WO2022263919A1

WO2022263919A1 - System and method to assist robotic process automation

Info

Publication number: WO2022263919A1
Application number: PCT/IB2021/058125
Authority: WO
Inventors: Vignesh Kumar
Original assignee: Vignesh Kumar
Priority date: 2021-06-18
Filing date: 2021-09-07
Publication date: 2022-12-22

Abstract

System and method to assist in robotic process automation (RPA) are provided. The system includes an input module configured to receive or retrieve data in a pre-defined form; a data processing module configured to process the input to extract content; a data access module configured to access one or more networks; a model building module configured to build a ML model; the ML model is configured to determine a possibility to generate assistance for the process automation of functioning of the one or more sources, wherein the assistance is generated by a bot model, wherein the BOT model includes a BOT assistance module. The bot assistant module includes an analysis submodule configured to analyse parameters of a user device, networks associated to the user device, or a combination thereof; a server analysis submodule configured to analyse attributes associated to the user device; a process management submodule configured to provide assistance.

Description

SYSTEM AND METHOD TO ASSIST ROBOTIC PROCESS AUTOMATION

EARLIEST PRIORITY DATE:

This Application claims priority from a patent application filed in India having Patent Application No. 202141027341, filed on June 18, 2021 and titled “SYSTEM AND METHOD TO ASSIST ROBOTIC PROCESS AUTOMATION.”

FIELD OF INVENTION

Embodiments of a present disclosure relate to process automation, and more particularly, to system and method to assist robotic process automation.

With a linear growth in technology, use of artificial intelligence and machine learning technologies are finding applications in almost all areas. One such major application area is a process automation, where virtual assistants such as BOTs (build-operate- transfer tools) are being used. An operation or a method used to control and operate any process which may be associated to a specific application is referred to as a process automation or an automation system. Further, hots are used for digitally interpreting and providing responses to expression of intent such as online chat session, content recognition or web content for new and improved modes of interaction between human and computer system.

Several systems for process automations are present and are being used in day-to-day activities. In some conventional approaches, the system enables a user to customize the process automation for a corresponding use and application. However, in such approaches, there is very less depth of information available on assisting users to guide on Feasibility of Automation process for any application or a use case ideated by the user, which leads to lack of personalization involved while designing a hot. Such limitations make the conventional approach is less reliable and less efficient to suggest if the ideation of the user can be automated. In addition, the conventional approach is not suitable for all types of websites, it has some limitations for older technology websites such as HTML, CSS, Tables, iFrames, JSP, ASP, and the like. Also, the existing approach is not suitable for all types of Windows-based applications such as third-party windows controls, Group containers, and the like. Furthermore, the conventional approach has various limitations within a BOT Automation especially in regard to the system specific requirements. Otherwise, the hot will be failing on the system continuously and the user will not be able to identify what the exact system related issue is and have to rely on technical support.

Furthermore, developer of the RPA also faces certain challenges such as not being able to find the multiple iframe and xpaths of the elements using Web Recorders. Also, able to find the elements for the third-party complicated controls like Synfusion, Dev Express grids, and the like due to limited resources and guidance on designing of the RPA. Also, the developers may take additional time to analyze the unhandled exceptions while running the hots in the unsatisfied system configurations. Such limitations make the conventional approach slower and also less reliable.

Hence, there is a need for an improved system and method to assist robotic process automation to address the aforementioned issues.

BRIEF DESCRIPTION

In accordance with one embodiment of the disclosure, a system to assist in robotic process automation (RPA). The system includes one or more processors; an input module configured to receive or retrieve data in a pre-defined form from one or more sources; a data processing module configured to process the input to extract one or more content associated to the data received by the input module; a data access module configured to access one or more networks; a model building module configured to build a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof; the machine learning (ML) model is configured to determine a possibility to generate assistance for the process automation of functioning of the one or more sources, wherein the assistance is generated by a hot model, wherein the BOT model includes a BOT assistance module. The hot assistant module includes an analysis submodule configured to analyse at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof; a server analysis submodule configured to analyse one or more attributes associated to the user device, to generate an analysis result; a process management submodule configured to provide assistance based on the ML model and the analysis result.

In accordance with another embodiment of the disclosure, a method for assisting a robotic process automation (RPA) is provided. The method includes processing the input to extract one or more content associated to the data received; accessing one or more networks; building a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof; determining a possibility to generate assistance for the process automation of functioning of the one or more sources; analysing at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof; analysing one or more attributes associated to the user device, for generating an analysis result; providing assistance based on the ML model and the analysis result.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representation of a system to assist in robotic process automation (RPA) in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;

FIG. 3 is a flow chart representing steps involved in a method for assisting a robotic process automation (RPA) in accordance with an embodiment of the present disclosure; and FIG. 4a and 4b are flow charts of an exemplary embodiment representing steps involved in a method of automation assistant for an RPA of FIG. 3 in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting. In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate system and method for assisting a robotic process automation (RPA). As used herein, the term process automation is defined as operation or a method used to control and operate any process which may be associated to a specific application.

FIG. 1 is a block diagram representation of a system (10) to assist in robotic process automation (RPA) in accordance with an embodiment of the present disclosure. The system (10) includes one or more processors (20). The system (100) also includes an input module (30) configured to receive or retrieve data in a pre-defined form from one or more sources.

In one embodiment, the pre-defined form may include at least one of a URL for a web assistance, at least one extension file of a pre-defined format for a device assistance. Further, the one or more sources may be one of a website, a webpage, a window recorder, or the like. More specifically, in one embodiment, the input module (30) may fetch the website URL upon crawling through a server, or upon receiving the URL by the user. In another embodiment, the input module (30) may receive data from a window -based functions as the at least one extension file. In such embodiment, the extension file may be a .EXE extension file, or the like. The system (10) also includes a data processing module (40) configured to process the input to extract one or more content associated to the data received by the input module. In one embodiment, the one or more content may include at least one of one or more hyperlinks from the fetched website for the corresponding one or more inputs. Furthermore, the system (10) includes a data access module (50) configured to access one or more networks. In one embodiment, the one or more networks may include at least one extensions file such as DLL, OCX, DRV, or the like.

The system (10) also includes a model building module (60) configured to build a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof. As used herein, the term ‘artificial intelligence technique’ may be defined as a type of intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. Also, the term ‘machine learning technique’ may be defined as a study of computer algorithms that improve automatically through experience and using data, which is seen as a part of artificial intelligence. Further, the term ‘deep learning technique’ is defined as part of a broader family of machine learning methods based on artificial neural networks with representation learning. Further, the machine learning (ML) model is configured to determine a possibility to generate assistance for the process automation of functioning of the one or more sources. In one embodiment, determine the possibility to generate assistance for the process automation of functioning of the one or more sources by the ML model corresponds to determine a feasibility for processing data for at least one of the web assistance, the device assistance, or a combination thereof. The assistance is generated by a hot model which includes a BOT assistance module (70). In one embodiment, the deep learning model may be but not limited to a dynamic Machine Learning model with training data being collated dynamically during the run time.

In one specific embodiment, the hot model may be an avatar model which may be configured to design an avatar for the RPA of the corresponding application. As used herein, the term ‘avatar’ may be described as miniatures of corresponding user or any entity in a form of a digital worker to automate various aspects of any process through RPA. The avatar may be created by designing a hot in an Integrated Development Environment (IDE) and may be uploaded into a hot farm. Further the system (10) enables the user to design their own avatars by uploading platform recommended images abiding to data privacy policies. Upon designing the avatar, the same may then mapped to the respective hots. These avatars will be available in a dashboard of the user device, which allows the users to have a visual representation of the designed hots. Also, the avatar may provide one or more hot insights. Further, the avatar is substantially similar to the BOT assistance module and may be referred as the BOT assistance module (70) hereafter. The BOT assistance module (70) includes an analysis submodule (80) configured to analyse at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof. In one embodiment, the one or more parameters may include at least one of a central processing unit (CPU) of the user device, memory utilization a storage medium associated to the user device, API function request, or a combination thereof.

Furthermore, the BOT assistance module (70) includes an analysis submodule (80) configured to analyse at least one of the one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof. Also, the BOT assistance module (70) includes a server analysis submodule (90) configured to analyse one or more attributes associated to the user device, to generate an analysis result. In one embodiment, the one or more attributes may include at least one of a dynamic link library, one or more extension files of a pre-defined format one or more hyperlinks, or a combination thereof based on the corresponding one or more sources.

The BOT assistance module (70) includes a process management submodule (100) configured to provide assistance based on the ML model and the analysis result. In one embodiment, the assistance may be in a suggestion for the user to enable the process of automation for a corresponding application.

In one exemplary embodiment, the system (10) may include a notification module which may be configured to generate an issue notification to at least one authorized entity in case of at least one issue raised in the user device. In such embodiment, the issue notification may be associated to one or more issues which may be generated during the management of the RPA, during the assessment for providing assistance for the RPA or the like. In one specific embodiment, the issue notification may be a text notification, a message, a pop-up note, an alarm, or the like. FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (110) includes processor(s) (120), and a memory (130) coupled to a bus (140). As used herein, the processor(s) (120) and the memory (130) are substantially similar to the system (10) of FIG. 1. Here, the memory (180) is located in a local storage device.

The processor(s) (120), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (120).

The memory ( 130) includes a plurality of modules stored in the form of executable program which instructs the processor(s) (120) to perform method steps illustrated in FIG. 3. The memory (130) has following modules: an input module (30), a data processing module (40), a data access module (50), a model building module (60), a BOT assistance module (70), an analysis submodule (80), a server analysis submodule (90) and a process management submodule (100).

The input module (30) is configured to receive or retrieve data in a pre-defined form from one or more sources. The data processing module (40) is configured to process the input to extract one or more content associated to the data received by the input module. The data access module (50) is configured to access one or more networks. The model building module (60) is configured to build a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof. The hot assistant module (70) includes the analysis submodule (80) configured to analyse at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof. The server analysis submodule (90) is configured to analyse one or more attributes associated to the user device, to generate an analysis result. The process management submodule (100) is configured to provide assistance based on the ML model and the analysis result. FIG. 3 is a flow chart representing steps involved in a method (150) for assisting a robotic process automation (RPA) in accordance with an embodiment of the present disclosure. The method (150) includes receiving or retrieving data in a pre-defined form from one or more sources in step 160. In one embodiment, receiving or retrieving the data may include receiving or retrieving data by an input module. In one exemplary embodiment, receiving or retrieving the data may include receiving or retrieving data in at least one of a URL for a web assistance, at least one extension file of a pre-defined format for a device assistance. In such embodiment, receiving or retrieving the data may include receiving or retrieving data from one of a website, a webpage, a window recorder, or the like.

The method (150) also includes processing the input for extracting one or more content associated to the data received in step 170. In one embodiment, processing the input may include processing the input by a data processing module. In one exemplary embodiment, extracting the one or more content may include extracting at least one of one or more hyperlinks from the fetched website for the corresponding one or more inputs.

Furthermore, the method (150) includes accessing one or more networks in step 180. In one embodiment, accessing the one or more networks may include accessing the one or more networks by a data access module. In one exemplary embodiment, accessing the one or more networks may include accessing at least one extensions file such as DLL, OCX, DRV, or the like. The method ( 150) also includes building a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof in step 190. In one embodiment, building the machine learning (ML) model may include building the ML model by a model building module. The method (150) also includes determining a possibility for generating assistance for the process automation of functioning of the one or more sources in step 200. In one embodiment, determining the possibility for generating assistance may include determining the possibility by a machine learning (ML) model.

Furthermore, the method (150) includes analysing at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof in step 210. In one embodiment, analysing the one or more parameters may include analysing the one or more parameters by an analysis submodule. In one exemplary embodiment, analysing the one or more parameters may include analysing at least one of a central processing unit (CPU) of the user device, memory utilization a storage medium associated to the user device, API function request, or a combination thereof.

The method (150) further includes analysing one or more attributes associated to the user device, for generating an analysis result in step 220. In one embodiment, analysing the one or more attributes may include analysing the one or more attributes by a server analysis submodule. In one exemplary embodiment, analysing the one or more attributes may include analysing at least one of a dynamic link library, one or more extension files of a pre-defined format one or more hyperlinks, or a combination thereof based on the corresponding one or more sources.

The method (150) also includes providing assistance based on the ML model and the analysis result in step 230. In one embodiment, providing assistance may include providing assistance by a process management module. In one exemplary embodiment, providing assistance may include providing probability of building a successful architecture of the RPA for the corresponding application the user is intended to automate. In one exemplary embodiment, the method (150) may further include generating an issue notification to at least one authorized entity in case of at least one issue raised in the user device. In one embodiment, generating the issue notification may include generating the issue notification by a notification module. In one exemplary embodiment, generating the issue notification may include generating the issue notification in one of a text notification, a message, a pop-up note, an alarm, or the like.

FIG. 4a and 4b are flow charts of an exemplary embodiment representing steps involved in a method (240) for automation assistant for an RPA of FIG. 3 in accordance with an embodiment of the present disclosure. Turning to FIG. 4a, FIG. 4a is flow chart of an exemplary embodiment representing steps involved in a method (240) for automation of web recorder assistant of FIG. 3 in accordance with an embodiment of the present disclosure.

The method (240) includes retrieving a web site URL in step 250 from which one or more links associated to the website are extracted in step 260. The method (240) further include analysing for presence of any previous scripts, from the one or more extracted links in step 270. If the previous scripts are found to be present, the method (240) includes comparing the previous scripts with previous element changes in step 280. If any changes are found, the system generates a signal to re-record the website in step 290. If no changes are identified on the website, the method (240) includes returning a true value for execution of a BOT assistant in step 300.

Furthermore, upon rerecording the website in case of any changes are found, the method (240) includes extracting a HTML DOM from the website in the form of one or more links in step 310. The method (240) further includes extracting the xPATH in step 320. The method further includes calling an ML model for analysing a record feasibility in step 330. Consequently, the method (240) includes returning a percentage of record feasibility in step 340.

Subsequently, method (240) for automation of windows recorder assistant includes extracting windows application extension file in step 350. The method (240) further includes Gather all the third-party DLLs and group containers level in step 360. The method further includes Calling the ML model to decide the Windows recording feasibilities in step 370. Consequently, the method (240) includes returning a percentage of record feasibility in step 380.

Furthermore, turning to FIG. 4b, FIG. 4b is flow chart of an exemplary embodiment representing steps involved in a method (240) for automation of a BOT assistant of FIG. 3 in accordance with an embodiment of the present disclosure. The method (240) includes analysing a system or a computing device in step 390. The method (240) further includes analysing one or more networks in step 400. The method further includes analysing a worker server in step 410. Furthermore, the method (240) includes analysis of previous executions in step 420. Consequently, the method (240) includes generating a report or a notification if any issue occurs in step 430.

Various embodiments of the present disclosure enable the system to assist users to guide on Feasibility of Automation process for any application or a use case ideated by the user, which makes the system more reliable and efficient to suggest if the ideation of the user can be automated. In addition, the system is made suitable for all type of technologies and websites. Also, the system is made feasible for all types of Windows-based applications. Furthermore, the system eliminates the limitation on BOT Automation in regard to the system specific requirements. In addition, system eases the role of the developers upon providing guidance on designing of the RPA, which lessens the time of the developers, thereby making the approach faster and more reliable.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

I/WE CLAIM:

1. A system (10) to assist in robotic process automation (RPA), wherein the system comprises: one or more processors (20); an input module (30) operable by the one or more processors (20), and configured to receive or retrieve data in a pre-defined form from one or more sources; a data processing module (40) operable by the one or more processors (20), and configured to process the input to extract one or more content associated to the data received by the input module; a data access module (50) operable by the one or more processors (20), and configured to access one or more networks; a model building module (60) operable by the one or more processors (20), and configured to build a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof, wherein the machine learning (ML) model is configured to determine a possibility to generate assistance for the process automation of functioning of the one or more sources, wherein the assistance is generated by a bot model, wherein the BOT model comprises: a BOT assistance module (70) operable by the one or more processors (20), wherein the BOT assistance module (70) comprises: an analysis submodule (80) operable by the one or more processors (20), and configured to analyse at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof; a server analysis submodule (90) operable by the one or more processors (20), and configured to analyse one or more attributes associated to the user device, to generate an analysis result; and a process management submodule (100) operable by the one or more processors (20), and configured to provide assistance based on the ML model and the analysis result.

2. The system (10) as claimed in claim 1, wherein the pre-defined form comprises at least one of a URL for a web assistance, at least one extension file of a pre-defined format for a device assistance.

3. The system (10) as claimed in claim 2, wherein determine the possibility to generate assistance for the process automation of functioning of the one or more sources by the ML model corresponds to determine a feasibility for processing data for at least one of the web assistance, the device assistance, or a combination thereof.

4. The system (10) as claimed in claim 1, wherein the one or more sources comprises at least one of a webpage, a storage medium associated to the user device.

5. The system (10) as claimed in claim 1, wherein the one or more parameters comprises at least one of a central processing unit (CPU) of the user device, memory utilization a storage medium associated to the user device, API function request, or a combination thereof.

6. The system (10) as claimed in claim 1, wherein the one or more attributes comprises at least one of a dynamic link library, one or more extension files of a pre defined format one or more hyperlinks, or a combination thereof based on the corresponding one or more sources.

7. The system (10) as claimed in claim 1, comprising a notification module operable by the one or more processors (20), and configured to generate an issue notification to at least one authorized entity in case of at least one issue raised in the user device.

8. A method (150) for assisting a robotic process automation (RPA) comprising: receiving or retrieving, by an input module, data in a pre-defined form from one or more sources; (160) processing, by a data processing module, the input for extracting one or more content associated to the data received; (170) accessing, by a data access module, one or more networks; (180) building, by a model building module, a machine learning (ML) model using at least one of a machine learning technique, artificial intelligence technique, a deep learning technique, or a combination thereof; (190) determining, by a machine learning (ML) model, a possibility for generating assistance for the process automation of functioning of the one or more sources; (200) analysing, by an analysis submodule, at least one of one or more parameters of a user device, one or more networks associated to the user device, or a combination thereof; (210) analysing, by a server analysis submodule, one or more attributes associated to the user device, for generating an analysis result; and (220) providing, by a process management module, assistance based on the ML model and the analysis result. (230)

9. The method (150) as claim in claim 8, wherein determine the possibility to generate assistance for the process automation of functioning of the one or more sources by the ML model corresponds to determine a feasibility for processing data in the pre-defined form, wherein the pre-defined form comprises at least one of a URL for a web assistance, at least one extension file of a pre-defined format for a device assistance.

10. The method (150) as claim in claim 8, comprising generating, by a notification module, an issue notification to at least one authorized entity in case of at least one issue raised in the user device.