Abstract: Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop.

Abstract: A system for preparing vehicle repair estimates and sourcing parts for vehicles includes a processor, a display in communication with the processor, and an input device in communication with the processor. The display is configured to display information generated by the processor, while the input device is configured to receive inputs from a user and transmit the inputs to the processor. The processor is configured to identify records in a first electronic database that relate to a particular vehicle, identify records in a second {or multiple} electronic database relating to a source of parts for the particular vehicle, display a repair estimate illustrating recommended parts and part sources on the display, and receive user input identifying parts other than recommended, or identifying a different source for a recommended part, and automatically updating the estimate and notification of guideline exceptions.