Automated vehicle repair estimation by adaptive ensembling of multiple artificial intelligence functions

1. A method comprising:

receiving a vehicle damage object for a damaged vehicle, wherein the vehicle damage object includes a plurality of metadata objects of the damaged vehicle;

providing the vehicle damage object to a plurality of sources;

receiving a plurality of vehicle repair recommendation sets for the damaged vehicle from the sources, wherein each of the vehicle repair recommendation sets identifies at least one recommended vehicle repair operation of a plurality of the vehicle repair operations for at least one component of the damaged vehicle, and wherein at least two of the vehicle repair recommendation sets identify different recommended vehicle repair operations for one of the components of the damaged vehicle;

selecting, by a trained artificial intelligence function, one of the recommended vehicle repair operations for each component of the damaged vehicle based on a plurality of learned states associated with the recommended vehicle repair operations, wherein the learned states are weights, and wherein each source has a respective weight for each recommended vehicle repair operation for each component;

generating a composite vehicle repair recommendation set that identifies the selected recommended vehicle repair operation;

providing the composite vehicle repair recommendation set to one or more claims management systems; and

repeatedly retraining the trained artificial intelligence function by adjusting the learned states according to the vehicle damage objects received, and the corresponding composite vehicle repair recommendation generated, since the last retraining of the trained artificial intelligence function.

2. The method of claim 1 , wherein generating the composite vehicle repair recommendation set comprises:

selecting one or more of the metadata objects of the damaged vehicle, and

identifying the selected metadata objects in the generated composite vehicle repair recommendation set.

3. The method of claim 1 , wherein:

each of the vehicle repair recommendation sets identifies a score and/or confidence percentage for the recommended vehicle repair operation; and

the method further comprises: identifying the scores for the recommended vehicle repair operations in the generated composite vehicle repair recommendation set.

4. The method of claim 1 , wherein:

each of the vehicle repair recommendation sets is generated by a respective further artificial intelligence function.

5. The method of claim 4 , wherein:

each of the further artificial intelligence functions is trained.

6. A system, comprising:

a hardware processor; and

a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to perform a method comprising:

receiving a vehicle damage object for a damaged vehicle, wherein the vehicle damage object includes a plurality of metadata objects of the damaged vehicle;

providing the vehicle damage object to a plurality of sources;

receiving a plurality of vehicle repair recommendation sets for the damaged vehicle from the sources, wherein each of the vehicle repair recommendation sets identifies at least one recommended vehicle repair operation of a plurality of the vehicle repair operations for at least one component of the damaged vehicle, and wherein at least two of the vehicle repair recommendation sets identify different recommended vehicle repair operations for one of the components of the damaged vehicle;

selecting, by a trained artificial intelligence function, one of the recommended vehicle repair operations for each component of the damaged vehicle based on a plurality of learned states associated with the recommended vehicle repair operations, wherein the learned states are weights, and wherein each source has a respective weight for each recommended vehicle repair operation for each component;

generating a composite vehicle repair recommendation set that identifies the selected recommended vehicle repair operation;

providing the composite vehicle repair recommendation set to one or more claims management systems; and

repeatedly retraining the trained artificial intelligence function by adjusting the learned states according to the vehicle damage objects received, and the corresponding composite vehicle repair recommendation generated, since the last retraining of the trained artificial intelligence function.

7. The system of claim 6 , wherein generating the composite vehicle repair recommendation set comprises:

selecting one or more of the metadata objects of the damaged vehicle, and

identifying the selected metadata objects in the generated composite vehicle repair recommendation set.

8. The system of claim 6 , wherein:

each of the vehicle repair recommendation sets identifies a score and/or confidence percentage for the recommended vehicle repair operation; and

the method further comprises: identifying the scores for the recommended vehicle repair operations in the generated composite vehicle repair recommendation set.

9. The system of claim 6 , wherein:

each of the vehicle repair recommendation sets is generated by a respective further artificial intelligence function.

10. The system of claim 9 , wherein:

each of the further artificial intelligence functions is trained.

11. The system of claim 10 , the method further comprising:

requesting one or more of the further artificial intelligence functions be re-trained when a predetermined event occurs.

12. A non-transitory machine-readable storage medium encoded with instructions executable by a hardware processor of a computing component, the machine-readable storage medium comprising instructions to cause the hardware processor to perform a method comprising:

receiving a vehicle damage object for a damaged vehicle, wherein the vehicle damage object includes a plurality of metadata objects of the damaged vehicle;

providing the vehicle damage object to a plurality of sources;

receiving a plurality of vehicle repair recommendation sets for the damaged vehicle from the sources, wherein each of the vehicle repair recommendation sets identifies at least one recommended vehicle repair operation of a plurality of the vehicle repair operations for at least one component of the damaged vehicle, and wherein at least two of the vehicle repair recommendation sets identify different recommended vehicle repair operations for one of the components of the damaged vehicle;

selecting, by a trained artificial intelligence function, one of the recommended vehicle repair operations for each component of the damaged vehicle based on a plurality of learned states associated with the recommended vehicle repair operations, wherein the learned states are weights, and wherein each source has a respective weight for each recommended vehicle repair operation for each component;

generating a composite vehicle repair recommendation set that identifies the selected recommended vehicle repair operation;

providing the composite vehicle repair recommendation set to one or more claims management systems; and

repeatedly retraining the trained artificial intelligence function by adjusting the learned states according to the vehicle damage objects received, and the corresponding composite vehicle repair recommendation generated, since the last retraining of the trained artificial intelligence function.

13. The non-transitory machine-readable storage medium of claim 12 , wherein generating the composite vehicle repair recommendation set comprises:

selecting one or more of the metadata objects of the damaged vehicle, and

identifying the selected metadata objects in the generated composite vehicle repair recommendation set.

14. The non-transitory machine-readable storage medium of claim 12 , wherein:

each of the vehicle repair recommendation sets identifies a score and/or confidence percentage for the recommended vehicle repair operation; and

the method further comprises: identifying the scores for the recommended vehicle repair operations in the generated composite vehicle repair recommendation set.

15. The non-transitory machine-readable storage medium of claim 12 , wherein:

each of the vehicle repair recommendation sets is generated by a respective further artificial intelligence function.

16. The non-transitory machine-readable storage medium of claim 15 , wherein:

each of the further artificial intelligence functions is trained.

17. The non-transitory machine-readable storage medium of claim 16 , the method further comprising:

requesting one or more of the further artificial intelligence functions be re-trained when a predetermined event occurs.