# US 12,225,028 B2 · AI (artificial intelligence) driven dynamic self-remediation for wireless networks by dispatching robots

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- Number: US 12,225,028 B2
- Document ID: US12225028B2
- Status: Granted
- Kind: B2
- Application number: 17/710,739
- Filed: 2022-03-31
- Published: 2023-10-05
- Granted: 2025-02-11
- Earliest claimed: 2022-03-31
- Assignees: Fortinet, Inc.
- Applicants: Fortinet, Inc.
- Inventors: Sekhar Sumanth Gorajala Chandra
- Web page: https://patentgrep.com/patent/US12225028B2

## CPC

- H04L 63/1425: {Traffic logging, e.g. anomaly detection}
- H04L 63/02: {for separating internal from external traffic, e.g. firewalls}
- H04L 63/1416: {Event detection, e.g. attack signature detection}
- H04L 63/168: {above the transport layer}
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

An anomalous behavior is detected at an AI server device based on data communications managed by the wireless controller. In response to the detected behavior, a robot module can be deployed to a location of the anomalous behavior for testing. Once at the location, logs can be collected from testing or troubleshooting at the location and involving a remote access point proximate to the anomalous behavior (e.g., sniff and capture at specific channel or multiple channels in real-time). Solutions are generated from AI analysis concerning the anomalous behavior and priority level, including at least one automatically implemented solution to self-remediate the wireless network.

## Claims

### Claim 1

A network device communicatively coupled to a data communication network with a plurality of stations, for self-remediation of a wireless network using AI (artificial intelligence), the network device comprising:
a processor;
a network interface communicatively coupled to the processor and communicatively coupled to exchange data packets over the data communication network; and
a memory communicatively coupled to the processor and storing:
an anomalous behavior module to detect anomalous behavior by an AI server device based on data communications managed by the network device;
a robot controller to deploy a robot app to a remote location on the data communication network of the anomalous behavior for testing;
wherein the robot controller collects logs from testing or troubleshooting at the location and involving a remote access point proximate to the anomalous behavior; and
a self-remediation module to generate solutions from AI analysis concerning the anomalous behavior and priority level, including at least one automatically implemented solution to self-remediate the wireless network.

### Claim 2 (depends on 1)

The network device of claim 1, wherein the remote location comprises at least one of a remote access point and a remote station.

### Claim 3

In a network device on a data communication network with a plurality of access points, a method for self-remediation of a wireless network using AI (artificial intelligence), the method comprising the steps of:
detecting anomalous behavior by an AI server device based on data communications managed by the network device;
deploying a robot app to a location of the anomalous behavior for testing;
collecting logs from testing or troubleshooting at the location and involving a remote access point proximate to the anomalous behavior; and
generating solutions from AI analysis concerning the anomalous behavior and priority level, including at least one automatically implemented solution to self-remediate the wireless network.

### Claim 4

In a non-transitory computer-readable media in a network device communicatively coupled to a data communication network with a plurality of stations, a method for self-remediation of a wireless network using AI (artificial intelligence), the method comprising the steps of:
detecting anomalous behavior by an AI server device based on data communications managed by the network device;
deploying a robot app to a location of the anomalous behavior for testing;
collecting logs from testing or troubleshooting at the location and involving a remote access point proximate to the anomalous behavior; and
generating solutions from AI analysis concerning the anomalous behavior and priority level, including at least one automatically implemented solution to self-remediate the wireless network.