System for generating an architecture diagram

Patent metadata

Field	Value
Publication number	US-11018949-B2
Application number	US-201715730495-A
Country	US
Kind code	B2
Filing date	Oct 11, 2017
Priority date	Jul 14, 2017
Publication date	May 25, 2021
Grant date	May 25, 2021

Abstract

A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

First claim

We claim:

1. A system for generating an architecture diagram, the system comprising:
an input processor configured to:
generate a graphical user interface (GUI) viewable on the terminal that facilitates a user drawing an architecture diagram on the GUI displayed on the terminal, the architecture diagram comprising an architecture having a plurality of entities and a plurality of paths that interconnect the entities; and
receive, from the terminal, entity data associated with the plurality of entities of the architecture and path data associated with the plurality of paths that correspond to the interconnections between the plurality of entities, wherein the entity data and path data are converted by the input processor into a computer processable format from the plurality of entities and plurality of paths drawn by the user on the GUI on the terminal depicting the architecture;

a machine learning processor that utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data,
wherein the training dataset includes a matrix comprising features and categories, each row of the matrix including cells containing features that include values corresponding to pre-defined object IDs associated with the architecture entities in a path and a cell containing a value corresponding to a predefined assessment category, the predefined assessment category including an object ID of an entity missing in the path, an incorrect path indicator or a correct path indicator,
wherein the machine learning processor includes machine learning algorithms that are trained with the training dataset to classify the interconnected architecture entities as corresponding to the predefined assessment category, and
wherein the machine learning processor generates an assessment that indicates a proposed change to either insert a new entity between entities of the architecture in a path or to replace an existing entity of the architecture with the new entity in a path based on the predefined assessment category determined to correspond to the received entity data for that path; and
an advice generator that receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal, wherein the recommendation includes the proposed change displayed on the GUI and includes a selectable button for the user to accept or reject the proposed change;
wherein the input processor is further configured to receive an indication of the acceptance or rejection of the proposed change based on the button selection by the user, and in response to the acceptance selection, automatically update the architecture diagram on the GUI with the proposed change.

2. The system according to claim 1 , wherein the advice generator is further configured to:
receive an indication of acceptance or rejection of the recommendation from the terminal; and
when the recommendation is rejected, the advice generator is configured to communicate an indication to the machine learning processor that the proposed change in the recommendation was rejected,
wherein in response to the indication that the proposed change was rejected, the machine learning processor is further configured to update information in the training dataset to reflect that the proposed change was rejected.

3. The system according to claim 2 , wherein in addition to the proposed change to either insert a new entity between entities of the architecture or to replace an existing entity of the architecture with the new entity, the assessment indicates a proposed change to delete a path between entities of the architecture.

4. The system according to claim 1 , wherein the entity data and path data are converted to a JavaScript Object Notation (JSON) format.

5. The system according to claim 1 , wherein the machine learning processor utilizes statistical classification algorithms to classify the entity data and path data as belonging to a class of interconnected entities defined in the training dataset.

6. A non-transitory computer readable medium that includes instruction code that facilitates generating an architecture diagram, the instruction code being executable by a machine for causing the machine to perform acts comprising:
generating, on a terminal, a graphical user interface (GUI) that facilitates a user drawing an architecture diagram on the GUI displayed on the terminal, the architecture diagram comprising an architecture having a plurality of entities and a plurality of paths that interconnect the entities;
receiving, from the terminal, entity data associated with the plurality of entities of the architecture and path data associated with the plurality of paths that correspond to the interconnections between the plurality of entities, wherein the entity data and path data are converted to a computer processable format from the plurality of entities and plurality of paths drawn by the user on the GUI on the terminal depicting the architecture;
controlling a machine learning processor that utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data,
wherein the training dataset includes a matrix comprising features and categories, each row of the matrix including cells containing features that include values corresponding to pre-defined object IDs associated with the architecture entities in a path and a cell containing a value corresponding to a predefined assessment category, the predefined assessment category including an object ID of an entity missing in the path, an incorrect path indicator or a correct path indicator,
wherein the machine learning processor includes machine learning algorithms that are trained with the training dataset to classify the interconnected architecture entities as corresponding to the predefined assessment category, and
wherein the machine learning processor generates an assessment that indicates a proposed change to either insert a new entity between entities of the architecture in a path or to replace an existing entity of the architecture with the new entity in a path based on the predefined assessment category determined to correspond to the received entity data for that path; and
preparing a recommendation based on the assessment, and communicating the recommendation to the terminal, wherein the recommendation includes the proposed change displayed on the GUI and includes a selectable button for the user to accept or reject the proposed change;
wherein the input processor is further configured to receive an indication of the acceptance or rejection of the proposed change based on the button selection by the user, and in response to the acceptance selection, automatically update the architecture diagram on the GUI with the proposed change.

7. The non-transitory computer readable medium according to claim 6 , wherein the instruction code is executable to cause the machine to perform further acts comprising:
receiving an indication of acceptance or rejection of the recommendation from the terminal; and
when the recommendation is rejected, communicate an indication to the machine learning processor that the proposed change in the recommendation was rejected,
wherein in response to the indication that the proposed change was rejected, the machine learning processor updates information in the training dataset to reflect that the proposed change was rejected.

8. The non-transitory computer readable medium according to claim 7 , wherein in addition to the proposed change to either insert a new entity between entities of the architecture or to replace an existing entity of the ar

Assignees

• Accenture Global Solutions Ltd

Inventors

• Ramasamy Manoharan
• Sawant Nitin Madhukar
• Baskaran Vijay
• Waghmale Ganesh Dadasaheb
• Pandey Abhishek Kumar
• Besta Balasubramanyam
• Kanyal Rakesh Singh
• Kumar Anil

Classifications

• G06F18/24

  Classification techniques · CPC title

• G06F18/214

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

• H04L41/145

  involving simulating, designing, planning or modelling of a network · CPC title

• H04L41/22

  comprising specially adapted graphical user interfaces [GUI] · CPC title

• G06F2111/12

  Symbolic schematics · CPC title