Abstract: Systems and methods are provided for integrating damage evidence with appraisal management system to create a unified user experience for improving a virtual damage appraisal process. The system may display a curated collection of evidence providing an overview of the vehicle and sections of a vehicle damaged during an adverse incident. A user may select a damaged section from a plurality of damaged sections to view damage evidence determined by machine learning algorithms to best reflect the selected damaged section may be displayed. The damage evidence may be displayed concurrently with vehicle part selection and repair line editing functionality in a configurable graphical user interface (GUI) of a virtual appraisal application. Additionally, the system may generate set of recommendations for repairing or replacing the damaged parts in the selected section. The user may add the recommendations to a repair estimate as repair estimate lines.

Abstract: Systems and methods are provided for integrating damage evidence with appraisal management system to create a unified user experience for improving a virtual damage appraisal process. The system may display a curated collection of evidence providing an overview of the vehicle and sections of a vehicle damaged during an adverse incident. A user may select a damaged section from a plurality of damaged sections to view damage evidence determined by machine learning algorithms to best reflect the selected damaged section may be displayed. The damage evidence may be displayed concurrently with vehicle part selection and repair line editing functionality in a configurable graphical user interface (GUI) of a virtual appraisal application. Additionally, the system may generate set of recommendations for repairing or replacing the damaged parts in the selected section. The user may add the recommendations to a repair estimate as repair estimate lines.

Abstract: A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify the part of the vehicle that has sustained damage. Damage data on an extent of the damage in the identified part of the vehicle is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified generic part of the vehicle is used to obtain a corresponding Part ID Code by using a parts dictionary. Part Qualifier information obtained using VIN information is then used in conjunction with the Part ID to obtain the OEM-specific part and then generate one or more repair lines for the repair estimate.

Abstract: A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage. Damage data on an extent of the damage in the identified area of the property is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified damaged part and may be used to determine one or more adjacent parts based on the vehicle information and the repair operation type.

Abstract: A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage. Damage data on an extent of the damage in the identified area of the property is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified area of the property with the damage is mapped to one of a plurality of stored repair procedure templates to generate a list of one or more parts and one or more repair lines to make a repair. The generated data list for the identified area of the property with the damage is provided.