US 12,002,108 B2
Systems and methods for generating a home score and modifications for a user
Sharon Gibson, Carlock, IL (US); Daniel Wilson, Glendale, AZ (US); Phillip Michael Wilkowski, Phoenix, AZ (US); Jason Goldfarb, Bloomington, IL (US); Arsh Singh, Frisco, TX (US); and Dustin Helland, Morton, 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 Oct. 25, 2022, as Appl. No. 17/973,108.
Application 17/973,108 is a continuation in part of application No. 17/816,391, filed on Jul. 29, 2022.
Claims priority of provisional application 63/410,101, filed on Sep. 26, 2022.
Claims priority of provisional application 63/333,519, filed on Apr. 21, 2022.
Claims priority of provisional application 63/332,972, filed on Apr. 20, 2022.
Prior Publication US 2023/0342859 A1, Oct. 26, 2023
Int. Cl. G06Q 40/08 (2012.01)
CPC G06Q 40/08 (2013.01) 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for evaluating and gamifying maintenance for a property by a user, the computer-implemented method comprising:
retrieving, by one or more processors, home data for a first property;
determining, by the one or more processors and using a first trained machine learning evaluation model, one or more home score factors based upon at least the home data;
weighting, by the one or more processors, each of the one or more home score factors to generate one or more weighted home score factors;
generating, by the one or more processors and based upon the one or more weighted home score factors, a home score for the first property;
determining, by the one or more processors and using a second trained machine learning evaluation model, that one or more additional properties are similar to the first property;
retrieving, by the one or more processors, past hazard data associated with a second property of the one or more additional properties;
generating, by the one or more processors and based upon at least the past hazard data and at least one of the one or more weighted home score factors, a home modification recommendation for the first property; and
performing, by the one or more processors and responsive to an indication to train the first trained machine learning evaluation model including a condition that the home score is accurately representative of the first property, additional training of the first trained machine learning evaluation model using at least (1) the one or more home score factors, (ii) the home data for the first property, and (iii) home data for at least some of the one or more additional properties.