US 12,290,339 B2
Edge computing system with low power wide area network connectivity and autonomous or semi-autonomous machine learning
Adam G. Russek-Sobol, Chicago, IL (US); Joseph T. Kreidler, Arlington Heights, IL (US); Brian A. Donlin, Chicago, IL (US); Jon G. Ledwith, Palatine, IL (US); Patrick J. McVey, Wheeling, IL (US); Ross D. Moore, Winnetka, IL (US); Peter Nanni, Algonquin, IL (US); Dwayne D Forsyth, Deer Park, IL (US); Paul Sheldon, Arlington Heights, IL (US); Todd Sobol, Dayton, OH (US); and John D. Reed, Dayton, OH (US)
Assigned to CareBand Inc., Chicago, IL (US)
Filed by CareBand Inc., Chicago, IL (US)
Filed on Sep. 6, 2024, as Appl. No. 18/826,646.
Application 18/668,655 is a division of application No. 17/486,250, filed on Sep. 27, 2021, granted, now 12,023,137, issued on Jul. 2, 2024.
Application 17/486,250 is a division of application No. 16/233,462, filed on Dec. 27, 2018, granted, now 11,147,459, issued on Oct. 19, 2021.
Application 18/826,646 is a continuation of application No. 18/668,655, filed on May 20, 2024.
Claims priority of provisional application 62/709,129, filed on Jan. 5, 2018.
Prior Publication US 2025/0009237 A1, Jan. 9, 2025
Int. Cl. A61B 5/02 (2006.01); A61B 5/00 (2006.01); A61B 5/01 (2006.01); A61B 5/0205 (2006.01); A61B 5/029 (2006.01); A61B 5/11 (2006.01); A61B 5/1455 (2006.01); A61B 5/318 (2021.01); G06N 3/00 (2023.01); G06N 20/00 (2019.01); G08B 21/02 (2006.01); G08B 25/01 (2006.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); H04B 1/3827 (2015.01); H04W 4/02 (2018.01); H04W 4/029 (2018.01); H04W 4/38 (2018.01); H04W 4/80 (2018.01); H04W 84/12 (2009.01); A61B 5/021 (2006.01); A61B 5/024 (2006.01); A61B 5/08 (2006.01); A61B 5/145 (2006.01); G08C 17/02 (2006.01)
CPC A61B 5/02055 (2013.01) [A61B 5/0002 (2013.01); A61B 5/0015 (2013.01); A61B 5/0022 (2013.01); A61B 5/01 (2013.01); A61B 5/029 (2013.01); A61B 5/1112 (2013.01); A61B 5/1113 (2013.01); A61B 5/1118 (2013.01); A61B 5/14551 (2013.01); A61B 5/318 (2021.01); A61B 5/4088 (2013.01); A61B 5/6803 (2013.01); A61B 5/6804 (2013.01); A61B 5/681 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 5/7435 (2013.01); G06N 3/00 (2013.01); G06N 20/00 (2019.01); G08B 21/0211 (2013.01); G08B 21/0269 (2013.01); G08B 21/0272 (2013.01); G08B 21/0288 (2013.01); G08B 25/016 (2013.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); H04B 1/385 (2013.01); H04W 4/025 (2013.01); H04W 4/029 (2018.02); H04W 4/38 (2018.02); H04W 4/80 (2018.02); H04W 84/12 (2013.01); A61B 5/021 (2013.01); A61B 5/02438 (2013.01); A61B 5/0816 (2013.01); A61B 5/14532 (2013.01); A61B 5/14542 (2013.01); A61B 5/6822 (2013.01); A61B 5/6829 (2013.01); A61B 5/686 (2013.01); A61B 2560/0214 (2013.01); A61B 2560/0242 (2013.01); A61B 2560/0252 (2013.01); A61B 2560/0257 (2013.01); A61B 2562/0219 (2013.01); A61B 2562/029 (2013.01); A61B 2562/06 (2013.01); G08C 17/02 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A mobile edge computing system comprising:
a platform;
a communication module disposed on the platform and configured to establish signal communication over a plurality of wireless communication protocols at least one of which comprises a low power wide area network protocol such that the communication module transmits, over at least one of the wireless communication protocols, a signal that corresponds to an output that has been generated by a trained machine learning model;
at least one sensor;
a non-transitory computer-readable medium disposed on the platform and storing machine code thereon; and
a logic device disposed on the platform and comprising a distributed set of processing units comprising a central processing unit and at least one processor configured for performing machine learning operations and comprising at least one of a graphical processor unit and a tensor processing unit, wherein the mobile edge computing system, upon receipt of event data that has been acquired by at least one of the communication module and the at least one sensor, performs at least one of (i) event data preprocessing, (ii) feature extraction of the event data, (iii) segmentation of at least a portion of the event data that has undergone at least one of preprocessing and feature extraction into a training data set and a validation data set, (iv) utilization of at least one machine learning algorithm to provide an inference on at least a portion of the training data set and (v) validation of the inference with at least a portion of the validation data set to define the trained machine learning model that, upon receipt of at least one of (a) a portion of the event data that was not a part of at least one of the training data set and the validation data set and (b) subsequently acquired event data, either updates the trained machine learning model or executes the trained machine learning model.