State Farm Mutual Automobile Insurance Company

Joshua M Mast

Sharon Gibson

Douglas L Dewey

Taryn M Madey

Todd Binion

Rosemarie Geier Grant

Jeffrey Feid

Nathan Lee Tofte

Chad Junior Turner

Jeffrey Wyrick

Kai-Hsiang Lin

Charlie K Dagli

Apparatuses, systems and methods are provided for performing insurance property loss mitigation processes based on an event driven probable roof loss confidence score based on a particular weather event, the event driven probable roof loss confidence score associated with a roof of a building. An example method may include receiving a base-line probable roof loss confidence score for the building. The method may further include receiving weather data associated with the particular weather event, that occurred in the geographic area of the building, and hail data associated with the particular weather event. The method also may include estimating at least one attribute of the one or more attributes, and generating the event driven probable roof loss confidence score based on the base-line probable roof loss confidence score, the weather data, the hail data, and the at least one estimated attribute.

Apparatuses, systems and methods for mitigating property loss based on an event driven probable roof loss confidence score

Systems and methods are provided for automating property assessment using probable roof loss confidence scores. More particularly, the systems and methods may generate a base-line probable roof loss confidence score based upon buildings, roof, weather, hail, and climate data. The systems and methods may predict a specific event and a set of characteristics of the specific event. The systems and methods may predict a level of roof damage and a cost of roof damage based upon the predicted level of roof damage. The systems and methods may adjust a risk-related factor, and may further adjust policy parameters based upon the risk-related factor. A probable roof loss confidence score may be generated, as well as property insurance claims data or property insurance loss mitigation data based upon probable roof loss confidence score data.

Systems and methods for automating property assessment using probable roof loss confidence scores

Systems and methods for analyzing image data to identify cabinet products are disclosed. A computer-implemented method may include receiving, from an electronic device via a network connection, at least one digital image depicting a cabinet. The method also may include analyzing, by one or more processors, the at least one digital image to determine a first set of characteristics of the cabinet. Additionally, the method may include accessing, by the one or more processors from memory, a second set of characteristics corresponding to a plurality of cabinet products and comparing the first set of characteristics to the second set of characteristics to identify a cabinet product of the plurality of cabinet products that matches the cabinet. Further, the method may include transmitting, to the electronic device via the network connection, an indication of the cabinet product.

System and method for identifying cabinetry

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

A computer-implemented method for identifying whether tread of a tire of a vehicle is insufficient is presented. Image data corresponding to at least one image of the tire of the vehicle may be captured using an image sensor. The image data may be processed to determine at least one indication of a tread wear of the tire. At least one of the at least one indication of the tread wear may be analyzed based on tread wear data corresponding to the tire, with the tire being of a particular type. The tread wear data corresponding to the tire may be available to one or more processors that perform the analyzing from a provider of tires of the particular type. Based on the analyzing of the at least one of the at least one indication of the tread wear, it may be identified whether the tread of the tire is insufficient.

System and method for using image data to determine tire quality

A siding evaluation apparatus for use in identifying a characteristic of building siding is disclosed, together with methods and systems for identifying a characteristic of building siding using the siding evaluation apparatus. The siding evaluation apparatus includes a protrusion, clip, or other means for removably attaching to building siding by friction or pressure. The siding evaluation apparatus further includes a color chart holder adapted to hold a color chart for reference. The siding evaluation apparatus is attached to the siding and may be adjusted to display the color chart, which may be photographed together with a portion of the siding. Characteristics of the siding may then be determined based upon the captured photograph and a known reference area of the color chart.

Siding evaluation apparatus and method

Methods and systems for identifying an unidentified plumbing supply product are provided. According to certain aspects, an application executing on an electronic device may be configured to capture image data encoding an image of the unidentified plumbing supply product therein. The application may cause the electronic device to transmit the image data to an identification server. The identification server may analyze the image data to determine the unidentified plumbing supply product encoded in the image data matches a known plumbing supply product in a plumbing supply products database. The identification server may then transmit an indication to the electronic device that the unidentified plumbing supply product was successfully identified. The identification of the unidentified plumbing supply product may be used to process and/or subrogate an insurance claim.

Systems and methods for identifying unidentified plumbing supply products

In a computer-implemented method of roofing material identification, image data corresponding to one or more images of roofing materials may be received. The image data may be processed to determine pertinent characteristics of the roofing materials. The determined characteristics and a characteristics database storing data indicative of associations between a plurality of roofing material products and characteristics of the plurality of roofing material products, may be used to identify a roofing material product associated with the pertinent characteristics. An indication of the identified roofing material product may be provided (e.g., to facilitate claim processing).

Method for field identification of roofing materials

A method for identifying siding includes receiving particular texture data associated with a physical texture of a particular siding sample, accessing a reference database including reference texture data associated with physical texture of multiple different reference siding samples, performing an automated texture data analysis by analyzing the particular texture data associated with the particular siding sample and the reference texture data associated with different reference siding samples, and automatically identifying at least one reference siding sample that matches the particular siding sample based at least on the automated texture data analysis. The particular texture data associated with the physical texture of the particular siding sample may comprise a digital image, such as a photographic or scanned image of the particular siding sample, or a photographic or scanned image of an ink image physically transferred from the particular siding sample onto a substrate.

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Bloomington, IL, United States of America

Joshua M Mast