Abstract: A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

Abstract: A system for capturing images of damage to an object configured to (i) store an orientation model associated with an object; (ii) receive, from a user, a request to analyze damage to the object; (iii) instruct the user to position a camera at a first position relative to the object; (iv) receive an image of the object from the camera; (v) determine whether the received image is properly framed; (vi) if the received image is not properly framed, instruct the user to adjust the position of the camera; and (vii) if the received image is properly framed, instruct the user to position the camera at a second position relative to the object. As a result, obtaining images of sufficient quality for proper processor analysis may be facilitated.

Abstract: Systems and methods are described for the identification and classification of a social media posting. In various aspects, image-based data and text-based data may be collected from one or more social media data sources associated with a social media posting. Event related data may then be extracted from each of the image-based data and the text-based data, in which the event related data is indicative of one or more life events associated with a social media user who posted the social media posting. One or more life event classifications may be determined from the event related data and an activity, such as sending a message or article to the social media user, may be triggered as a result.

Abstract: In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.

Abstract: Systems and methods for analyzing image data to automatically ensure image capture consistency are described. According to certain aspects, a server may access aerial and other image data and identify a set of parameters associated with the capture of the image data. The server may access a corresponding set of acceptable image capture parameters, and may compare the set of parameters to the set of acceptable parameters to determine whether image data is consistent with the set of acceptable parameters. In some embodiments, if the image data is not consistent, the server may generate a notification or instruction to cause an image capture component to recapture additional image data.

Abstract: One or more processing elements may be trained to identify vehicle damages or vehicle damages based upon training data. A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed using the trained processing elements to identify a damage to the vehicle or defect of the vehicle.