US 12,332,734 B2
Disentangled graph learning for incremental causal discovery and root cause analysis
Zhengzhang Chen, Princeton Junction, NJ (US); Haifeng Chen, West Windsor, NJ (US); Liang Tong, Lawrenceville, NJ (US); and Dongjie Wang, Orlando, FL (US)
Assigned to NEC Corporation, Tokyo (JP)
Filed by NEC Laboratories America, Inc., Princeton, NJ (US)
Filed on Jul. 26, 2023, as Appl. No. 18/359,350.
Claims priority of provisional application 63/442,155, filed on Jan. 31, 2023.
Claims priority of provisional application 63/397,955, filed on Aug. 15, 2022.
Prior Publication US 2024/0061740 A1, Feb. 22, 2024
Int. Cl. G06F 11/00 (2006.01); G06F 11/07 (2006.01); G06F 11/34 (2006.01)
CPC G06F 11/079 (2013.01) [G06F 11/0709 (2013.01); G06F 11/076 (2013.01); G06F 11/3447 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for locating root causes, the method comprising:
detecting a trigger point from entity metrics data and key performance indicator (KPI) data;
generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph; and
locating the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.