US 11,777,813 B2
Systems and methods for event assignment of dynamically changing islands
Jeremy Kolter, Redwood City, CA (US); Giuseppe Barbaro, Redwood City, CA (US); Mehdi Maasoumy Haghighi, Redwood City, CA (US); Henrik Ohlsson, Redwood City, CA (US); and Umashankar Sandilya, Redwood City, CA (US)
Assigned to C3.AI, Inc., Redwood City, CA (US)
Filed by C3.AI, INC., Redwood City, CA (US)
Filed on May 4, 2022, as Appl. No. 17/736,631.
Application 17/736,631 is a continuation of application No. 17/479,929, filed on Sep. 20, 2021.
Application 17/479,929 is a continuation of application No. PCT/US2020/023738, filed on Mar. 19, 2020.
Claims priority of provisional application 62/822,300, filed on Mar. 22, 2019.
Prior Publication US 2022/0261695 A1, Aug. 18, 2022
Int. Cl. H04L 29/08 (2006.01); H04L 41/16 (2022.01); H04L 67/12 (2022.01); G06N 20/00 (2019.01); G01R 19/25 (2006.01); G06N 7/01 (2023.01)
CPC H04L 41/16 (2013.01) [G01R 19/2513 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); H04L 67/12 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method of training a machine learning model, comprising:
receiving an island-level event data describing an island-level event occurring at an island that is capable of dynamically changing by splitting or merging with another island, wherein:
the island is a part of a network comprising a plurality of islands including the another island and comprises one or more individual components; and
the island-level event data fails to immediately identify a source individual component of the island-level event from the one or more individual components;
estimating a prior probability of each of the one or more individual components causing the island-level event;
performing a posterior inference, using the machine learning model and based on the island-level event data and the prior probability of each of the one or more individual components, to determine a probability estimate of each of the one or more individual components causing the island-level event;
updating the prior probability of each of the one or more individual components based on a result of the performance of the posterior inference; and
executing the receiving the island-level event data, the performing the posterior inference, and the updating the prior probability repeatedly until a difference between the updated and estimated prior probability of each of the one or more individual components is less than a threshold difference,
wherein, after the executing, the machine learning model is trained to receive the island-level event data describing the island-level event occurring at the island, and identify the source individual component as a cause of the island-level event based on the received island-level event data as the island is dynamically changing.