US 12,043,280 B2
Systems and methods for graph-based AI training
Robert Chess Stetson, Altadena, CA (US); Kris Chaisanguanthum, Kansas City, MO (US); Robert Ferguson, Tujunga, CA (US); and Boris Revechkis, Pasadena, CA (US)
Assigned to dRISK, Inc., Pasadena, CA (US)
Filed by dRISK, Inc., Pasadena, CA (US)
Filed on Nov. 21, 2022, as Appl. No. 18/057,695.
Application 18/057,695 is a continuation of application No. 16/566,776, filed on Sep. 10, 2019, granted, now 11,507,099.
Claims priority of provisional application 62/789,955, filed on Jan. 8, 2019.
Claims priority of provisional application 62/729,368, filed on Sep. 10, 2018.
Prior Publication US 2023/0202513 A1, Jun. 29, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. B60W 60/00 (2020.01); G05D 1/00 (2006.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06V 10/774 (2022.01); G06V 10/84 (2022.01); G06V 20/56 (2022.01)
CPC B60W 60/001 (2020.02) [G05D 1/0221 (2013.01); G05D 1/0246 (2013.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06N 5/02 (2013.01); G06N 5/04 (2013.01); G06V 10/774 (2022.01); G06V 10/84 (2022.01); G06V 20/56 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A graph interface system comprising:
a processor; and
a memory configured to store a graph interface application, where the graph interface application directs the processor to:
obtain a set of training data, where the set of training data describes a plurality of scenarios;
encode the set of training data into a first knowledge graph as a plurality of nodes representing spatiotemporal features of each scenario in the plurality of scenarios;
calculate a similarity measure between each scenario in the first knowledge graph based on their spatiotemporal features; and
embed the scenarios into a manifold linked to the first knowledge graph by connecting scenarios via edges with an edge weight equal to the similarity measure between a first scenario and a second scenario connected by their respective edge; and
train an artificial intelligence (AI) model by traversing the manifold.