US 11,682,080 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 Jan. 23, 2020, as Appl. No. 16/750,741.
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.
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;
a user interface;
an object sensor configured to scan an interior structure of a room to capture the 3D image of the room, wherein the room includes a plurality of objects; 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 an object of the plurality of objects;
transmit an instruction to the object sensor to capture the 3D image of the object;
analyze the 3D image captured by the drone to identify features of the object using image analysis trained using one or more machine learning algorithms for identifying insurable assets;
automatically identify the object of the plurality of objects within the 3D image based upon the image analysis trained using the one or more machine learning algorithms for identifying insurable assets;
determine that the identified object is an insurable asset using a database lookup of insurable assets;
in response to determining that the identified object is one of the insurable assets, determine a nature and an extent of damage to a damaged feature of the object; and
determine a cost of repair of the damaged feature of the object based upon the nature and extent of the damage.