Artificial Intelligence-Driven Multi-Intelligence Multi-Attribute Metadata Enabling All-Domain Preemptive Measures

In response to the Navy’s need for an AI-enabled multi-attribute generation system, IERUS Technologies proposes to determine the technical feasibility, design and prototype for a system that can be fully integrated with proper associative databases to monitor and track developing activities/signals in all operational domains. This prototype architecture will extract meta-data attributes from existing operational data sets/data feeds.  That meta-data extraction will be things such as events, entities, observations, and relationships between various objects collected from disparate multi-INT data sets that correlate to each other.  This prototype architecture will include three relevant scenarios with sample data illustrating the usefulness of an AI-enabled multi-INT data fusion architecture. The output of this data fusion will be an ontological framework representing the events and relationships that are extracted from each data set as well as the events and relationships that span multiple INTs. As part of this architecture, algorithms will be identified specific to each data set that are necessary to extract meta-data information that is not readily available in the data feed.