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 initial office action is based on the application filed on 03/25/2024, which claims 1-20 have been presented for examination.
Status of Claim
2. Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 11 and 19 are presented in independent form.
Priority
3. No priority has been filed in this application.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 07/22/2024, 06/06/2025, 09/23/2025 and 02/20/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Objections
6. Claims 1-20 are objected to because of the following informalities:
Claims 1, 11 and 19 recite “based on based on analysis” in lines 17, 12 and 13 respectively. Repeating “based on” should be corrected/removed.
Claims 11 and 19, first occurrence of acronym, “AI” in lines 14 and 15, should be spelled out. Further, “the one or more custom models” in line 13 of claim 11 and in line 14 of claim 19 lacks proper antecedent basis.
Claims 2-11, 12-18 and 20 depend on claims 1, 11 and 19 are also objected.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
8. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “user interface component configured to…”; “generative artificial intelligence (AI) component configured to …” and “project generation component configured to…” in claims 1-3 and 8-10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
6. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 - 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending application No. 18/614,925. Herein the independent claims are shown mapped to claim 1 of the co-pending application. The dependent claims are ODP in view of dependent claims of claim 1 or in view of the prior art used in the art rejection below associated with that dependent claim.
Instant Application 18/614,956
Co-pending Application 18/614,925
Claim 1:
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:
a user interface component configured to render a project development interface and to receive, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative Al component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
a project generation component configured to generate, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
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:
a user interface component configured to render an integrated development environment (IDE) interface and to receive, via interaction with the IDE interface, industrial control code input that defines an industrial control program; and a project generation component configured to generate, based on the industrial control programming input, an executable control program file that,
in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program, wherein the user interface component is further configured to receive a natural language request for control code to be included in the industrial control program, wherein the natural language request specifies one or more requirements of the control code, the executable components further comprise a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language request, formulate a prompt, directed to a generative Al model, designed to obtain a response from the generative Al model comprising information used by the generative A component to generate control code inferred to satisfy the one or more requirements, wherein the prompt is generated based on analysis of the natural language request and industry knowledge encoded in the one or more custom models, and the project generation component is further configured to integrate the control code into the industrial control program.
Claim 11
A method, comprising:
rendering, by a system comprising a processor, a project development interface;
receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating, by the system, a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, by the system based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
1. 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:a user interface component configured to render an integrated development environment (IDE) interface and to receive, via interaction with the IDE interface, industrial control code input that defines an industrial control program; and a project generation component configured to generate, based on the industrial control programming input, an executable control program file that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program, wherein the user interface component is further configured to receive a natural language request for control code to be included in the industrial control program, wherein the natural language request specifies one or more requirements of the control code, the executable components further comprise a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language request, formulate a prompt, directed to a generative Al model, designed to obtain a response from the generative Al model comprising information used by the generative A component to generate control code inferred to satisfy the one or more requirements, wherein the prompt is generated based on analysis of the natural language request and industry knowledge encoded in the one or more custom models, and the project generation component is further configured to integrate the control code into the industrial control program.
Claim 19
A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
rendering a project development interface;
receiving, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
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:
a user interface component configured to render an integrated development environment (IDE) interface and to receive, via interaction with the IDE interface, industrial control code input that defines an industrial control program; and a project generation component configured to generate, based on the industrial control programming input, an executable control program file that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program, wherein the user interface component is further configured to receive a natural language request for control code to be included in the industrial control program, wherein the natural language request specifies one or more requirements of the control code, the executable components further comprise a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language request, formulate a prompt, directed to a generative Al model, designed to obtain a response from the generative Al model comprising information used by the generative A component to generate control code inferred to satisfy the one or more requirements, wherein the prompt is generated based on analysis of the natural language request and industry knowledge encoded in the one or more custom models, and the project generation component is further configured to integrate the control code into the industrial control program.
Claims 1 - 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending application No. 18,614,966. Herein the independent claims are shown mapped to claim 1 of the co-pending application. The dependent claims are ODP in view of dependent claims of claim 1 or in view of the prior art used in the art rejection below associated with that dependent claim.
Instant Application 18/614,956
Co-pending Application 18/614,966
Claim 1:
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:
a user interface component configured to render a project development interface and to receive, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative Al component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
a project generation component configured to generate, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1:
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:
a user interface component configured to render an integrated development environment (IDE) interface and to receive, via interaction with the IDE interface, design input that defines aspects of an industrial system project;
a project generation component configured to generate system project data based on the design input, the system project data comprising at least an industrial control program that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program; and
a generative artificial intelligence (AI) component configured to formulate and send a prompt to a generative Al model
in response to receipt of a natural language input, submitted via the IDE interface, comprising at least one of a request to generate a portion of the industrial system project or a question about the industrial system project, wherein the generative AI component formulates the prompt to obtain a response from the generative AI model comprising information used by the generative AI component to at least one of generate the portion of the industrial system project or generate a natural language answer to the question about the industrial system project, and the generative Al component formulates the prompt based on analysis of the natural language input and industry-specific information encoded in the one or more custom models.
Claim 11
A method, comprising:
rendering, by a system comprising a processor, a project development interface;
receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating, by the system, a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, by the system based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
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:
a user interface component configured to render an integrated development environment (IDE) interface and to receive, via interaction with the IDE interface, design input that defines aspects of an industrial system project; a project generation component configured to generate system project data based on the design input, the system project data comprising at least an industrial control program that,
in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program; and a generative artificial intelligence (Al) component configured to formulate and send a prompt to a generative Al model in response to receipt of a natural language input, submitted via the IDE interface, comprising at least one of a request to generate a portion of the industrial system project or a question about the industrial system project, wherein the generative AI component formulates the prompt to obtain a response from the generative Al model comprising information used by the generative AI component to at least one of generate the portion of the industrial system project or generate a natural language answer to the question about the industrial system project, and the generative Al component formulates the prompt based on analysis of the natural language input and industry-specific information encoded in the one or more custom models.
Claim 19
A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
rendering a project development interface;
receiving, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
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:
a user interface component configured to render an integrated development environment (IDE) interface and to
receive, via interaction with the IDE interface, design input that defines aspects of an industrial system project; a project generation component configured to generate system project data based on the design input, the system project data comprising at least an industrial control program that,
in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program; and a generative artificial intelligence (AI) component configured to formulate and send a prompt to a generative Al model in response to receipt of a natural language input, submitted via the IDE interface, comprising at least one of a request to generate a portion of the industrial system project or a question about the industrial system project, wherein the generative Al component formulates the prompt to obtain a response from the generative AI model comprising information used by the generative Al component to at least one of generate the portion of the industrial system project or generate a natural language answer to the question about the industrial system project, and the generative Al component formulates the prompt based on analysis of the natural language input and industry-specific information encoded in the one or more custom models.
Claims 1-20 are further rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending application No. 18/952,567. Herein the independent claims are shown mapped to claim 1 of the co-pending application. The dependent claims are ODP in view of dependent claims of claim 1 or in view of the prior art used in the art rejection below associated with that dependent claim.
Instant Application 18/614,956
Co-pending Application 18/852,567
Claim 1
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:
a user interface component configured to render a project development interface and to
receive, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative Al component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
a project generation component configured to generate, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
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:
a user interface component configured to render an integrated development environment (IDE) interface and to
receive, via interaction with the IDE interface, a natural language request for control code to be included in an industrial control program, wherein the natural language request specifies one or more requirements of the control code;
a generative artificial intelligence (AI) component configured to,
in response to receipt of the natural language request: generate control code inferred to satisfy the one or more requirements based on content of the natural language request and responses prompted from a generative Al model, generate natural language documentation for the control code based on the content of the natural language request and the responses prompted from a generative Al model, and embed the natural language documentation into the control code; and a project generation component configured to generate, based on the control code, an executable control program file that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 11
A method, comprising:
rendering, by a system comprising a processor, a project development interface;
receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project,
wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating, by the system, a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, by the system based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
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:
a user interface component configured to render an integrated development environment (IDE) interface and to
receive, via interaction with the IDE interface, a natural language request for control code to be included in an industrial control program,
wherein the natural language request specifies one or more requirements of the control code; a generative artificial intelligence (Al) component configured to, in response to receipt of the natural language request:
generate control code inferred to satisfy the one or more requirements based on content of the natural language request and responses prompted from a generative Al model, generate natural language documentation for the control code based on the content of the natural language request and the responses prompted from a generative Al model, and embed the natural language documentation into the control code; and a project generation component configured to generate, based on the control code, an executable control program file that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 19 A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
rendering a project development interface;
receiving, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
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:
a user interface component configured to render an integrated development environment (IDE) interface and to
receive, via interaction with the IDE interface, a natural language request for control code to be included in an industrial control program, wherein the natural language request specifies one or more requirements of the control code; a generative artificial intelligence (AI) component configured to,
in response to receipt of the natural language request: generate control code inferred to satisfy the one or more requirements based on content of the natural language request and responses prompted from a generative Al model, generate natural language documentation for the control code based on the content of the natural language request and the responses prompted from a generative Al model, and embed the natural language documentation into the control code; and
a project generation component configured to generate, based on the control code, an executable control program file that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claims 1-20 are further rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending application No. 18/459,822. Herein the independent claims are shown mapped to claim 1 of the co-pending application. The dependent claims are ODP in view of dependent claims of claim 1 or in view of the prior art used in the art rejection below associated with that dependent claim.
Instant Application 18/614,956
Co-pending Application 18/459,822
Claim 1
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:
a user interface component configured to render a project development interface and to
receive, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative Al component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
a project generation component configured to generate, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
A system, comprising:
a memory that stores executable components and
a generative artificial intelligence (AI) model that has been trained using training data comprising at least one of industrial control code samples, industrial standards data, or industrial protocol data; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to render a chat interface configured to receive industrial design input data as plain language input data; a generative artificial intelligence (AI) component configured to infer functional requirements of an industrial control system for an industrial automation system based on generative AI analysis of the industrial design input data using the generative AI model; and a project generation component configured to generate, based on the generative AI analysis, industrial control code designed to satisfy the functional requirements.
Claim 11
A method, comprising:
rendering, by a system comprising a processor, a project development interface;
receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project,
wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating, by the system, a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, by the system based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
A system, comprising:
a memory that stores executable components and a generative artificial intelligence (AI) model that has been trained using training data comprising at least one of industrial control code samples, industrial standards data, or industrial protocol data; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to render a chat interface configured to receive industrial design input data as plain language input data; a generative artificial intelligence (AI) component configured to infer functional requirements of an industrial control system for an industrial automation system based on generative AI analysis of the industrial design input data using the generative AI model; and a project generation component configured to generate, based on the generative AI analysis, industrial control code designed to satisfy the functional requirements.
Claim 19 A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
rendering a project development interface;
receiving, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
1. A system, comprising: a memory that stores executable components and a generative artificial intelligence (AI) model that has been trained using training data comprising at least one of industrial control code samples, industrial standards data, or industrial protocol data; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a user interface component configured to render a chat interface configured to receive industrial design input data as plain language input data; a generative artificial intelligence (AI) component configured to infer functional requirements of an industrial control system for an industrial automation system based on generative AI analysis of the industrial design input data using the generative AI model; and a project generation component configured to generate, based on the generative AI analysis, industrial control code designed to satisfy the functional requirements.
Claims 1-20 are further rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending application No. 18/497,191. Herein the independent claims are shown mapped to claim 1 of the co-pending application. The dependent claims are ODP in view of dependent claims of claim 1 or in view of the prior art used in the art rejection below associated with that dependent claim.
Instant Application 18/614,956
Co-pending Application 18/497,191
Claim 1
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:
a user interface component configured to render a project development interface and to
receive, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative Al component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
a project generation component configured to generate, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
A system, comprising:
a memory that stores executable components and a generative artificial intelligence (AI) model; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to receive, as natural language input, a prompt comprising a request for control code for an industrial system project in development, wherein the request specifies one or more requirements of the control code;
a generative AI component configured to
perform contextual analysis on the industrial system project to determine at least one of a type of industrial application or an industrial vertical for which the industrial system project is being developed, and to generate, using the generative AI model, control code inferred to satisfy the one or more requirements based on analysis of the prompt and a result of the contextual analysis; and a project generation component configured to integrate the control code into the industrial system project.
Claim 11
A method, comprising:
rendering, by a system comprising a processor, a project development interface;
receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project,
wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating, by the system, a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, by the system based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
A system, comprising: a memory that stores executable components and a generative artificial intelligence (AI) model; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to receive, as natural language input,
a prompt comprising a request for control code for an industrial system project in development, wherein the request specifies one or more requirements of the control code; a generative AI component configured to perform contextual analysis on the industrial system project to determine at least one of a type of industrial application or an industrial vertical for which the industrial system project is being developed, and to generate, using the generative AI model, control code inferred to satisfy the one or more requirements based on analysis of the prompt and a result of the contextual analysis; and a project generation component configured to integrate the control code into the industrial system project..
Claim 19 A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
endering a project development interface;
receiving, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
A system, comprising: a memory that stores executable components and a generative artificial intelligence (AI) model; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to receive, as natural language input, a prompt comprising a request for control code for an industrial system project in development, wherein the request specifies one or more requirements of the control code; a generative AI component configured to perform contextual analysis on the industrial system project to determine at least one of a type of industrial application or an industrial vertical for which the industrial system project is being developed, and to generate, using the generative AI model, control code inferred to satisfy the one or more requirements based on analysis of the prompt and a result of the contextual analysis; and a project generation component configured to integrate the control code into the industrial system project.
Claims 1-20 are further rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending application No. 18/610,444. Herein the independent claims are shown mapped to claim 1 of the co-pending application. The dependent claims are ODP in view of dependent claims of claim 1 or in view of the prior art used in the art rejection below associated with that dependent claim.
Instant Application 18/614,956
Co-pending Application 18/610,444
Claim 1
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:
a user interface component configured to render a project development interface and to
receive, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative Al component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
a project generation component configured to generate, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
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:
a user interface component configured to receive, via an integrated development environment (IDE) interface, a natural language query directed to industrial control code that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control code; and a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language query, 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, and a response prompted from a generative AT model comprising information used by the generative Al component to formulate the natural language answer, wherein the user interface component is configured to render the natural language answer on the IDE interface.
Claim 11
A method, comprising:
rendering, by a system comprising a processor, a project development interface;
receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project,
wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating, by the system, a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, by the system based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1.
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:
a user interface component configured to receive, via an integrated development environment (IDE) interface, a natural language query directed to industrial control code that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control code; and a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language query, 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, and a response prompted from a generative AT model comprising information used by the generative Al component to formulate the natural language answer, wherein the user interface component is configured to render the natural language answer on the IDE interface.
Claim 19
A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
rendering a project development interface;
receiving, via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project:
generating control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and
creating a smart object definition representing the control code;
in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition; and
generating, based on the design input, an executable control program file that, in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code.
Claim 1
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:
a user interface component configured to receive, via an integrated development environment (IDE) interface, a natural language query directed to industrial control code that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control code; and
a generative artificial intelligence (AI) component configured to, in response to receipt of the natural language query, 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, and a response prompted from a generative AT model comprising information used by the generative Al component to formulate the natural language answer, wherein the user interface component is configured to render the natural language answer on the IDE interface.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, lines 15, 27 and 35, "the control code" is unclear whether it refers to "control code" in line 13 or 14 of the claim. For the examination purposes, "the control code" in lines 15, 27 and 35 will be treated as -the generated control code--.
Claims 11 and 19 have the same issue and, for the examination purposes, "the control code" in lines 16, 22 and 29 of claim 11 and in lines 16, 22 and 28 of claim 19 will be treated as --the generated control code- respectively.
Claims 2-10, 12-18, and 20 depend on the rejected claims and inherit the same issue.
Claim Rejections - 35 USC § 101
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.
9. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims 1, 11 and 19 are being presented below.
Claims 1, 11 and 19:
Step 1 Analysis:
Claims 1-10 of the instant application is direct to apparatus.
Claims 11-18 of the instant application is direct to process.
Claims 19-20 of the instant application is direct to product.
Step 2 Analysis:
Claims 1, 11 and 19 recite:
(a) rendering, by a system comprising a processor, a project development interface;
(b) receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
(c) in response to receiving a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project: generating, by the system, control code inferred to satisfy the one or more requirements, wherein the generating comprises generating the control code based on based on analysis of the first natural language request, industry- specific information encoded in the one or more custom models, and a response prompted from a generative AI model, and creating, by the system, a smart object definition representing the control code;
(d) in response to receiving a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined as part of the industrial system project and representing an industrial controller, recording, by the system, a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition;
Step 2A -- Prong 1:
The claims 1, 11 and 19 recite the limitations of:
(b) receiving, by the system via interaction with the project development interface, design input that defines aspects of an industrial system project, wherein the design input comprises natural language requests received via a chat interface;
Limitation (b) is limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “receiving”/collecting can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitations of “A system”, “a memory”, “a processor”, “an industrial controller” and “an industrial automation system”. The limitations of “A system”, “a memory”, “a processor”, “an industrial controller” and “an industrial automation system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitations “a user interface component”; “a generative artificial intelligence” and “a project generation component” recited as tools perform abstract idea. The claim 11 recites the additional limitations of “a system”, “a processor”, “an industrial controller” and “an industrial automation system”. The limitations of “a system”, “a processor”, “an industrial controller” and “an industrial automation system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitations “a project development interface”; “a generative AI model” recited as tools perform abstract idea. The claim 19 recites the additional limitations of “A non-transitory computer-readable medium”, “a processor”, “an industrial controller” and “an industrial automation system”. The limitations of “a system”, “a processor”, “an industrial controller” and “an industrial automation system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitations “a project development interface”; “a generative AI model” recited as tools perform abstract idea. Additionally, limitation (a) perform as well-understood, routine and conventional activity, limitations (c) and (e) are merely insignificant extra solution activity of gathering data, analyzing data and outputting data. Limitation (d) perform as well-understood, routine and conventional activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving and storing/uploading data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g) . Therefore, claims are ineligible.
Dependent claims
Additionally, claim 2, 12 and 20 recite “wherein the generative AI component is further configured to, in response to receipt of a third natural language request, of the natural language requests, to create the controller definition, create the controller definition in accordance with a controller description included in the third natural language request” is merely insignificant extra solution activity of comparation/judgment data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 2 and 12 are ineligible.
Additionally, claim 3 recites “wherein the controller description included in the third natural language request describes at least one of a vendor and model of the industrial controller, a name of the industrial controller, identities of one or more I/O modules installed on the industrial controller, or an identify of a production line that the controller will be monitoring and controlling, and the generative AI component is configured to set properties of the controller definition in accordance with the controller description” is merely insignificant extra solution activity of defining and observing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 3 is ineligible.
Additionally, claim 4 recites “wherein the third natural language request comprises a request for a recommended industrial controller and associated configuration capable of satisfying a control requirement described by the third natural language request, and the generative Al component is configured to set, based on the industry- specific information encoded in the one or more custom models and another response prompted from a generative Al model, properties of the controller description inferred to satisfy the control requirement” is merely insignificant extra solution activity of evaluating and observing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 4 is ineligible.
Additionally, claims 5 and 15 recite “wherein the control requirement described by the third natural language request comprises at least one of a type of industrial application to be executed by the industrial controller, an identity of one or more devices or machines to be monitored or controlled by the industrial controller, an industrial vertical in which the control application will operate, or a minimum or maximum control performance metric” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 5 is ineligible.
Additionally, claims 6 and 16 recite “wherein the first natural language request describes, as the one or more requirements of the control code, at least one of a type of industrial application for which the control code is being developed, an identity of one or more machines or industrial assets that are part of the industrial automation system” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 6 is ineligible.
Additionally, claims 7 and 17 recite “wherein the industry-specific information 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” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 7 and 17 are ineligible.
Additionally, claims 8 and 18 recite “wherein the generative Al component is configured to generate the control code as at least one of ladder logic, structured text, a function block diagram, or an industrial domain-specific language (DSL)” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 8 and 18 are ineligible.
Additionally, claim 9 recites “wherein the user interface component is further configured to render a navigation tree that comprises browsable smart object nodes representing smart object definitions, including the smart object definition, that are defined for the industrial system project” perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 9 is ineligible.
Additionally, claim 10 recites “wherein the generative AI component is configured to, in response to receipt of the first natural language request, formulate a prompt, directed to the generative AI model, designed to obtain the response from the generative Al model comprising information used by the generative AI component to generate the control code inferred to satisfy the one or more requirements” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 10 is ineligible.
Additionally, claim 13 recites “wherein the controller description included in the third natural language request describes at least one of a vendor and model of the industrial controller, a name of the industrial controller, identities of one or more I/O modules installed on the industrial controller, or an identify of a production line that the controller will be monitoring and controlling, and the creating of the controller definition comprises setting properties of the controller definition in accordance with the controller description” is merely insignificant extra solution activity of defining data, monitoring data and creating data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 13 is ineligible.
Additionally, claim 14 recites “wherein the third natural language request comprises a request for a recommended industrial controller and associated configuration capable of satisfying a control requirement described by the third natural language request, and the creating of the controller definition comprises setting, by the system based on the industry-specific information encoded in the one or more custom models and another response prompted from a generative AI model, properties of the controller description inferred to satisfy the control requirement” is merely insignificant extra solution activity of data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 14 is ineligible.
Claim Rejections - 35 USC § 103
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.
10. Claim(s) 1-2, 6-12 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stump et al. (US Pub. No. 2023/0058094 A1 – IDS filed on 02/20/2026 --herein after Stump) in view of Heiko Koziolek (ChatGPT for PLC/DCS Control Logic Generation, 2023 – IDS filed on 07/22/2024 – herein after Koziolek).
Claim 1
Stump discloses
A system (a system – See Fig. 2), comprising:
a memory that stores executable components (a memory that stores executable components – See paragraph [0003]) and one or more custom models (project model – See paragraph [0076]); and
a processor (processor 218 – See Fig. 2), operatively coupled to the memory, that executes the executable components (a memory that stores executable components – See paragraph [0003]), the executable components comprising:
a user interface component configured to render a project development interface (user interface component 204 renders design feedback 518 designed to assist the developer in connection with developing a system project 302 for configuration, control, and visualization of an industrial automation system – See paragraph [0058]) and to receive, via interaction with the project development interface (IDE system 202 can be configured to receive digital engineering drawings (e.g., computer-aided design (CAD) files) as design input 512 – See paragraph [0059]), design input that defines aspects of an industrial system project (design input that defines aspects of an industrial control and monitoring project --- See paragraph [0004]),
a project generation component configured to generate, based on the design input (a project generation component configured to generate system project data based on the design input – See paragraph [0003]), an executable control program file that (a system project comprising at least one of an executable industrial control program – See paragraphs [0003-0004]. Executable files that can be executed on the respective target devices – See paragraph [0055]), in response to execution on the industrial controller represented by the controller definition, configures the industrial controller in accordance with the controller definition (the project generation component 206 will generate portions of the system project 302 to satisfy the specified design goals and constraints. Portions of the system project 302 that can be generated in this manner can include, but are not limited to, device and equipment selections (e.g., definitions of how many pumps, controllers, stations, conveyors, drives, or other assets will be needed to satisfy the specified goal), associated device configurations (e.g., tuning parameters, network settings, drive parameters, etc.), control coding, or HMI screens suitable for visualizing the automation system being designed – See paragraph [0062]. Upon completion of project development, a user can identify which target devices—including an industrial controller 118, an HMI terminal 114, and a motor drive 710—are to execute or receive these respective aspects of the system project 302. Project deployment component 208 can then translate the controller code defined by the system project 302 to a control program file 702 formatted for execution on the specified industrial controller 118 and send this control program file 702 to the controller 118 (e.g., via plant network 116) – See paragraph [0080]) and causes the industrial controller to monitor and control an industrial automation system in accordance with the control code (industry-standard or recommended control code for monitoring and controlling the asset represented by the automation object 222, a 2D or 3D graphical object that can be used to visualize operational or statistical data for the asset, alarm conditions associated with the asset, analytic or reporting scripts designed to yield actionable insights into the asset's behavior, or other such properties – See paragraph [0077]. Control logic that can be executed as part of the system project 302 to monitor and control the represented assets – See paragraphs [0089-0090]).
Stump discloses project generation component 206 can apply artificial intelligence (AI) or traditional analytic approaches to this information to determine whether existing equipment specified in design in put 512 can be repurposed or leveraged – See paragraph [0072])
Stump does not disclose
wherein the design input comprises natural language requests received via a chat interface;
a generative artificial intelligence (AI) component configured to,
in response to receipt of a first natural language request, of the natural language requests, describing one or more requirements of control code to be created as part of the industrial system project, generate control code inferred to satisfy the one or more requirements and create a smart object definition representing the control code, wherein the generative AI component is configured to generate the control code based on based on analysis of the first natural language request, industry-specific information encoded in the one or more custom models, and
a response prompted from a generative AI model, and in response to receipt of a second natural language request, of the natural language requests, to allocate an instance of the smart object definition to a controller definition defined in the industrial system project and representing an industrial controller, record a binding between the instance of the smart object definition and the controller definition, wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition;
Koziolek discloses
wherein the design input comprises natural language requests received via a chat interface (the prompts to OpenAI’s popular ChatGPT with the GPT-4 LLM and analyzed the generated answers – See page 1, right column. Using ChatGPT code generation and formulating many natural language prompts may require more efforts than simply writing the code manually – See page 7, left column);
a generative artificial intelligence (AI) component configured to (generative AI – See page 1, left column),
in response to receipt of a first natural language request, of the natural language requests (multiple concurrent requests – See page 4, right column), describing one or more requirements of control code to be created as part of the industrial system project (Metrics are needed to evaluate the quality of LLM source code generation, so that different LLMs could be compared or the effectiveness of a custom training could be verified – See page 1, left column. Various Engineering Inputs: ChatGPT showed an ability to complete control logic requirements, by synthesizing plausible control narratives, I/O lists, or P&IDs – See page 6, left column), generate control code inferred to satisfy the one or more requirements (ChatGPT-generated IEC 61131-3 ST code for an EtherCAT state machine with correctly synthesized state transitions… Code generation can also support control engineers not only in creating code, but also in fixing bugs, optimizing algorithms, or translating existing programs. For (‘FIX TRAFFIC LIGHT CODE’) we asked ChatGPT to find and fix errors in code generated with GPT-3.5. It correctly found four issues in the code – See Fig. 4, page 6, left column) and create a smart object definition representing the control code (derived object-oriented models from piping-and-instrumentation diagrams (P&IDs) and then applied pre-specified rules to automatically identify topological patterns and generate IEC 61131-3 ST—See page 1, right column. Prompted for a function block for an EtherCAT slave device. The device is controlled with a state machine, where specific function are only available if the device is in a particular state – See page 5, right column), wherein the generative AI component is configured to generate the control code based on based on analysis of the first natural language request (we created 100 LLM prompts in 10 representative categories to analyze control logic generation for of PLCs and DCS from natural language – See Abstract, left column. Provides reference points for specific sensors and actuators, which can then be used in follow-up prompts to generate required interlocking code – See page 5, right column), industry-specific information encoded in the one or more custom models (generate code for startup/shutdown sequences and batch applications, which involve a number of different steps. The correct sequence of steps for an abstractly formulated task requires specific application domain knowledge encoded in LLMs – See page 3, left column. The states are predefined and only specific transitions are allowed. ChatGPT managed to encode the correctly allowed transitions in the generate IEC 61131-3 ST code – See page 5, right column), and
a response prompted from a generative AI model, and in response to receipt of a second natural language request, of the natural language requests (Various Engineering Inputs: ChatGPT showed an ability to complete control logic requirements – See page 6, left column. Using ChatGPT code generation and formulating many natural language prompts may require more efforts – See page 7, left column. Examiner respectfully notes that various engineering inputs could be first, second and third requests), to allocate an instance of the smart object definition to a controller definition defined in the industrial system project (the programs are assigned cyclically executing tasks in the PLC runtime. We also successfully generated a few examples for cascade control and ratio control. The device is controlled with a state machine, where specific function are only available if the device is in a particular state. The states are predefined and only specific transitions are allowed. ChatGPT managed to encode the correctly allowed transitions in the generate IEC 61131-3 ST code -- See page 5) and representing an industrial controller (Process Control: In this category, we tested generating code for different kinds of PID controllers, e.g., for level, flow or pressure control in specific situations – See page 5, left column), record a binding between the instance of the smart object definition and the controller definition (Interlocks are safety mechanisms, often linking an individual sensor reading with a concrete actuator response. While the code to express interlocks is simple, the prompts in this category also check an LLMs ability to create alternative notations (e.g., cause and effect matrices) as well as the ability to suggest required interlocks for a given situation. Prompts for Diagnostics and Communication test whether an LLM can deal with specific requests regarding communication protocols often linked to control logic – See page 3, right column and Table 1), wherein the binding configures the industrial system project to assign a copy of the control code associated with the smart object definition to the industrial controller represented by the controller definition (We found that ChatGPT can often generate sophisticated, syntactically correct IEC 61131-3 code given natural language prompts. In addition, we perceived interesting reasoning skills and a vast amount of domain knowledge retrievable from GPT-4 – See page 1, right column. ChatGPT generated syntactically correct code for all prompts in this category, often with useful code comments and additional explanations below the code. The generated code utilizes keywords and standard functions defined in the latest version 3.0 of the IEC 61131-3 specification, such as ‘METHOD’ or ‘CONCAT’ – See page 3, right column).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to create Large Language Model prompts to analyze control logic generation for control program from natural language as suggested by Koriolek (Abstract).
Regarding claim 2, the system of claim 1,
Koriolek discloses
wherein the generative AI component is further configured to, in response to receipt of a third natural language request, of the natural language requests, to create the controller definition, create the controller definition in accordance with a controller description included in the third natural language request (The category Advanced Process Control includes prompts that for example ask for model-predictive control schemes in MATLAB, Python or C++, since these are typical languages for this kind of control logic. ChatGPT generated syntactically correct code for all prompts in this category, often with useful code comments and additional explanations below the code. The generated code utilizes keywords and standard functions defined in the latest version 3.0 of the IEC 61131-3 specification – See page 3. Examiner respectfully notes that Various Engineering inputs could be first, second and third requests).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to create prompts to analyze control logic and generate standard function that defined in specification as suggested by Koriolek (See page 3).
Regarding claim 6, the system of claim 1,
Koziolek discloses
wherein the first natural language request describes, as the one or more requirements of the control code (Various Engineering Inputs: ChatGPT showed an ability to complete control logic requirements – See page 6, left column), at least one of a type of industrial application for which the control code is being developed, an identity of one or more machines or industrial assets that are part of the industrial automation system (Control engineering for industrial automation application can involve custom control logic programming, e.g., in IEC 61131-3 Structured Text (ST). Such logic can involve special mathematical algorithms, control strategies, or optimization routines – See page 1, left column)
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to involve special mathematical algorithms, control strategies, or optimization routines as suggested by Koriolek (See page 1, left column).
Regarding claim 7, the system of claim 1,
Stump discloses
wherein the industry-specific information 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 (encodes one or more of control programming; HMI, AR, and/or VR visualizations; device or sub-system configuration data (e.g., drive parameters, vision system configurations, telemetry device parameters, safety zone definitions, etc.); or other such aspects of an industrial automation system being designed… Project generation component 206 can leverage guardrail templates 506 to implement rules-based programming, whereby programming feedback (a subset of design feedback 518) such as dynamic intelligent autocorrection, type-aheads, or coding suggestions are rendered based on encoded industry expertise and best practices (e.g., identifying inefficiencies in code being developed and recommending appropriate corrections) – See paragraphs [0055-0057 and 0066]).
Regarding claim 8, the system of claim 1,
Koziolek discloses
wherein the generative Al component is configured to generate the control code as at least one of ladder logic, structured text, a function block diagram, or an industrial domain-specific language (DSL) (We generated IEC 61131-3 ST as a representative textual control programming language, which is standardized and supported by many PLC and DCS programming environments. – See page 8, left column).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to generate a representative textual control programming language as suggested by Koriolek (See page 8, left column).
Regarding claim 9, the system of claim 1,
Stump discloses
wherein the user interface component is further configured to render a navigation tree that comprises browsable smart object nodes representing smart object definitions, including the smart object definition, that are defined for the industrial system project (the IDE editor 224 can allow a user to modify attributes of selected automation objects 222 that are stored in the library 502. To this end, user interface component 204 can generate and deliver user interfaces to a client device 504 (e.g., via an IDE client 514) that allow the user to browse the available automation objects 222 and submit edits 1102 to selected objects 222. Any of the attributes described above in connection with FIG. 9 can be modified in this manner for any of the defined automation objects 222. For example, a designer may wish to modify the control code associated with a particular industrial asset (e.g., a pump, a tank, a stamping press, etc.) having a defined automation object 222 stored in the library 502 – See paragraph [0104] and Fig. 5, 302, system project including nodes and tree).
Regarding claim 10, the system of claim 1,
Koziolek discloses
wherein the generative AI component is configured to, in response to receipt of the first natural language request, formulate a prompt (how to formulate prompts to yield effective results – See page 1, left column), directed to the generative AI model (targets exploring the capability of Generative AI to generate control logic – See page 7 – right column), designed to obtain the response from the generative Al model comprising information used by the generative AI component to generate the control code inferred to satisfy the one or more requirements (A generative artificial intelligence (AI) is a special kind of AI system that can generate text, images, videos, and also program code – See page 1, left column. ChatGPT showed an ability to complete control logic requirements, by synthesizing plausible control narratives, I/O lists, or P&IDs – See page 6, left column).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to formulate prompts to yield effective results as suggested by Koriolek (See page 1, left column).
Regarding claim 11.
A method, comprising:
Regarding claim 11, recites the same limitations as rejected claim 1 above.
Regarding claim 12, recites the same limitations as rejected claim 2 above.
Regarding claim 16, recites the same limitations as rejected claim 6 above.
Regarding claim 17, recites the same limitations as rejected claim 7 above.
Regarding claim 18, recites the same limitations as rejected claim 8 above.
Regarding claim 19.
A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
Regarding claim 19, recites the same limitations as rejected claim 1 above.
Regarding claim 20, recites the same limitations as rejected claim 2 above.
11. Claim(s) 3-5 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stump and Koriolek as applied to claims 2 and 12 respectively above, and further in view of Lu et al. (US Pub. No. 2025/0291559 A – herein after Lu).
Regarding claim 3, the system of claim 2,
Lu discloses
wherein the controller description included in the third natural language request describes at least one of a vendor and model of the industrial controller, a name of the industrial controller, identities of one or more I/O modules installed on the industrial controller (an input/output controller(s) that may be controlled by software and may be used for receiving I/O signals that are uncommitted to a specific role – See paragraph [0213]), or an identify of a production line that the controller will be monitoring and controlling (applied to a language model (e.g., large language model, vision language model, etc.) that is trained to interact with a simulation system and/or control various aspects of a simulation. In some examples, the language model may generate code for, among other things, creating and/or customizing a virtual environment associated with the simulation - - See paragraph [0025]. Monitoring the status and health of the controller(s) 1036 and/or infotainment SoC 1030 – See paragraph [0221]), and
the generative AI component is configured to set properties of the controller definition in accordance with the controller description (The AI controller may compute desired steering, acceleration, and/or braking, and may apply those values to the virtual objects. The vehicle properties used may include mass, max RPM, torque curves, and/or other properties – See paragraph [0041]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Lu’s teaching into Stump’s and Koriolek’s invention because incorporating Lu’s teaching would enhance Stump and Koriolek to enable to compute desired steering, acceleration, and/or braking, and may apply those values to the virtual objects as suggested by Lu (See paragraph [0141]).
Regarding claim 4, the system of claim 2, wherein
Koriolek discloses
the third natural language request comprises a request for a recommended industrial controller and associated configuration capable of satisfying a control requirement described by the third natural language request (ChatGPT showed an ability to complete control logic requirements, by synthesizing plausible control narratives, I/O lists, or P&IDs. For illustrative purposes, Figure 5 shows an excerpt from a control narrative generated specifically for ethanol production– See page 6, left column), and
the generative Al component is configured to set, based on the industry- specific information encoded in the one or more custom models and another response prompted from a generative Al model (provide concrete setpoints and ranges, which are specially relevant for the control logic engineering – See page 6, left column),
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to create prompts to complete control logic requirements as suggested by Koriolek (See page 6, left column).
Stump and Koriolek do not disclose
properties of the controller description inferred to satisfy the control requirement.
Lu discloses
the generative Al component is configured to set, based on the industry- specific information encoded in the one or more custom models and another response prompted from a generative Al model, properties of the controller description inferred to satisfy the control requirement (provide structured entity information to the LLM/VLM/MMLM/etc. by combining the structured entity textual description with its many properties and relationships, allowing for deeper insights by the model – See paragraph [0094]. CPUs are oftentimes unable to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption – See paragraph [0216]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Lu’s teaching into Stump’s and Koriolek’s invention because incorporating Lu’s teaching would enhance Stump and Koriolek to enable to provide structured entity information with its many properties as suggested by Lu (paragraph [0094]).
Regarding claim 5, the system of claim 4,
Koziolek discloses
wherein the control requirement described by the third natural language request comprises at least one of a type of industrial application to be executed by the industrial controller, an identity of one or more devices or machines to be monitored or controlled by the industrial controller, an industrial vertical in which the control application will operate, or a minimum or maximum control performance metric (ChatGPT sometimes provided very specific technical information. For example, Fig. 4 shows an code excerpt for (‘ETHERCAT STATE MACHINE’), which prompted for a function block for an EtherCAT slave device. The device is controlled with a state machine, where specific function are only available if the device is in a particular state – See page 5, right column).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to control the device with a state machine, where specific function are only available if the device is in a particular state as suggested by Koriolek (See page 5, right column).
Regarding claim 13, the method of claim 12,
Lu discloses
wherein the controller description included in the third natural language request describes at least one of a vendor and model of the industrial controller, a name of the industrial controller, identities of one or more I/O modules installed on the industrial controller, or an identify of a production line that the controller will be monitoring and controlling (The AI controller may compute desired steering, acceleration, and/or braking, and may apply those values to the virtual objects. The vehicle properties used may include mass, max RPM, torque curves, and/or other properties - - See paragraph [0141]), and
the creating of the controller definition comprises setting properties of the controller definition in accordance with the controller description (The AI controller may compute desired steering, acceleration, and/or braking, and may apply those values to the virtual objects. The vehicle properties used may include mass, max RPM, torque curves, and/or other properties – See paragraph [0041]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Lu’s teaching into Stump’s and Koriolek’s invention because incorporating Lu’s teaching would enhance Stump and Koriolek to enable to compute desired steering, acceleration, and/or braking, and may apply those values to the virtual objects as suggested by Lu (See paragraph [0141]).
Regarding claim 14, the method of claim 12,
Koriolek discloses
wherein the third natural language request comprises a request for a recommended industrial controller and associated configuration capable of satisfying a control requirement described by the third natural language request (ChatGPT showed an ability to complete control logic requirements, by synthesizing plausible control narratives, I/O lists, or P&IDs. For illustrative purposes, Figure 5 shows an excerpt from a control narrative generated specifically for ethanol production– See page 6, left column), and
the creating of the controller definition comprises setting, by the system based on the industry-specific information encoded in the one or more custom models and another response prompted from a generative AI model (provide concrete setpoints and ranges, which are specially relevant for the control logic engineering – See page 6, left column),
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to create prompts to complete control logic requirements as suggested by Koriolek (See page 6, left column).
Stump and Koriolek do not disclose
properties of the controller description inferred to satisfy the control requirement.
Lu discloses
the creating of the controller definition comprises setting, by the system based on the industry-specific information encoded in the one or more custom models and another response prompted from a generative AI model properties of the controller description inferred to satisfy the control requirement (provide structured entity information to the LLM/VLM/MMLM/etc. by combining the structured entity textual description with its many properties and relationships, allowing for deeper insights by the model – See paragraph [0094]. CPUs are oftentimes unable to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption – See paragraph [0216]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Lu’s teaching into Stump’s and Koriolek’s invention because incorporating Lu’s teaching would enhance Stump and Koriolek to enable to provide structured entity information with its many properties as suggested by Lu (paragraph [0094]).
Regarding claim 15, the method of claim 14,
Koziolek discloses
wherein the control requirement described by the third natural language request comprises at least one of a type of industrial application to be executed by the industrial controller, an identity of one or more devices or machines to be monitored or controlled by the industrial controller, an industrial vertical in which the control application will operate, or a minimum or maximum control performance metric (ChatGPT sometimes provided very specific technical information. For example, Fig. 4 shows an code excerpt for (‘ETHERCAT STATE MACHINE’), which prompted for a function block for an EtherCAT slave device. The device is controlled with a state machine, where specific function are only available if the device is in a particular state – See page 5, right column).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Koriolek’s teaching into Stump’s invention because incorporating Koriolek’s teaching would enhance Stump to enable to control the device with a state machine, where specific function are only available if the device is in a particular state as suggested by Koriolek (See page 5, right column).
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ngiam et al. (US Pub. No. 2025/0045027 A1) discloses when execution runs into an error, the LCIEE may decide to redo the code at operation 314 and then try to execute the operation again. This means asking the LLM to regenerate the code for that operation. For example, the prompt for the LLM includes information about the code being executed, the error generated, and the operation, and then request to generate new code to try again – See paragraph [0086]).
Lee et al. (US Patent No. 12130923 B2) discloses receives natural language data for performing an identified cybersecurity task. The processor can provide the natural language data to a first machine learning (ML) model. The first ML model can automatically infer a template query based on the natural language data. The processor can receive user input indicating a finalized query and to provide the finalized query as input to a system configured to perform the identified computational task. The processor can provide the finalized query as a reference phrase to a second ML model, the second ML model configured to generate a set of natural language phrases similar to the reference phrase. The processor can generate supplemental training data using the set of natural language phrases similar to the reference phrase to augment training data used to improve performance of the first ML model and/or the second ML model – See Abstract and specification for more details.
Grover et al. (US Pub. No. 2025/0278268 A) discloses a plurality of sets input of parameters are captured. The captured plurality of sets of input parameters are input into a first Artificial Intelligence (AI) algorithm that generates a plurality of corresponding AI generated source code. Each set of the captured plurality of sets of input parameters comprises one or more input parameters. The plurality of corresponding AI generated source code are scanned to identify an issue – See Abstract and specification for more details.
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/MONGBAO NGUYEN/Examiner, Art Unit 2192
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