Abstract: 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.

Abstract: A method and system for analyzing images of a target vehicle according to a base image model corresponding to the target vehicle. According to certain aspects, the method and system receive a set of images from an electronic device and analyze the set of images to identify the target vehicle and a set of information associated with the target vehicle. The method and system may determine that the set of images does not meet a threshold criteria for further image analysis using a base image model corresponding to the target vehicle. In response, the method and system may generate and transmit a notification to the electronic device indicating that the set of images does not meet the threshold criteria.

Abstract: An image processing system and method obtains one or more source images in which a damaged vehicle is represented, and performs one or more image processing techniques on the obtained images to determine, predict, estimate, and/or detect damage that has occurred at various locations on the vehicle. Specifically, the system/method generates damage parameter values based on the image-processed images, where each value corresponds to damage at a particular location on the vehicle. The image processing techniques may include generating a composite image of the damaged vehicle, aligning and/or isolating the image, applying convolutional neural network techniques to the image to generate the damage parameter values, etc. Based on the generated values, the image processing system/method determines, predicts, estimates, and/or selects a set of parts that are needed to repair the vehicle and, in some embodiments, orders the parts needed to repair the vehicle.

Abstract: 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.

Abstract: A method and system for analyzing images of a target vehicle according to a base image model corresponding to the target vehicle. According to certain aspects, the method and system receive a set of images from an electronic device and analyze the set of images to identify the target vehicle and a set of information associated with the target vehicle. The method and system may determine that the set of images does not meet a threshold criteria for further image analysis using a base image model corresponding to the target vehicle. In response, the method and system may generate and transmit a notification to the electronic device indicating that the set of images does not meet the threshold criteria.

Abstract: A method to analyze popularity of user defined topics by identifying correlations between grams contained in user identified anchor documents and the grams contained in raw documents includes the following steps: (a) a user input data that includes (i) user identified topics for user identified subject matter, (ii) user identified topical anchor documents, and (iii) a plurality of user identified raw documents internet source with respective source addresses; (b) the raw document sources is accessed using the source addresses to retrieve and store data in a database; (c) grams and gram document dictionaries together with gram values for each topical anchor document and raw document are identified and stored; and (d) the grams in each of the topical anchor documents against the grams in all the raw documents are analyzed to determine a relative popularity of the topical anchor documents.

Abstract: An image processing system detects changes in objects, such as damage to automobiles, by comparing a base object model, which depicts the object in an expected condition, to one or more target images of the object in the changed condition. The image processing system first processes a target object image to detect one or more predefined landmarks in the target object image and corrects for camera and positional distortions by determining a camera model for the target object image based on the detected landmarks.

Abstract: An image processing system detects changes in an object, such as damage to an automobile, by comparing a base object model, which depicts the object in an expected condition, to one or more target object images depicting the object in the changed condition. The image processing system aligns the object, as depicted in the base object model, with the object as depicted in a target object image. The image processing system then determines contours of the target object within the target object image by overlaying the aligned base object model with the target object image, and removes background pixels or other extraneous information based on this comparison. The image processing system may also determine various different components, such as body panels of an automobile, based on this overlay.

Abstract: An umbrella assembly is provided that includes a frame, a canopy, an extension and a base. The frame includes a post and top piece, a number of arms, a number of connectors, a lift ring and a locking pin. The post includes a number of rod members attached together in adjacent parallel space orientation with a top piece which rotatably attaches to each of a number of arms each arm having a number of arm rod members attached together in adjacent parallel space orientation. Each arm is rotatably attached to one end of a connector. The other end of each connector is rotatably attached to the lift ring. Raising and lowering the lift ring opens and closes the umbrella arms. The locking pin holds the lift ring in an upper position.

Abstract: A system for analyzing images of objects such as vehicles. According to certain aspects, the system includes a user interface device configured to capture a set of images depicting a target vehicle, and transfer the set of images to a server that stores a set of base image models. The server analyzes the set of images using a base image model corresponding to the target vehicle, a set of correlational filters, and a set of convolutional neural networks (CNNs) to determine a set of changes to the target vehicle as depicted in the set of images. The server further transmits, to the user interface device, information indicative of the set of changes for a user to view or otherwise access.