Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This Office Action is in response to the amendment filed on 12/10/2025. Claims 1-34 are pending in this application. Claims 1, 15 and 25 are independent claims. This Office Action is made Final.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3. Claims 1-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 15 and 25 are corresponding to one of four statutory categories including method, system, and method respectively under step 1. The claims 1, 15 and 25 similarly recites “a non-transitory computer-readable medium storing a computer program for a smart analyzer, the computer program configured to cause at least one processor to: monitor robotic process automation (RPA) workflow development in an RPA designer application while the user is using the RPA designer application; provide information pertaining to the RPA workflow development to a cognitive artificial intelligence (Al) layer, the cognitive Al layer comprising at least one Al model and configured to perform at least one of repairing logic mistakes in the RPA workflow, improving runtime efficiency of the RPA workflow, and improving security of the RPA workflow by analyzing the RPA workflow development information and outputting at least one of one or more suggestions for repairing the RPA workflow and one or more suggestions for improving performance of the RPA workflow; receive output from the cognitive Al layer comprising the at least one of the one or more suggestions for repairing the RPA workflow and the one or more suggestions for improving performance of the RPA workflow; and
display the one or more suggestions from the cognitive Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow to a user of the RPA designer application via an interface of the RPA designer application, automatically modify the RPA workflow using the one or more suggestions from the cognitive Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow via the RPA designer application, or both”.
The claim 15 additionally recites “wherein the cognitive AI layer comprises a generative AI model configured to facilitate understanding of an intent of the RPA workflow, how prior activities in an RPA workflow and/or a logical flow of the RPA workflow affect a given activity, one or more best courses of action to take to repair or improve the RPA workflow, or any combination thereof, and the generative AI layer comprises one or more other AI models configured to use output from the generative AI model to provide intelligent analysis functionality for the smart analyzer”.
The limitation of the claims 1, 15 and 25 of “improving security of the RPA workflow by analyzing the RPA workflow development information and” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “analyzing”. For example, a human may analyze the RPA workflow development information for improving security of the RPA workflow with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
The limitation of the claims 1, 15 and 25 of “automatically modify the RPA workflow using the one or more suggestions from the cognitive Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow via the RPA designer application, or both” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “modifying (changing)”. For example, a human may modify the RPA workflow using the one or more suggestions from the cognitive Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow via the RPA designer application, or both with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 15 and 25 recite additional elements such as “monitor robotic process automation (RPA) workflow development in an RPA designer application while the user is using the RPA designer application; provide information pertaining to the RPA workflow development to a cognitive artificial intelligence (Al) layer, the cognitive Al layer comprising at least one Al model”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 15 and 25 recite additional elements such as “receive output from the cognitive Al layer comprising the at least one of the one or more suggestions for repairing the RPA workflow and the one or more suggestions for improving performance of the RPA workflow”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 15 and 25 recite additional elements such as “outputting at least one of one or more suggestions for repairing the RPA workflow and one or more suggestions for improving performance of the RPA workflow”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data outputting under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 15 and 25 recite additional elements such as “display the one or more suggestions from the cognitive Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow to a user of the RPA designer application via an interface of the RPA designer application”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data outputting under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 2, 16 and 26 recite additional elements such as “the information pertaining to the RPA workflow development provided to the cognitive AI layer comprises activities in the RPA workflow and their parameters, relationships between the activities of the RPA workflow, most frequently used RPA workflows and activities and how users configured the most frequently used RPA workflows and activities, logs with information pertaining to how respective automations ran during production, available application programming interfaces (APIs) and/or native operating system (OS) functionality that are pertinent to RPA workflow activities, telemetry data regarding how users are using the RPA designer application, or any combination thereof”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 3, 17 and 27 recite additional elements such as “the output from the cognitive AI layer comprises one or more suggested error corrections and/or improvements for the RPA workflow, one or more suggested changes to the RPA workflow or a respective automation to improve efficiency, increase execution speed of the RPA workflow, consume less processing resources when executing the RPA workflow, and/or consume less memory when executing the RPA workflow, one or more suggestions to remove activities and/or add activities, one or more suggestions of what to change for future versions of RPA workflows in order to avoid a same problem, one or more suggestions for security improvements, one or more automatically generated blocks of code, one or more respective confidence scores, or any combination thereof”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
The limitation of the claims 4, 18 and 28 of “continue monitoring further development of the RPA workflow by the user after providing the one or more suggestions from the cognitive AI model to the user of the RPA designer application, automatically modifying the RPA workflow using the one or more suggestions from the cognitive AI model, or both” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “monitoring” and “modifying”. For example, a human may monitor development of the RPA workflow by the user after providing the one or more suggestions from the cognitive AI model to the user of the RPA designer application, automatically modifying the RPA workflow using the one or more suggestions from the cognitive AI model, or both with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claims 5, 19 and 29 recite additional elements such as “sending instructions to the RPA designer application to modify the RPA workflow”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 6 recites additional elements such as “the smart analyzer is part of an RPA designer application”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 7 recites additional elements such as “the smart analyzer is an RPA robot”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 8 and 15 recite additional elements such as “a generative AI model configured to facilitate understanding of an intent of the RPA workflow, how prior activities in an RPA workflow and/or a logical flow of the RPA workflow affect a given activity, one or more best courses of action to take to repair or improve the RPA workflow, or any combination thereof; and one or more other AI/ML models configured to use output from the generative AI model to provide intelligent analysis functionality for the smart analyzer”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 9 and 20 recite additional elements such as “the generative AI model is configured to generate code, provide sematic associations between text on a screen, determine actions to address issues in RPA workflows or runtime automations, or any combination thereof”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
The limitation of the claims 10, 21 and 31 of “suggesting breaking a workflow down further instead of using a loop and/or suggest decoupling nested loops” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “suggesting”. For example, a human may be suggested to break a workflow down further instead of using a loop and/or suggest decoupling nested loops with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
This judicial exception is not integrated into a practical application. In particular, the claim 11 recites additional elements such as “the RPA workflow is a previously created workflow”.
Examiner would like to point out that with the broad reasonable interpretation, this element
amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which
does not impose any meaningful limits on practicing the mental process. Accordingly, this additional
element does not integrate the abstract idea into a practical application because it does not impose any
meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
The limitation of the claims 12, 22 and 32 of “replacing one or more old dependencies and pieces of code that present a potential threat and/or blocking one or more websites that are malicious, unsecure, or not approved” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “suggesting”. For example, a human may be suggested to break a workflow down further instead of using a loop and/or suggest decoupling nested loops with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
The limitation of the claims 13 and 23 of “read one or more logs with information pertaining to how the automation ran during runtime, the log information comprising timestamps for execution of each activity of the RPA workflow, values of variables in the RPA workflow, or a combination thereof” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “reading”. For example, a human may read one or more logs with information pertaining to how the automation ran during runtime, the log information comprising timestamps for execution of each activity of the RPA workflow, values of variables in the RPA workflow, or a combination thereof with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
The limitation of the claims 14, 24 and 34 of “the cognitive AI layer is trained based on automations built by other users to find patterns in RPA workflow code from the automations and make suggestions for the RPA workflow based on the learned patterns” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “finding”. For example, a human may find patterns in RPA workflow code from the automations and make suggestions for the RPA workflow based on the learned patterns with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong I.
Dependent claims 2-14, 16-24 and 26-34 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-14, 16-24 and 26-34 are also rejected for incorporating the deficiency of their independent claims 1, 15 and 25 respectively.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
5. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
6. Claims 1, 2, 5, 6, 7, 13, 14, 16, 19, 23, 24, 25, 26, 29, 33 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Stocker (US PGPub 20210191843), in view of Sengupta (US PGPub 20170344889), and further in view of Cella (US PGPub 20210342836).
As per Claim 1, Stocker teaches of a non-transitory computer-readable medium storing a computer program for a smart analyzer, the computer program configured to cause at least one processor to: monitor robotic process automation (RPA) workflow development in an RPA designer application while the user is using the RPA designer application; (Claim 1, receive the workflow of the test automation associated with the RPA application; analyze, via an AI model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules; and par 22, … analyze workflow of test automation associated with a RPA application for identifying and removing potential flaws in test automation workflow (also called as “workflow of test automation”) of the RPA. In some embodiments, the computing system receives the workflow of the test automation from a design module and analyzes the received workflow for identifying and removing the flaws.)
provide information pertaining to the RPA workflow development to a [cognitive] artificial intelligence (Al) layer, the [cognitive] Al layer comprising at least one Al model and configured to (Par 22, In some embodiments, the computing system receives the workflow of the test automation from a design module and analyzes the received workflow for identifying and removing the flaws. For example, the computing system uses Artificial Intelligence (AI) model to analyze the workflow based on a set of pre-defined test automation rules. The AI model is pre-trained with standard workflows of test automation, all possible errors in the workflows, and standard robotic enterprise framework documents. In some example embodiments, standard RPA workflows or any RPA workflows are converted into test cases or imported as test cases from test automation projects for training the AI model. From the analyzed workflow of the test automation, one or more metrics are determined for generating corrective activity data.)
outputting at least one of one or more suggestions for repairing the RPA workflow and one or more suggestions for improving performance of the RPA workflow; (Par 22, Some embodiments pertain to a system (hereinafter referred to as a “computing system”) configured to analyze workflow of test automation associated with a RPA application for identifying and removing potential flaws in test automation workflow (also called as “workflow of test automation”) of the RPA. In some embodiments, the computing system receives the workflow of the test automation from a design module and analyzes the received workflow for identifying and removing the flaws. For example, the computing system uses Artificial Intelligence (AI) model to analyze the workflow based on a set of pre-defined test automation rules. Par 47, In some embodiments, the UI automation activities 330 include activities, which are related to debugging flaws or correcting flaws in the workflows.)
receive output from the [cognitive] Al layer comprising the at least one of the one or more suggestions for repairing the RPA workflow and the one or more suggestions for improving performance of the RPA workflow; and (Par 23, the AI model generates corrective activity data based on the one or more determined metrics. The corrective activity data is used for performing corrective activity for the analyzed workflow of the test automation. The corrective activity data includes suggestion-messages (e.g., assertions) or details instructing a user (e.g., a developer or a tester) on how to perform the corrective activity for the analyzed workflow. The modified test automation file is configured to have improved execution time and storage requirements in comparison with the received workflow of the test automation. Further, the improvements in execution time and storage requirements reduce computational overhead on the computing system. In this way, the workflow of the test automation is analyzed to debug the flaws prior to deployment, using the computing system and the computer-implemented method disclosed herein. Par 47, In some embodiments, the UI automation activities 330 include activities, which are related to debugging flaws or correcting flaws in the workflows. Par 73, In some embodiments, the corrective module provides feedback to the user regarding better possibility of the workflow of the test automation.)
display the one or more suggestions from the [cognitive] Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow to a user of the RPA designer application via an interface of the RPA designer application, (Par 73, In some embodiments, the corrective module provides feedback to the user regarding better possibility of the workflow of the test automation. According to some example embodiments, the feedback includes a modified workflow of the test automation or a suggestion message to modify the analyzed workflow of the test automation. The suggestion message comprises assertions or any other information for modifying the workflow of the test automation. Par 74, The warning message or the error message includes a summary comprising details or information related to flaws of the analyzed workflow of the test automation.)
automatically modify the RPA workflow using the one or more suggestions from the [cognitive] Al layer for repairing the at least one of the RPA workflow and the one or more suggestions for improving performance of the RPA workflow via the RPA designer application, or both. (Par 75, Further, the AI model 624 modifies the workflow of the test automation to remove the one or more flaws. Par 91, At step 940, method 900 includes, generating, via the AI model, corrective activity data based on the one or more metrics. In some embodiments, the corrective activity data is used for performing corrective activity for the workflow of the test automation. The corrective activity comprises predicting, via the AI model one or more flaws in the workflow of the test automation based on the determined one or more metrics and modifying, via the AI model, the workflow to remove the one or more flaws.)
Stocker does not specifically teach, however Sengupta teaches of cognitive AI layer (Par 44, The machine cognition engines of the AI layer 516, for example, machine learning agents, may be trained to identify attributes of the external entity based on the characteristic information. The AI layer 516 may apply the learned attributes of the external entity to formulate the response to the captured message structure or to use in responding to future messages captured by the orchestration layer 512. Par 34, The AI layer 516 may include multiple machine cognition engines, for example, one or more of: a data platform analytics agent, a sentiment/emotion analyzer, a natural language understanding processing agent, a natural language question and answering agent, a dynamic logic agent, a user behavior analysis agent, a machine learning agent, a conversation service and a natural language generation agent. In some systems, the AI layer 516 may be implemented using systems such as MICROSOFT® AZURE®, machine learning (ML) technologies, IBM® WATSON ANALYTICS® and other available or proprietary AI technologies. However, the application is not limited to any specific AI technology and any suitable AI software may be utilized. Each of the AI technologies may have a machine cognition engine or module to process a user's input and formulate a response, and may be optimized with custom code, for example, to improve natural language processing and intent classifications.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add cognitive AI layer and cognitive AI model, as conceptually seen from the teaching of Sengupta, into that of Soler because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
Cella teaches of improving security of the RPA workflow by analyzing the RPA workflow development information and (Par 161, Referring to FIG. 36, a set of opportunity miners 3446 may be provided as part of the adaptive intelligence layer 3304, which may be configured to seek and recommend opportunities to improve one or more of the elements of the platform 3300, such as via addition of artificial intelligence 3448, automation (including robotic process automation 3446), or the like to one or more of the systems, sub-systems, components, applications or the like of the platform 100 or with which the platform 100 interacts. In embodiments, analytics 3419 may be used to identify which environments or activities would most benefit from automation for purposes of labor saving, profit optimization, yield optimization, increased up time, increased throughput, increased transaction flow, improved security, improved reliability, or other factors.)
As per Claim 2, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the information pertaining to the RPA workflow development provided to the cognitive AI layer comprises activities in the RPA workflow and their parameters, relationships between the activities of the RPA workflow, most frequently used RPA workflows and activities and how users configured the most frequently used RPA workflows and activities, logs with information pertaining to how respective automations ran during production, available application programming interfaces (APIs) and/or native operating system (OS) functionality that are pertinent to RPA workflow activities, telemetry data regarding how users are using the RPA designer application, or any combination thereof. (Par 65 and Claim 1, In some embodiments, the AI model 624 corresponds to a pre-trained AI model that analyzes the received workflow of the test automation. In some embodiments, the AI model 624 is pre-trained based on training data. In some example embodiments, the training data is stored in the training sub-module 622. The training data comprises at least one of standard test automation workflows, errors in test automation workflows, and standard framework documents. The training data also includes sequences within test automation workflows, and all possible flaws (also solutions to tackle the flaws) associated with the test automation workflows. In some example embodiments, the training data is based on previous functional testing of web and mobile applications, visual testing of user interfaces, and UI element location and auto-correcting element selectors. In some embodiments, the flaws include human errors such as a wrong data input for the test automation or missing data input in the test automation. In another example, the AI model 624 predicts flaws associated with the test automation workflow using knowledge of the training data and output the analyzed workflow of the test automation (also called as “analyzed test automation workflow”). The analyzed test automation workflow comprises the workflow of the test automation and the respective predicted flaw information. determining one or more metrics associated with the analyzed workflow of the test automation; and generating, via the AI model, corrective activity data based on the determined one or more metrics. Par 41-42, The REST API is consumed by both the web application 232 and the agent 214. The agent 214 is the supervisor of the one or more robots on the client computer in this embodiment. The REST API in this embodiment covers configuration, logging, monitoring, and queuing functionality.)
As per Claim 5, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the providing the one or more suggestions from the cognitive AI model to the user of the RPA designer application, automatically modifying the RPA workflow using the one or more suggestions from the cognitive AI model, or both, comprises sending instructions to the RPA designer application to modify the RPA workflow. (Claim 4, performing the corrective activity further comprises: predicting, via the AI model, one or more flaws in the workflow of the test automation based on the determined one or more metrics; and modifying, via the AI model, the workflow of the test automation to remove the one or more flaws. Par 23, The corrective activity data includes suggestion-messages (e.g., assertions) or details instructing a user (e.g., a developer or a tester) on how to perform the corrective activity for the analyzed workflow. The modified test automation file is configured to have improved execution time and storage requirements in comparison with the received workflow of the test automation. Further, the improvements in execution time and storage requirements reduce computational overhead on the computing system. In this way, the workflow of the test automation is analyzed to debug the flaws prior to deployment, using the computing system and the computer-implemented method disclosed herein.)
As per Claim 6, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the smart analyzer is part of an RPA designer application. (Par 61, In some embodiments, the workflow analyzer module 600 is similar to, or the same as, workflow analyzer module 534 illustrated in FIG. 5. Also, in some embodiments, the workflow analyzer module 600 is embodied within the designer 110. Par 24, The RPA system 100 includes a designer 110 that allows a developer or a user to design and implement workflows. The designer 110 provides a solution for application integration, as well as automating third-party applications, administrative Information Technology (IT) tasks, and business IT processes. The designer 110 facilitates development of an automation project, which is a graphical representation of a business process. Simply put, the designer 110 facilitates the development and deployment of workflows and robots.)
As per Claim 7, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the smart analyzer is an RPA robot. (Par 24, The designer 110 facilitates development of an automation project, which is a graphical representation of a business process. Simply put, the designer 110 facilitates the development and deployment of workflows and robots. Par 27, Once a workflow is developed in the designer 110, execution of business processes is orchestrated by a conductor 120, which orchestrates one or more robots 130 that execute the workflows developed in the designer 110.)
As per Claim 13, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the RPA workflow pertains to an existing automation and the computer program is further configured to cause the at least one processor to: read one or more logs with information pertaining to how the automation ran during runtime, the log information comprising timestamps for execution of each activity of the RPA workflow, values of variables in the RPA workflow, or a combination thereof. (Par 37, in some embodiments, the designer 216 is not running on the robot application 210. The executors 212 are running processes. Several business projects (i.e. the executors 212) run simultaneously, as shown in FIG. 2. The agent 214 (e.g., the Windows® service) is the single point of contact for all the executors 212 in this embodiment. All messages in this embodiment is logged into a conductor 230, which processes them further via a database server 240, an indexer server 250, or both. As discussed above with respect to FIG. 1, the executors 212 are robot components.)
As per Claim 14, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the cognitive AI layer is trained based on automations built by other users to find patterns in RPA workflow code from the automations and make suggestions for the RPA workflow based on the learned patterns. (Par 65-66, In some embodiments, the AI model 624 includes an ML model such as a Recurrent Neural Network model (for instance, a Long Short-Term Memory (LS™) model) and the like. Also, in certain embodiments, the ML model is self-trained. For instance, the ML model is trained to learn one or more patterns in the workflow of test automation. The one or more patterns correspond to recurring tests in previous workflows of the test automation. The ML model provides the one or more patterns to the AI model 624 for the analysis of a workflow of test automation associated with an RPA application. In some example embodiments, the ML model is pre-built ML model stored in the memory 530. In some alternate embodiments, the ML model is customized by the user or accessed from an open platform (e.g., open source community), a third-party organization, or the like. For example, when a flaw occurs in the workflow of the test automation at run-time, the ML model learns the flaw, and then learns a way to tackle the flaw.)
Re Claim 16, it is the system claim, having similar limitations of claim 2. Thus, claim 16 is also rejected under the similar rationale as cited in the rejection of claim 2.
Re Claim 19, it is the system claim, having similar limitations of claim 5. Thus, claim 19 is also rejected under the similar rationale as cited in the rejection of claim 5.
Re Claim 23, it is the system claim, having similar limitations of claim 13. Thus, claim 23 is also rejected under the similar rationale as cited in the rejection of claim 13.
Re Claim 24, it is the system claim, having similar limitations of claim 14. Thus, claim 24 is also rejected under the similar rationale as cited in the rejection of claim 14.
Re Claim 25, it is the method claim, having similar limitations of claim 1. Thus, claim 25 is also rejected under the similar rationale as cited in the rejection of claim 1.
Re Claim 26, it is the method claim, having similar limitations of claim 2. Thus, claim 26 is also rejected under the similar rationale as cited in the rejection of claim 2.
Re Claim 29, it is the method claim, having similar limitations of claim 5. Thus, claim 29 is also rejected under the similar rationale as cited in the rejection of claim 5.
Re Claim 33, it is the method claim, having similar limitations of claim 13. Thus, claim 33 is also rejected under the similar rationale as cited in the rejection of claim 13.
Re Claim 34, it is the method claim, having similar limitations of claim 14. Thus, claim 34 is also rejected under the similar rationale as cited in the rejection of claim 14.
7. Claims 3, 4, 8, 9, 15, 17, 18, 20, 27, 28 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Stocker (US PGPub 20210191843), in view of Sengupta (US PGPub 20170344889), in view of Cella (US PGPub 20210342836), and further in view of Iyer (US PGPub 20220075605).
As per Claim 3, Stocker teaches of the non-transitory computer-readable medium of claim 1, wherein the output from the cognitive AI layer comprises one or more suggested error corrections and/or improvements for the RPA workflow, one or more suggested changes to the RPA workflow or a respective automation to improve efficiency, increase execution speed of the RPA workflow, consume less processing resources when executing the RPA workflow, and/or consume less memory when executing the RPA workflow, one or more suggestions to remove activities and/or add activities, one or more suggestions of what to change for future versions of RPA workflows in order to avoid a same problem, one or more suggestions for security improvements, one or more automatically generated blocks of code, (Par 23, the AI model generates corrective activity data based on the one or more determined metrics. The corrective activity data is used for performing corrective activity for the analyzed workflow of the test automation. The corrective activity data includes suggestion-messages (e.g., assertions) or details instructing a user (e.g., a developer or a tester) on how to perform the corrective activity for the analyzed workflow. The modified test automation file is configured to have improved execution time and storage requirements in comparison with the received workflow of the test automation. Further, the improvements in execution time and storage requirements reduce computational overhead on the computing system. In this way, the workflow of the test automation is analyzed to debug the flaws prior to deployment, using the computing system and the computer-implemented method disclosed herein.)
Neither Stocker nor Sengupta specifically teaches, however Iyer teaches of one or more respective confidence scores, or any combination thereof. (Par 84, Neural network 600 is trained to assign a confidence score to graphical elements believed to have been found in the image. In order to reduce matches with unacceptably low likelihoods, only those results with a confidence score that meets or exceeds a confidence threshold may be provided in some embodiments. For instance, if the confidence threshold is 80%, outputs with confidence scores exceeding this amount may be used and the rest may be ignored. In this case, the output layer indicates that two text fields, a text label, and a submit button were found. Neural network 600 may provide the locations, dimensions, images, and/or confidence scores for these elements without deviating from the scope of the invention, which can be used subsequently by an RPA robot or another process that uses this output for a given purpose. Par 113, In some embodiments, the confidence score(s) of the suggestion(s) are also passed to the RPA designer application to make the determination of whether to suggest the next sequence(s).)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add one or more respective confidence scores, or any combination thereof, as conceptually seen from the teaching of Iyer, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
As per Claim 4, neither Stocker nor Sengupta specifically teaches, however Iyer teaches of the non-transitory computer-readable medium of claim 1, wherein the computer program is further configured to cause the at least one processor to: continue monitoring further development of the RPA workflow by the user after providing the one or more suggestions from the cognitive AI model to the user of the RPA designer application, automatically modifying the RPA workflow using the one or more suggestions from the cognitive AI model, or both. (Par 131, Once deployed or made available, the RPA designer application monitors user activities during RPA workflow development and provides these to at least one of the one or more ML models at 1520 (e.g., as XAML, JSON, etc.). In some embodiments, multiple ML models may be called and executed in series if a previously executed ML model does not detect a next sequence of activities. If the ML model(s) do not detect one or more potential next sequences of activities meeting or exceeding a suggestion confidence threshold at 1525 (e.g., as determined on the server side or by the RPA designer application itself based on the confidence score(s) for the suggestion(s)), the process returns to step 1520. However, if one or more potential next sequences of activities meeting or exceeding the suggestion confidence threshold are detected at 1525, the sequence(s) are suggested to the user at 1530. Par 132, If the user accepts the suggestion or if the sequence exceeds a second, higher autocompletion threshold that does not require user selection at 1535, the suggested sequence of activities is automatically added to the RPA workflow at 1540. However, if the user rejects the suggestion at 1535, the RPA designer application waits for the user to complete the RPA workflow and then causes the completed RPA workflow to be stored at 1545 (e.g., by sending the completed RPA workflow to a cloud RPA system). The completed RPA workflow and potentially some or many other completed RPA workflows including negative examples (and potentially positive examples) are then used to retrain the ML model(s) at 1550, and the retrained ML model(s) are deployed or made available at 1555.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add continue monitoring further development of the RPA workflow by the user after providing the one or more suggestions from the cognitive AI model to the user of the RPA designer application, automatically modifying the RPA workflow using the one or more suggestions from the cognitive AI model, or both, as conceptually seen from the teaching of Iyer, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
As per Claim 8, neither Stocker nor Sengupta specifically teaches, however Iyer teaches of the non-transitory computer-readable medium of claim 1, wherein the cognitive AI layer comprises: a generative AI model configured to facilitate understanding of an intent of the RPA workflow, how prior activities in an RPA workflow and/or a logical flow of the RPA workflow affect a given activity, one or more best courses of action to take to repair or improve the RPA workflow, or any combination thereof; and (Par 5, a system includes a developer computing system executing an RPA designer application and a model serving server hosting one or more AI/ML models trained to analyze sequences of activities in an RPA workflow as input and provide suggestions of next sequences of activities and respective confidence scores as an output. The RPA designer application is configured to capture a sequence of the activities in an RPA workflow, send the captured sequence of activities to the model serving server, receive one or more suggested next sequences of activities from the one or more trained AI/ML models via the model serving server, and display the one or more suggested next sequences of activities to the developer. Claim 1, a developer computing system executing a robotic process automation (RPA) designer application; and a model serving server hosting one or more artificial intelligence (AI) / machine learning (ML) models trained to analyze sequences of activities in an RPA workflow as input and provide suggestions of next sequences of activities and respective confidence scores as an output, wherein the RPA designer application is configured to: capture a sequence of the activities in an RPA workflow, send the captured sequence of activities to the model serving server, receive one or more suggested next sequences of activities from the one or more trained AI/ML models via the model serving server, and display the one or more suggested next sequences of activities to the developer.)
one or more other AI/ML models configured to use output from the generative AI model to provide intelligent analysis functionality for the smart analyzer. (Par 7, The computer program instructions are configured to cause the at least one processor to receive a captured sequence of activities in an RPA workflow under development from an RPA designer application of a developer computing system via a communication network, provide the captured sequence of activities as input to one or more trained AI/ML models, receive one or more suggested next sequences of activities and respective confidence scores as an output from the one or more trained AI/ML models, and send the one or more suggested next sequences of activities to the designer computing system.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add wherein the cognitive AI layer comprises a generative AI model configured to facilitate understanding of an intent of the RPA workflow, how prior activities in an RPA workflow and/or a logical flow of the RPA workflow affect a given activity, one or more best courses of action to take to repair or improve the RPA workflow, or any combination thereof, and the generative AI layer comprises one or more other AI/ML models configured to use output from the generative AI model to provide intelligent analysis functionality for the smart analyzer, as conceptually seen from the teaching of Iyer, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
As per Claim 9, neither Stocker nor Sengupta specifically teaches, however Iyer teaches of the non-transitory computer-readable medium of claim 8, wherein the generative AI model is configured to generate code, provide sematic associations between text on a screen, determine actions to address issues in RPA workflows or runtime automations, or any combination thereof. (Par 104, If the user tends to include this sequence of activities repeatedly following adding a certain activity, the ML model(s) may learn to predict that the user will likely perform this sequence of actions based on a certain context and beginning activity (e.g., when the user adds an activity that launches a web browser, the user then adds activities to visit the website and copy-and-paste the table into the Excel® spreadsheet). Par 108, FIG. 7 is a flowchart illustrating a process 700 for training AI/ML model(s) to provide suggestions to automatically add to (i.e., supplement) and/or complete RPA workflows, according to an embodiment of the present invention. The process begins with providing labeled screens (e.g., with graphical elements and text identified), RPA workflows in XAML or any other suitable format for processing, words and phrases, a “thesaurus” of semantic associations between words and phrases such that similar words and phrases for a given word or phrase can be identified, etc. at 710. The AI/ML model is then trained over multiple epochs at 720 and results are reviewed at 730. Par 102, This may collectively allow the AI/ML models to enable semantic automation, for instance. CV and OCR may be performed using convolutional and/or recurrent neural networks (RNNs), for example.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add the generative AI model is configured to generate code, provide sematic associations between text on a screen, determine actions to address issues in RPA workflows or runtime automations, or any combination thereof, as conceptually seen from the teaching of Iyer, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
As per Claim 15, Stocker teaches of one or more computing systems, comprising: memory storing computer program instructions; and at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to: monitor robotic process automation (RPA) workflow development in an RPA designer application; (Claim 1, receive the workflow of the test automation associated with the RPA application; analyze, via an AI model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules; and par 22, … analyze workflow of test automation associated with a RPA application for identifying and removing potential flaws in test automation workflow (also called as “workflow of test automation”) of the RPA. In some embodiments, the computing system receives the workflow of the test automation from a design module and analyzes the received workflow for identifying and removing the flaws.)
provide information pertaining to the RPA workflow development to a [cognitive] artificial intelligence (AI) layer; (Par 22, In some embodiments, the computing system receives the workflow of the test automation from a design module and analyzes the received workflow for identifying and removing the flaws. For example, the computing system uses Artificial Intelligence (AI) model to analyze the workflow based on a set of pre-defined test automation rules. The AI model is pre-trained with standard workflows of test automation, all possible errors in the workflows, and standard robotic enterprise framework documents. In some example embodiments, standard RPA workflows or any RPA workflows are converted into test cases or imported as test cases from test automation projects for training the AI model. From the analyzed workflow of the test automation, one or more metrics are determined for generating corrective activity data.)
receive output from the [cognitive] AI layer comprising one or more suggestions for repairing the RPA workflow, improving performance of the RPA workflow, or both; and (Par 23, the AI model generates corrective activity data based on the one or more determined metrics. The corrective activity data is used for performing corrective activity for the analyzed workflow of the test automation. The corrective activity data includes suggestion-messages (e.g., assertions) or details instructing a user (e.g., a developer or a tester) on how to perform the corrective activity for the analyzed workflow. The modified test automation file is configured to have improved execution time and storage requirements in comparison with the received workflow of the test automation. Further, the improvements in execution time and storage requirements reduce computational overhead on the computing system. In this way, the workflow of the test automation is analyzed to debug the flaws prior to deployment, using the computing system and the computer-implemented method disclosed herein.)
provide the one or more suggestions from the [cognitive] AI model to a user of the RPA designer application, automatically modifying the RPA workflow using the one or more suggestions from the [cognitive] AI model, or both, (Par 91, At step 940, method 900 includes, generating, via the AI model, corrective activity data based on the one or more metrics. In some embodiments, the corrective activity data is used for performing corrective activity for the workflow of the test automation. The corrective activity comprises predicting, via the AI model one or more flaws in the workflow of the test automation based on the determined one or more metrics and modifying, via the AI model, the workflow to remove the one or more flaws.)
Stocker does not specifically teach, however Sengupta teaches of cognitive AI layer and cognitive AI model (Par 44, The machine cognition engines of the AI layer 516, for example, machine learning agents, may be trained to identify attributes of the external entity based on the characteristic information. The AI layer 516 may apply the learned attributes of the external entity to formulate the response to the captured message structure or to use in responding to future messages captured by the orchestration layer 512. Par 34, The AI layer 516 may include multiple machine cognition engines, for example, one or more of: a data platform analytics agent, a sentiment/emotion analyzer, a natural language understanding processing agent, a natural language question and answering agent, a dynamic logic agent, a user behavior analysis agent, a machine learning agent, a conversation service and a natural language generation agent. In some systems, the AI layer 516 may be implemented using systems such as MICROSOFT® AZURE®, machine learning (ML) technologies, IBM® WATSON ANALYTICS® and other available or proprietary AI technologies. However, the application is not limited to any specific AI technology and any suitable AI software may be utilized. Each of the AI technologies may have a machine cognition engine or module to process a user's input and formulate a response, and may be optimized with custom code, for example, to improve natural language processing and intent classifications.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add cognitive AI layer and cognitive AI model, as conceptually seen from the teaching of Sengupta, into that of Stocker because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
Neither Stocker nor Sengupta specifically teaches, however Iyer teaches of wherein the cognitive AI layer comprises a generative AI model configured to facilitate understanding of an intent of the RPA workflow, how prior activities in an RPA workflow and/or a logical flow of the RPA workflow affect a given activity, one or more best courses of action to take to repair or improve the RPA workflow, or any combination thereof, and (Par 5, a system includes a developer computing system executing an RPA designer application and a model serving server hosting one or more AI/ML models trained to analyze sequences of activities in an RPA workflow as input and provide suggestions of next sequences of activities and respective confidence scores as an output. The RPA designer application is configured to capture a sequence of the activities in an RPA workflow, send the captured sequence of activities to the model serving server, receive one or more suggested next sequences of activities from the one or more trained AI/ML models via the model serving server, and display the one or more suggested next sequences of activities to the developer. Claim 1, a developer computing system executing a robotic process automation (RPA) designer application; and a model serving server hosting one or more artificial intelligence (AI) / machine learning (ML) models trained to analyze sequences of activities in an RPA workflow as input and provide suggestions of next sequences of activities and respective confidence scores as an output, wherein the RPA designer application is configured to: capture a sequence of the activities in an RPA workflow, send the captured sequence of activities to the model serving server, receive one or more suggested next sequences of activities from the one or more trained AI/ML models via the model serving server, and display the one or more suggested next sequences of activities to the developer.)
the generative AI layer comprises one or more other AI/ML models configured to use output from the generative AI model to provide intelligent analysis functionality for the smart analyzer. (Par 7, The computer program instructions are configured to cause the at least one processor to receive a captured sequence of activities in an RPA workflow under development from an RPA designer application of a developer computing system via a communication network, provide the captured sequence of activities as input to one or more trained AI/ML models, receive one or more suggested next sequences of activities and respective confidence scores as an output from the one or more trained AI/ML models, and send the one or more suggested next sequences of activities to the designer computing system.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add wherein the cognitive AI layer comprises a generative AI model configured to facilitate understanding of an intent of the RPA workflow, how prior activities in an RPA workflow and/or a logical flow of the RPA workflow affect a given activity, one or more best courses of action to take to repair or improve the RPA workflow, or any combination thereof, and the generative AI layer comprises one or more other AI/ML models configured to use output from the generative AI model to provide intelligent analysis functionality for the smart analyzer, as conceptually seen from the teaching of Iyer, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data.
Re Claim 17, it is the system claim, having similar limitations of claim 3. Thus, claim 17 is also rejected under the similar rationale as cited in the rejection of claim 3.
Re Claim 18, it is the system claim, having similar limitations of claim 4. Thus, claim 18 is also rejected under the similar rationale as cited in the rejection of claim 4.
Re Claim 20, it is the system claim, having similar limitations of claim 9. Thus, claim 20 is also rejected under the similar rationale as cited in the rejection of claim 9.
Re Claim 27, it is the method claim, having similar limitations of claim 3. Thus, claim 27 is also rejected under the similar rationale as cited in the rejection of claim 3.
Re Claim 28, it is the method claim, having similar limitations of claim 4. Thus, claim 28 is also rejected under the similar rationale as cited in the rejection of claim 4.
Re Claim 30, it is the method claim, having similar limitations of claims 8 and 9. Thus, claim 30 is also rejected under the similar rationale as cited in the rejection of claims 8 and 9.
8. Claims 10, 21 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Stocker (US PGPub 20210191843), in view of Sengupta (US PGPub 20170344889), in view of Cella (US PGPub 20210342836), and further in view of Volkov (US PGPub 20170076246).
As per Claim 10, neither Stocker nor Sengupta specifically teaches, however Volkov teaches of the non-transitory computer-readable medium of claim 1, wherein the one or more suggestions provided by the cognitive AI layer comprise suggesting breaking a workflow down further instead of using a loop and/or suggest decoupling nested loops. (Claim 4, wherein the recommendation comprises identifying at least one task in the workflow to be split into one or more sub-tasks. Par 51, As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. For example, a financial document may list several transactions in a single document. However, the original workflow may have been configured to handle only a single transaction per document. The workflow optimization may suggest splitting up this single task into various subtasks. Thus, the workflow may be modified to accommodate unforeseen changes in workflows.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add suggesting breaking a workflow down further instead of using a loop and/or suggest decoupling nested loops, as conceptually seen from the teaching of Iyer, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data to minimize human error for robotic automation.
Re Claim 21, it is the system claim, having similar limitations of claim 10. Thus, claim 21 is also rejected under the similar rationale as cited in the rejection of claim 10.
Re Claim 31, it is the method claim, having similar limitations of claim 10. Thus, claim 31 is also rejected under the similar rationale as cited in the rejection of claim 10.
9. Claims 11, 12, 22 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Stocker (US PGPub 20210191843), in view of Sengupta (US PGPub 20170344889), in view of Cella (US PGPub 20210342836), and further in view of Cella (US PGPub 20220187847).
As per Claim 11, neither Stocker nor Sengupta specifically teaches, however Cella teaches of the non-transitory computer-readable medium of claim 1, wherein the RPA workflow is a previously created workflow. (Par 367, The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people. Par 234, Other useful information that a workflow definition system may utilize from a robot configuration library 12314 may include template, preconfigured or default workflows, such as workflows developed for a previous execution of the job. Par 1510, In embodiments, machine learning models may perform anomaly detection or outlier detection. For example, machine learning models can identify input data that does not conform to an expected pattern or other characteristic (e.g., as previously observed from previous input data). As examples, the anomaly detection can be used for fraud detection or system failure detection.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add the RPA workflow is a previously created workflow, as conceptually seen from the teaching of Cella, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data to minimize human error for robotic automation.
As per Claim 12, neither Stocker nor Sengupta specifically teaches, however Cella teaches of the non-transitory computer-readable medium of claim 1, wherein the automatically modifying the RPA workflow comprises replacing one or more old dependencies and pieces of code that present a potential threat and/or blocking one or more websites that are malicious, unsecure, or not approved. (Par 2236, A job workflow definition system may examine task to task dependency (e.g., performing a second task is dependent on completing a first task) to identify potential workflow independence and dependence for among other things configuring a job execution plan that may include parallelized use of fleet resources, such as teams and the like. Par 1373, In embodiments, a feedback of the at least one expert may be used to modify the set of inputs to the expert agent and/or used to identify and characterize at least one error by the expert agent. Par 1405, In embodiments, a feedback of the expert may be solicited to train the artificial intelligence system to replicate the expertise of the expert in the role, used to modify the set of inputs to the artificial intelligence system, and or used to identify and characterize at least one error by the artificial intelligence system. Par 1625, For example, the network enhancement chip 9200 may be configured to reconfigure the network or a segment thereof (e.g., by performing traffic shaping or otherwise modifying data flows or other data received as inputs 9292) and/or to instruct other devices to reconfigure the network or a segment thereof.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add replacing one or more old dependencies and pieces of code that present a potential threat and/or blocking one or more websites that are malicious, unsecure, or not approved, as conceptually seen from the teaching of Cella, into that of Stocker and Sengupta because this modification can help increase contextual understanding, handling of complex and unstructured data while improving adaptability and dynamic learning from feedback data to minimize human error for robotic automation.
Re Claim 22, it is the system claim, having similar limitations of claim 12. Thus, claim 22 is also rejected under the similar rationale as cited in the rejection of claim 12.
Re Claim 32, it is the method claim, having similar limitations of claim 12. Thus, claim 32 is also rejected under the similar rationale as cited in the rejection of claim 12.
Response to Arguments
Applicant's arguments with respect to the claims 1, 15 and 25 and their dependent claims have been fully considered but they are not persuasive.
Regarding the first argument of the remark on pages 18-20 that the mental-process limitations cannot be practically performed by human mind and limitations that encompassing AI in a way that cannot be practically performed in a human mind, the examiner would like to point out that a claim that requires a computer such as AI may still recite a mental process and the concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept can be still considered as a mental process.
C. A Claim That Requires a Computer May Still Recite a Mental Process
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Regarding the argument of the remark on pages 23-25 that Sengupta does not appear to teach the RPA workflow development and not performing workflow monitoring during development, the examiner would like to point out that Stocker teaches in par 24 “The RPA system 100 includes a designer 110 that allows a developer or a user to design and implement workflows. The designer 110 provides a solution for application integration, as well as automating third-party applications, administrative Information Technology (IT) tasks, and business IT processes. The designer 110 facilitates development of an automation project, which is a graphical representation of a business process. Simply put, the designer 110 facilitates the development and deployment of workflows and robots.” Par 46, FIG. 3 is an architectural diagram illustrating a relationship 300 between a designer 310, user-defined activities 320, User Interface (UI) automation activities 330, and drivers 340, according to an embodiment of the present invention. Per the above, a developer uses the designer 310 to develop workflows that are executed by robots. According to some embodiments, the designer 310 is a design module of an integrated development environment (IDE), which allows the user or the developer to perform one or more functionalities related to the workflows. The functionalities include editing, coding, debugging, browsing, saving, modifying and the like for the workflows. In some example embodiments, the designer 310 facilitates in analyzing the workflows. Thus, the examiner believes that Stocker teaches of the amendment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JAE U JEON/Primary Examiner, Art Unit 2193