Ccc Information Services Inc.

Ccc Intelligent Solutions Inc.

Ronald Nelson

John L Haller

Christoph Plenio

Ranjini Vaidyanathan

Sean Fitzgerald

Ehsan Mohammady Ardehaly

Jesse Horski

Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop. User-facing draft estimates may be of a WYSIWYG format, and draft estimate contents and/or guidance annotations are updated, via the nested loops, in-line upon each individual/unitary user modification.

Intelligent vehicle repair estimation system

A vehicle image capture and labeling system operates to enable a user to capture vehicle photos, pictures, and/or images that are automatically labeled, i.e., annotated. In particular, the captured vehicle photos, pictures, or images are automatically labeled with certain vehicle identifier information, like a vehicle identification number (VIN), and pose information, that identifies a portion or view of the vehicle depicted within the image of the vehicle. The vehicle images may also be automatically labeled with one or more other image attributes or indicia, such as geospatial information corresponding to a location at which the photo or image was captured (e.g., global positioning system (GPS) data), time and date of image capture data, etc. The captured vehicle image and its label(s) may then be stored and used by other applications such as vehicle insurance claim applications, automobile repair estimate applications, etc.

Image capturing and automatic labeling system

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Los Angeles, CA, United States of America

Ronald Nelson