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:
Apr. 23, 2024

Filed:

Aug. 31, 2021
Applicant:

Lg Electronics Inc., Seoul, KR;

Inventors:

Amir Hossein Khalili, Santa Clara, CA (US);

Bhooshan Supe, Santa Clara, CA (US);

Jung Ick Guack, Santa Clara, CA (US);

Shantanu Patel, Santa Clara, CA (US);

Gaurav Saraf, Santa Clara, CA (US);

Baisub Lee, Santa Clara, CA (US);

Helder Silva, Santa Clara, CA (US);

Julie Huynh, Santa Clara, CA (US);

Jaigak Song, Santa Clara, CA (US);

Assignee:

LG ELECTRONICS INC., Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 20/20 (2012.01); G01G 19/414 (2006.01); G06F 18/25 (2023.01); G06N 3/02 (2006.01); G06N 20/00 (2019.01); G06Q 10/087 (2023.01); G06T 7/246 (2017.01); G06T 7/73 (2017.01); G06V 20/52 (2022.01); G06V 40/10 (2022.01); G06V 40/16 (2022.01); G06V 40/20 (2022.01); H04N 23/61 (2023.01);
U.S. Cl.
CPC ...
G06Q 20/208 (2013.01); G01G 19/4144 (2013.01); G06F 18/251 (2023.01); G06N 3/02 (2013.01); G06N 20/00 (2019.01); G06Q 10/087 (2013.01); G06Q 20/203 (2013.01); G06T 7/251 (2017.01); G06T 7/73 (2017.01); G06V 20/52 (2022.01); G06V 40/107 (2022.01); G06V 40/161 (2022.01); G06V 40/168 (2022.01); G06V 40/172 (2022.01); G06V 40/28 (2022.01); H04N 23/61 (2023.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01); G06T 2207/30232 (2013.01);
Abstract

Disclosed is a method for identifying and monitoring a shopping behavior in a user. The method includes capturing images from a depth camera mounted on a shelf unit, identifying a user from the captured image, identifying joints of the identified user by performing a deep neural network (DNN) body joint detection on the captured images; detecting and tracking actions of the identified user over a first time period; tracking an object from the bins over a second time period by associating the object with one or more joints among the identified joints that have entered the bins within the shelf unit, and determining an action of the identified user based at least in part on the associated object with the one or more joints and results from the deep learning identification on the bounding box.


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