DETAILED ACTION
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 .
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 6-9, 11, 14-18, 21, 22 are rejected under 35 U.S.C. 102(a)(1) ss being anticipated by Marin et al (US Pub 2024/0142917).
Regarding claim 1, Marin et al discloses a non-transitory computer-readable medium storing a computer program (sec 0088, 0092), the computer program configured to cause at least one processor to:
provide a natural language description of a process to a cognitive artificial intelligence (AI) layer [figs. 3, 9; sec 0078, 0127, 0128-0136; Listener 330 communicates to and from with database 355; database 355 is part of layer 336 and layer 366 comprises an AI layer 360; listener 330 provides a natural language description of a processor also known as a process definition document (PDD) or recorder of user actions, a task capture, a process mining, etc to the layer 336 having the AI layer 360], the cognitive Al layer configured to analyze and automatically generate annotations describing activities for a robotic process automation (RPA) workflow (AI generating and analyzing or editing annotations for an RPA workflow for the robot; sec 0035, 0036, 0038) or an RPA workflow pertaining to a process described in the PDD (sec 0035, 0037, 0038);
process the natural language description (figs. 3, 9; restructuring, reconfiguring, etc; sec 0078, 0127, 0128-0136), by the cognitive AI layer; and
generate an annotated (RPA) workflow (sec 0127; builder 903 generates a workflows for the RPA 905 using ML from natural language described task) by the cognitive AI layer,
wherein the annotations comprise description of changes between a current version of the RPA workflow and one or more previous versions of the RPA workflow (RPA workflows are updated; figs. 8&9; sec 0116, 0121-0129; the RPA 905 can be a complex flow with multiple steps being added to one or more written automation tasks).
Regarding claim 3, Marin et al discloses the non-transitory computer-readable medium of claim 1, wherein the natural language description is a process definition document (PDD) [figs. 3, 9; sec 0078, 0127, 0128-0136].
Regarding claim 6, Marin et al discloses the non-transitory computer-readable medium of claim 1, wherein the computer program is further configured to cause the at least one processor to:
generate a process definition document (PDD) for the RPA workflow when the natural language description of the process does not include a PDD [figs. 3, 9; sec 0078, 0127, 0128-0136.].
Regarding claim 7, Marin et al discloses the non-transitory computer-readable medium of claim 1, wherein the annotations provide a description of an overall process of the RPA workflow, describe what each activity in the RPA workflow is doing, or both [figs. 3, 9; sec 0078, 0127, 0128-0136.].
Regarding claim 8, Marin et al discloses the non-transitory computer-readable medium of claim 1, wherein the cognitive AI layer comprises:
a generative AI model configured to generate annotated RPA workflows, generate process definition documents (PDDs) and/or other documents, logically group classes of RPA workflow activities, or any combination thereof [figs. 3, 9; sec 0078, 0127, 0128-0136].
Regarding claim 9, Marin et al discloses One or more computing systems (sec 0088, 0092), comprising:
memory storing computer program instructions (sec 0088, 0092); and
at least one processor configured to execute the computer program instructions (sec 0088, 0092), wherein the computer program instructions are configured to cause the at least one processor to:
provide a natural language description of a process to a cognitive artificial intelligence (AI) layer [figs. 3, 9; sec 0078, 0127, 0128-0136; Listener 330 communicates to and from with database 355; database 355 is part of layer 336 and layer 366 comprises an AI layer 360; listener 330 provides a natural language description of a processor also known as a process definition document (PDD) or recorder of user actions, a task capture, a process mining, etc to the layer 336 having the AI layer 360], the cognitive Al layer configured to analyze and automatically generate annotations describing activities for a robotic process automation (RPA) workflow (AI generating and analyzing or editing annotations for an RPA workflow for the robot; sec 0035, 0036, 0038) or an RPA workflow pertaining to a process described in the PDD (sec 0035, 0037, 0038);
process the natural language description (figs. 3, 9; restructuring, reconfiguring, etc; sec 0078, 0127, 0128-0136), by the cognitive AI layer; and
generate an annotated (RPA) workflow (sec 0127; builder 903 generates a workflows for the RPA 905 using ML from natural language described task), by the cognitive AI layer,
wherein the annotations comprise description of changes between a current version of the RPA workflow and one or more previous versions of the RPA workflow (RPA workflows are updated; figs. 8&9; sec 0116, 0121-0129; the RPA 905 can be a complex flow with multiple steps being added to one or more written automation tasks).
Regarding claim 11, Marin et al discloses the one or more computing systems of claim 9, wherein the natural language description is a process definition document (PDD) [figs. 3, 9; sec 0078, 0127, 0128-0136.].
Regarding claim 14, Marin et al discloses the one or more computing systems of claim 9, wherein the computer program instructions are further configured to cause the at least one processor to:
generate a process definition document (PDD) for the RPA workflow when the natural language description of the process does not include a PDD [figs. 3, 9; sec 0078, 0127, 0128-0136].
Regarding claim 15, Marin et al discloses the one or more computing systems of claim 9, wherein the annotations provide a description of an overall process of the RPA workflow, describe what each activity in the RPA workflow is doing, or both [figs. 3, 9; sec 0078, 0127, 0128-0136.].
Regarding claim 16, Marin et al discloses the one or more computing systems of claim 9, wherein the cognitive Al layer comprises:
a generative AI model configured to generate annotated RPA workflows, generate process definition documents (PDDs) and/or other documents, logically group classes of RPA workflow activities, or any combination thereof [figs. 3, 9; sec 0078, 0127, 0128-0136].
Regarding claim 17, Marin et al discloses a computer-implemented method (sec 0088, 0092), comprising:
providing a natural language description of a process to a cognitive artificial intelligence (AI) layer on one or more computing systems [figs. 3, 9; sec 0078, 0127, 0128-0136; Listener 330 communicates to and from with database 355; database 355 is part of layer 336 and layer 366 comprises an AI layer 360; listener 330 provides a natural language description of a processor also known as a process definition document (PDD) or recorder of user actions, a task capture, a process mining, etc to the layer 336 having the AI layer 360], the cognitive Al layer configured to analyze and automatically generate annotations describing activities for a robotic process automation (RPA) workflow (AI generating and analyzing or editing annotations for an RPA workflow for the robot; sec 0035, 0036, 0038; OR an RPA workflow pertaining to a process described in the PDD; sec 0035, 0037, 0038);
processing the natural language description (figs. 3, 9; restructuring, reconfiguring, etc; sec 0078, 0127, 0128-0136), by the cognitive AI layer; and
generating an annotated (RPA) workflow, by the cognitive AI layer (sec 0127; builder 903 generates a workflows for the RPA 905 using ML from natural language described task),
wherein the annotations comprise at least one of a description of an overall process of the RPA workflow and what each activity in the RPA workflow is doing [figs. 3, 9; sec 0078, 0127, 0128-0136], as well as a description of changes between a current version of the RPA workflow and one or more previous versions of the RPA workflow (sec 0129; the RPA 905 can be a complex flow with multiple steps being added to one or more written automation tasks).
Regarding claim 18, Marin et al discloses the computer-implemented method of claim 17, wherein the natural language description is a process definition document (PDD) [figs. 3, 9; sec 0078, 0127, 0128-0136].
Regarding claim 21, Marin et al discloses the computer-implemented method of claim 17, further comprising:
generating a process definition document (PDD) for the RPA workflow when the natural language description of the process does not include a PDD, by the cognitive AI layer [figs. 3, 9; sec 0078, 0127, 0128-0136].
Regarding claim 22, Marin et al discloses the computer-implemented method of claim 17, wherein the cognitive Al layer comprises:
a generative AI model configured to generate annotated RPA workflows, generate process definition documents (PDDs) and/or other documents, logically group classes of RPA workflow activities, or any combination thereof [figs. 3, 9; sec 0078, 0127, 0128-0136].
Claim Rejections - 35 USC § 103
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 4, 12, 19 are rejected under 35 U.S.C. 103 as being obvious over Marin et al (US Pub 2024/0142917) in view of Subramanian (US 20240028586)
The applied reference has a common Assignee, UiPath, Inc with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 4, Marin et al discloses the non-transitory computer-readable medium of claim 1, but did not recite, a large language model (LLM) “wherein the cognitive AI layer comprises a large language model (LLM) that has been fine-tuned during training to understand natures of and interrelationships between RPA workflow activities.”
However, Subramanian (sec 0052, 0059) teaches of the limitation, “wherein the cognitive AI layer comprises a large language model (LLM) that has been fine-tuned during training to understand natures of and interrelationships between RPA workflow activities.”
Therefore, it would have been to one having ordinary skill in the art at the time then invention was filed to modify Marin as taught by Subramanian for the purpose of augmenting the Marin device with a cognitive layer that comprises a large language model (LLM), to provide a more advanced and sophisticated user experience, as well as provide access to state-of-the-art natural language processing (NLP) and other ML capabilities that these companies offer (see Subramanian; sec 0059).
Regarding claim 12, Marin et al discloses the one or more computing systems of claim 9, but did not recite, a large language model (LLM), “wherein the cognitive AI layer comprises a large language model (LLM) that has been fine-tuned during training to understand natures of and interrelationships between RPA workflow activities.”
However, Subramanian (sec 0052, 0059) teaches of the limitation, “wherein the cognitive AI layer comprises a large language model (LLM) that has been fine-tuned during training to understand natures of and interrelationships between RPA workflow activities.”
Therefore, it would have been to one having ordinary skill in the art at the time then invention was filed to modify Marin as taught by Subramanian for the purpose of augmenting the Marin device with a cognitive layer that comprises a large language model (LLM), to provide a more advanced and sophisticated user experience, as well as provide access to state-of-the-art natural language processing (NLP) and other ML capabilities that these companies offer (see Subramanian; sec 0059).
Regarding claim 19, Marin et al discloses the computer-implemented method of claim 17, but did not recite, a large language model (LLM), “wherein the cognitive AI layer comprises a large language model (LLM) that has been fine-tuned during training to understand natures of and interrelationships between RPA workflow activities.”
However, Subramanian (sec 0052, 0059) teaches of the limitation, “wherein the cognitive AI layer comprises a large language model (LLM) that has been fine-tuned during training to understand natures of and interrelationships between RPA workflow activities.”
Therefore, it would have been to one having ordinary skill in the art at the time then invention was filed to modify Marin as taught by Subramanian for the purpose of augmenting the Marin device with a cognitive layer that comprises a large language model (LLM), to provide a more advanced and sophisticated user experience, as well as provide access to state-of-the-art natural language processing (NLP) and other ML capabilities that these companies offer (see Subramanian; sec 0059).
Claim 5, 13, 20 are rejected under 35 U.S.C. 103 as being obvious over Marin et al (US Pub 2024/0142917) in view of Dines (US Pub 2025/0104458).
Regarding claim 5, Marin et al discloses the non-transitory computer-readable medium of claim 1, but did not particularly recite the limitation, “generate a runtime automation for execution by one or more RPA robots using the RPA workflow.”
However, Dines (sec 0023, 0138-0140) teaches of a computer program is further configured to cause the at least one processor to:
generate a runtime automation for execution by one or more RPA robots using the RPA workflow (sec 0023, 0138-0140).
Therefore, it would have been to one having ordinary skill in the art at the time then invention was filed to modify Marin as taught by Dines for the purpose of undoing failure, etc in an automation process (Dines, sec 0023, 0138-0140).
Regarding claim 13, Marin et al discloses the one or more computing systems of claim 9, but did not particularly recite the limitation, “generate a runtime automation for execution by one or more RPA robots using the RPA workflow.”
However, Dines (sec 0023, 0138-0140) teaches of one or more computing systems, wherein computer program instructions are further configured to cause the at least one processor to generate a runtime automation for execution by one or more RPA robots using the RPA workflow (Dines, sec 0023, 0138-0140).
Therefore, it would have been to one having ordinary skill in the art at the time then invention was filed to modify Marin as taught by Dines for the purpose of undoing failure, etc in an automation process (Dines, sec 0023, 0138-0140).
Regarding claim 20, Marin et al discloses the computer-implemented method of claim 17, but did not particularly recite the limitation, “generate a runtime automation for execution by one or more RPA robots using the RPA workflow.”
However, Dines (sec 0023, 0138-0140) teaches of a computer-implemented method, comprising generating a runtime automation for execution by one or more RPA robots using an RPA workflow (Dines, sec 0023, 0138-0140).
Therefore, it would have been to one having ordinary skill in the art at the time then invention was filed to modify Marin as taught by Dines for the purpose of undoing failure, etc in an automation process (Dines, sec 0023, 0138-0140).
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered but they are not persuasive.
Applicant is appreciated for pointing out the error for inadvertently referring to Bron instead of Marin in the office action. Corrections have been made accordingly.
In addition, applicant argues that the prior art, Marin does not read on the claims. The examiner respectfully disagrees. The applicant particularly argues that Marin does not disclose, “wherein the annotations comprise description of changes between a current version of the RPA workflow and one or more previous versions of the RPA workflow”. The examiner respectfully disagrees. The RPA workflows are constantly updated, as such when an RPA version is modified or updated or parameters thereof changed or updated or modified the robotic process automation (RPA) that results from such modifications, updated, or changed parameters are a new version of RPA that is different from a previous RPA. Applicant is referred to Marin where , RPA workflows are updated; figs. 8&9; sec 0116, 0121-0129; the RPA 905 can be a complex flow with multiple steps being added to one or more written automation tasks.
In addition, the also respectfully disagrees with applicant’s assertions. The examiner notes that the prior art, Marin at least at sec 0036-0038 uses AI to automatically generate annotations that describe activities for a Robotic Process Automation (RPA). Merlin for example performs a Robotic Process Automation (RPA) automatically e.g. by using AI (Artificial Intelligence). In performing the RPA Merlin disclose that, “One or more AI/ML models 132 may be employed to uncover recurring task patterns in the data. Repetitive tasks that are ripe for automation may then be identified. This information may initially be provided by listeners and analyzed on servers of core hyper-automation system 120, such as server 130, in some embodiments. The findings from task mining (e.g., extensive application markup language (XAML) process data) may be exported to process documents or to a designer application”. The again Merlin discloses that, “Task mining in some embodiments may include taking screenshots with user actions (e.g., mouse click locations, keyboard inputs, application windows and graphical elements the user was interacting with, timestamps for the interactions, etc.), collecting statistical data (e.g., execution time, number of actions, text entries, etc.), editing and annotating screenshots, specifying types of actions to be recorded, etc”. For describing the RPA process, Merlin uses markup language which is a system of tags that annotates a document to describe the structure and presentation thereof.. Markup language uses tags to define elements like headings, paragraphs, or bold text, instructing a computer on how to display the content. Common examples include HTML (Hypertext Markup Language), XML (eXtensible Markup Language), and Markdown.
As such Merlin disclose the claimed limitations.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONNIE MANCHO whose telephone number is (571)272-6984. The examiner can normally be reached Mon-Thurs.
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/RONNIE M MANCHO/Primary Examiner, Art Unit 3657