# US 12,120,529 B1 · System, method, and apparatus for providing optimized network resources > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,120,529 B1 - Document ID: US12120529B1 - Status: Granted - Kind: B1 - Application number: 18/764,829 - Filed: 2024-07-05 - Published: 2024-11-07 - Granted: 2024-10-15 - Earliest claimed: 2022-08-02 - Assignees: Digital Global Systems, Inc. - Applicants: Digital Global Systems, Inc. - Inventors: Armando Montalvo - Web page: https://patentgrep.com/patent/US12120529B1 ## CPC - H04W 16/10: Dynamic resource partitioning - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/048: Activation functions - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 5/025: Extracting rules from data - G06N 5/04: Inference or reasoning models - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - H04L 41/147: for predicting network behaviour - H04L 41/16: using machine learning or artificial intelligence - H04L 41/40: using virtualisation of network functions or resources, e.g. SDN or NFV entities - H04W 16/14: Spectrum sharing arrangements {between different networks} - H04W 24/02: Arrangements for optimising operational condition - H04W 24/04: Arrangements for maintaining operational condition - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W 28/0925: {using policies} - H04W 28/0967: {Quality of Service [QoS] parameters} - H04W 72/0453: Resources in frequency domain, e.g. a carrier in FDMA - H04L 41/0894: Policy-based network configuration management - H04L 41/0895: Configuration of virtualised networks or elements, e.g. virtualised network function or OpenFlow elements - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources. ## Claims ### Claim 1 A system for dynamic spectrum utilization management in an electromagnetic environment comprising: a network slice or a subnetwork, wherein the network slice or the subnetwork includes a radio access network (RAN), a core network, and a Multi-Access Edge Computing (MEC) layer; at least one physical monitoring sensor operable to monitor the electromagnetic environment and to send measured data to at least one data analysis engine; wherein the at least one physical monitoring sensor includes at least one antenna, at least one antenna array, and/or at least one radio server; a wireless network resource optimization application, wherein the wireless network resource optimization application includes the at least one data analysis engine for analyzing the measured data; wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data for optimizing network resources; wherein the wireless network resource optimization application creates a customer goals index vector, wherein the customer goal index vector is a vector of binary values; wherein the customer goals index vector is used to optimize customer utilization of spectrum in the MEC layer. ### Claim 2 (depends on 1) The system of claim 1, wherein the MEC layer is operable to determine a location of at least one user equipment (UE) device. ### Claim 3 (depends on 2) The system of claim 2, wherein the MEC layer includes procedures for migration and/or service continuity using MEC host pre-allocation based on a predicted future location of the at least one UE device. ### Claim 4 (depends on 1) The system of claim 1, wherein the wireless network resource optimization application includes a rules and policy editor, wherein one or more of at least one rule and/or at least one policy of the rules and policy editor is defined by requirements of at least one customer. ### Claim 5 (depends on 1) The system of claim 1, wherein the wireless network resource optimization application generates at least one RAN command to change at least one RAN parameter. ### Claim 6 (depends on 1) The system of claim 1, wherein the network resources are optimized by reconfiguring at least one parameter of the core network and/or the MEC layer associated with the network slice or the subnetwork. ### Claim 7 (depends on 1) The system of claim 1, wherein the at least one physical monitoring sensor is included in a base station. ### Claim 8 (depends on 1) The system of claim 1, wherein the data analysis engine is operable to provide a quality of one more channels. ### Claim 9 (depends on 1) The system of claim 1, wherein the MEC layer is operable to use a statistical inference engine and/or a ML engine interface with at least one application. ### Claim 10 A method for dynamic spectrum utilization management in an electromagnetic environment comprising: providing a radio access network (RAN), a core network, and a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork and a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing measured data; monitoring the electromagnetic environment using at least one physical monitoring sensor to create and send the measured data to the at least one data analysis engine; wherein the at least one physical monitoring sensor includes at least one antenna, at least one antenna array, and/or at least one radio server; analyzing the measured data using the at least one data analysis engine to create analyzed data; the wireless network resource optimization application creates a customer goals index vector, wherein the customer goal index vector is a vector of binary values; wherein the customer goals index vector is used to optimize customer utilization of spectrum in the MEC layer. ### Claim 11 (depends on 10) The method of claim 10, further comprising determining channel availability in the electromagnetic environment. ### Claim 12 (depends on 10) The method of claim 10, further comprising providing a quality of a channel to a network operator and receiving a request for use of the channel from a network operator based on a quality of the channel. ### Claim 13 (depends on 10) The method of claim 10, further comprising the MEC layer determining a location of at least one user equipment (UE) device. ### Claim 14 (depends on 10) The method of claim 10, further comprising providing optimization of service to at least one UE device based on a predicted future location of at least one UE device. ### Claim 15 (depends on 14) The method of claim 14, further comprising providing MEC host pre-allocation based on the predicted future location of the at least one UE device. ### Claim 16 A method for dynamic spectrum utilization management in an electromagnetic environment comprising: providing a radio access network (RAN), a core network, and a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork and a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing measured data; monitoring the electromagnetic environment using at least one physical monitoring sensor to create and send the measured data to the at least one data analysis engine; wherein the at least one physical monitoring sensor includes at least one antenna, at least one antenna array, and/or at least one radio server; the wireless network resource optimization application creates a customer goals index vector, wherein the customer goal index vector is a vector of binary values; wherein the customer goals index vector is used to optimize customer utilization of spectrum in the MEC layer; analyzing the measured data using the at least one data analysis engine to create analyzed data; and the MEC layer determining a location of at least one user equipment (UE) device. ### Claim 17 (depends on 16) The method of claim 16, further comprising determining channel availability in the electromagnetic environment. ### Claim 18 (depends on 16) The method of claim 16, further comprising providing a quality of a channel to a network operator and receiving a request for use of the channel from a network operator based on a quality of the channel. ### Claim 19 (depends on 16) The method of claim 16, further comprising the MEC layer providing procedures for migration and/or service continuity based on a predicted future location of the at least one UE device. ### Claim 20 (depends on 16) The method of claim 16, further comprising creating a relocation group including at least one MEC host pre-configured to run at least one application.