Abstract: An image processing system automatically processes a plurality of images of a damaged vehicle and determines replacements parts that are needed to repair the vehicle therefrom. The system includes a network interface via which the images are received, an image extraction component that generates a set of image attributes from the received images, a parts identifier component that generates indications of the needed replacement parts based on the image attributes and an information identification model, and an output interface via which the generated indications are provided. Additionally, the system includes a data storage entity storing data from multiple historical vehicle insurance claims, and a model generation component that generates the information identification model based on the historical claim data.

Abstract: An image processing system automatically processes a plurality of images of a damaged vehicle and determines replacements parts that are needed to repair the vehicle therefrom. The system includes a network interface via which the images are received, an image extraction component that generates a set of image attributes from the received images, a parts identifier component that generates indications of the needed replacement parts based on the image attributes and an information identification model, and an output interface via which the generated indications are provided. Additionally, the system includes a data storage entity storing data from multiple historical vehicle insurance claims, and a model generation component that generates the information identification model based on the historical claim data.

Abstract: An image processing system includes a first image processor that creates a composite 3-D image or model of a damaged vehicle from a plurality of 2-D images of the damaged vehicle. The first image processor or another image processor of the system generates deformation data that is indicative or descriptive of points/areas of damage on the vehicle based on a comparison of the composite 3-D image/model with a 3-D image/model of a pristine vehicle. Based on the deformation data, a deformation model component generates indications of replacement parts and/or other items corresponding to repairing the vehicle by utilizing a model generated from historical claim data. Aside from receiving the initial set of images of the damaged vehicle, the system does not require any user input, and may generate the indications of replacement parts and/or other items corresponding to repairing the vehicle in real-time, such as at FNOL.

Abstract: A system includes a parts prediction model module. The parts prediction model module causes the system to, based on information obtained via a network interface and indicative of damage to a specific vehicle, filter vehicle parts data to obtain filtered vehicle parts data that is indicative of a subset of a plurality of parts that were utilized to repair a subset of a plurality of vehicles. The parts prediction model module further causes the system to obtain an indication of at least one specific part determined as being needed to repair the damage to the specific vehicle, and analyze the filtered vehicle parts data to determine one or more recommended parts based on the indication. Still further, the parts prediction model module causes an indication of the one or more recommended parts to be transmitted to at least one of a remote computing device or a user interface.

Abstract: Techniques for determining or predicting re-inspection of a vehicle insurance claim are disclosed. The probability of an occurrence of a re-inspection of a claim (e.g., a re-inspection score) is determined by using a predictive re-inspection model generated based on a data analysis of historical claim data from a plurality of sources. The re-inspection score may be determined prior to a repair facility initially reviewing the claim or viewing the damage to the vehicle, e.g., at FNOL. Inputs to the predictive re-inspection model may include a settlement estimate, and optionally one or more other claim attributes that are strongly correlated to re-inspection. Other re-inspection information may be additionally or alternatively predicted by using the predictive re-inspection model. Candidate claims for re-inspection may be identified by ranking re-inspection scores and/or other re-inspection information.