State Farm Mutual Automobile Insurance Company

Ryan Michael Gross

Joseph Robert Brannan

Brian N Harvey

Matthew Eric Riley, Sr

J Lynn Wilson

Aaron C Williams

Benjamin Joel Tucker

Michael Aaron Myers

Darwin Gene Beachy

Scott T Christensen

Eric R Moore

Jody Ann Thoele

Craig Benjamin Cope

Kristopher Keith Gaudin

Joseph P Harr

Michael K Cook

Jessica Lynn Shull

Melissa C Miles

M Eric Riley, Sr

Vicki King

Mark E Clauss

Jennifer L Crawford

Steven Joseph Balbach

Rick Lovings

Daryoush Hakimi

A computer system for flood monitoring and insurance provider notification, the computer system may include one or more processors configured to: detect a flood event in a structure, transmit information associated with the structure and a prompt for flood reimbursement services to a machine learning (ML) chatbot to cause the ML chatbot to file a flood reimbursement claim with an insurance provider having an insurance policy associated with the structure via telephone by converting a text output into a voice output.

Artificial intelligence for flood monitoring and insurance claim filing

A computing device for generating on-demand products based upon real-time geolocation data is configured to receive and store at least one product definition associated with a respective product. Each product definition defines an availability of the product and includes a product availability location. The computing device is also configured to receive real-time location data from a user computing device, and periodically query a memory device with the real-time location data. The computing device detects that the user computing device has entered a first location by retrieving, in response to a successful query of the memory device, a first product definition associated with a first product having a product availability location including the first location. The computing device is also configured to generate a product identification message including at least a portion of the first product definition, and transmit the product identification message to the user computing device.

Systems and methods for generating on-demand products responsive to real-time geolocation and telematics

Systems and methods are described for analyzing user data to generate a dialogue output. The method may include: (1) receiving, by one or more processors, an indication of a user identity for a user at a generative artificial intelligence (AI) model; (2) retrieving, by the one or more processors and based upon at least the user identity, user data from one or more publicly accessible sources; (3) determining, by the one or more processors and based upon at least the user data, one or more personalization characteristics associated with at least an information retention rate for the user via the generative AI model; and (4) generating, by the one or more processors and based upon at least the one or more personalization characteristics, a personalized dialogue output (or visual or virtual output for display) for the user via the generative AI model.

Systems and methods for analysis of user telematics data using generative AI

A personalized insurance ('PI') computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

Systems and methods for generating mobility insurance products using ride-sharing telematics data

Described herein are methods and systems for generating and applying vehicle maintenance models to vehicle telematics data. A vehicle telematics analytics (VTA) computing device is configured to receive vehicle telematics data associated with operation of a subject vehicle, and apply the received vehicle telematics data to a vehicle maintenance model to determine whether the operation of the subject vehicle is better or worse than standard operation of like vehicles. The VTA computing device may adjust a maintenance cost and/or determine if a user of the vehicle is eligible for a reward based upon this determination. The VTA computing device is configured to transmit an itemized report to the user including the adjusted maintenance cost and/or the reward as well as one or more other vehicle costs.

Vehicle telematics systems and methods

Embodiments described herein, inter alia, receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by a potential renter during the period of time; identify, upon analyzing the telematics data, driving behavior(s) of the renter during the period of time; determine, for each driving behavior, a corresponding state of an environment of the vehicle when the driving behavior occurred; determine renter eligibility value(s) for the renter based on the driving behavior(s) and the corresponding state of an environment of the vehicle; compare the renter eligibility value(s) to user preference value(s) of a profile of a vehicle owner, wherein the user preference value(s) define one or more criteria for vehicle renters with whom the vehicle can be shared; and cause an indication of the vehicle associated with the profile to be displayed only if the renter satisfies the criteria.

System and methods for determining rental eligibility based on contextual telematics data

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

A matching computer system for electronically generating, matching, and providing online user profiles, and determining a trust score for a user based upon at least social media data and insurance data is provided. The matching computer system may be configured to register users within the matching computer system, receive consent from the users to capture the social media data, and collect the social media data and the insurance data from each registered user. The matching computer system may also be configured to retrieve the social media data and the insurance data associated with each registered user. The matching computer system may be further configured to determine a trust score for each registered user based upon each respective social media data and each respective insurance data. Each trust score represents a level of trustworthiness of the user.

Systems and methods for electronically matching online user profiles

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

A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.

Systems and methods for generating refined risk models responsive to vehicle-based telematics data

A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

Systems and methods for dynamically generating optimal routes for vehicle delivery management

Systems and methods for dynamically generating optimal routes for vehicle operation management

Systems and methods for dynamically generating optimal routes for management of multiple vehicles

A method of facilitating first mile/last mile transfer of a vehicle includes: analyzing a first route and a second route using a trained machine learning model to determine, respectively, a first cost of the first route and a second cost of the second route to a user; wherein the trained machine learning model is trained using at least one of a detour in time, a delay in miles, a time of day, a traffic congestion factor, or profile information about a ride share passenger; causing a message including the first cost and the second cost to be displayed to the user via an electronic device of the user; and receiving an indication of whether the user selects the first route or the second route.

First mile and last mile ride sharing method and system

Provided herein is a computing system including a processor in communication with at least one memory. The processor is configured to (i) build a model using historical vehicle telematics data and historical vehicle data, the model configured to: (a) predict an operating cost of a vehicle based upon a vehicle type and user data and (b) output operational parameters for operating the vehicle, (ii) receive a vehicle acquisition request for a selected vehicle including vehicle data for the selected vehicle and selected user data for the user of the selected vehicle, (iii) input the vehicle acquisition request data into the model, and (iv) output from the model an acquisition cost and operational parameters for the selected vehicle and the selected user including a procurement cost for the selected vehicle and an operating cost for operating the selected vehicle within the operational parameters for a predetermined time period.

Growing community of inventors

Normal, IL, United States of America

Ryan Michael Gross