Methods and systems for automatically predicting the repair costs of a damaged vehicle from images

A system for automatically predicting the labor, hours, and parts costs for repair of a vehicle, said system comprising:

• an image training database including a plurality of damaged vehicle images and corresponding metadata;
• a historical claims database including a plurality of claims, each claim of the plurality of claims associated with one or more of the plurality of damaged vehicle images and the corresponding metadata, each claim including total labor costs, total parts costs, and total hours for repair data;
• a damage assessment model corresponding to features of vehicle damage based on the plurality of damaged vehicle images in the image training database;
• and
• a processor coupled to said image training database and said historical claims database, said processor programmed to:
• receive one or more images of the vehicle from a policyholder;
• access the damage assessment model;
• compare the damage assessment model to the received one or more images of the vehicle to identify vehicle damage to the vehicle based on the received one or more images of the vehicle;
• in response to identifying the vehicle damage, predict total labor costs, total parts costs, and total hours for repair of the vehicle based on associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database;
• and
• determining whether the vehicle is repairable based on comparing the predicted total labor costs, total parts costs, and total hours for repair of the vehicle to a threshold and wherein if the vehicle is determined to not be repairable, processing a damage claim indicating the vehicle is a total loss.