US 12,169,777 B2
Artificial-intelligence-based waterway information system
Joseph Celano, San Diego, CA (US); David Sathiaraj, San Diego, CA (US); Eric Ho, San Diego, CA (US); Andrew Nolan Smith, San Diego, CA (US); and Eric Vincent Rohli, Baton Rouge, LA (US)
Assigned to TRABUS, San Diego, CA (US)
Filed by Trabus, San Diego, CA (US)
Filed on Mar. 30, 2023, as Appl. No. 18/128,839.
Application 18/128,839 is a continuation of application No. 17/720,782, filed on Apr. 14, 2022, granted, now 11,620,523.
Application 17/720,782 is a continuation of application No. 17/190,254, filed on Mar. 2, 2021, granted, now 11,334,794, issued on Apr. 27, 2022.
Claims priority of provisional application 63/017,508, filed on Apr. 29, 2020.
Claims priority of provisional application 63/016,568, filed on Apr. 28, 2020.
Prior Publication US 2023/0237327 A1, Jul. 27, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G01C 21/00 (2006.01); B63B 79/40 (2020.01); G01C 21/20 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G08G 3/00 (2006.01)
CPC G06N 3/08 (2013.01) [B63B 79/40 (2020.01); G01C 21/203 (2013.01); G06N 3/04 (2013.01); G08G 3/00 (2013.01); B63B 2213/00 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising using at least one hardware processor to:
use a training dataset, comprising positional and non-positional data representing trips with known travel times along a waterway, to train a travel time prediction model to predict a travel time along the waterway for a given trip;
for each trip of one or more trips by a maritime vessel along the waterway,
receive a request that specifies that trip and a time of that trip, and
in response to the request,
use the travel time prediction model to predict a travel time for that trip, and
display a representation of that trip on a virtual map within a graphical user interface with an indication of the predicted travel time.