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:
Sep. 19, 2023

Filed:

Nov. 05, 2019
Applicant:

Lg Electronics Inc., Seoul, KR;

Inventors:

Hyo Jin An, Goyang-si, KR;

Young Min Lee, Incheon, KR;

Heung Sik Choi, Seoul, KR;

Assignee:

LG ELECTRONICS INC., Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A47J 27/21 (2006.01); G01G 19/52 (2006.01); G01H 9/00 (2006.01); F24C 7/08 (2006.01); G01G 19/00 (2006.01); F24C 15/10 (2006.01); A47J 36/32 (2006.01); G01H 17/00 (2006.01); G01G 23/06 (2006.01);
U.S. Cl.
CPC ...
A47J 27/21158 (2013.01); A47J 36/321 (2018.08); F24C 7/087 (2013.01); F24C 15/105 (2013.01); G01G 19/00 (2013.01); G01G 19/52 (2013.01); G01H 9/008 (2013.01); G01G 23/06 (2013.01); G01H 17/00 (2013.01);
Abstract

The present disclosure relates to an apparatus and a method for estimating a state of an object to be heated based on sound which is generated when the object to be heated is heated, and providing the estimated information to other devices in an Internet of Things (IoT) environment through a 5G communication network. The heating apparatus may include a housing having a receiving space therein, a heating member disposed within the housing, a power supplier for supplying power to the heating member, a top plate disposed on the top of the housing to support the object to be heated, a sound sensor disposed on the bottom of the top plate, and a controller for predicting the state of the object to be heated by using a deep neural network model that has been trained through machine learning based on a sound signal received from the sound sensor.


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