Systems and methods for identifying and remediating architecture design defects

Patent metadata

Field	Value
Publication number	US-12314646-B2
Application number	US-202217653805-A
Country	US
Kind code	B2
Filing date	Mar 7, 2022
Priority date	Mar 7, 2022
Publication date	May 27, 2025
Grant date	May 27, 2025

Abstract

Systems and methods for identifying and remediating architecture design defects are disclosed. In one aspect, a method includes generating a new architecture graph pattern based on an architecture design document of an evaluated architecture; determining a model graph pattern, wherein a shape of the model graph pattern is similar to a shape of the architecture graph pattern; determining, based on a comparison of the shape of the model graph pattern with the shape of the new architecture graph pattern, that the new architecture graph pattern includes a design defect; generating, based on the shape of the model graph pattern, a remediated graph pattern; and determining, based on the differences between the remediated graph pattern and the new architecture graph pattern, a suggested remedial change to the architecture design document.

First claim

The invention claimed is:

1. A method of evaluating architecture design, comprising:
generating a new architecture graph pattern based on an architecture design document of an evaluated architecture and based on one or more environmental interactions found using a packet capture tool, the new architecture graph pattern comprising a first node, a second node, and an edge connecting the first node and the second node, the edge describing a relationship between the first node and the second node, wherein a first machine learning model infers an unknown relationship between the first node and the second node;
determining a model graph pattern, wherein a shape of the model graph pattern is similar to a shape of the architecture graph pattern;
determining, based on a comparison of the shape of the model graph pattern with the shape of the new architecture graph pattern, that the new architecture graph pattern includes a design defect;
generating, based on the shape of the model graph pattern, a remediated graph pattern; and
determining, based on the differences between the remediated graph pattern and the new architecture graph pattern, a suggested remedial change to the architecture design document.

2. The method of claim 1 , wherein the suggested remedial change is generated as a natural language statement.

3. The method of claim 2 , wherein the natural language statement is formatted as a behavior driven architecture language statement.

4. The method of claim 1 , wherein the suggested remedial change is presented via an electronic interface.

5. The method of claim 4 , wherein the electronic interface is an integrated development environment.

6. The method of claim 1 , wherein the new architecture graph pattern is generated by processing the architecture design document with a natural language processing engine.

7. The method of claim 1 , comprising:
training a second machine learning model to recognize the model graph pattern within a knowledge graph, wherein the knowledge graph represents a technology infrastructure of an evaluating organization.

8. A system for evaluating architecture design comprising at least one server including a processor and a memory, wherein the at least one server is configured for operative communication on a technology infrastructure of an evaluating organization, and wherein instructions stored on the memory instruct the processor to:
generate a new architecture graph pattern based on an architecture design document of an evaluated architecture and based on one or more environmental interactions found using a packet capture tool, the new architecture graph pattern comprising a first node, a second node, and an edge connecting the first node and the second node, the edge describing a relationship between the first node and the second node, wherein a first machine learning model infers an unknown relationship between the first node and the second node;
determine a model graph pattern, wherein a shape of the model graph pattern is similar to a shape of the architecture graph pattern;
determine, based on a comparison of the shape of the model graph pattern with the shape of the new architecture graph pattern, that the new architecture graph pattern includes a design defect;
generate, based on the shape of the model graph pattern, a remediated graph pattern; and
determine, based on the differences between the remediated graph pattern and the new architecture graph pattern, a suggested remedial change to the architecture design document.

9. The system of claim 8 , wherein the suggested remedial change is generated as a natural language statement.

10. The system of claim 9 , wherein the natural language statement is formatted as a behavior driven architecture language statement.

11. The system of claim 8 , wherein the suggested remedial change is presented via an electronic interface.

12. The system of claim 11 , wherein the electronic interface is an integrated development environment.

13. The system of claim 8 , wherein the new architecture graph pattern is generated by processing the architecture design document with a natural language processing engine.

14. The system of claim 8 , wherein instructions stored on the memory instruct the processor to train a second machine learning model to recognize the model graph pattern within a knowledge graph, wherein the knowledge graph represents a technology infrastructure of an evaluating organization.

15. A non-transitory computer readable storage medium, including instructions stored thereon for evaluating architecture design, which when read and executed by one or more computers cause the one or more computers to perform steps comprising:
generating a new architecture graph pattern based on an architecture design document of an evaluated architecture and based on one or more environmental interactions found using a packet capture tool, the new architecture graph pattern comprising a first node, a second node, and an edge connecting the first node and the second node, the edge describing a relationship between the first node and the second node, wherein a first machine learning model infers an unknown relationship between the first node and the second node;
determining a model graph pattern, wherein a shape of the model graph pattern is similar to a shape of the architecture graph pattern;
determining, based on a comparison of the shape of the model graph pattern with the shape of the new architecture graph pattern, that the new architecture graph pattern includes a design defect;
generating, based on the shape of the model graph pattern, a remediated graph pattern; and
determining, based on the differences between the remediated graph pattern and the new architecture graph pattern, a suggested remedial change to the architecture design document.

Assignees

• Jpmorgan Chase Bank Na

Inventors

• Eavy Ryan
• Okpoko Olisa Obinna

Classifications

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06F30/398

  Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] (optical proximity correction [OPC] design processes G03F1/36) · CPC title

• G06F40/20

  Natural language analysis (semantic analysis of natural language G06F40/30) · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F30/337 Primary

  Design optimisation · CPC title