US 11,900,535 B1
Systems and methods for a 3D model for visualization of landscape design
Nicholas Carmelo Marotta, Scottsdale, AZ (US); Laura Kennedy, Gilbert, AZ (US); and J D Johnson Willingham, Phoenix, AZ (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 Apr. 26, 2021, as Appl. No. 17/241,008.
Claims priority of provisional application 63/027,201, filed on May 19, 2020.
Claims priority of provisional application 63/025,600, filed on May 15, 2020.
Claims priority of provisional application 63/016,168, filed on Apr. 27, 2020.
Int. Cl. G06T 17/00 (2006.01); G06T 17/05 (2011.01); G01S 17/89 (2020.01); G06F 16/29 (2019.01); G06N 3/049 (2023.01); G06N 3/08 (2023.01); B64C 39/02 (2023.01); B64U 101/30 (2023.01)
CPC G06T 17/05 (2013.01) [B64C 39/024 (2013.01); G01S 17/89 (2013.01); G06F 16/29 (2019.01); G06N 3/049 (2013.01); G06N 3/08 (2013.01); B64U 2101/30 (2023.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method for visualization of landscape design, the method comprising, via one or more processors, sensors, servers, and/or transceivers:
receiving light detection and ranging (LIDAR) data generated from a LIDAR camera;
measuring a plurality of dimensions of a landscape based upon processor analysis of the LIDAR data;
building a 3D model of the landscape based upon the measured plurality of dimensions, the 3D model including: (i) a structure, and (ii) a vegetation;
displaying a representation of the 3D model;
receiving object data from a user, wherein the object comprises one of: a patio; a shed; a garage; a fence; a tree; a flower; or a pathway; and
inputting, into a machine learning algorithm: (i) data of the 3D model of the landscape, and (ii) the received object data to generate a recommendation for placement of the object in the landscape;
wherein the machine learning algorithm is trained based upon preexisting data of object placement in landscapes.