Methods and systems for identifying potential vehicle defects

The method of claim 11, wherein:

• the first set of vehicle defects does not indicate a vehicle defect of a first type being present in the vehicle;
• the second set of vehicle defects indicates a likelihood of the vehicle defect of the first type being present in the vehicle;
• and
• generating the indication of agreement or discrepancy comprises generating an indication of discrepancy with respect to the vehicle defect of the first type when the likelihood exceeds a threshold.

A system, comprising:

• at least one hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by the at least one computer hardware processor perform a method for using trained machine learning (ML) models to process data about a vehicle in furtherance of identifying one or more vehicle defects, the method comprising:
• obtaining an initial vehicle condition report comprising inspection results, the inspection results indicating a first set of zero, one or more vehicle defects that the inspector determined to be present in the vehicle;
• obtaining data about the vehicle gathered at least in part during inspection of the vehicle by the inspector;
• applying the trained ML models to the data about the vehicle to obtain a second set of zero, one or more vehicle defects that are indicated by the trained ML models to be present in the vehicle;
• generating an indication of agreement or discrepancy between the first set of vehicle defects that the inspector determined to be present in the vehicle and the second set of vehicle defects that are indicated by the trained ML models to be present in the vehicle;
• and
• outputting the initial vehicle condition report and the indication of agreement or discrepancy.

At least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by the at least one computer hardware processor perform a method for using trained machine learning ML models to process data about a vehicle in furtherance of identifying one or more vehicle defects, the method comprising:

• obtaining an initial vehicle condition report comprising inspection results, the inspection results indicating a first set of zero, one or more vehicle defects that the inspector determined to be present in the vehicle;
• obtaining data about the vehicle gathered at least in part during inspection of the vehicle by the inspector;
• applying the trained (ML) models to the data about the vehicle to obtain a second set of zero, one or more vehicle defects that are indicated by the trained ML models to be present in the vehicle;
• generating an indication of agreement or discrepancy between the first set of vehicle defects that the inspector determined to be present in the vehicle and the second set of vehicle defects that are indicated by the trained ML models to be present in the vehicle;
• and
• outputting the initial vehicle condition report and the indication of agreement or discrepancy.