IP Library › Granted Patent US 12,292,864
Granted Patent B2
US 12,292,864 · App. 18/502,546 · Granted May 6, 2025

Method and apparatus for visualizing a process map

Inventors: Oleksandr Reminnyi (Vinnytsia, UA); Nataliia Zasoba (Lviv, UA); Volodymyr Skalskyi (Vyshivka, UA); Christian Berg (Seattle, WA); Charles Park (Bellevue, WA); Dmitrii Leonidovich Kondratiuk (Kharkov, UA)
Assignee: UiPath, Inc.
G06F16/212G06F16/26
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Quick Facts
Patent No.
US 12,292,864
App. No.
18/502,546
Granted
May 6, 2025
Kind
B2
Abstract

A method for visualizing a process map is executed by a process map server. The method includes receiving a flowchart and a step-by-step recording related to a process. Generating a process map by combining the flowchart and the step-by-step recording and displaying the process map. The process map displays a task, step, and action related to the process. A detail window shows information associated with the process, and portions of the process, in response to user input. The action is based on information from the step-by-step recording.

Claims (47)

1. A method for visualizing a process as a process map, the method comprising:

receiving a flowchart of the process;

receiving a step-by-step recording of a user's input to a computer and images the computer displays while the user is performing the process;

generating metadata based on analysis of the images; and

generating, at a process map server, a process map by combining the flowchart and the step-by-step recording, the process map comprising at least one of the images and a mapping based on clustering of the generated metadata.

2. The method of claim 1 , wherein the metadata is generated based on at least one of optical character recognition, a document object model (DOM) tree, a hardware event, a causality score, or a word vector.

3. The method of claim 1 , further comprising:

displaying the process map on a display.

4. The method of claim 1 , wherein the clustering of the generated metadata is iterative clustering.

5. The method of claim 1 , wherein the clustering of the generated metadata is hierarchical clustering.

6. The method of claim 1 , further comprising:

receiving a petri net model of the process,

wherein the generating a process map further comprises combining the petri net model with the flowchart and the step-by-step recording.

7. The method of claim 1 , further comprising:

receiving a business process model/notation model of the process,

wherein the generating a process map further comprises combining the business process model/notation model with the flowchart and the step-by-step recording.

8. An apparatus comprising:

a processor; and

a memory to store computer program instructions, the computer program instructions when executed on the processor cause the processor to perform operations comprising:

receiving a flowchart of the process;

receiving a step-by-step recording of a user's input to a computer and images the computer displays while the user is performing the process;

generating metadata based on analysis of the images; and

generating, at the processor, a process map by combining the flowchart and the step-by-step recording, the process map comprising at least one of the images and a mapping based on clustering of the generated metadata.

9. The apparatus of claim 8 , wherein the metadata is generated based on at least one of optical character recognition, a document object model (DOM) tree, a hardware event, a causality score, or a word vector.

10. The apparatus of claim 8 , the operations further comprising:

displaying the process map on a display.

11. The apparatus of claim 8 , wherein the clustering of the generated metadata is iterative clustering.

12. The apparatus of claim 8 , wherein the clustering of the generated metadata is hierarchical clustering.

13. The apparatus of claim 8 , the operations further comprising:

receiving a petri net model of the process,

wherein the generating a process map further comprises combining the petri net model with the flowchart and the step-by-step recording.

14. The apparatus of claim 8 , the operations further comprising:

receiving a business process model/notation model of the process,

wherein the generating a process map further comprises combining the business process model/notation model with the flowchart and the step-by-step recording.

15. A computer readable medium storing computer program instructions, which, when executed on a processor, cause the processor to perform operations comprising:

receiving a flowchart of the process;

receiving a step-by-step recording of a user's input to a computer and images the computer displays while the user is performing the process;

generating metadata based on analysis of the images; and

generating, at the processor, a process map by combining the flowchart and the step-by-step recording, the process map comprising at least one of the images and a mapping based on clustering of the generated metadata.

16. The computer readable medium of claim 15 , wherein the metadata is generated based on at least one of optical character recognition, a document object model (DOM) tree, a hardware event, a causality score, or a word vector.

17. The computer readable medium of claim 15 , the operations further comprising:

displaying the process map on a display.

18. The computer readable medium of claim 15 , wherein the clustering of the generated metadata is iterative clustering.

19. The computer readable medium of claim 15 , wherein the clustering of the generated metadata is hierarchical clustering.

20. The computer readable medium of claim 15 , the operations further comprising:

receiving a petri net model of the process,

wherein the generating a process map further comprises combining the petri net model with the flowchart and the step-by-step recording.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 6, 2023
From: REMINNYI, OLEKSANDR; ZASOBA, NATALIIA; SKALSKYI, VOLODYMYR; PARK, CHARLES; KONDRATIUK, DMITRII LEONIDOVICH; BERG, CHRISTIAN
To: UIPATH, INC.
Reel/Frame 065469/0253 →
Continuity (2)
Continuation 16917861 · Jun 30, 2020
Related Publication 20240070127A1 · Feb 29, 2024
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