US 11,868,444 B2
Creating synthetic visual inspection data sets using augmented reality
Michael Charles Hollinger, Austin, TX (US); Mal Pattiarachi, Boston, MA (US); and Abhinav Pratap Singh, Milton (CA)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jul. 20, 2021, as Appl. No. 17/380,075.
Prior Publication US 2023/0027216 A1, Jan. 26, 2023
Int. Cl. G06F 18/40 (2023.01); G06T 19/00 (2011.01); G06T 19/20 (2011.01); G06N 20/00 (2019.01); G06V 20/20 (2022.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01)
CPC G06F 18/40 (2023.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 20/00 (2019.01); G06T 19/006 (2013.01); G06T 19/20 (2013.01); G06V 20/20 (2022.01); G06T 2200/24 (2013.01); G06T 2219/2016 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by one or more processors, a plurality of images of an anchor object from a user, wherein the anchor object is a known fixed reference object comprised of a plurality of sub-objects;
creating, by the one or more processors, an Artificial Intelligence (AI) computer vision deep learning model;
training, by the one or more processors, the AI computer vision deep learning model using a synthetic visual inspection data set to identify the anchor object in a three-dimensional representation;
and
enabling, by the one or more processors, the user to interact with the trained AI computer vision deep learning model in an access mode, wherein the access mode is one of a training mode and a validation mode by superimposing one or more target objects on the anchor object and capturing one or more images of the one or more target objects superimposed on the anchor object.