US 2026/0200563 A1Published application

Systems and methods for maritime abduction for region generation to expose dark vessel trajectories

AssigneesArizona Board of Regents on behalf of Arizona State University, EpochGeo LLC

InventorsPaulo Shakarian, Divyagna Bavikadi, Nathaniel Ezra Lee, Jason Ribeiro, Chad Parvis

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Abstract

A system can predict potential future locations of maritime vessels given historical maritime vessel trajectory data using abductive reasoning strategies. The system includes a learning component and reasoning component. The learning component creates a rule-based model that maps partial trajectory information to potential new locations, which the reasoning component can use to predict areas where a vessel may be based on partial trajectory information. This can be either area-based or trajectory based. The rule-based model also enables the system to learn normal trajectory behavior and characterize why such behavior is normal. The system can also infer multiple geospatial areas where a vessel may be located.

Claims20

Dependent claims

A system, comprising:

a processor in communication with a memory, the memory storing instructions executable by the processor to:
(a) access input data including (i) partial trajectory information for an agent and (ii) model information defining movement behavior of agents over geospatial regions partitioning an area of interest;
(b) generate a plurality of candidate future region assignments for the agent at a future time;
(c) for each candidate future region assignment, execute an evaluation operation that applies the model information to the partial trajectory information and the candidate future region assignment to produce an evaluation result that identifies (i) one or more model conditions defined by the model information which are satisfied by the candidate future region assignment and (ii) a measure of fit of the candidate future region assignment to the model information;
(d) generate, from the evaluation results, a region set including top-k candidate future region assignments by computing respective scores for the candidate future region assignments from the evaluation results and selecting the top-k candidate future region assignments according to the respective scores;
and
(e) output, to a display device, (i) a visual representation of the region set overlaid on the area of interest and (ii) an explanation identifying one or more model conditions satisfied by at least one selected candidate future region assignment.

4 more elements

A system, comprising:

a processor in communication with a memory, the memory storing instructions executable by the processor to:
(a) access historical trajectory information for a plurality of agents within an area of interest, the historical trajectory information including, for each respective agent, a respective historical trajectory represented as a time-ordered sequence of observed geospatial region identifiers for the respective agent;
(b) generate, from the time-ordered sequences of observed geospatial region identifiers of the historical trajectory information, model information comprising a plurality of movement behavior rules that relate (i) a first observed geospatial region identifier of a time-ordered sequence at a first time to (ii) a second observed geospatial region identifier of the time-ordered sequence at a second time;
(c) assign, to each movement behavior rule, a respective weight value based on the historical trajectory information;
and
(d) store the movement behavior rules and the respective weight values in a machine-readable rule representation configured for use by a reasoning component to (i) evaluate candidate future region assignments for a target agent based on partial trajectory information for the target agent and (ii) output an explanation that identifies at least one movement behavior rule supporting a selected candidate future region assignment.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/450,477Filed 15 Jan 2026
Priority: 15 Jan 2025
Published: 16 Jul 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification14 codes

B63B 79/20
Monitoring properties or operating parameters of vessels in operation: using models or simulation, e.g. statistical models or stochastic models
G06N 3/006
Artificial life, i.e. computing arrangements simulating life: based on simulated virtual individual or collective life forms, e.g. social…
G06N 3/044
Architecture, e.g. interconnection topology: recurrent networks, e.g. Hopfield networks
G06N 3/08
Neural networks: learning methods