US 12,002,107 B2
Systems and methods for generating a home score and modifications for a user
Sharon Gibson, Carlock, IL (US); Nicholas Carmelo Marotta, Scottsdale, AZ (US); Daniel Wilson, Glendale, AZ (US); David Frank, Tempe, AZ (US); Phillip Michael Wilkowski, Phoenix, AZ (US); and Jason Goldfarb, Bloomington, 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. 24, 2022, as Appl. No. 17/972,275.
Application 17/972,275 is a continuation of application No. 17/816,391, filed on Jul. 29, 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 2023/0342858 A1, Oct. 26, 2023
Int. Cl. G06Q 40/08 (2012.01); G06N 20/00 (2019.01); G06Q 50/163 (2024.01)
CPC G06Q 40/08 (2013.01) [G06N 20/00 (2019.01); G06Q 50/163 (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, training telematics sensor data captured by one or more sensors associated with one or more properties or one or more users;
retrieving, by the one or more processors, at least one of home data for a property or user data for a user;
calculating, by the one or more processors and using a trained machine learning evaluation model, one or more weighted home score factors based upon the at least one of the home data or the user data, wherein the trained machine learning evaluation model is trained with the training telematics sensor data and the calculating includes:
receiving, by the one or more processors, the at least one of the home data or the user data as an input at the trained machine learning evaluation model,
calculating, by the one or more processors, one or more home score factors based on the at least one of the home data or the user data, and
weighting, by the one or more processors, the one or more home score factors to generate the one or more weighted home score factors;
receiving, by the one or more processors and from the user, a home modification indication;
modifying, by the one or more processors and based upon the home modification indication, at least one of the one or more weighted home score factors to create one or more modified home score factors;
generating, by the one or more processors and based upon the one or more modified home score factors, a home score for the property; and
training, by the one or more processors and responsive to an indication to train the trained machine learning evaluation model including a condition that the home score is accurately representative of the property, the trained machine learning evaluation model using at least (i) the one or more weighted home score factors, (ii) the home score, and (iii) the home data or the user data.