State Farm Mutual Automobile Insurance Company

Other

Nicholas U Christopulos

Jonathan D Maurer

Nathan Lee Tofte

James M Freeman

Jackie O Jordan, Ii

Patrick Harold Boyer

Roger D Schmidgall

Nicholas R Baker

Lee C Drinan

Benjamin F Bowne

Gregory L Hayward

Meghan Sims Goldfarb

Erik Donahue

Eric Bellas

Brian Mark Fields

Matthew Eric Riley, Sr

J Lynn Wilson

David W Thurber

Duane Lee Marzinzik

Bryan T Wilkerson

Jufeng “James” Peng

James Peng

Ii Jordan Jackie O

A method for identifying a primary vehicle associated with a user of a mobile device includes receiving an indication of a vehicle entry event from a mobile device and retrieving sensor data from the mobile device. The method further includes receiving an indication of a vehicle exit event from the mobile device, generating a trip log including portions of the sensor data, and storing the trip log in a trip database. A server, or other suitable computing device, then analyzes the trip log and a plurality of previously stored trip logs in the trip database to determine a primary vehicle corresponding to the user of the mobile device. The method may allow a computing device to assign gathered mobile device data to a specific household vehicle.

Assigning mobile device data to a vehicle

In a computer-implemented method and system for capturing the condition of a structure, the structure is scanned with an unmanned aerial vehicle (UAV). Data collected by the UAV corresponding to points on a surface of a structure is received and a 3D point cloud is generated for the structure, where the 3D point cloud is generated based at least in part on the received UAV data. A 3D model of the surface of the structure is reconstructed using the 3D point cloud.

Estimating a condition of a physical structure

Techniques for implementing machine learning to improve claim handling are disclosed. In some scenarios, the machine-learning, analytics model may be trained in accordance with data that is relevant to insurance products, such as life and health insurance. A set of labeled historical claims each corresponding to a settlement amount may be analyzed to train an artificial neural network. A claim may be received from a user mobile device, and may be analyzed using the trained artificial neural network to predict a claim settlement, which may be used to generate a settlement offer. The settlement offer may be transmitted to the user's mobile device, and if a manifestation of acceptance is received from the user, then the claim may be automatically paid.

Implementing machine learning for life and health insurance claims handling

A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle. The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.

Automobile monitoring systems and methods for detecting damage and other conditions

In a computer-implemented method and system for capturing the condition of a structure, the structure is scanned with an unmanned aerial vehicle (UAV). The UAV receives an instruction to collect information on at least one aspect of a property, and identifies one or more onboard sensors of the UAV to collect the information on the at least one aspect of the property, where the UAV is configured to identify a first set of one or more onboard sensors to collect a first type of data and to identify a second set of one or more onboard sensors to collect a second type of data. The UAV also collects the information on the at least one aspect of the property using the one or more onboard sensors, and identifies, based on the collected information, a type of damage incurred on the at least one aspect of the property.

Systems and methods for assessing a physical structure are provided. Information indicative of an infrared image that includes a portion of the physical structure is received, and one or more indicators within the infrared image exceeding a heat threshold are determined. A plurality of characteristics of the one or more indicators are determined. One or more edges of the portion of the physical structure are detected, and an area associated with an intended use of one or more fasteners associated with the physical structure is determined according to the detected edges. An amount of the one or more indicators corresponding to the area is determined, and the one or more indicators are classified as fasteners according to the determined amount. Based on the classification of the one or more indicators, a condition of the physical structure is determined.

System and method for assessing a physical structure

A shading system creates a shaded region on a structure, enabling 3D scanning techniques that rely on light detection to generate a 3D model. The shading system includes a vehicle or device that moves the shading system into place. A light, such as a laser dot or line, can be projected onto the shaded region of the structure and detected by a 3D scanner.

Providing shade for optical detection of structural features

Machine learning systems, methods, and techniques for detecting damage and/or other conditions associated with a building, land, structure, or other real property using a real property monitoring system are disclosed. The property monitoring system is used in conjunction with machine learning techniques to determine and/or predict various conditions associated with the real property, including particular damage thereto, e.g., based upon dynamic characteristic data obtained via on-site sensors, static characteristic data, third-party input descriptive of an event impacting the building, etc. Accordingly, damage and/or loss associated with the building/real property is more quickly and/or accurately ascertained so that suitable mitigation techniques may be applied. In some scenarios, previously undetectable or uncharacterized damage and/or other conditions may be discovered and mitigated.

Real property monitoring systems and methods for detecting damage and other conditions

The method and system may be used to provide an indication of a color value for a particular siding sample and to color match a specific siding product to the color value of the siding sample. The system receives a digital image of a siding sample and a desired color value to be matched. A color query module plots this desired color value as a desired color point in a multidimensional color space together with a plurality of color reference points. Each color reference point represents the color value of an existing siding product. The system determines a 'distance' between the desired color point and each plotted color reference point within the color space and identifies the siding product associated with the color reference point that is located the shortest distance to the desired color point within the color space.

System and method for automatically identifying and matching a color of a structure's external surface

In a computer-implemented method and system for capturing the condition of a structure, the structure is scanned with a three-dimensional (3D) scanner. The 3D scanner generates 3D data. A point cloud or 3D model is constructed from the 3D data. The point cloud or 3D model is then analyzed to determine the condition of the structure.

Growing community of inventors

Bloomington, IL, United States of America

Nicholas U Christopulos