US 12,380,256 B2
Techniques for generating subjective style comparison metrics for B-reps of 3D CAD objects
Peter Meltzer, London (GB); Amir Hosein Khas Ahmadi, Toronto (CA); Pradeep Kumar Jayaraman, Toronto (CA); Joseph George Lambourne, London (GB); Aditya Sanghi, Toronto (CA); and Hooman Shayani, London (GB)
Assigned to AUTODESK, INC., San Francisco, CA (US)
Filed by AUTODESK, INC., San Francisco, CA (US)
Filed on Nov. 10, 2021, as Appl. No. 17/523,725.
Claims priority of provisional application 63/113,755, filed on Nov. 13, 2020.
Prior Publication US 2022/0156415 A1, May 19, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 30/10 (2020.01); G06F 30/27 (2020.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01)
CPC G06F 30/10 (2020.01) [G06F 30/27 (2020.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating a style comparison metric for pairs of different three dimensional (3D) computer-aided design (CAD) objects, the method comprising:
executing a trained neural network one or more times to map inputs of the trained neural network comprising a plurality of 3D CAD objects to outputs of the trained neural network comprising a plurality of feature maps, wherein the trained neural network is generated using unsupervised learning techniques that do not receive labeled training data as input;
computing a plurality of style signals based on the plurality of feature maps;
determining a plurality of values for a plurality of weights based on the plurality of style signals, wherein a parameterized style comparison metric combines a plurality of style distances based on the plurality of weights; and
generating the style comparison metric based on the plurality of weights and the parameterized style comparison metric.