US 11,836,648 B2
System and method for vessel risk assessment
Renata Queiroz Dividino, St. Catharines (CA); Ana Luisa Alfaro Suzan, North York (CA); Dhivya Jayaraman, Halifax (CA); Benjamin Kurtis Friedrich, Halifax (CA); and Robert Michael Marshy, Ottawa (CA)
Assigned to Global Spatial Technology Solutions Inc., Dartmouth (CA)
Filed by Global Spatial Technology Solutions Inc., Dartmouth (CA)
Filed on Feb. 9, 2021, as Appl. No. 17/171,516.
Prior Publication US 2022/0253763 A1, Aug. 11, 2022
Int. Cl. G06Q 10/00 (2023.01); G06N 3/00 (2023.01); G06Q 10/0635 (2023.01); G06Q 10/083 (2023.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01)
CPC G06Q 10/0635 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 10/083 (2013.01)] 28 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing a risk assessment user interface associated with a vessel, the method comprising:
receiving, at a processor, vessel data from at least one source, the vessel data comprising vessel tracking data received from a vessel tracking device associated with the vessel, the vessel tracking data comprising a plurality of Automatic Identification System (AIS) messages, the vessel tracking device comprising an AIS transceiver associated with the vessel, the at least one source comprising at least one vessel tracking system;
receiving, at the processor, updated vessel data from the at least one source, the updated vessel data comprising one or more updated vessel tracking messages;
comparing, at the processor, at least one parameter of the vessel data to at least one predetermined threshold, wherein the at least one predetermined threshold corresponds to at least one of: an upper parameter limit, a lower parameter limit, an upper quartile limit, or a lower quartile limit;
generating, at the processor, at least one vessel profile based on the vessel data, wherein each vessel profile provides indication of expected behavior events for one vessel based on the comparing, wherein the at least one parameter is within the at least one predetermined threshold and abnormal behavior events for one vessel based on the comparing, wherein the at least one parameter is outside the at least one predetermined threshold;
determining, at the processor, at least one abnormal behavior event of the vessel based on the at least one vessel profile, each event in the at least one abnormal behavior event having a time of occurrence;
determining, at the processor, at least one frequency of occurrence of abnormal behavior events of the vessel based on the time of occurrence of each abnormal behavior event of the vessel;
using at least one machine-learning model to determine a risk assessment associated with the vessel based on the at least one frequency of occurrence of abnormal behavior events of the vessel, a first machine-learning model in the at least one machine-learning model selected from the group of a Naïve Bayesian model, a linear regression model, a multiple class classifier, and a Neural Network;
outputting a user interface comprising a map, a vessel icon positioned on the map based on the vessel tracking data, and the risk assessment associated with the vessel; and
wherein the map, the vessel icon and the risk assessment are updated automatically based on the updated vessel data.