IP Library Granted Patent US 11,816,736
Granted Patent B2
US 11,816,736 · App. 17/977,999 · Granted Nov 14, 2023

Insurance underwriting and re-underwriting implementing unmanned aerial vehicles (UAVs)

Inventors: Nathan L. Tofte (Downs, IL); Rosemarie Geier Grant (Ellsworth, IL); Michael Shawn Jacob (Le Roy, IL); Timothy W. Ryan (Hudson, IL); Nathan W. Baumann (Bloomington, IL); Joshua David Lillie (Bloomington, IL); Brian N. Harvey (Bloomington, IL); Roxane Lyons (Chenoa, IL)
Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
G06Q40/08B64C39/024B64D47/08G01C11/02G06F18/22G06Q40/00G06T7/00G06T7/20G06T7/246G06T7/75G06T11/60G06T17/05G06V10/42G06V20/10G06V20/41H04N5/44H04N7/185B64U10/00B64U2101/00B64U2101/30G06T2207/10032G06T2207/30232G06T2207/30236G06T2207/30252G06T2215/16G06V20/44
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Quick Facts
Patent No.
US 11,816,736
App. No.
17/977,999
Granted
Nov 14, 2023
Kind
B2
Abstract

Unmanned aerial vehicles (UAVs) may facilitate insurance-related tasks. UAVs may actively be dispatched to an area surrounding a property, and collect data related to property. A location for an inspection of a property to be conducted by a UAV may be received, and one or more images depicting a view of the location may be displayed via a user interface. Additionally, a geofence boundary may be determined based on an area corresponding to a property boundary, where the geofence boundary represents a geospatial boundary in which to limit flight of the UAV. Furthermore, a navigation route may be determined which corresponds to the geofence boundary for inspection of the property by the UAV, the navigation route having waypoints, each waypoint indicating a location for the UAV to obtain drone data. The UAV may be directed around the property using the determined navigation route.

Claims (64)

1 . A computer-implemented method of underwriting insurance based upon drone data, the method comprising:

capturing drone data, by one or more unmanned aerial vehicles communicatively coupled to one or more sensors configured to capture the drone data, including:

navigating to an area surrounding an asset;

collecting data; and

storing the data as the drone data,

wherein the one or more sensors include at least one of a soil sample extractor, a wood sample extractor, a spectrometer, a heat sensor, an ultrasonic sensor, an image sensor, or a volumetric moisture content sensor, and wherein the drone data corresponds to the asset;

receiving, at one or more processors, the drone data captured by the one or more sensors communicatively coupled to the one or more unmanned aerial vehicles, wherein the drone data corresponds to the asset; and

analyzing, by the one or more processors, the drone data corresponding to the asset to identify one or more risk elements associated with the asset.

2 . The computer-implemented method of claim 1 , wherein the drone data further corresponds to the area which surrounds the asset, and further comprising:

combining, by the one or more processors, a first amount of risk associated with each of the one or more risk elements corresponding to the asset with a second amount of risk associated with each of the one or more risk elements corresponding to the area which surrounds the asset; and

determining, by the one or more processors, a total amount of risk associated with the asset based upon the combined amounts of risk.

3 . The computer-implemented method of claim 1 , wherein the drone data is current drone data and analyzing the drone data to identify one or more risk elements includes:

obtaining, at the one or more processors, previous drone data corresponding to the asset and which was captured before the current drone data; and

comparing, by the one or more processors, the previous drone data to the current drone data to determine whether an amount of risk associated with the asset has increased or decreased from a time in which the previous drone data was captured.

4 . The computer-implemented method of claim 3 , wherein when the amount of risk associated with the asset has increased or decreased from the time in which the previous drone data was captured based upon the comparison, adjusting, by the one or more processors, an insurance premium upon renewal of an insurance policy for the asset.

5 . The computer-implemented method of claim 1 , further comprising:

directing, by the one or more processors, the one or more unmanned aerial vehicles to an area defined by a geofenced geographic location surrounding the asset; and

directing, by the one or more processors, the one or more unmanned aerial vehicles to capture the drone data at a plurality of positions within the geofenced geographic location.

6 . The computer-implemented method of claim 1 , wherein the asset is a home, the drone data includes a thermal signature for the home, and when the thermal signature exceeds a predetermined threshold temperature based upon the analysis of the thermal signature, the method further comprising:

providing, by the one or more processors, an alert to emergency personnel that the home is at an increased risk of fire; and

adjusting, by the one or more processors, an insurance premium upon renewal of an insurance policy for the asset based upon the increased risk of fire.

7 . The computer-implemented method of claim 1 , wherein the asset is a home, the drone data includes a soil sample of soil surrounding the home, and when soil moisture content exceeds a predetermined threshold moisture content level based upon the analysis of the soil sample, the method further comprising:

determining, by the one or more processors, a risk of sewer or drain backup associated with the home based upon the soil moisture content;

adjusting, by the one or more processors, an insurance premium upon renewal of an insurance policy for the asset based upon the risk of sewer or drain backup; and

determining, by the one or more processors, a sump pump above a predetermined threshold size, type, capacity, or redundancy recommended for the house to mitigate the risk of sewer or drain backup based upon the soil moisture content.

8 . The computer-implemented method of claim 1 , wherein the asset is a home, the drone data includes a wood and a soil sample of a tree surrounding the home and further comprising:

analyzing, by the one or more unmanned aerial vehicles, the wood and soil sample to determine a number and size of dead sections of the tree, a degree of root damage, a number of dead branches, or an age of the tree;

receiving, at the one or more processors, the analysis of the wood and soil sample; and

determining, by the one or more processors, a risk of the tree falling based upon the received analysis.

9 . The computer-implemented method of claim 1 , wherein the drone data includes at least one of: (i) temperature data indicative of a current temperature associated with the asset; (ii) chemical and biological data; (ii) image data; (iii) audio data; (iv) location data; or (v) size data and material characteristics for the asset.

10 . The computer-implemented method of claim 1 , wherein the one or more risk elements include at least one of: (i) a risk based upon a current condition of a component of the asset; (ii) a natural disaster risk associated with the asset; (iii) a risk of pests associated with the asset; (iv) a risk based upon a hazardous object or activity associated with the asset; or (v) a risk based upon a current condition of vegetation or other organic matter at or around the asset.

11 . A computer system for underwriting insurance based upon drone data comprising:

one or more unmanned aerial vehicles communicatively coupled to one or more sensors including at least one of a soil sample extractor, a wood sample extractor, a spectrometer, a heat sensor, an ultrasonic sensor, an image sensor, or a volumetric moisture content sensor, the one or more unmanned aerial vehicles configured to capture drone data including:

navigate to an area surrounding an asset;

collect data; and

store the data as the drone data,

wherein the drone data corresponds to the asset;

one or more processors;

a communication network;

a non-transitory computer-readable memory coupled to the one or more processors, and the communication network, and storing thereon instructions that, when executed by the one or more processors, cause the system to:

receive, via the communication network, the drone data captured by the one or more sensors communicatively coupled to the one or more unmanned aerial vehicles;

analyze the drone data corresponding to the asset to determine one or more risk elements associated with the asset.

12 . The computer system of claim 11 , wherein the drone data further corresponds to the area which surrounds the asset, and the instructions further cause the system to:

combine a first amount of risk associated with each of the one or more risk elements corresponding to the asset with a second amount of risk associated with each of the one or more risk elements corresponding to the area which surrounds the asset; and

determine a total amount of risk associated with the asset based upon the combined amounts of risk.

13 . The computer system of claim 11 , wherein the drone data is current drone data and to analyze the drone data to determine one or more risk elements, the instructions cause the system to:

obtain previous drone data corresponding to the asset and which was captured before the current drone data; and

compare the previous drone data to the current drone data to determine whether the amount of risk associated with the asset has increased or decreased from a time in which the previous drone data was captured.

14 . The computer system of claim 13 , wherein when the amount of risk associated with the asset has increased or decreased from the time in which the previous drone data was captured based upon the comparison, the instructions cause the system to adjust an insurance premium upon renewal of an insurance policy for the asset.

15 . The computer system of claim 11 , wherein the instructions further cause the system to:

direct, via the communication network, the one or more unmanned aerial vehicles to an area defined by a geofenced geographic location surrounding the asset; and

direct, via the communication network, the one or more unmanned aerial vehicles to capture the drone data at a plurality of positions within the geofenced geographic location.

16 . The computer system of claim 11 , wherein the asset is a home, the drone data includes a thermal signature for the home, and when the thermal signature exceeds a predetermined threshold temperature based upon the analysis of the thermal signature, the instructions further cause the system to:

provide, via the communication network, an alert to emergency personnel that the home is at an increased risk of fire; and

adjust an insurance premium upon renewal of an insurance policy for the asset based upon the increased risk of fire.

17 . The computer system of claim 11 , wherein the asset is a home, the drone data includes a soil sample of soil surrounding the home, and when soil moisture content exceeds a predetermined threshold moisture content level based upon the analysis of the soil sample, the instructions further cause the system to:

determine a risk of sewer or drain backup associated with the home based upon the soil moisture content;

adjust an insurance premium upon renewal of an insurance policy for the asset based upon the risk of sewer or drain backup; and

determine a sump pump above a predetermined threshold size, type, capacity, or redundancy recommended for the house to mitigate the risk of sewer or drain backup based upon the soil moisture content.

18 . The computer system of claim 11 , wherein the asset is a home, the drone data includes a wood and a soil sample of a tree surrounding the home and the instructions further cause the system to:

receive, via the communication network, an analysis of the wood and soil sample by the one or more unmanned aerial vehicles, wherein the analysis includes a number and size of dead sections of the tree, a degree of root damage, a number of dead branches, or an age of the tree; and

determine a risk of the tree falling based upon the received analysis.

19 . The computer system of claim 11 , wherein the drone data includes at least one of: (i) temperature data indicative of a current temperature associated with the asset; (ii) chemical and biological data; (ii) image data; (iii) audio data; (iv) location data; or (v) size data and material characteristics for the asset.

20 . The computer system of claim 11 , wherein the one or more risk elements include at least one of: (i) a risk based upon a current condition of a component of the asset; (ii) a natural disaster risk associated the asset; (iii) a risk of pests associated with the asset; (iv) a risk based upon a hazardous object or activity associated with the asset; or (v) a risk based upon a current condition of vegetation or other organic matter at or around the asset.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 18, 2025
From: STATE FARM MUTUAL AUTOMOBILE INSURANCE CO.
To: NEARMAP US, INC.
Reel/Frame 070548/0732 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 3, 2023
From: TOFTE, NATHAN L.; GRANT, ROSEMARIE GEIER; JACOB, MICHAEL SHAWN; RYAN, TIMOTHY W.; BAUMANN, NATHAN W.; LILLIE, JOSHUA DAVID; HARVEY, BRIAN N.; LYONS, ROXANE
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 062262/0133 →
Continuity (12)
Continuation 17723900 · Apr 19, 2022
Continuation 16838372 · Apr 2, 2020
Continuation 16509631 · Jul 12, 2019
Continuation 14858075 · Sep 18, 2015
Provisional Application 62209963 · Aug 26, 2015
Provisional Application 62209755 · Aug 25, 2015
Provisional Application 62209627 · Aug 25, 2015
Provisional Application 62208201 · Aug 21, 2015
Provisional Application 62207421 · Aug 20, 2015
Provisional Application 62207127 · Aug 19, 2015
Provisional Application 62053519 · Sep 22, 2014
Related Publication 20230056605A1 · Feb 23, 2023