| CPC H04W 16/10 (2013.01) [G06F 30/27 (2020.01); G06N 3/02 (2013.01); G06N 5/022 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); H04L 41/0893 (2013.01); H04W 24/02 (2013.01); H04W 72/0453 (2013.01); G06N 3/042 (2023.01); G06N 3/045 (2023.01); H04L 41/0894 (2022.05); H04W 16/14 (2013.01); H04W 24/08 (2013.01)] | 20 Claims |

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1. A system for predicting and mitigating interference in an electromagnetic environment comprising:
at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment, wherein the at least one monitoring sensor is in communication with a learning engine, at least one data analysis engine, a semantic engine, and at least one server;
wherein the learning engine is operable to learn the electromagnetic environment; and
wherein the at least one server is operable to create actionable data;
wherein the learning engine is operable to provide predictive analytics for the electromagnetic environment based on the measured data using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques;
wherein the predictive analytics includes performing interference modeling;
wherein the learning engine is operable to predict interference in the electromagnetic environment based on the interference modeling;
wherein the at least one data analysis engine includes an identification engine operable to identify a device or an emitter transmitting at least one signal of interest;
wherein the at least one server creates the actionable data based on the predicted interference, analyzed data from the at least one data analysis engine, and information relating to a rule or a policy;
wherein the semantic engine is operable to establish the rule or the policy based on converting a user data input into the actionable data using natural language processing (NLP);
wherein the user data input includes at least one use case and/or at least one objective; and
wherein the at least one server is operable to send at least one notification based on the actionable data in real time or near real time.
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