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:
Jun. 30, 2026

Filed:

Apr. 15, 2022
Applicant:

Innolcon Medical Technology (Suzhou) Co., Ltd., Suzhou, CN;

Inventors:

Longyang Yao, Suzhou, CN;

Fuyuan Wang, Suzhou, CN;

Fei Ding, Suzhou, CN;

Zhenzhong Liu, Suzhou, CN;

Wei Luo, Suzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 17/32 (2006.01); A61B 17/00 (2006.01); G05D 23/19 (2006.01);
U.S. Cl.
CPC ...
A61B 17/320068 (2013.01); G05D 23/1904 (2013.01); G05D 23/1917 (2013.01); A61B 2017/00017 (2013.01);
Abstract

The present invention discloses a temperature control method and a system for a blade shaft of an ultrasonic scalpel based on a temperature distribution function model. The system includes the blade shaft of the ultrasonic scalpel and a transducer that are coupled to each other, and is connected to a generator through a cable. When the blade shaft of the ultrasonic scalpel works, actual temperature of the blade shaft is distributed along a one-dimensional space of the blade shaft. The temperature distribution on the blade shaft is determined by a set of the real-time working feedback parameter, the physical structure feature parameter, and the surrounding environmental parameter of the blade shaft. Each temperature distribution corresponds to a solution of the temperature distribution function, and the function can be approximated by a machine leaning algorithm. When the blade shaft of the ultrasonic scalpel works, the real-time temperature distribution of the blade shaft can be estimated by inputting, into a machine learning algorithm model, feature parameters such as the real-time resonance frequency, voltage, current, impedance, power, shape, and environment parameters of the blade shaft. Power control is performed based on the estimated temperature, which is accurate and effective.


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