US 12,412,095 B2
Cooperative learning neural networks and systems
Fa-Long Luo, San Jose, CA (US); Tamara Schmitz, Scotts Valley, CA (US); Jeremy Chritz, Seattle, WA (US); and Jaime Cummins, Bainbridge Island, WA (US)
Assigned to Micron Technology, Inc., Boise, ID (US)
Filed by Micron Technology, Inc., Boise, ID (US)
Filed on Mar. 19, 2024, as Appl. No. 18/609,221.
Application 18/609,221 is a continuation of application No. 16/114,923, filed on Aug. 28, 2018, granted, now 11,941,518.
Application 16/114,923 is a continuation of application No. 15/693,142, filed on Aug. 31, 2017, granted, now 11,941,516.
Prior Publication US 2024/0256869 A1, Aug. 1, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); H04W 4/02 (2018.01); H04W 4/38 (2018.01); H04W 4/40 (2018.01); H04W 4/90 (2018.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01); G06N 3/045 (2023.01); H04W 4/02 (2013.01); H04W 4/023 (2013.01); H04W 4/38 (2018.02); H04W 4/40 (2018.02); H04W 4/90 (2018.02)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
identifying, at a first wireless communication device, first data from a first sensor of the first wireless communication device;
receiving, at the first wireless communication device, second data from a second sensor of a second wireless communication device, wherein the second data is received at the first wireless communication device while the first wireless communication device is in communication with the second wireless communication device;
training one or more weights of a cooperative learning neural network at the first wireless communication device using a combination of the first data and the second data received from the second sensor of the second wireless communication device, wherein the one or more weights are associated with one or more crash conditions or one or more objects, wherein, responsive to identifying that the second wireless communication device is within a threshold distance of the first wireless communication device, and responsive to the receiving of the second data, training the cooperative learning neural network at the first wireless communication device using the combination of the first data and the second data occurs while the second wireless communication device is within the threshold distance of the first wireless communication device, and training the cooperative learning neural network using the combination of the first data and the second data does not occur otherwise;
detecting at least one crash condition or at least one object based on the trained cooperative neural network; and
adjusting, by a controller, a direction or a speed, or both associated with the first wireless communication device based at least in part on the detection of the at least one crash condition or the at least one object.