DYNAMIC PRODUCT DESIGN VIA GENERATIVE ARCHITECTURES

Number: 20260311236
Published: 2026-10-08
Filed: 2025-04-02
Assignee: DISNEY ENTERPRISES, INC.
Inventors: VAZ; Tatiana Marie, MURDOCK; Malcolm Edward, KHALFAN; Alif S.
CPC: G06Q30/0631; G06Q30/0206
Verdict: Set aside recommendation
In edition: 2026-W41
Source: Google Patents · FreePatentsOnline

The keeper's note

The present invention sets forth techniques for generating customized product designs.

Abstract

The present invention sets forth techniques for generating customized product designs. The techniques include generating, via one or more machine learning models based at least on information associated with a user, candidate product data comprising (i) one or more candidate customized product suggestions, (ii) one or more product images corresponding to the one or more candidate customized product suggestions, and (iii) one or more product descriptions for the one or more candidate customized product suggestions. The techniques also include determining, via one or more additional machine learning models based on the candidate product data, at least a subset of the one or more candidate customized product suggestions that meets a set of evaluation criteria, and outputting at least the subset of the one or more candidate customized product suggestions to the user.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to machine learning and generative models and, more specifically, to techniques for dynamic product design via generative architectures. Description of the Related Art

In the fields of product design and product creation, users may wish to customize the appearance or material of an existing product, rather than limiting their choices to versions of products that have already been designed and/or created. For example, a customer may attempt to search a product website for a coffee mug including one or more personalized or otherwise customized design elements that are not available on pre-designed or already created mugs.

However, multiple manual, human-performed steps are typically performed to create, review, update, approve, and/or manufacture a product with a given design. These manual, labor-intensive steps may limit the number and variety of product designs available to a user.

One existing technique for creating on-demand custom product designs may allow a user to specify a customizable product and select one or more options from a list of available colors, designs, patterns, and/or textual content via, e.g., a custom design page included in a manufacturer's or vendor's website. The manufacturer or vendor then applies the selected options to the specified customizable product and produces the customized product for sale and/or delivery. This tech

Claims

1. A computer-implemented method for generating customized product designs, the computer-implemented method comprising: generating, via one or more machine learning models based at least on information associated with a user, candidate product data comprising (i) one or more candidate customized product suggestions, (ii) one or more product images corresponding to the one or more candidate customized product suggestions, and (iii) one or more product descriptions for the one or more candidate customized product suggestions; determining, via one or more additional machine learning models based on the candidate product data, at least a subset of the one or more candidate customized product suggestions that meets a set of evaluation criteria; and outputting the at least the subset of the one or more candidate customized product suggestions to the user. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: generating, via one or more machine learning models based at least on information associated with a user, candidate product data comprising (i) one or more candidate customized product suggestions, (ii) one or more product images corresponding to the one or more candidate customized product suggestions, and (iii) one or more product descriptions for the one or more candidate customized product suggestions; determining, via one or more additional machine learning models based on the candidate product data, at least a subset of the one or more candidate customized product suggestions that meets a set of evaluation criteria; and outputting the at least the subset of the one or more candidate customized product suggestions to the user. || 19. A system comprising: one or more memories storing instructions; and one or more processors for executing the instructions to: generate, via one or more machine learning models based at least on information associated with a user, candidate product data comprising (i) one or more candidate customized product suggestions, (ii) one or more product images corresponding to the one or more candidate customized product suggestions, and (iii) one or more product descriptions for the one or more candidate customized product suggestions; determine, via one or more additional machine learning models based on the candidate product data, at least a subset of the one or more candidate customized product suggestions that meets a set of evaluation criteria; and output the at least the subset of the one or more candidate customized product suggestions to the user.