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. 16, 2024

Filed:

Dec. 06, 2020
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);

Andrew James Weitz, Bishop, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61F 2/58 (2006.01); G06F 3/01 (2006.01); G06F 3/0346 (2013.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
A61F 2/583 (2013.01); G06F 3/015 (2013.01); G06F 3/016 (2013.01); G06F 3/0346 (2013.01); G06N 20/00 (2019.01);
Abstract

A mobility augmentation system monitors data representative of a user's motor intent and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.


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