Abstract: Automated vehicle repair estimation by voting ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets, each identifying (i) at least one component of a damaged vehicle, (ii) a recommended vehicle repair operation for each identified component, and (iii) a score and/or confidence percentage for each operation; when a plurality of the sets identify recommended operations for one of the components, selecting the operation having the highest score, and unselecting the other operations for the component; generating a composite vehicle repair recommendation set, wherein the composite vehicle repair recommendation set identifies the selected recommended vehicle repair operation, and wherein the composite vehicle repair recommendation set does not identify the unselected recommended vehicle repair operation; and providing the composite vehicle repair recommendation set to one or more claims management systems.

Abstract: A method, non-transitory computer readable medium, and an apparatus for automated estimation of repair data includes applying a first generated artificial intelligence model on a received vehicle damage image associated with an electronic claim to identify damaged component(s) on a vehicle without using any metadata. A heat map analysis is performed on the received actual vehicle damage image to identify a damage severity value associated with at least one of the identified damaged component(s). A second generated artificial intelligence model is applied on the received actual vehicle damage image and the damage severity value associated with the identified damaged component(s) to determine repair data and a repair-or-replace designation. The determined repair data and the determined repair-or-replace designation for at least one of the identified one or more damaged components is provided in response to the received actual vehicle damage image associated with the electronic claim.

Abstract: Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.

Abstract: Automated vehicle repair estimation by voting ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets, each identifying (i) at least one component of a damaged vehicle, (ii) a recommended vehicle repair operation for each identified component, and (iii) a score and/or confidence percentage for each operation; when a plurality of the sets identify recommended operations for one of the components, selecting the operation having the highest score, and unselecting the other operations for the component; generating a composite vehicle repair recommendation set, wherein the composite vehicle repair recommendation set identifies the selected recommended vehicle repair operation, and wherein the composite vehicle repair recommendation set does not identify the unselected recommended vehicle repair operation; and providing the composite vehicle repair recommendation set to one or more claims management systems.

Abstract: A computer-implemented method comprises obtaining an image of a first damaged vehicle; selecting a set of images of second damaged vehicles that are similar to the first damaged vehicle; finding a set of images of the second vehicles showing damage similar to the damage to the first vehicle; obtaining a set of vehicle repair claims corresponding to the set of one or more images of the second vehicles; adding a selected subset to a repair estimate data structure; presenting a user interface that represents the selected subset of line items; receiving first user input that represents line items chosen by the user; generating a vector that represents the chosen line items; and applying the vector to a trained machine learning model, wherein the trained machine learning model outputs a refined subset of line items.

Abstract: Systems and methods are provided for a dynamic and iterative process for determining a weighted decision using a combination of weighted output from multiple, trained machine learning (ML) models. Key data can be identified and efficient decision-based processing can be achieved. In some examples, the system calculates a weighted decision of a repair or total loss determination for a motor vehicle, yet any industry or data set may be implemented with the use of the dynamic and iterative decision process.

Abstract: A computer-implemented method comprises: generating a user interface operable by a user to generate one or more vehicle repair estimate lines for repairing a damaged vehicle and/or request an automated review of the line(s); generating one or more first vehicle repair estimate lines, adding the one or more first vehicle repair estimate lines to a vehicle repair estimate data structure, and presenting a first view of the data structure in the user interface; obtaining images of the damaged vehicle, providing the images to one or more trained machine learning (ML) models, which provide first output comprising second vehicle repair estimate lines for the vehicle repair estimate, adding second vehicle repair estimate lines to the data structure, and presenting a second view of the data structure in the user interface; and generating a vehicle repair estimation document based on the vehicle repair estimate data structure.

Abstract: A computer-implemented method comprises receiving an image of a vehicle having damage to a first exterior body panel; providing the image to one or more trained machine learning models that are configured to identify a first region of the first exterior body panel to be repaired and a second region to be paint-blended, when the second region contains a second exterior body panel of the vehicle other than the first exterior body panel, generating an exterior body panel repainting list that includes the identification of the first exterior body panel of the vehicle and the identification of the second exterior body panel of the vehicle; querying a repainting cost database using the exterior body panel repainting list; and receiving a repainting cost estimate from the repainting cost database responsive to the querying.

Abstract: Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.

Abstract: Automated vehicle repair estimation by random ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a vehicle damage object that includes a plurality of metadata objects of the damaged vehicle; fragmenting the object into fragments each including at least one of the metadata objects; providing each of the fragments to a respective artificial intelligence function; receiving a respective vehicle repair recommendation set from each of the artificial intelligence functions, wherein each of the vehicle repair recommendation sets identifies a recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle; selecting a plurality of the recommended vehicle repair operations; generating a composite vehicle repair recommendation set that identifies the selected recommended vehicle repair operations; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.

Abstract: Automated vehicle repair estimation by preferential ensembling of multiple artificial intelligence functions is provided. A method comprises receiving, from each source of a plurality of the sources, a respective vehicle repair recommendation set for a damaged vehicle, wherein each vehicle repair recommendation set identifies a recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle; determining a respective source rank of each source from a plurality of the source ranks; generating a composite vehicle repair recommendation set that identifies the recommended vehicle repair operations in an order determined according to the source ranks of the respective sources; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.

Abstract: Automated vehicle repair estimation by voting ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets, each identifying (i) at least one component of a damaged vehicle, (ii) a recommended vehicle repair operation for each identified component, and (iii) a score and/or confidence percentage for each operation; when a plurality of the sets identify recommended operations for one of the components, selecting the operation having the highest score, and unselecting the other operations for the component; generating a composite vehicle repair recommendation set, wherein the composite vehicle repair recommendation set identifies the selected recommended vehicle repair operation, and wherein the composite vehicle repair recommendation set does not identify the unselected recommended vehicle repair operation; and providing the composite vehicle repair recommendation set to one or more claims management systems.

Abstract: Automated vehicle repair estimation by adaptive ensembling of multiple artificial intelligence functions is provided. A method comprises receiving, from a plurality of sources, vehicle repair recommendation sets identifying recommended vehicle repair operations for damaged vehicle components; selecting, by a trained artificial intelligence function, one of the operations for each component based on a plurality of learned states; generating a composite vehicle repair recommendation set identifying the selected 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.

Abstract: Systems and methods for managing predictions for vehicle repair estimates are provided. A method includes providing one or more images of a damaged vehicle as input to a machine learning model, wherein the machine learning model has been trained with images of other damaged vehicles and corresponding vehicle operations, wherein each of the vehicle operations represents the repair or replacement of a vehicle component; receiving output of the machine learning model responsive to the input, wherein the output comprises a plurality of values each corresponding to one of a plurality of the vehicle operations; determining a confidence metric based on the values; making a comparison between the confidence metric and a confidence threshold value; and selecting the one of the plurality of the vehicle operations corresponding to the highest value as a predicted operation based on the comparison.

Abstract: Automated vehicle repair estimation by aggregate ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets for a damaged vehicle, 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 the damaged vehicle; aggregating a plurality of the recommended vehicle repair operations; generating a composite vehicle repair recommendation set that identifies the aggregated recommended vehicle repair operations; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.

Abstract: Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.

Abstract: Vehicle insurance claim data is categorized into a plurality of strata. The categorized vehicle insurance claim data is mapped to corresponding geographic regions and aggregated. When the number of samples in the aggregated data meets a sampling threshold size, the aggregated data is clustered into clusters based on certain criteria and sampled to generate component synthetic peer data sets. A synthetic peer data set is generated by applying a bootstrap aggregation machine learning algorithm on the plurality of component synthetic peer data sets. The performance of a target vehicle insurance company is analyzed by comparing target vehicle insurance claim data of the target vehicle insurance company with the synthetic peer data set. The results of the comparison between the target vehicle insurance claim data and the synthetic peer are presented in a graphical representation.

Abstract: Methods, non-transitory computer readable media, and computing apparatus that assist with analyzing data includes obtaining vehicle data from one of the plurality of data sources in a plurality of formats. The obtained vehicle data is aggregated based on one or more geographic locations obtained from one of the plurality of sources. A sampling threshold size is determined for sampling the aggregated vehicle data based on one or more threshold rules. One or more machine learning algorithms are applied to the aggregated vehicle data to generate sampling data when the aggregated vehicle data is greater than the determined sampling threshold size. The generated sampling data is represented in a graphical representation format via a graphical user interface.

Abstract: Vehicle repair workflow automation with natural language processing is disclosed.

Abstract: Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.