US 12,333,555 B2
Electronic management of license data
Shawn Everly, Bloomington, IL (US); Randall Kirchner, Dawsonville, GA (US); Steve Schierholz, Bloomington, IL (US); Joseph Waldmann, Bloomington, IL (US); Nancy Warden, Cumming, GA (US); William T. Malone, Decatur, IL (US); Troy Steffen, Fairbury, IL (US); Alex George Sarris, Eureka, IL (US); Matthew Jared Schroeder, Bloomington, IL (US); Matthew Jeffrey Wilkens, Bloomington, IL (US); and William G. Baker, Towanda, 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 May 18, 2023, as Appl. No. 18/320,033.
Application 18/320,033 is a continuation of application No. 17/112,205, filed on Dec. 4, 2020, granted, now 11,694,213.
Claims priority of provisional application 62/943,410, filed on Dec. 4, 2019.
Prior Publication US 2023/0289826 A1, Sep. 14, 2023
Int. Cl. G06Q 30/01 (2023.01); G06F 16/23 (2019.01); G06F 16/2455 (2019.01); G06F 16/9535 (2019.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/10 (2023.01); G06Q 10/105 (2023.01); G06Q 10/1093 (2023.01); G06Q 30/018 (2023.01); G06Q 30/0204 (2023.01); G06Q 40/08 (2012.01); G06Q 50/20 (2012.01)
CPC G06Q 30/0185 (2013.01) [G06F 16/2379 (2019.01); G06F 16/2455 (2019.01); G06F 16/9535 (2019.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/103 (2013.01); G06Q 10/105 (2013.01); G06Q 10/1095 (2013.01); G06Q 30/0205 (2013.01); G06Q 40/08 (2013.01); G06Q 50/2057 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer system comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:
store a respective user profile for each respective user of a plurality of users, the plurality of users including a candidate user associated with a candidate user profile, wherein each respective user profile includes at least one of a role of the respective user, one or more locations of practice of the respective user, or license data associated with the respective user;
input the candidate user profile to a model for determining licensing requirements, wherein the model utilizes at least one of machine learning or artificial intelligence techniques, and wherein the model is trained based upon sample user profiles comprising at least one of sample roles, sample locations of practice, or sample license data and is configured to determine licensing requirements for the candidate user by:
determining a role of the candidate user and a location of practice of the candidate user based upon the candidate user profile;
identifying license data associated with user profiles matching the role and the location of practice;
comparing the license data associated with user profiles matching the role and the location of practice to license data associated with the candidate user; and
identifying a licensure discrepancy for the candidate user based upon the comparison, the licensure discrepancy associated with a license included in the license data for one or more of the user profiles matching the role and the location of practice and not included in the license data associated with the candidate user;
receive an output from the model identifying the licensure discrepancy; and
transmit a notification to a user computing device associated with the candidate user that causes the user computing device to display the licensure discrepancy.