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 .
This action is in response to response filed on 6/19/2026. This action if 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.
Claims 1-2, 5-6, 8, 11-12, 14-15, 17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al. (US 2025/0117195 A1 ) and further in view of and Stump et al. (US 2023/0058094 A1).
As per claim 1 (Amended), Rieken et al. teaches the invention as claimed including, “A system, comprising:
a memory that stores executable components and one or more custom models; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:”
See figures 1 and 7
“a user interface component configured to receive, via an integrated development environment (IDE) interface, a natural language query directed to industrial control code in development
Rieken et al. teaches a developer can provide a request via an artificial intelligence prompt, requesting for an artificial intelligence model to implement a particular change to the code (0014). The artificial intelligence prompt is written in natural language (0015). An input box includes a text field that is configured to receive an artificial intelligent prompt that specifies a modification to be performed on the code (0068-0069 and figure 6). Also see 0017. The developer tool is part of an IDE (0001). Also see 0025, 0029 and 0038.
“a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language query,
perform contextual analysis of the industrial control code to determine an industrial vertical for which the industrial control code is being developed, wherein the industrial vertical is at least one of automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, marine, die casting, or power generation,
generate a natural language answer to the natural language query based on analysis of the industrial control code, industry knowledge encoded in the one or more custom models, a result of the contextual analysis and a response prompted from a generative AT model comprising information used by the generative Al component to formulate the natural language answer, and,
, via the user interface component the natural language answer on the IDE interface;”
An artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). AI modified code recommendation logic is configured to provide a recommendation of AI modified code (I.e., code that is modified using artificial intelligence). An interface element in a user interface of the developer tool (IDE) is provided. The AI-modified code recommendation logic automatically causes the artificial intelligence model to perform the modification by providing the artificial intelligence prompt together with the snippet as input to the artificial intelligent model. A modified snippet results from the artificial intelligent model performing the modification specified by the artificial intelligence prompt. The AI modified code recommendation logic provides a recommendation to replace the snippet in the code with modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer too (0029). Also see figure 6. Also see 0035, 0036, 00038-0040, 0051-0052 and 0069.
However, Rieken et al. does not explicitly appear to teach, ‘…an integrated development environment (IDE) interface, a natural language query directed to industrial control code in development;” and
“perform contextual analysis of the industrial control code to determine an industrial vertical for which the industrial control code is being developed, wherein the industrial vertical is at least one of automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, marine, die casting, or power generation,
generate a natural language answer to the natural language query based on analysis of the industrial control code, industry knowledge encoded in the one or more custom models, a result of the contextual analysis” and
“a project generation component configured to translate the industrial control code to an executable control program file; and
a project deployment component configured to send the executable control program file to an industrial controller, wherein sending the executable control program file to the industrial controller configures the industrial controller to monitor and control an industrial automation system in accordance with the industrial control code.”
Stump et al. teaches, an integrated development environment used for control code development (0047-0048). Also see 0079-0080. Stump et al. further teaches code modules and/or automation objects can be categorized according to one or more of an industrial vertical (e.g, automative, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.), an industrial application, or type of machine or device to which the code modular automation object is applicable. In some embodiments project generation component can infer a programmer’s current programming task or design goal based on programmatic input being provided by the programmer (as a subset of design input) and determine based on this task or goal, whether one of the pre-defined code modules or automation objects may be appropriately added to the control program being developed (0068-0069). Project deployment component can translate the controller code defined by the system project to a control program file formatted for execution on the specified industrial controller and send this control program file to the controller. Files can be deployed for execution and/or device configuration (0080). Also see 0033, 0055 and 0078.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Rieken et al. with Stump et al. Both Rieken et al. and Stump et al. teach the use of integrated development environments to generate programs. Rieken et al. teaches the use of artificial intelligence prompts written in natural language to generate code (0014-0017) and Stump teaches the use of a IDE to generate industrial control code. It would have been obvious to one of ordinary skill in the art to modify Rieken et al. with Stump et al. because Rieken et al. teach a method of controlling an IDE using natural language which is similar to using an IDE manually as taught in Stump et al. Applying artificial intelligence to the IDE of Stump et al. would produce similar results, both generate code from an IDE. Using natural language simplifies the use of an IDE and would have been obvious to try. Rieken et al. states, “It may be desirable to use artificial intelligence (AI) to modify code that is being developed in context of a developer tool so that the modified code can be suggested to a developer of the code in the context of the developer tool (0002). Therefore, it would have been obvious to one of ordinary skill for the IDE of Rieken et al. to be used to generate code in the context of a controller for a specific industrial vertical. This is nothing more than a design choice.
As per claim 2, Rieken et al. further teaches, “The system of claim 1, wherein the generative AI component is configured to, in response to receipt of the natural language query, formulate a prompt, directed to the generative AI model, designed to obtain the response from the generative AI model comprising the information used by the generative AI component to formulate the natural language answer, and the prompt is formulated based on analysis of the natural language query and the industry knowledge encoded in the one or more custom models.”
An artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). AI modified code recommendation logic is configured to provide a recommendation of AI modified code (I.e., code that is modified using artificial intelligence). An interface element in a user interface of the developer tool (IDE) is provided. The AI-modified code recommendation logic automatically causes the artificial intelligence model to perform the modification by providing the artificial intelligence prompt together with the snippet as input to the artificial intelligent model. A modified snipped results from the artificial intelligent model performing the modification specified by the artificial intelligence prompt. The AI modified code recommendation logic provides a recommendation to replace the snippet in the code with modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer too (0029). Prompt may be written in natural language 0015. Also see figure 6. Also see 0003, 0015, 0034-0036, and 0069.
As per claim 5 (Amended), Rieken et al. and Stump et al. further teach, “The system of claim 1, wherein the generative Al component is further configured , based on the contextual analysis the type of industrial application.”
Rieken et al. teaches identifying the language (type of industrial application) in which the code is written. In accordance with this aspect, the artificial intelligence model is caused to generate the documentation in a style that corresponds to the programming language (0051-0052). Rieken et al. states, “It may be desirable to use artificial intelligence (AI) to modify code that is being developed in context of a developer tool so that the modified code can be suggested to a developer of the code in the context of the developer tool (0002).
Stump et al. teaches, an integrated development environment used for control code development (0047-0048). Also see 0079-0080. Stump et al. further teaches code modules and/or automation objects can be categorized according to one or more of an industrial vertical (e.g, automative, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.), an industrial application, or type of machine or device to which the code modular automation object is applicable. In some embodiments project generation component can infer a programmer’s current programming task or design goal based on programmatic input being provided by the programmer (as a subset of design input) and determine based on this task or goal, whether one of the pre-defined code modules or automation objects may be appropriately added to the control program being developed (0068-0069).
As per claim 6, Rieken et al. and Stump et al. further teach, “The system of claim 1, wherein the industry knowledge encoded in the one or more custom models comprises at least one of libraries of control code instructions, libraries of add-on instructions, libraries of control code samples, libraries of user- defined data types (UDTs), libraries of product manuals for industrial devices or software platforms, specification data for industrial devices, training data, information defining industrial standards, design standards for respective different types of industrial control applications, design standards for respective different industrial verticals, knowledge of industrial best practices, control design rules, or industrial domain-specific language (DSL) syntax data.”
Rieken et al. teaches, an artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). AI modified code recommendation logic is configured to provide a recommendation of AI modified code (I.e., code that is modified using artificial intelligence) (0029). The artificial intelligence model is a large language model (LLM). A large language model is trained using self-supervised learning and semi-supervised learning (0036).
Stump et al. teaches, an integrated development environment use for control code development (0047-0048). Also see 0079-0080. Stump et al. further teaches code modules and/or automation objects can be categorized according to one or more of an industrial vertical (e.g, automative, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.), an industrial application, or type of machine or device to which the code modular automation object is applicable. In some embodiments project generation component can infer a programmer’s current programming task or design goal based on programmatic input being provided by the programmer (as a subset of design input) and determine based on this task or goal, whether one of the pre-defined code modules or automation objects may be appropriately added to the control program being developed (0068-0069).
As per claim 8, Rieken et al. further teaches, “The system of claim 1, wherein
the user interface component is configured to:
in response to receipt of a user interaction at a location on a workspace canvas area of the IDE interface in which the industrial control code is being displayed, render an in-line chat window as an overlay on the workspace canvas area, wherein the location corresponds with an element of the industrial control code, and
receive the natural language query via interaction with the in-line chat window, and
the generative AI component is configured to generate the natural language answer to the natural language query based on the analysis using the control code element as a parameter of the natural language query.”
Rieken et al. teaches the AI-modified code recommendation logic provides an interface element in a user interface of the developer tool. The interface element is configured to receive an artificial intelligence prompt that specifies a modification to be performed on the code. Based at least on receipt of the artificial intelligence prompt via the interface element, the AI-modified code recommendation logic automatically causes an artificial intelligence model to perform the modification on at least the snippet of the code. The AI-modified code recommendation logic provides a recommendation to replace the snippet in the code with the modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer tool (0029). Prompt may be written in natural language 0015. Also see 0035 and 0039-0041. Also see figure 6.
As per claim 11-12, 14-15, 17 and 19-20, the contain similar limitations to claims 1-2, 3-6 and 8 and are therefore rejected for similar reasons.
Claims 3,4,7, 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al. (US 2025/0117195 A1) and Stump et al. (US 2023/0058094 A1) as applied to claims 1 above, and further in view of Ligman et al. (US 2025/0272067 A1).
As per claim 3, Rieken et al. further teaches, “The system of claim 1, wherein the natural language answer comprises at least one of an explanation of functionality of a portion of the industrial control code specified in the natural language query, an explanation of a variable used in the industrial control code and specified in the natural language query, an explanation of a unit of equipment controlled by the industrial control code and specified in the natural language query, an indication of a portion of the industrial control code responsible for performing a control function specified in the natural language query, or a recommendation for rewriting the industrial control code in a manner predicted to improve a control performance metric specified in the natural language query.”
Rieken et al. teaches, an artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). AI modified code recommendation logic is configured to provide a recommendation of AI modified code (I.e., code that is modified using artificial intelligence). An interface element in a user interface of the developer tool (IDE) is provided. The AI-modified code recommendation logic automatically causes the artificial intelligence model to perform the modification by providing the artificial intelligence prompt together with the snippet as input to the artificial intelligent model. A modified snipped results from the artificial intelligent model performing the modification specified by the artificial intelligence prompt. The AI modified code recommendation logic provides a recommendation to replace the snippet in the code with modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer too (0029). Also see 0039. The artificial intelligence model can generate documentation regarding the snippet by providing a second artificial intelligence prompt that requests the documentation regarding the snippet together with the snippet (0051).
However, Rieken et al. does not explicitly appear to teach, “a recommendation for rewriting the industrial control code in a manner predicted to improve a control performance metric specified in the natural language query.”
Ligman et al. teaches, inferring by a machine learning model, edits to the input code block and outputting by the machine learning model an optimized code block based on the input code block (0005). Suggested code edits are formulated to improve the carbon efficiency (performance) of a computer program (0030). A models output inference can generate an optimal energy conserving version of the input code, but with less carbon cost created upon execution of the program that includes the inferred code (0032). Also see 0042 and 0055.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Rieken et al. with Ligman et al. because Rieken et al. teaches its AI model is capable of performing any tasks a human is capable of. A human is able to modify/optimize code to improve its performance. Ligman et al. teaches optimizing code for performance using a ML model that is similar to Rieken et al. Therefore, it would have been obvious for Rieken et al. to do the same to produce similar results.
As per claim 4, Ligman et al. further teaches, “The system of claim 3, wherein the control performance metric comprises at least one of an energy consumption, a part cycle time, a number of machine downtime occurrences.”
Ligman et al. teaches, inferring by a machine learning model, edits to the input code block and outputting by the machine learning model an optimized code block based on the input code block (0005). Suggested code edits are formulated to improve the carbon efficiency (performance) of a computer program (0030). A models output inference can generate an optimal energy conserving version of the input code, but with less carbon cost created upon execution of the program that includes the inferred code (0032). Also see 0042 and 0055.
As per claim 7, Rieken et al. further teaches, “The system of claim 1, wherein
the user interface component is further configured to receive a natural language request for a proposed modification to the industrial control code that will address a control performance issue specified in the natural language request,
the generative AI component is further configured to, in response to receipt of the natural language request, determine one or more proposed modifications to the industrial control code designed to address the control performance issue based on analysis of the industrial control code, the industry knowledge encoded in the one or more custom models, and another response prompted from the generative AI model comprising information used by the generative AI component to formulate the proposed modifications, and
the user interface component is configured to render the one or more proposed modifications on the IDE interface as natural language descriptions.”
Rieken et al. teaches a developer can provide a request via an artificial intelligence prompt, requesting for an artificial intelligence model to implement a particular change to the code (0014). The artificial intelligence prompt is written in natural language (0015). An input box includes a text field that is configured to receive an artificial intelligent prompt that specifies a modification to be performed on the code (0068-0069 and figure 6). Also see 0017.
An artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). AI modified code recommendation logic is configured to provide a recommendation of AI modified code (I.e., code that is modified using artificial intelligence). An interface element in a user interface of the developer tool (IDE) is provided. The AI-modified code recommendation logic automatically causes the artificial intelligence model to perform the modification by providing the artificial intelligence prompt together with the snippet as input to the artificial intelligent model. A modified snipped results from the artificial intelligent model performing the modification specified by the artificial intelligence prompt. The AI modified code recommendation logic provides a recommendation to replace the snippet in the code with modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer too (0029).
However Rieken et al. does not explicitly appear to teach, “determine one or more proposed modifications to the industrial control code designed to address the control performance issue based on analysis of the industrial control code”
Ligman et al. teaches, inferring by a machine learning model, edits to the input code block and outputting by the machine learning model an optimized code block based on the input code block (0005). Suggested code edits are formulated to improve the carbon efficiency (performance) of a computer program (0030). A models output inference can generate an optimal energy conserving version of the input code, but with less carbon cost created upon execution of the program that includes the inferred code (0032). Also see 0042 and 0055.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Rieken et al. with Ligman et al. because Rieken et al. teaches its AI model is capable of performing any tasks a human is capable of. A human is able to modify/optimize code to improve its performance. Ligman et al. teaches optimizing code for performance using a ML model that is similar to Rieken et al. Therefore, it would have been obvious for Rieken et al. to do the same to produce similar results.
As per claims 13 and 16, they contain similar limitations to claims 3 and 7 and are therefore rejected for similar reasons.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al. (US 2025/0117195 A1) and Stump et al. (US 2023/0058094 A1) as applied to claims 1 and 11 above, and further in view of Ravindar et al. (US 2021/0286718 A1).
As per claim 9, Rieken et al. further teaches, “The system of claim 1, wherein
the analysis is a first analysis,
the generative AI component is further configured to identify, based on a second analysis of the industrial control code, a potential modification to the industrial control code predicted to at least one of reduce a size of the industrial control code without altering control functionality of the industrial control code, reduce a number or distance of movements of a machine controlled using the industrial control code, eliminate an inconsistency in a naming convention for variables used in the industrial control code, or modularize portions of the industrial control code,
the user interface is configured to render, via the IDE interface, a natural language recommendation to implement the potential modification, and
the second analysis is performed on the industrial control code based on the industry knowledge encoded in the one or more custom models and another response prompted from the generative AI model comprising information used by the generative AI component to identify the potential modification.”
Rieken et al. teaches, an artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). Rieken et al. further teaches the AI-modified code recommendation logic provides an interface elements in a user interface of the developer tool. The interface element is configured to receive an artificial intelligence prompt that specifies a modification to be performed on the code. Based at least on receipt of the artificial intelligence prompt via the interface element, the AI-modified code recommendation logic automatically causes an artificial intelligence model to perform the modification on at least the snippet of the code. The AI-modified code recommendation logic provides a recommendation to replace the snippet in the code with the modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer tool (0029). Prompt may be written in natural language 0015. Also see 0035 and 0039-0041. Also see figure 6.
However, Rieken et al. does not explicitly appear to teach “a potential modification to the industrial control code predicted to at least one of reduce a size of the industrial control code without altering control functionality of the industrial control code”.
Ravindar et al. teaches a code optimizer that can apply optimization techniques to reduce the code size of source code (0047).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Rieken et al. with Ravindar et al. because Rieken et al. teaches its AI model is capable of performing any tasks a human is capable of, such as code modification suggestions. A human is able to modify/optimize code to reduce its size. Ravindar et al. teaches a code optimizer that can apply optimization techniques to reduce the code size of source code. Therefore, it would have been obvious for the machine learning model of Rieken et al. to also determine code modification to reduce the code size. This is nothing more than a design choice and would have been obvious to try.
As per claim 18, contains similar limitations as claim 9 and is therefore rejected for similar reasons.
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al. (US 2025/0117195 A1) and Stump et al. (US 2023/0058094 A1) as applied to claim 1 above, and further in view of Stump et al. (US 2022/0299982 A1) herein forth Stump_2.
As per claim 10, Rieken et al. further teaches, “The system of claim 1, wherein
the user interface component is further configured to receive a natural language request to modify the industrial control code in a manner that brings the industrial control code into compliance with a specified programming standard, and
the generative AI component is further configured to, in response to receipt of the natural language request, implement one or more modifications to the industrial control code that bring the industrial control code into compliance with the specified programming standard,
wherein the generative AI component is configured to determine the one or more modifications based on information about the specified programming standard defined in the one or more custom models and another response prompted from the generative AI model.
Rieken et al. teaches, an artificial intelligence model is a model that utilizes artificial intelligence to generate an answer, that is responsive to an artificial intelligence prompt. The AI model is capable of performing any task that a human is capable of performing (0016). Rieken et al. further teaches the AI-modified code recommendation logic provides an interface elements in a user interface of the developer tool. The interface element is configured to receive an artificial intelligence prompt that specifies a modification to be performed on the code. Based at least on receipt of the artificial intelligence prompt via the interface element, the AI-modified code recommendation logic automatically causes an artificial intelligence model to perform the modification on at least the snippet of the code. The AI-modified code recommendation logic provides a recommendation to replace the snippet in the code with the modified snippet by causing the processed version of the modified snippet to be displayed via the user interface of the developer tool (0029). Prompt may be written in natural language 0015. Also see 0035 and 0039-0041. Also see figure 6.
However, Rieken et al. does not explicitly appear to teach “the user interface component is further configured to receive a natural language request to modify the industrial control code in a manner that brings the industrial control code into compliance with a specified programming standard, and
the generative AI component is further configured to, in response to receipt of the natural language request, implement one or more modifications to the industrial control code that bring the industrial control code into compliance with the specified programming standard,
wherein the generative AI component is configured to determine the one or more modifications based on information about the specified programming standard defined in the one or more custom models and another response prompted from the generative AI model.
Stump_2 teaches that users can run internal guardrail templates against code provided by outside vendors to ensure code compiles with in-house programing standards. A user interface of an IDE can based on results of the analysis display suggestions for modifying the code in order to bring the code into compliance (0066).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Rieken et al. with Stump_2 because Rieken et al. teaches its AI model is capable of performing any tasks a human is capable of, such as code modification suggestions. A human is able to modify/optimize code to bring the code into compliance with a programming standard. Stump et al. teaches based on analysis suggesting code modifications in order to bring the code into compliance with programming standards. Therefore, it would have been obvious for the AI model of Rieken et al. to also determine code modification to bring the code into compliance with a programming standard. This is nothing more than a design choice and would have been obvious to try.
Response to Arguments
Applicant's arguments filed 6/19/2026 have been fully considered but they are moot due to amendments. Please see above rejection in light of new additional reference Stump et al. (US 2023/0058094 A1).
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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/MARK A GOORAY/ Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/ Supervisory Patent Examiner, Art Unit 2199
