# US 11,910,368 B2 · Dynamically capturing location at networks in motion > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,910,368 B2 - Document ID: US11910368B2 - Status: Granted - Kind: B2 - Application number: 17/144,037 - Filed: 2021-01-07 - Published: 2022-07-07 - Granted: 2024-02-20 - Earliest claimed: 2021-01-07 - Assignees: Hewlett Packard Enterprise Development LP - Applicants: Hewlett Packard Enterprise Development LP - Inventors: Ian Beyer - Web page: https://patentgrep.com/patent/US11910368B2 ## CPC - H04W 72/0453: Resources in frequency domain, e.g. a carrier in FDMA - G06N 20/00: Machine learning - H04W 24/02: Arrangements for optimising operational condition - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W 64/003: {locating network equipment} - H04W 84/005: {Moving wireless networks} - 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 ## Abstract Systems and methods of controlling a mobile network include processors and memory storing instructions that cause the processors to perform receiving, from an external source or receiver, dynamically updated location data of a controller of the mobile network; determining, based on the dynamically updated location data, one or more permitted radiofrequency (RF) channels through which RF signals are transmitted from a mobile access point associated with the mobile network and traffic data corresponding to the permitted radiofrequency channels; and adjusting a frequency behavior of the mobile access point based on the permitted RF channels and the traffic data. ## Claims ### Claim 1 A computer-implemented method of a controller of a mobile network, comprising: receiving, from an external source or receiver, dynamically updated location data of the controller, at an infrastructural level of the mobile network; determining a first regulation associated with a particular location is associated with the dynamically updated location data, wherein the first regulation comprises a local privacy law in accordance with the dynamically updated location data; determining, based on the dynamically updated location data and the first regulation, one or more permitted radiofrequency (RF) channels through which RF signals are transmitted from a mobile access point associated with the mobile network and traffic data corresponding to the permitted RF channels; determining that the first regulation associated with the particular location is replaced by a second regulation; identifying a permitted channel of the one or more permitted RF channels comprising an updated RF channel based on the second regulation; and adjusting a frequency behavior of the mobile access point based on the permitted RF channels associated with the second regulation and the traffic data. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the adjusting of the frequency behavior of the mobile access point is further based on a distance between the controller, at a location indicated by the dynamically updated location data, and a nearest port, docking station, or landing station. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: determining, based on the traffic data, a probability of congestion, interference, noise, or outage associated with the one or more permitted RF channels at a location indicated by the dynamically updated location data; and wherein: the adjusting the frequency behavior of the mobile access point is further based on the probability of congestion, interference, noise, or outage at the location. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the probability of congestion, interference, noise, or outage is determined by a machine learning model, the machine learning model being trained by a first dataset including historical data related to congestion, interferences, noise, usage, or outage of frequency channels at particular locations, and a second training dataset including trends or cyclical patterns of the historical data at the particular locations. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the adjusting of the frequency behavior of the mobile access point comprises terminating signal transmission in a frequency channel that was previously transmitting RF signals. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the adjusting of the frequency behavior of the mobile access point comprises decreasing a signal transmission power of the mobile access point based on a probability of RF signals from the mobile access point causing interference at a location indicated by the dynamically updated location data. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the controller is disposed in a vehicle; and the method further comprises: updating a navigation path of the vehicle based on the determined permitted RF channels and the traffic data. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: transmitting the dynamically updated location data to the mobile access point. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the dynamically updated location data comprises a longitude, a latitude, and an altitude. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the method further comprises: determining, based on the traffic data, a probability of congestion, interference, noise, or outage associated with the one or more permitted RF channels at a location indicated by the dynamically updated location data; and adjusting a sampling rate of sampling for the dynamically updated location data, at the infrastructural level of the mobile network, based on the probability of congestion, interference, noise, or outage in the one or more permitted RF channels at the location. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, further comprising: determining, based on the dynamically updated location data, requirements of General Data Protection Regulation (GDPR); and reconfiguring the mobile access point to restrict a transmission of one or more types of traffic that fails to comply with the requirements of GDPR. ### Claim 12 A computing system that controls a mobile network, the computing system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform: receiving, from an external source or receiver, dynamically updated location data of a controller of a mobile network; determining a first regulation associated with a particular location is associated with the dynamically updated location data, wherein the first regulation comprises a local privacy law in accordance with the dynamically updated location data; determining, based on the dynamically updated location data and first regulation, one or more permitted radiofrequency (RF) channels through which RF signals are transmitted from a mobile access point associated with the mobile network and traffic data corresponding to the permitted RF channels; determining that the first regulation associated with the particular location is replaced by a second regulation; identifying a permitted channel of the one or more permitted RF channels comprising an updated RF channel based on the second regulation; and adjusting a frequency behavior of the mobile access point based on the permitted RF channels associated with the second regulation and the traffic data. ### Claim 13 (depends on 12) The computing system of claim 12, wherein the adjusting of the frequency behavior of the mobile access point is further based on a distance between the controller, at a location indicated by the dynamically updated location data, and a nearest port, docking station, or landing station. ### Claim 14 (depends on 12) The computing system of claim 12, wherein the instructions further cause the one or more processors to perform: determining, based on the traffic data, a probability of congestion, interference, noise, or outage associated with the one or more permitted RF channels at a location indicated by the dynamically updated location data; and wherein: the adjusting the frequency behavior of the mobile access point is further based on the probability of congestion, interference, noise, or outage at the location. ### Claim 15 (depends on 14) The computing system of claim 14, wherein the probability of congestion, interference, noise, or outage is determined by a machine learning model, the machine learning model being trained by a first dataset including historical data related to congestion, interferences, noise, usage, or outage of frequency channels at particular locations, and a second training dataset including trends or cyclical patterns of the historical data at the particular locations. ### Claim 16 (depends on 12) The computing system of claim 12, wherein the adjusting of the frequency behavior of the mobile access point comprises terminating signal transmission in a frequency channel that was previously transmitting RF signals. ### Claim 17 (depends on 12) The computing system of claim 12, wherein the adjusting of the frequency behavior of the mobile access point comprises decreasing a signal transmission power of the mobile access point based on a probability of RF signals from the mobile access point causing interference at a location indicated by the dynamically updated location data. ### Claim 18 (depends on 12) The computing system of claim 12, wherein the controller is disposed in a vehicle; and the instructions further cause the one or more processors to perform: updating a navigation path of the vehicle based on the determined permitted RF channels and the traffic data. ### Claim 19 (depends on 12) The computing system of claim 12, wherein the instructions further cause the one or more processors to perform: transmitting the dynamically updated location data to the mobile access point. ### Claim 20 (depends on 12) The computing system of claim 12, wherein the instructions further cause the one or more processors to perform: determining, based on the traffic data, a probability of congestion, interference, noise, or outage associated with the one or more permitted RF channels at a location indicated by the dynamically updated location data; and adjusting a sampling rate of sampling for the dynamically updated location data based on a probability of congestion, interference, noise, or outage in the one or more permitted RF channels at the location.