US 12,461,841 B2
Generative artificial intelligence for a network security scanner
Aaron Williams, Congerville, IL (US); Joseph Harr, Bloomington, IL (US); Scott T. Christensen, Salem, OR (US); and Ryan M. Gross, Normal, IL (US)
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed by STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed on Jun. 29, 2023, as Appl. No. 18/215,992.
Claims priority of provisional application 63/463,383, filed on May 2, 2023.
Claims priority of provisional application 63/456,704, filed on Apr. 3, 2023.
Prior Publication US 2024/0333746 A1, Oct. 3, 2024
Int. Cl. G06F 11/362 (2025.01); G06F 21/62 (2013.01); G06N 20/00 (2019.01); H04L 9/40 (2022.01); H04L 51/02 (2022.01)
CPC G06F 11/3624 (2013.01) [G06F 21/6245 (2013.01); G06N 20/00 (2019.01); H04L 51/02 (2013.01); H04L 63/1416 (2013.01); H04L 63/1433 (2013.01); H04L 63/1441 (2013.01); H04L 63/20 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer system for network security vulnerability inspection, the computer system comprising:
one or more processors;
a memory storing executable instructions thereon that, when executed by the one or more processors, cause the one or more processors to:
transmit a prompt for a network security vulnerability testing code to a machine learning (ML) chatbot to cause an ML model to generate the network security vulnerability testing code, and
receive the network security vulnerability testing code from the ML chatbot,
wherein the network security vulnerability testing code comprises further instructions that, when executed by the one or more processors, cause the one or more processors to:
scan a network to identify network computing devices,
scan one or more of the network computing devices to identify security vulnerabilities and vulnerable network computing devices, and
communicate an identification of the security vulnerabilities and/or the vulnerable network computing devices to a user
wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
receive a security vulnerability announcement,
transmit a prompt for updated network security vulnerability testing code and the security vulnerability announcement to the ML chatbot to cause the ML model to generate the updated network security vulnerability testing code,
receive the updated network security vulnerability testing code from the ML chatbot, and
alert the user regarding the security vulnerability announcement and/or the updated network security vulnerability testing code.