State Farm Mutual Automobile Insurance Company

Jeremy Carnahan

John Andrew Schirano

Bryan R Nussbaum

Ryan Knuffman

Todd Binion

Jennifer Criswell Kellett

Michael Stine McGraw

Matt Megyese

Vicki King

Mike McGraw

Michael Stine Mcgraw

A feature mapping computer system configured to (i) receive a localized image including a photo depicting a driving environment and location data associated with the photo, (ii) identify, using an image recognition module, a roadway feature depicted in the photo, (iii) generate, using a photogrammetry module, a point cloud based upon the photo and the location data, wherein the point cloud comprises a set of data points representing the driving environment in a three dimensional ('3D') space, (iv) localize the point cloud by assigning a location to the point cloud based upon the location data, and (v) generate an enhanced base map that includes a roadway feature.

Systems and methods for enhanced base map generation

A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to receive two-dimensional (2D) images, analyze the images using a trained deep network to generate points, process the labeled points to identify tie points, and combine the 2D dimensional images into a three-dimensional (3D) point cloud using structure-from-motion. A method for generating a semantically-segmented 3D point cloud from 2D data includes receiving 2D images, analyzing the images using a trained deep network to generate labeled points, processing the points to identify tie points, and combining the 2D images into a 3D point cloud using structure-from-motion. A non-transitory computer readable storage medium stores executable instructions that, when executed by a processor, cause a computer to receive 2D images, analyze the images using a trained deep network to generate labeled points, process the points to identify and combine tie points using structure-from-motion.

Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data

Methods, systems, and computer readable media for predictively determining a risk of damage to a property are provided. To determine the risk, a high resolution virtual model of a region that includes the property is obtained. The virtual model is imported into a simulation environment. One or more of the simulation parameters are set based on historic weather data for the region. For example, each parameter may be associated with a probability distribution derived based on the historic weather data that is sampled prior to executing the simulation. One or more simulations are executed in accordance with the sampled inputs to simulate the likely weather patterns the property will experience. The result of the simulation is analyzed to determine the predicted risk of damage to the property.

Systems and methods for predictive modeling via simulation

A computer-implemented method for labeling a three-dimensional (3D) model using virtual reality (VR) techniques implemented by a computer system including a processor is provided herein. The method may include (i) receiving a 3D model including an environmental feature that is unlabeled, (ii) displaying, through a VR device in communication with the processor, a VR environment to a user representing the 3D model, (iii) prompting a user to input labeling data for the environmental feature displayed within the VR environment of the VR device by prompting the user to select the environmental feature through user interaction with the VR device, and input labeling data for the environmental feature, wherein the labeling data identifies the environmental feature, and (iv) generating a labeled 3D model by embedding the labeling data associated with the selected environmental feature into the 3D model.

Systems and methods for labeling 3D models using virtual reality and augmented reality

A system for determining drone operation rules configured to (i) receive a plurality of telematics data from a plurality of missions; (ii) analyze the plurality of telematics data to determine one or more mission trends; and (iii) determine one or more rules based upon the one or more mission trends.

Systems and methods for analyzing unmanned aerial missions

A feature mapping computer system configured to (i) receive a first localized image including a first photo and a first location; (ii) receive a second localized image including a second photo and a second location; (iii) identify a roadway feature depicted in both the first and second photos; (iv) generate, using a photogrammetry module, a point cloud based upon the first and second photos and first and second locations; (v) generate a localized point cloud by assigning a location to the point cloud based upon at least one of the first and second locations; and (vi) generate an enhanced base map that includes a roadway feature by embedding an indication of the identified roadway feature onto the localized point cloud.

A virtual reality (VR) labeling computer system configured to receive a 3D model, process the 3D model using object recognition, identify at least one environmental feature within the 3D model, generate a processed 3D model including the at least one environmental feature, display a VR environment based upon the processed 3D model; receive user input including labeling data associated with the environmental feature; generate a labeled 3D model by embedding the labeling data into the processed 3D model; and generate training data based upon the labeled 3D model.

A system for analyzing unmanned aerial missions configured to (i) receive, from a user, pre-mission data for a planned unmanned aerial mission; (ii) determine an insurance policy, premium, rate, mission-based rate, or usage-based rate based upon the pre-mission data, one or more business rules, and a plurality of historical mission data; (iii) transmit, to the user, the insurance policy; (iv) receive acceptance of the insurance policy from the user; (v) receive a plurality of telematics information from the unmanned aerial mission; and (vi) update at least one of the one or more business rules and the plurality of historical mission data based upon the plurality of telematics information.

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Normal, IL, United States of America

Jeremy Carnahan