CPC G06Q 10/20 (2013.01) [G06Q 10/0875 (2013.01); G06V 10/764 (2022.01); G06V 20/00 (2022.01); G06Q 40/08 (2013.01)] | 20 Claims |
1. A method comprising, at a computing platform comprising at least one processor, memory, and a communication interface:
training, using the at least one processor, a machine learning model to predict whether clusters of vehicle parts should be repaired and/or replaced using training data comprising:
input data for a plurality of vehicles comprising one or more of:
damage information for the plurality of vehicles;
asymmetry information for the plurality of vehicles, the asymmetry information comprising one or more of an asymmetry array, an asymmetry matrix, or a magnitude of asymmetry; or
image information for the plurality of vehicles; and
target data indicating, for each of the plurality of vehicles, whether one or more clusters of vehicle parts should be repaired and/or replaced for the respective vehicle;
receiving, for a vehicle associated with a damage claim, one or more of damage information, asymmetry information, or image information;
generating, for the vehicle, using the at least one processor and the machine learning model, an indication that one or more clusters of parts for the vehicle should be repaired and/or replaced;
mapping, by the at least one processor, at least one identifier of a part for the vehicle to the one or more clusters of parts for the vehicle;
storing, in a database of the computing platform, mappings of the at least one identifier of a part for the vehicle to the one or more clusters of parts for the vehicle; and
transmitting an instruction indicating the at least one identifier.
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