Invention Grant
- Patent Title: Techniques for analyzing vehicle design deviations using deep learning with neural networks
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Application No.: US18045809Application Date: 2022-10-11
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Publication No.: US11842262B2Publication Date: 2023-12-12
- Inventor: Danil Nagy , Daniel Noviello , James Stoddart , David Benjamin , Damon Lau
- Applicant: AUTODESK, INC.
- Applicant Address: US CA San Francisco
- Assignee: AUTODESK, INC.
- Current Assignee: AUTODESK, INC.
- Current Assignee Address: US CA San Francisco
- Agency: Artegis Law Group, LLP
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/045 ; G06F17/15 ; G06N3/08 ; G06F30/15

Abstract:
A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.
Public/Granted literature
- US20230061993A1 TECHNIQUES FOR ANALYZING VEHICLE DESIGN DEVIATIONS USING DEEP LEARNING WITH NEURAL NETWORKS Public/Granted day:2023-03-02
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