Using machine learning techniques to calculate damage of vehicles involved in an accident

A system of iterating a machine learning model trained for calculating damage to a vehicle, the system comprising:

• one or more processors;
• and
• one or more memories, the memories including instructions that, when executed by the one or more processors, cause the system to:
• receive, from a client device, one or more digital images of a vehicle;
• retrieve, via a computer network, first data from one or more remote databases, the first data indicative of damages to other vehicles;
• identify, via the one or more processors, at least one of a make of the vehicle, a model of the vehicle, a year of the vehicle, or an area of the vehicle that is damaged;
• select, via the one or more processors, a machine learning algorithm from a plurality of different machine learning algorithms based upon at least one of the make, the model, the year, or the area, wherein the plurality of different machine learning algorithms are trained for respective different combinations of make, model, year and area;
• input, via the one or more processors, the first data and image data of the one or more digital images into the machine learning algorithm, the machine learning algorithm trained to calculate damage to the vehicle based upon the first data and the image data;
• obtain, via the one or more processors, the calculated damage to the vehicle from the machine learning algorithm;
• receive, via the computer network, an indication of actual damage to the vehicle;
• and
• in response to receiving the indication of the actual damage:
• compare, via the one or more processors, the actual damage to the calculated damage;
• and
• iterate, via the one or more processors, the machine learning algorithm to improve its damage calculation accuracy based on the comparison of the actual damage to the calculated damage.

A non-transitory computer-readable medium including computer-readable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to iterate a machine learning model trained for estimating damage to a vehicle by:

• receiving one or more digital images of a vehicle;
• retrieving, via a computer network, first data from one or more remote databases, the first data indicative of damages to other vehicles;
• identify at least one of a make of the vehicle, a model of the vehicle, a year of the vehicle, or an area of the vehicle that is damaged;
• selecting a machine learning algorithm from a plurality of different machine learning algorithms based upon at least one of the make, the model, the year, or the area, wherein the plurality of different machine learning algorithms are trained for respective different combinations of make, model, year and area;
• input the first data and image data of the one or more digital images into the machine learning algorithm, the machine learning algorithm trained to estimate damage to the vehicle based upon the first data and the image data;
• obtain an estimate of damage to the vehicle from the machine learning algorithm;
• receiving, via the computer network, an indication of actual damage to the vehicle;
• and
• in response to receiving the indication of the actual damage:
• comparing the actual damage to the estimated damage;
• and
• iterating the machine learning algorithm to improve its damage estimation accuracy based on the comparison of the actual damage to the estimated damage.