DETAILED ACTION
This second non-final office action is responsive to application 18/339,539 with applicant’s amendments and request for reconsideration as submitted 02 July 2026.
Claim status is currently pending and under examination for claims 1-6, 9-11 and 14-20 of which amended claims are 1, 3, 9, 11 and 15; canceled claims are 7-8 and 12-13; independent claims are 1, 11 and 15.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Remarks
Applicant’s responsive remarks filed 07/02/26 are considered together with amendments. Amendments roll up claims 7-8 with remarks that are at least partly persuasive concerning prior arts. Therefore, the prior rejection is withdrawn and this action is made non-final.
The rejection of claim 3 under 35 U.S.C. 101 as being directed to an abstract idea without significantly more is withdrawn. Examiner agrees with remarks [P.7-8 of 12] that practical application is satisfied by integrating an execution of a particular bot solution responsive to the confidence measure. Accordingly, the rejection is withdrawn.
The rejection under anticipation is withdrawn in light of amendments, and the obviousness remarks [P.9-10 of 12] over combination of Vlad and Mani is reasonably persuasive. Accordingly, the rejection is withdrawn. Updated search and consideration reveals pertinent prior art Bucur as a new reference applied below. Since this is a new grounds of rejection, this office action is made non-final.
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.
Claims 1-6, 9-11 and 14-20 (all pending claims) are rejected under 35 U.S.C. 103 as being unpatentable over:
Bucur et al., US Patent No 12,613,678B2 hereinafter Bucur (UI-Path).
With respect to claim 1, Bucur teaches:
A computer-implemented method, comprising: {Bucur [Col1 Lines37-54] “A method is provided… implemented as a system, a computer program product, and/or an apparatus” Fig 1. Note claim 1}
receiving, by a processor, plural runtime variables of a bot executing client-side web application code {Bucur [Col3 Lines7-27] “receive one or more configuration inputs for the sequence of HTTP request activities, which is now ready to be consumed (e.g., by on in a workflow) …HTTP recorder” describes workflow of RPA being ro(bot)ic process automation, shown Fig 19 “create a variable for each” such that the ‘sequence of HTTP request activities for input’ corresponds to runtime variables when read in light of instant specification [0059] “sequence of runtime variables”, noting Bucur [Col7 Lines2-3] “timestamps for the interactions”. Bucur’s RPA robot application executes client-side [Col13 Line13-25] “On the client side 301, a robot application 310 includes executors 312, an agent 314, and a designer”}, wherein at least one of the plural runtime variables of the bot comprises a parameter of a robotic process automation command; {Bucur [Col13 Lines41-42] “commands sent to the robot (e.g., start, stop, etc.)” similar [Col12 Line19] “start or stop jobs and change settings” teaches RPA commands. More particularly, parameters include settings that are changed per Fig 14 “Filter Settings” saved via interface. This may allow for [Col3 Lines23-24] “custom connections” again at [Col27 Line6] “custom connector builder of the engine 902” 902 being RPA workflow engine [Col24 Line18], and for use by Fig 19 UI-path RPA for sequence. The BRI of parameters are read in light of instant specification as per [0046,58] “single select, combo boxes, radio buttons, and other parameters …UI commands with parameters” in other words, not AI/ML parameters}
generating, by the processor, a network request from a machine learning model given input of the plural runtime variables of the bot {Bucur [Col3 Lines1-2] “generate a sequence of HTTP request activities” again at [Col24 Line34]. Fig 15 shows the request for export [Col6 Lines41-44] “export to the automation hub” automation hub employs AI/ML models, particularly Equations 3-4 [Col21-22] where ‘y’ and/or ‘o’ denotes output of machine learning model, input is ‘x’ in vector form. Furthermore, see [Col12 Lines39-60] “RPA robots that utilize AI/ML models… robot calls an AI/ML model” e.g. [Col15 Lines27-32] “automations (e.g., RPA robots) may call AI/ML models from AI/ML server” emphasis ‘from’ the AI/ML server, which hosts the model. See also [Col7 Lines30-33] “drag-and-drop modeling”}, wherein the network request comprises a hypertext transfer protocol request with payload data of a selection of a user interface control in the client-side web application code; and {Bucur discloses per [Col26 Lines50-51] “HTTP request …request body” body corresponds to payload shown Figs 14-16 interfaces, e.g. Fig 14 request body with clicked checkbox to accept and Fig 15 checkboxes allow user to deselect requests, the request again comprising request body/payload Fig 16. The interface described [Col26 Lines46-49] “user can save the HTTP requests as a Json file or as a sequence of activities that is viewable and editable in the engine 902” 902 being the RPA workflow engine [Col24 Line18]}
sending, by the processor, the network request generated from the machine learning model to the bot. {Bucur [Col12 Lines39-67] “RPA robots may send input for execution of the AI/ML model(s) and receive output therefrom” establishes (bi)directional communication between RPA bot and ML model, thus sending includes sending from ML model’s output to the RPA robot which receives it as therefrom. The communication comprises export/import and call operations for HTTP requests, e.g. [Col3 Lines21-4] “captured requests (e.g., the recorded HTTP requests) are exported” and/or [Col28 Lines2-3] “hypertext transfer protocol requests after importing”}
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to combine RPA robots with AI models in communication to arrive at the invention as claimed for a motivation [Col6 Lines28,43] “to get from automation discovery to build-out faster… speed up” e.g. [Col7 Lines46-48] “RPA and AI templates and solution may be provided to allow developers to automate a wide variety of processes more quickly”
With respect to claim 2, Bucur teaches the computer-implemented method of claim 1, further comprising:
inputting the plural runtime variables of the bot into the machine learning model; and
receiving output of the generated network request from the machine learning model.
{Bucur [Col12 Lines39-43] “RPA robots may send input for execution of the AI/ML model(s) and receive output therefrom” Fig 19 shows variables of a sequence for UIpath RPA, the ML model with input/output is detailed Eqs.3-4 [Col21-22]}
With respect to claim 3, Bucur teaches the computer-implemented method of claim 2, further comprising:
determining if a confidence measure in the output of the generated network request exceeds a predetermined threshold; and in response to determining that the confidence measure exceeds the predetermined threshold {Bucur [Col20 Lines27-57] “confidence threshold… For instance, if the confidence threshold is 80%, outputs with confidence scores exceeding this amount may be used” similar at [Col23 Line63 - Col24 Line34] decision to retrain model for the generated HTTP request, see Figs 14-19}
causing the generated network request to be executed by the bot to bypass execution of user interface (UI) commands. {Bucur Fig 15 Deselect requests allows for bypassing execution being of user interface (UI) commands – checkbox marked. Doing so may regard human supervision of supervised model [Col 22 Line16] subject to confidence threshold [Col24 Line2], and may entail validation e.g. see [Col7 Lines37-39] “When data is validated or exceptions are handled, this information may be used to retrain the respective AI/ML models” similar at [Col8 Lines61-67], [Col15 Lines30-32]}
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to use confidence threshold for deselecting requests as human-validated for supervised learning in combination to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or for a motivation [Col8 Lines59-67] “Performance of the AI/ML models may be monitored, and be trained and improved using human-validated data… For instance, human reviewers may validate that predictions by AI/ML models 132 are accurate or provide corrections otherwise” such correction by deselecting provides skilled artisan with a tool for [Col9 Lines5-6] “Both positive and negative examples may be stored and used for retraining.”
With respect to claim 4, Bucur teaches the computer-implemented method of claim 1, wherein
the machine learning model comprises a recurrent neural network trained with plural associations of runtime variables and network requests to a web application. {Bucur [Col20 Lines3,27] “recurrent neural network” as “neural network is trained” e.g. Eq.4 [Col22] where x is input vector, the input associations/affiliated with variables Fig 9 as a sequence of HTTP requests [Col3 Lines7-27]. The RNN comprises types of RNNs to include LSTM [Col5 Line66] and GRU [Col20 Line5]}
With respect to claim 5, Bucur teaches the computer-implemented method of claim 1, wherein
the machine learning model comprises a Long Short Term Memory (LSTM) neural network trained with plural associations of runtime variables and network requests to a web application. {Bucur [Col5 Lines65-66] “long short term memory (LSTM) deep learning” and/or [Col20 Lines4,27] “long/short term memory networks” as “neural network is trained” e.g. Eq.4 [Col22] where x is input, the input associations/affiliated with variables Fig 9 as a sequence of HTTP requests [Col3 Lines7-27]}
With respect to claim 6, Bucur teaches the computer-implemented method of claim 1, wherein
the machine learning model comprises a Gated Recurrent Unit (GRU) neural network trained with plural associations of runtime variables and network requests to a web application. {Bucur at [Col20 Lines5,27] “gated recurrent unit networks” abbrv. GRU as “neural network is trained” e.g. Eq.4 [Col22] where x is input vector, the input associations/affiliated with variables Fig 9 as a sequence of HTTP requests [Col3 Lines7-27]. The RNN comprises types of RNNs to include LSTM [Col5 Line66] and GRU [Col20 Line5]}
Claims 7-8 (Canceled)
With respect to claim 9, Bucur teaches the computer-implemented method of claim 1, further comprising
receiving a request for generation of the network request from the bot executing the client-side web application code. {Bucur [Col3 Lines1-27] “captured requests (e.g., the recorded HTTP requests)” for “generate a sequence of HTTP request activities” shown Fig 19 sequence workflow of application UI-path RPA software where [Col13 Lines13-25] “On the client side 301, a robot application 310 includes…designer 316” the designer module referenced at [Col3 Lines14-27]. See also [Col15 Lines9-10] “capture-related data, received from listener 330, installed on the client side”}
With respect to claim 10, Bucur teaches the computer-implemented method of claim 1, further comprising
storing the association of the plural runtime variables of the bot and the network request in persistent storage. {Bucur [Col15 Lines45-46] “stores and indexes the information logged by the robots” e.g. Fig 19 logged cookies “create a variable for each one, store and use them in subsequent calls” and/or Fig 15 [Col26 Lines46-49] “save the HTTP requests…HTTP recorder”}
With respect to claim 11, the rejection of claim 1 is incorporated. The difference in scope being a computer program product comprising one or more computer readable storage media storing program instructions executable to perform limitations of method claim 1. Bucur discloses [Col28 Lines18-24] “The computer program can be implemented in hardware, software, or a hybrid” including [Col17 Lines20-30] “non-transitory computer-readable media” and “instructions to be executed by processor” Figs 1, 5. The remainder of this claim is rejected for the same rationale as claim 1.
Claims 12-13 (Canceled).
With respect to claim 14, Bucur teaches the computer program product of claim 11, and further teaches the limitation of claims 4-6. Therefore, the rejection of claims 4-6 are applied to claim 14.
With respect to claim 15, the rejection of claim 1 is incorporated. The difference in scope being a system comprising a processor set, computer readable storage media storing program instructions executable to perform limitations of method claim 1. Bucur discloses [Col17 Lines20-30] “Computing system 500 further includes a memory 515 for storing information and instructions to be executed by processor(s) …Non-transitory computer-readable media may be any available media that can be accessed by processor(s).” The remainder of this claim is rejected for the same rationale as claim 1.
With respect to claim 16, Bucur teaches the system of claim 15, and further teaches the limitation of claim 3. Therefore, the rejection of claim 3 is applied to claim 16.
With respect to claim 17, Bucur teaches the system of claim 15, wherein the program instructions are further executable to
identify the runtime variables of the bot. {Bucur [Col11 Lines37-38] “robot identification data” e.g. Fig 19 sequence variables in workflow [Col6 Line51] “identifies and aggregates workflows”}
With respect to claim 18, Bucur teaches the system of claim 15, wherein
the machine learning model is trained with plural associations of runtime variables and network requests to generate the network request given input of the runtime variables of the bot. {Bucur [Col10 Lines5-8] “AI/ML models may be trained and retrained, and the process may repeat itself” e.g. Eq.4 [Col22] where x is input vector, the input associations/affiliated with variables Fig 9 as a sequence of HTTP requests to generate HTTP requests [Col3 Lines1-27]}
With respect to claim 19, Bucur teaches the system of claim 15, and further teaches the limitation of claim 4. Therefore, the rejection of claim 4 is applied to claim 19.
With respect to claim 20, Bucur teaches the system of claim 15, wherein the program instructions are further executable to
track plural network requests from client-side web application code sent to the web application. {Bucur [Col8 Lines39-41] “track, measure, and manage the performance of deployed automations” e.g. tracked by recording and/or monitoring HTTP request [Col3 Lines1-25] with application from client-side [Col13 Lines13-43]. See also [Col11 Lines37-39] “keeping track of robot identification data… indexing logs”}
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hecht et al., US PG Pub No 2024/0403328A1 transformer ML/AI based workflow automation
Singh et al., US Patent No 11,815,880B2 UI-path RPA human-in-the-loop training
Grigore et al., US PG Pub No 2023/0191601 UI-path discloses skipping akin to bypass
Vlad et al., US PG Pub No 2024/0256370A1 UI-path at Figs 3,6 and [0079] HTTP req. post
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chase P Hinckley whose telephone number is (571)272-7935. The examiner can normally be reached M-F 9:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda M. Huang can be reached at 571-270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124
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