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. 16, 2026

Filed:

Aug. 25, 2021
Applicant:

Cionic, Inc., San Francisco, CA (US);

Inventors:

Jeremiah Robison, San Francisco, CA (US);

Michael Dean Achelis, Walnut Creek, CA (US);

Lina Avancini Colucci, Los Altos, CA (US);

Sidney Rafael Primas, Los Altos, CA (US);

Jonathan Sakai, Rocky River, OH (US);

Andrew James Weitz, Bishop, CA (US);

Assignee:

Cionic, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/11 (2006.01); A61B 5/251 (2021.01); A61M 5/172 (2006.01); A61P 25/16 (2006.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
A61B 5/4082 (2013.01); A61B 5/112 (2013.01); A61B 5/251 (2021.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61M 5/1723 (2013.01); A61P 25/16 (2018.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); G16H 50/20 (2018.01); A61M 2205/3303 (2013.01); A61M 2205/50 (2013.01); A61M 2205/502 (2013.01); A61M 2230/06 (2013.01); A61M 2230/08 (2013.01); A61M 2230/42 (2013.01); A61M 2230/62 (2013.01); A61M 2230/63 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30196 (2013.01); G06V 2201/03 (2022.01);
Abstract

A symptom intervention system monitors data representative of a user's movement, identifies an onset of a symptom of a physical condition, and applies an actuation to intervene with the identified onset. A machine-learned model is trained to identify an onset of a symptom based on the monitored data. The system may use the machine-learned model to determine whether to modify an upcoming administration of a chemical stimulus that is administered to the user to treat their physical condition. The system may determine a modification to a dose or a time associated with the upcoming administration of the stimulus and apply the stimulus to the user based on the determined modification. The system may use the machine-learned model to determine that the user is exhibiting a particular symptom of their physical condition. Depending on the symptom, the system may depolarize or hyperpolarize neurons of the user.


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