US 12,081,571 B2
Graphics processing unit optimization
Damion Irving, Brooklyn, NY (US); James Korge, Brooklyn, NY (US); Jeffrey L. Thomas, Columbus, OH (US); and Donald Bathurst, Denver, CO (US)
Assigned to Reveald Holdings, Inc., New York, NY (US)
Filed by Reveald Holdings, Inc., New York, NY (US)
Filed on Jul. 29, 2022, as Appl. No. 17/877,133.
Claims priority of provisional application 63/227,977, filed on Jul. 30, 2021.
Prior Publication US 2023/0032249 A1, Feb. 2, 2023
Int. Cl. H04L 29/06 (2006.01); G06N 20/00 (2019.01); H04L 9/40 (2022.01)
CPC H04L 63/1425 (2013.01) [G06N 20/00 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A method for optimizing resources privately, comprising:
mapping, by an algorithmic framework processor, a first structure represented by initial graph data to a second structure, the second structure having a lower dimension than the first structure and containing real numbers, thereby generating an initial client embedding data set;
computing, by an information gain processor, an initial information gain corresponding to the initial client embedding data set;
training, by a dedicated server, a machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;
mapping, using the algorithmic framework processor, a third structure representing a second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;
computing, using the information gain processor, a second information gain corresponding to the second client embedding data set;
computing, using a difference processor, a difference between the first information gain and the second information gain; and
retraining, using the dedicated server, the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data
scoring, by a scoring processor the at least one initial attack path, thereby generating at least one scored initial attack path;
ranking, by a ranking processor the at least one scored initial attack path, thereby generating a ranking of initial attack paths;
providing the ranking of new attack paths to a user interface;
scoring, by the scoring processor the at least one new attack path, thereby generating at least one scored new attack path;
ranking, by the ranking processor the at least one scored new attack path, thereby generating a ranking of new attack paths; and
providing the ranking of new attack paths to the user interface.