US 12,229,217 B1
Machine learning based gesture recognition
Jonathan Marsden, San Mateo, CA (US); Raffi Bedikian, San Francisco, CA (US); and David Samuel Holz, San Francisco, CA (US)
Assigned to ULTRAHAPTICS IP TWO LIMITED, Bristol (GB)
Filed by ULTRAHAPTICS IP TWO LIMITED, Bristol (GB)
Filed on Dec. 11, 2023, as Appl. No. 18/536,151.
Application 18/536,151 is a continuation of application No. 15/432,869, filed on Feb. 14, 2017, granted, now 11,841,920.
Claims priority of provisional application 62/335,497, filed on May 12, 2016.
Claims priority of provisional application 62/296,561, filed on Feb. 17, 2016.
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 18/214 (2023.01); G06F 3/01 (2006.01); G06F 18/24 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 7/246 (2017.01); G06T 7/285 (2017.01); G06T 7/73 (2017.01); G06V 10/70 (2022.01); G06V 20/64 (2022.01); G06V 40/20 (2022.01)
CPC G06F 18/214 (2023.01) [G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/248 (2017.01); G06T 7/285 (2017.01); G06T 7/74 (2017.01); G06V 10/70 (2022.01); G06V 20/64 (2022.01); G06V 40/28 (2022.01); G06F 3/011 (2013.01); G06F 3/017 (2013.01); G06T 2207/10021 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30196 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method of preparing a plurality of neural network systems to recognize hand positions, the method including:
generating a plurality of simulated hand position images, each hand position image labeled with a plurality of labeled hand position parameters, the simulated hand position images organized as gesture sequences;
producing reduced dimensionality images from the simulated hand position images;
training a first set of atemporal generalist neural networks with the simulated hand position images to produce estimated hand position parameters, using the reduced dimensionality images and the labeled hand position parameters for the reduced dimensionality images;
subdividing the simulated hand position images into a plurality of at least partially overlapping specialist categories and training a plurality of corresponding atemporal specialist neural networks to produce estimated hand position parameters;
training a first set of atemporal specialist neural networks using the reduced dimensionality images from the plurality of at least partially overlapping corresponding specialist categories using the reduced dimensionality images from the simulated hand position images and the labeled hand position parameters for the reduced dimensionality images; and
saving parameters from training the first set of atemporal generalist neural networks and the first set of atemporal specialist neural networks in tangible machine readable memory.