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:
Oct. 28, 2025

Filed:

Aug. 18, 2022
Applicant:

Advanced Neuromodulation Systems, Inc., Plano, TX (US);

Inventors:

Mary Khun Hor-Lao, Prosper, TX (US);

Binesh Balasingh, Prosper, TX (US);

Scott Debates, Frisco, TX (US);

Douglas Alfred Lautner, Frisco, TX (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61N 1/372 (2006.01); A61B 5/00 (2006.01); A61B 5/11 (2006.01); A61N 1/36 (2006.01); G06F 21/62 (2013.01); G06T 11/00 (2006.01); G06T 19/00 (2011.01); G06V 10/82 (2022.01); G06V 40/20 (2022.01); G16H 20/30 (2018.01); G16H 40/40 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 80/00 (2018.01); H04N 5/272 (2006.01); H04N 7/14 (2006.01);
U.S. Cl.
CPC ...
A61N 1/37282 (2013.01); A61B 5/1101 (2013.01); A61B 5/1114 (2013.01); A61B 5/1118 (2013.01); A61B 5/112 (2013.01); A61B 5/4082 (2013.01); A61B 5/742 (2013.01); A61B 5/7455 (2013.01); A61N 1/36135 (2013.01); A61N 1/37247 (2013.01); A61N 1/37264 (2013.01); G06F 21/6254 (2013.01); G06T 11/00 (2013.01); G06T 19/006 (2013.01); G06V 10/82 (2022.01); G06V 40/25 (2022.01); G16H 20/30 (2018.01); G16H 40/40 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 80/00 (2018.01); H04N 5/272 (2013.01); H04N 7/141 (2013.01); A61N 1/36067 (2013.01); A61N 1/36071 (2013.01); A61N 1/36132 (2013.01); G06T 2210/41 (2013.01);
Abstract

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.


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