US 11,704,737 B1
Structural characteristic extraction using drone-generated 3D image data
Timothy J. Spader, Bloomington, IL (US); George T. Dulee, Jr., Bloomington, IL (US); Donald Yuhas, Bloomington, IL (US); Aaron Brucker, Bloomington, IL (US); Chris Stroh, Bloomington, IL (US); and Jeffrey Mousty, Bloomington, IL (US)
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed by State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed on Oct. 16, 2020, as Appl. No. 17/73,043.
Application 17/073,043 is a continuation of application No. 16/750,741, filed on Jan. 23, 2020.
Application 16/750,741 is a continuation of application No. 15/245,746, filed on Aug. 24, 2016, granted, now 10,832,332.
Claims priority of provisional application 62/299,658, filed on Feb. 25, 2016.
Claims priority of provisional application 62/290,233, filed on Feb. 2, 2016.
Claims priority of provisional application 62/290,215, filed on Feb. 2, 2016.
Claims priority of provisional application 62/266,454, filed on Dec. 11, 2015.
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/08 (2012.01); G01C 11/06 (2006.01); G01C 11/02 (2006.01)
CPC G06Q 40/08 (2013.01) [G01C 11/025 (2013.01); G01C 11/06 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A structural analysis computing device for generating an insurance claim for an object pictured in a three-dimensional (3D) image, the structural analysis computing device coupled to a drone, the structural analysis computing device comprising:
a memory;
an object sensor; and
at least one processor in communication with the memory and the object sensor, wherein the at least one processor is programmed to:
transmit an instruction to the drone to navigate to a room;
transmit an instruction to the object sensor scan an interior structure of the room to capture a 3D image of the room, wherein the room includes a plurality of objects, the 3D image including an object of the plurality of objects;
analyze the 3D image to identify features of the object using image analysis trained using one or more machine learning algorithms;
based upon the analysis, automatically identify the object of the plurality of objects within the 3D image as an insurable asset;
determine whether an insurance policy covers a damaged feature of the identified object based upon a nature and extent of damage to the identified object; and
automatically determine, when the insurance policy covers the feature, a cost of repair of the damaged feature of the object based upon the nature and extent of the damage.