each GAN is trained using image dataset for corresponding ones of the first and second product categories, to generate an output image replacing at least a portion of first input image with the second input image, the replacement based on the match between the product categories.

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