# US 11,910,478 B2 · Adaptive wireless client steering > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,910,478 B2 - Document ID: US11910478B2 - Status: Granted - Kind: B2 - Application number: 17/239,770 - Filed: 2021-04-26 - Published: 2021-08-05 - Granted: 2024-02-20 - Earliest claimed: 2018-07-26 - Assignees: Hewlett Packard Enterprise Development LP - Applicants: Hewlett Packard Enterprise Development LP - Inventors: Sachin Ganu; Bernd Bandemer; Jose Tellado; Gaurav Patwardhan - Web page: https://patentgrep.com/patent/US11910478B2 ## CPC - H04B 17/391: Modelling the propagation channel - H04W 8/14: between corresponding nodes - G06N 5/025: Extracting rules from data - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - H04B 17/3912: {Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region} - H04L 41/16: using machine learning or artificial intelligence - H04W 60/04: using triggered events - H04W 24/02: Arrangements for optimising operational condition - H04W 84/12: WLAN [Wireless Local Area Networks] - H04B: TRANSMISSION - 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 An example network information aggregator is disclosed. The network information aggregator includes a network interface, a memory, and processing circuitry. The processing circuitry is to receive a machine learning model trained using initialization data from a model creation device. The processing circuitry is also to generate a device steering rule to steer a client device from a first radio to a second radio using the machine learning model. The processing circuitry is also to send a steer command to the first radio. ## Claims ### Claim 1 A network information aggregator of a first network comprising a network interface, a memory, and processing circuitry to: train a machine learning model using initialization data stored in a training data repository, the initialization data including roaming information generated by a second network information aggregator of a second network, the roaming information comprising a result of steering a device on the second network; generate, using the machine learning model, a device steering rule including radio environment criteria; and transmit the device steering rule to a network device. ### Claim 2 (depends on 1) The network information aggregator of claim 1, wherein a steer command is sent to a first radio in response to, in part, radio environment characteristics of the first radio matching radio environment criteria of the device steering rule. ### Claim 3 (depends on 2) The network information aggregator of claim 2, wherein the processing circuitry is also to receive a response to the steer command, including a result of applying the steer command. ### Claim 4 (depends on 2) The network information aggregator of claim 2, wherein the network information aggregator is a component of an access point including the first radio. ### Claim 5 (depends on 1) The network information aggregator of claim 1, the processing circuitry further to: receive a roaming record from the network device; and update the machine learning model using data including the roaming record from the network device. ### Claim 6 (depends on 1) The network information aggregator of claim 1, wherein the second network is dissimilar to the first network. ### Claim 7 (depends on 6) The network information aggregator of claim 6, wherein generate, using the machine learning model, a device steering rule including radio environment criteria comprises: generating different device steering rules for the first network and the second network. ### Claim 8 (depends on 1) The network information aggregator of claim 1, wherein the second network is similar to the first network. ### Claim 9 (depends on 1) The network information aggregator of claim 1, wherein the results of steering a device on the second network comprises whether or not the steering was successful. ### Claim 10 A method, comprising: training a machine learning model using initialization data stored in a training data repository, the initialization data including roaming information generated by a second network information aggregator of a second network, the roaming information comprising a result of steering a device on the second network; generating, by a network information aggregator of a first network using the machine learning model, a device steering rule including radio environment criteria; and transmitting, by the network information aggregator, the device steering rule to a network device. ### Claim 11 (depends on 10) The method of claim 10, wherein a steer command is sent to a first radio in response to, in part, radio environment characteristics of the first radio matching radio environment criteria of the device steering rule. ### Claim 12 (depends on 11) The method of claim 11, further comprising: receiving a response to the steer command, including a result of applying the steer command. ### Claim 13 (depends on 11) The method of claim 11, wherein the network information aggregator is a component of an access point including the first radio. ### Claim 14 (depends on 10) The method of claim 10, further comprising: receiving a roaming record from the network device; and updating the machine learning model using data including the roaming record from the network device. ### Claim 15 (depends on 10) The method of claim 10, wherein the results of steering a device on the second network comprises whether or not the steering was successful. ### Claim 16 A non-transitory, computer-readable medium comprising instructions that, when executed by processing circuitry, cause the processing circuitry to: train a machine learning model using initialization data stored in a training data repository, the initialization data including roaming information generated by a second network information aggregator of a second network, the roaming information comprising a result of steering a device on the second network; generate, using the machine learning model, a device steering rule for a first network, the device steering rule including radio environment criteria; and transmit the device steering rule to a network device on the first network. ### Claim 17 (depends on 16) The computer readable medium of claim 16, wherein a steer command is sent to a first radio in response to, in part, radio environment characteristics of the first radio matching radio environment criteria of the device steering rule. ### Claim 18 (depends on 17) The computer readable medium of claim 17, wherein the processing circuitry is also to receive a response to the steer command, including a result of applying the steer command. ### Claim 19 (depends on 17) The computer readable medium of claim 17, wherein the network information aggregator is a component of an access point including the first radio. ### Claim 20 (depends on 16) The computer readable medium of claim 16, the processing circuitry further to: receive a roaming record from the network device; and update the machine learning model using data including the roaming record from the network device.