Patent ID: 11922524
Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A damage assessment (DA) computing device for determining roof damage to a building, the DA computing device comprising a processor and a memory communicatively coupled to the processor, wherein the processor is programmed to:
train a machine learning damage model using historical damage data associated with a plurality of historical weather damage incidents for a respective plurality of buildings, the historical damage data including parameters associated with the respective building, a respective historical weather event, and a known damage status representing an extent of damage to the respective building following the historical weather event;
identify a plurality of buildings that are susceptible to potential damage from an upcoming weather event, the plurality of buildings including the building;
input data associated with a roof of the building to the trained damage model, the data including a plurality of parameters associated with the building and with the upcoming weather event;
receive a model output from the trained damage model, the model output including a damage status of the roof representing the predicted extent of damage to the roof;
when the predicted extent of damage to the roof exceeds a threshold, automatically generate a claim initiation message including instructions that cause display of the model output including the damage status, a first link that, upon selection thereof, causes initiation of an insurance claim for the roof based upon the model output and the parameters associated with the building, and a second link that, upon selection thereof, declines initiation of the insurance claim;
transmit the claim initiation message to a remote computing device of a user associated with the building for display of the damage status, the first link, and the second link on a graphical user interface of the computing device;
store the model output and an identification of whether the first or second link was selected, as updated parameters; and
re-train the damage model using the updated parameters.