Systems and methods for automatically predicting and scheduling vehicle repairs

Abstract

Systems, methods, and computer-readable media are provided for automatically predicting and scheduling vehicle repairs. Particularly, a model (such as a natural language processing model, for example) may be trained using historical data relating to prior vehicle DTCs and actions taken to resolve the DTCs. The trained model may then be used to determine multiple potential resolutions to current DTCs provided by a vehicle. The model may also output probabilities associated with the various resolutions. Finally, the model may be configured to consider historical data associated with different types of vehicles than the current vehicle providing the DTC code. This allows for a larger data set to be considered by the model than if the model only considered data associated with the same type of vehicle.