State Farm Mutual Automobile Insurance Company

Jody Ann Thoele

Jaime Skaggs

Ryan Michael Gross

Angela Glusick

Joseph Robert Brannan

Eric R Moore

Scott T Christensen

Ashish Sawhney

Neill Broadstone

Gustufus Phillip Theofanis

Benjamin Joel Tucker

Shawn Harbaugh

M Eric Riley, Sr

Rick Lovings

Jenny L Jacobs

Erik Skyten

Jordan Jeffers

Joann C Yant

Systems and methods are described for performing analysis of parametric events. The method may include: (1) receiving weather data from a weather oracle network; (2) calculating, using a first trained machine learning algorithm, a likelihood of a trigger activation for a parametric event (or a trigger event) for a user based at least upon the weather data; (3) calculating, using a second trained machine learning algorithm, an estimated loss for the user based at least upon the likelihood of the trigger activation; (4) determining an initial coverage for the user; and (5) determining whether to offer the user updated coverage for the parametric event based at least upon a comparison of the initial coverage and the estimated loss for the user.

Parametric engine to implement methods using parametric analytics

Provided herein is a computing system that includes a processor in communication with a memory. The processor is configured to (1) receive a plurality of data records associated with a plurality of users that include historical user data; (2) generate a model based upon the plurality of data records, wherein the model (i) predicts travel behavior of a user, and/or (ii) outputs an insurance policy and associated premium for the user based upon the predicted travel behavior; (3) retrieve current user data associated with the candidate user; (4) apply the model to (i) determine a user trial travel behavior, and/or (ii) output a trial insurance policy and associated premium for the candidate user; and/or (5) transmit a notification to the user computing device that includes a prompt for the user to register for the insurance policy.

Systems and methods for modeling telematics data

The following relates generally to tracking effectiveness of an update to a vehicle feature using a distributed ledger. In some embodiments, a distributed ledger including a vehicle feature is added to or constructed. Information indicating an update to the vehicle feature, and accident record information may then be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets, and added to the distributed ledger.

Distributed ledger for vehicle feature updates

An adaptive mapping (AM) computing device having at least one processor in communication with at least one memory device is provided. The AM computing device may be configured to retrieve a plurality of tasks associated with a user, and retrieve geographic mapping data. The AM computing device may also generate a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data. The AM computing device may execute the route model to determine an optimal route, and transmit, to the user, an optimized travel plan based upon the optimal route.

Systems and methods for adaptive route optimization for learned task planning

An on-demand insurance (ODI) server for generating dynamic user offerings may be provided. The ODI server may include at least one processor in communication with a memory device. The at least one processor may be programmed to (i) receive, from a user computing device associated with a user, an insurance policy request for a trip from a start location to an end location, (ii) determine at least one transportation mode available for the trip, (iii) access contextual data associated with the trip, (iv) assess a risk associated with the at least one transportation mode, (v) calculate a risk score associated with the at least one transportation mode based upon at least the contextual data, (vi) generate an insurance offering associated with the at least one transportation mode, and/or (vii) transmit the insurance offering in real time to the user computing device for purchase by the user.

Systems and methods for generating on-demand insurance policies

Systems and methods for building a vehicle data repository (VDR) are provided. In some embodiments, the VDR is constructed by adding standardized build sheets to it. The standardized build sheets may be constructed by selecting data from various data sources (e.g., original equipment manufacturer (OEM) databases, National Highway Traffic Safety Administration (NHTSA) databases, Highway Loss Data Institute (HLDI) databases, and/or Insurance Institute for Highway Safety (IIHS) databases). Furthermore, a common ontology may be created and applied to the various data sources to aide in the selection of data between the data sources to construct the standardized build sheets.

Systems and methods of building a consistent vehicle data repository

The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets.

Systems and methods of determining effectiveness of vehicle safety features

Systems and methods for determining a reparability of a vehicle are provided. In some embodiments, vehicle data is obtained, and a list of variables is generated from the obtained data. A machine learning algorithm may then be trained to generate a reparability metric by: (i) generating correlation metrics between the variables and costs to repair the vehicle, (ii) removing variables with correlation metrics below a threshold, and (iii) training the machine learning algorithm based upon unremoved variables.

Growing community of inventors

Bloomington, IL, United States of America

Jody Ann Thoele