# US 12,604,203 B2 · 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 12,604,203 B2 - Document ID: US12604203B2 - Status: Granted - Kind: B2 - Application number: 19/324,884 - Filed: 2025-09-10 - Published: 2026-01-08 - Granted: 2026-04-14 - Earliest claimed: 2022-08-02 - Assignees: Digital Global Systems, Inc. - Applicants: Digital Global Systems, Inc. - Inventors: Armando Montalvo - Web page: https://patentgrep.com/patent/US12604203B2 ## CPC - H04W 16/10: Dynamic resource partitioning - 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 - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## 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 spectrum utilization management in a wireless network comprising: a Multi-Access Edge Computing (MEC) layer in a network slice; a management and orchestration (MANO) module; and a wireless network resource optimization application in the MEC layer; wherein the wireless network resource optimization application analyzes detected signal information from a radio frequency (RF) environment; wherein the detected signal information includes a center frequency and bandwidth of at least one signal; wherein the wireless network resource optimization application is configured to obtain statistical information and analyze possible interactions based on the center frequency and the bandwidth of the at least one signal to create analyzed data; wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment; wherein the wireless network resource optimization application is configured to create a set by adding the possible interactions to the vector ensemble class; and wherein the wireless network resource optimization application is configured to create actionable data for optimizing network resources by combining the set with a customer goals index vector of binary values; wherein each binary value of the customer goals index vector represents whether or not a specific piece of the detected signal information is relevant to satisfying customer goals; wherein the wireless network resource optimization application is configured to optimize network resources based on the actionable data; and wherein the MANO module is configured to coordinate data from the wireless network resource optimization application with at least one core network to meet a customer network performance preference. ### Claim 2 (depends on 1) The system of claim 1, wherein the MEC layer is configured to send network traffic data to an Internet of Things (IoT) device. ### Claim 3 (depends on 1) The system of claim 1, wherein the MEC layer supports cloud computing for the network slice. ### Claim 4 (depends on 1) The system of claim 1, wherein the MEC layer is configured to transfer network traffic to a MEC application with sufficient resources for the network traffic. ### Claim 5 (depends on 1) The system of claim 1, wherein the MEC layer is configured to provide traffic steering to route network traffic. ### Claim 6 (depends on 1) The system of claim 1, wherein the MEC layer is configured to provide gaming services for at least one application. ### Claim 7 (depends on 1) The system of claim 1, wherein the MEC layer is configured to provide vehicle-to-everything (V2X) services for at least one application. ### Claim 8 (depends on 1) The system of claim 1, wherein the MANO module is configured to create network slices. ### Claim 9 (depends on 1) The system of claim 1, wherein the MEC layer is configured to provide media streaming services for at least one application. ### Claim 10 (depends on 1) The system of claim 1, further comprising at least one user equipment (UE) device, wherein the UE device includes an application instance of at least one application from a first MEC host, wherein the application instance of the at least one application is configured to be transferred to a second MEC host upon a change of location of the UE device without disruption of service of the at least one application to the UE device. ### Claim 11 A system for spectrum utilization management in a wireless network comprising: a Multi-Access Edge Computing (MEC) layer in a network slice; a wireless network resource optimization application in the MEC layer; a machine learning (ML) engine receiving detected signal information from a radio frequency (RF) environment; and a management and orchestration (MANO) module; wherein the MEC layer is in communication with at least one core network; wherein the wireless network resource optimization application analyzes detected signal information from a radio frequency (RF) environment; wherein the detected signal information includes a center frequency and/or bandwidth of at least one signal; wherein the wireless network resource optimization application is configured to analyze possible interactions based on the center frequency and/or the bandwidth of the at least one signal to create analyzed data; wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment; wherein the wireless network resource optimization application is configured to create a set by adding the possible interactions to the vector ensemble class; and wherein the wireless network resource optimization application is configured to create actionable data for optimizing network resources by combining the set with a customer goals index vector of binary values; wherein each binary value of the customer goals index vector represents whether or not a specific piece of the detected signal information is relevant to satisfying customer goals, wherein the wireless network resource optimization application is configured to optimize network resources based on the actionable data and a quality of service (QoS) required by at least one application; and wherein the MANO module is configured to coordinate data from the wireless network resource optimization application with the at least one core network to meet a customer network performance preference. ### Claim 12 (depends on 11) The system of claim 11, wherein the ML engine is configured to make predictions about the RF environment based on the detected signal information. ### Claim 13 (depends on 11) The system of claim 11, wherein the MANO module is configured to create network slices. ### Claim 14 (depends on 11) The system of claim 11, wherein the MEC layer is configured to provide media streaming services for the at least one application. ### Claim 15 (depends on 11) The system of claim 11, wherein the MEC layer is configured to provide gaming services for the at least one application. ### Claim 16 (depends on 11) The system of claim 11, wherein the MEC layer is configured to provide vehicle-to-everything (V2X) services for the at least one application. ### Claim 17 A method for spectrum utilization management in a wireless network comprising: a machine learning (ML) engine receiving detected signal information from at least one sensor in the electromagnetic environment; and a wireless network resource optimization application in a Multi-Access Edge Computing (MEC) layer analyzing the detected signal information; wherein the detected signal information includes a center frequency or bandwidth of at least one signal; the wireless network resource optimization application obtaining statistical information and analyzing possible interactions based on the center frequency or the bandwidth of the at least one signal to create analyzed data; wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment; the wireless network resource optimization application creating a set by adding the possible interactions to the vector ensemble class; and the wireless network resource optimization application creating actionable data for optimizing network resources by combining the set with a customer goals index vector of binary values; wherein each binary value of the customer goals index vector represents whether or not a specific piece of the detected signal information is relevant to satisfying customer goals, the wireless network resource optimization application optimizing network resources based on the actionable data; and a MANO module coordinating data from the wireless network resource optimization application with at least one core network to meet a customer network performance preference. ### Claim 18 (depends on 17) The method of claim 17, further comprising the MEC layer routing network traffic to at least one cloud service system from a MEC application. ### Claim 19 (depends on 17) The method of claim 17, further comprising the MANO module creating network slices. ### Claim 20 (depends on 17) The method of claim 17, further comprising transferring an application instance of at least one application running on at least one user equipment (UE) device from a first MEC host to a second MEC host upon a change of location of the UE device without disruption of service of the at least one application to the UE device.