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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.

Patent No.:

US 11821844 B1

PDFFull Text
Date of Patent:
Nov. 21, 2023

Filed:

Nov. 23, 2022

System for detecting surface type of object and artificial neural network-based method for detecting surface type of object

Applicant:

Getac Technology Corporation, New Taipei, TW;

Inventor:

Kun-Yu Tsai, Taipei, TW;

Assignee:

GETAC TECHNOLOGY CORPORATION, Hsinchu County, TW;

Attorney:
Primary Examiner:

Boubacar Abdou Tchoussou

Int. Cl.
CPC ...
G01N 21/88 (2006.01); G06N 3/08 (2023.01); G01J 3/28 (2006.01); G01N 21/956 (2006.01); G01N 21/952 (2006.01); G06T 7/586 (2017.01); G06T 7/00 (2017.01); G01N 21/3581 (2014.01); G06T 7/40 (2017.01); G06N 3/04 (2023.01); G06T 7/11 (2017.01); G06T 7/45 (2017.01); G06F 17/16 (2006.01); G06N 3/063 (2023.01); G01N 21/01 (2006.01); G06V 10/22 (2022.01); G06V 20/64 (2022.01); G06F 18/214 (2023.01); G06N 3/047 (2023.01); G06V 10/145 (2022.01); G06V 10/25 (2022.01); G06V 10/60 (2022.01); G06V 10/143 (2022.01); G06V 10/24 (2022.01);
U.S. Cl.
CPC ...
G01N 21/8806 (2013.01); G01J 3/2823 (2013.01); G01N 21/01 (2013.01); G01N 21/3581 (2013.01); G01N 21/8851 (2013.01); G01N 21/952 (2013.01); G01N 21/956 (2013.01); G06F 17/16 (2013.01); G06F 18/2148 (2023.01); G06N 3/04 (2013.01); G06N 3/047 (2023.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06T 7/0004 (2013.01); G06T 7/11 (2017.01); G06T 7/40 (2013.01); G06T 7/45 (2017.01); G06T 7/586 (2017.01); G06T 7/97 (2017.01); G06V 10/143 (2022.01); G06V 10/145 (2022.01); G06V 10/22 (2022.01); G06V 10/242 (2022.01); G06V 10/25 (2022.01); G06V 10/60 (2022.01); G06V 20/64 (2022.01); G06V 20/647 (2022.01); G01N 2021/8887 (2013.01); G06T 2207/10152 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

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