US 11,957,605 B2
Machine-learned movement determination based on intent identification
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); and Andrew James Weitz, Bishop, CA (US)
Assigned to Cionic, Inc., San Francisco, CA (US)
Filed by Cionic, Inc., San Francisco, CA (US)
Filed on Dec. 6, 2020, as Appl. No. 17/113,058.
Prior Publication US 2022/0175555 A1, Jun. 9, 2022
Int. Cl. A61F 2/58 (2006.01); G06F 3/01 (2006.01); G06F 3/0346 (2013.01); G06N 20/00 (2019.01)
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)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
collecting a first set of motor intent data of one or more users from a database;
labeling the first set of motor intent data with an intent label representative of intended motion characterized by the first set of motor intent data;
creating a first training set based on the labeled first set of motor intent data;
training a machine learning model using the first training set, the machine learning model configured to output, based on monitored motor intent data, a movement prediction corresponding to likely motion characterized by the monitored motor intent data;
creating a second training set based on the movement prediction and a labeled second set of motor intent data corresponding to movement signals of a target user; and
re-training the machine learning model using the second training set such that the machine learning model is customized to motions of the target user.