US 2023/0146501 A1Published application

Techniques for graph data structure augmentation

AssigneeOracle International Corporation

InventorsAmit Agarwal, Kulbhushan Pachauri, Iman Zadeh, Jun Qian

Life of this patent

Estimating the term…

Loading patent…

Abstract

A computing device may receive a set of user documents. Data may be extracted from the documents to generate a first graph data structure with one or more initial graphs containing key-value pairs. A model may be trained on the first graph data structure to classify the pairs. Until a set of evaluation metrics for the model exceeds a set of deployment thresholds: generating, a set of evaluation metrics may be generated for the model. The set of evaluation metrics may be compared to the set of deployment thresholds. In response to a determination that the set of evaluation metrics are below the set of deployment thresholds: one or more new graphs may be generated from the one or more initial graphs in the first graph data structure to produce a second graph data structure. The first and second graph can be used to train the model.

Claims20

Dependent claims

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 user document, 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 a first augmentation module of 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 at least one of the first graph data structure or the second graph data structure.

9 more elements

A computer-readable storage medium storing a set of instructions, that, when executed by one or more processors of a computer device, cause the one or more processors to perform instructions comprising:

receiving a set of user documents;
extracting data from the set of user documents;
generating a first graph data structure with one or more initial graphs containing the data extracted from the user document, the data including a set of key-value pairs;
training 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 the set of evaluation metrics for the model;
comparing 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 a first augmentation module of 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 the model on at least one of the first graph data structure or the second graph data structure.

9 more elements

A system, comprising:

memory storing computer-executable instructions;
and
one or more processors configured to access the memory, and execute the computer-executable instructions to at least:
receive a set of user documents;
extract data from the set of user documents;
generate a first graph data structure with one or more initial graphs containing the data extracted from the user document, the data including a set of key-value pairs;
train 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:
generate the set of evaluation metrics for the model;
compare 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:
generate, by a first augmentation module, 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
train the model on at least one of the first graph data structure or the second graph data structure.

12 more elements

Dependencies are inferred from the claim text.