US 12,461,951 B1
Parcel growth model training system
Kien Trong Trinh, San Diego, CA (US); Daniel Lawrence Gossett, Round Rock, TX (US); Charles Presley Reynolds, Austin, TX (US); Hans Christian Dumke, Lafayette, CO (US); and Bin He, Philadelphia, PA (US)
Assigned to CoreLogic Solutions, LLC, Irvine, CA (US)
Filed by CoreLogic Solutions, LLC, Irvine, CA (US)
Filed on Sep. 20, 2023, as Appl. No. 18/370,779.
Claims priority of provisional application 63/409,141, filed on Sep. 22, 2022.
Int. Cl. G06F 16/29 (2019.01); G06F 16/2457 (2019.01)
CPC G06F 16/29 (2019.01) [G06F 16/24573 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system for training a parcel growth artificial intelligence model, the system comprising:
memory that stores computer-executable instructions; and
a processor in communication with the memory, wherein the computer-executable instructions, when executed by the processor, cause the processor to:
obtain a graph model corresponding to a geographic area, wherein the graph model comprises a first node that corresponds to a first parcel and that is connected to a second node that corresponds to a second parcel;
update a status of the first node to reflect a development status of the first parcel at a first time;
update a status of the second node to reflect a development status of the second parcel at the first time;
train the parcel growth artificial intelligence model using the graph model with the updated status of the first node corresponding to the first time and the updated status of the second node corresponding to the first time;
update a status of the first node to reflect a development status of the first parcel at a second time;
update a status of the second node to reflect a development status of the second parcel at the second time;
apply the graph model with the updated status of the first node corresponding to the second time and the updated status of the second node corresponding to the second time as an input to the trained parcel growth artificial intelligence model to obtain a growth probability; and
validate the trained parcel growth artificial intelligence model based on a comparison of the growth probability with historical data corresponding to a development or lack of development of the first and second parcels by the second time.