US 11,989,964 B2
Techniques for graph data structure augmentation
Amit Agarwal, Kolkata (IN); Kulbhushan Pachauri, Bangalore (IN); Iman Zadeh, Los Angeles, CA (US); and Jun Qian, Bellevue, WA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Nov. 11, 2021, as Appl. No. 17/524,157.
Prior Publication US 2023/0146501 A1, May 11, 2023
Int. Cl. G06V 30/41 (2022.01); G06N 20/00 (2019.01); G06V 30/18 (2022.01)
CPC G06V 30/41 (2022.01) [G06N 20/00 (2019.01); G06V 30/18181 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving, at a computing device, a set of user documents;
extracting, by the computing device, data from the set of user documents;
generating, by the computing device, a first graph data structure with one or more initial graphs containing the data extracted from the set of user documents, the data including a set of key-value pairs;
training, by the computing device, a model on the first graph data structure to classify the set of key-value pairs;
until a set of evaluation metrics for the model exceeds a set of deployment thresholds:
generating, by the computing device, the set of evaluation metrics for the model;
comparing, by the computing device, the set of evaluation metrics to the set of deployment thresholds; and
in response to a determination that the set of evaluation metrics are below the set of deployment thresholds:
generating, by the computing device, one or more new graphs from the one or more initial graphs in the first graph data structure to produce a second graph data structure; and
training, by the computing device, the model on the second graph data structure.