Methods and systems for automatic processing of images of a damaged vehicle and estimating a repair cost

1 . A system, comprising:

a processor; and

a computer-readable media storing instructions that, when executed by the processor, cause the processor to perform operations including:

receiving image data via a network connection with a mobile device, the image data corresponding to a damaged vehicle;

determining, based on the image data and using a damage assessment model, a damaged part of the damaged vehicle, the damage assessment model comprising a machine learning program trained to identify damage to the damaged vehicle using an initial image dataset of damaged vehicles;

processing the image data using the damage assessment model to identify one or more replacement parts from the one or more damaged parts, wherein processing the image data using the damage assessment model comprises:

inferring a level of internal damage for the damaged part;

determining that the inferred level of internal damage is below a predetermined threshold; and

determining, based on determining that the inferred level of internal damage is below the predetermined threshold, and using estimated part repair cost data:

to exclude the damaged part from the one or more replacement parts, and

a labor cost associated with repair of the damaged part; and

determining a cost associated with repair of the damaged vehicle based on the labor cost, the one or more replacement parts, and excluding a replacement cost for the damaged part.

2 . The system of claim 1 , wherein processing the image data using the damage assessment model further comprises extracting one or more vehicle features from the image data for use in identifying the one or more replacement parts from the one or more damaged parts.

3 . The system of claim 1 , wherein the inferred level of internal damage represents damage to one or more portions of the damaged part that are not visible in the image data.

4 . The system of claim 1 , wherein the operations further comprise:

processing the image data to determine a second damaged part of the damaged vehicle using the damage assessment model;

inferring a second level of internal damage for the second damaged part;

determining that the second inferred level of internal damage meets or exceeds a second predetermined threshold;

determining, based on determining that the second inferred level of internal damage meets or exceeds the second predetermined threshold, a replacement cost associated with replacement of the second damaged part; and

determining the cost associated with repair of the damaged vehicle further based on the replacement cost.

5 . The system of claim 4 , wherein determining the second inferred level of internal damage for the second damaged part comprises:

inferring a level of external damage for the second damaged part;

determining that the inferred level of external damage for the second damaged part is below the second predetermined threshold; and

based on determining that the inferred level of external damage for the second damaged part is below the second predetermined threshold, determining the second inferred level of internal damage for the second damaged part.

6 . The system of claim 4 , wherein the operations further comprise generating a parts list identifying the damaged part, a repair cost associated with repair of the damaged part, the second damaged part, and the replacement cost associated with replacement of the second damaged part.

7 . The system of claim 1 , wherein inferring the level of internal damage for the damaged part comprises:

inferring a level of external damage for the damaged part;

determining that the inferred level of external damage for the damaged part is below the predetermined threshold; and

based on determining that the inferred level of external damage for the damaged part is below the predetermined threshold, determining the inferred level of internal damage for the damaged part.

8 . A computer-implemented method, comprising:

receiving, by a processor, image data via a network connection with a mobile device, the image data corresponding to a damaged vehicle;

determining, based on the image data and using a damage assessment model, a damaged part of the damaged vehicle, the damage assessment model comprising a machine learning program trained to identify damage to the damaged vehicle using an initial image dataset of damaged vehicles;

processing, by the processor, the image data using the damage assessment model to identify one or more replacement parts from the one or more damaged parts, wherein processing the image data using the damage assessment model comprises:

inferring a level of internal damage for the damaged part;

determining, that the inferred level of internal damage is below a predetermined threshold; and

determining, based on determining that the inferred level of internal damage is below the predetermined threshold, and using estimated part repair cost data:

to exclude the damaged part from the one or more replacement parts, and

a labor cost associated with repair of the damaged part; and

determining, by the processor, a cost associated with repair of the damaged vehicle based on the labor cost, the one or more replacement parts, and excluding a replacement cost for the damaged part and excluding a replacement cost for the damaged part.

9 . The computer-implemented method of claim 8 , further comprising:

determining, by the processor, based on determining that the inferred level of internal damage is below the predetermined threshold, and using estimated part repair cost data, materials associated with repair of the damaged part; and

determining, by the processor, the cost associated with repair of the damaged vehicle further based on the materials associated with repair of the damaged part.

10 . The computer-implemented method of claim 8 , wherein determining, by the processor, the inferred level of internal damage for the damaged part comprises:

inferring, by the processor, using the damage assessment model, a level of external damage for the damaged part;

determining, by the processor, that the inferred level of external damage for the damaged part is below the predetermined threshold; and

based on determining that the inferred level of external damage for the damaged part is below the predetermined threshold, inferring, by the processor, using the damage assessment model, the inferred level of internal damage for the damaged part.

11 . The computer-implemented method of claim 8 , wherein the image data comprises data representing at least one of a make of the damaged vehicle or a model of the damaged vehicle.

12 . The computer-implemented method of claim 8 , wherein the mobile device is configured to generate metadata for the image data comprising vehicle pose data.

13 . The computer-implemented method of claim 12 , wherein processing the image data to determine the damaged part of the damaged vehicle is based on the vehicle pose data.

14 . The computer-implemented method of claim 8 , wherein determining the labor cost is further based on policyholder data associated with the damaged vehicle.

15 . A non-transitory computer-readable storage medium configured to store instructions, the instructions when executed by a processor causing the processor to perform operations comprising:

receiving image data via a network connection with a mobile device, the image data corresponding to a damaged vehicle;

determining, based on the image data and using a damage assessment model, a damaged part of the damaged vehicle, the damage assessment model comprising a machine learning program trained to identify damage to the damaged vehicle using an initial image dataset of damaged vehicles;

processing the image data using the damage assessment model to identify one or more replacement parts from the one or more damaged parts, wherein processing the image data using the damage assessment model comprises:

inferring a level of internal damage for the damaged part;

determining that the inferred level of internal damage is below a predetermined threshold; and

determining, based on determining that the inferred level of internal damage is below the predetermined threshold, and using estimated part repair cost data:

to exclude the damaged part from the one or more replacement parts, and

a labor cost associated with repair of the damaged part; and

determining a cost associated with repair of the damaged vehicle based on the labor cost, the one or more replacement parts, and excluding a replacement cost for the damaged part.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise:

processing the image data to determine a second damaged part of the damaged vehicle using the damage assessment model;

inferring, using the damage assessment model, a second level of internal damage for the second damaged part;

determining that the second inferred level of internal damage meets or exceeds a second predetermined threshold;

determining, based on determining that the second inferred level of internal damage meets or exceeds the second predetermined threshold, a replacement cost associated with replacement of the second damaged part; and

determining the cost associated with repair of the damaged vehicle further based on the replacement cost.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein determining the second inferred level of internal damage for the second damaged part comprises:

inferring, using the damage assessment model, a level of external damage for the second damaged part;

determining that the inferred level of external damage for the second damaged part is below the second predetermined threshold; and

based on determining that the inferred level of external damage for the second damaged part is below the second predetermined threshold, determining the second inferred level of internal damage for the second damaged part.

18 . The non-transitory computer-readable storage medium of claim 16 , wherein the operations further comprise generating a parts list identifying the damaged part, a repair cost associated with repair of the damaged part, the second damaged part, and the replacement cost associated with replacement of the second damaged part.

19 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise retrieving the estimated part repair cost data from a parts repair database.

20 . The non-transitory computer-readable storage medium of claim 15 , wherein the image data comprises a plurality of images of the damaged vehicle captured by the mobile device.