The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 11, 2025

Filed:

Dec. 20, 2023
Applicant:

3shape A/s, Copenhagen K, DK;

Inventors:

Jens Peter Träff, Copenhagen K, DK;

Jens Christian Jørgensen, Seattle, WA (US);

Alejandro Alonso Diaz, Copenhagen K, DK;

Mathias Bøgh Stokholm, Copenhagen K, DK;

Asger Vejen Hoedt, Vallensbæk, DK;

Assignee:

3SHAPE A/S, Copenhagen K, DK;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/00 (2006.01); A61C 7/00 (2006.01); A61C 9/00 (2006.01); A61C 13/00 (2006.01); G06T 7/00 (2017.01); G06T 9/00 (2006.01); G06T 11/00 (2006.01); G06T 17/20 (2006.01); G06T 19/20 (2011.01); G06V 10/774 (2022.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06T 17/20 (2013.01); A61C 7/002 (2013.01); A61C 9/0053 (2013.01); A61C 13/0004 (2013.01); A61C 13/0019 (2013.01); G06T 7/0012 (2013.01); G06T 9/002 (2013.01); G06T 11/00 (2013.01); G06T 17/00 (2013.01); G06T 19/20 (2013.01); G06V 10/7747 (2022.01); G16H 30/40 (2018.01); G06T 2207/30036 (2013.01); G06T 2210/41 (2013.01); G06T 2219/2021 (2013.01);
Abstract

A computer-implemented method for generating a 2D or 3D object, including training an autoencoder on a first set of training data to identify a first set of latent variables and generate a first set of output data; training an hourglass predictor on a second set of training data, where the hourglass predictor encoder converts a set of related but different training input data to a second set of latent variables, which decode into a second set of output data of the same type as the first set of output data; and using the hourglass predictor to predict a 2D or 3D object of the same type as the first set of output data based on a 2D or 3D object of the same type as the second set of input data.


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