DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Note
Claims 15- 20 recite the limitation “Computer-readable storage media”. Examiner did not assign 101 rejections for the claims because the published paragraph 143 states “In no case is the computer readable storage media a propagated or transitory signal.”.
Specification
The disclosure is objected to because of the following informalities: In par. 22, the specification only refers to Figs. 8D-E, when the drawings include Figs. 8A-E.
Appropriate correction is required.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1,2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stump et al. (US 20210096827 A1.), and in view of Reissner et al. (US 20200104723 A1.).
As per claim 1, Stump et al. teaches a computer-implemented method for providing industrial design suggestions, the method comprising (para 4-5, fig 5):
receiving, via a user interface of an industrial design application an industrial design selection from a user of a plurality of users of the industrial design application (Fig.5, para 46, system 202 receive user design input/user selection #512 via user interface #204. Also see Fig. 5, para 57, client IDE 514 on a client computer 504 is inputting design input 512, #512 teaches design selection; Fig. 9, para 15 and para 86 teach multiple users ), wherein the industrial design selection comprises a selection of a configuration option in a design of an industrial automation project (para 57, the inputs are to design industrial automation system; also see para 5 “industrial design input that defines control design aspects of an industrial automation project;”) ;
generating a prompt to elicit a reply from a General Artificial Intelligence (GAI) model trained on data including previous industrial design submissions from the plurality of users, the prompt comprising (paras 42 and 47- and double-sided arrow between #204 & #206, user interface 204 sending the user selection information to #206, the information that is passed from 204 to 206 teaches the prompt):
a description of the industrial design selection and the design (para 47, “Project generation component 206 can be configured to create a system project comprising one or more project files based on design input received via the user interface component 204,”), and
the GAI model (paras 42 and 47, Project generation component 206 generate system project based on a training module generated based on analysis of multiple sets of project data submitted to the industrial IDE, therefore request to use the training module/GAI is understood) to compare the design with the industrial design selection against common selections learned from the previous industrial design submissions to identify uncommon selections (para 85,“common design approaches and relationships between project components can be learned, as well runtime production data collected after these system projects have been commissioned for operation.”; para 73 “discovery of common develop behaviors; learned associations between design goals and control code or visualizations, and other such intelligent decision-making”; para 65 “autocorrection” best on best practices. User selection that is uncommon based on best practices was identified; para 42 – “commonly used control code, configurations, etc.”);
submitting the prompt to the GAI model (para 57, Fig. 5, developers can submit design input and it goes through predefined code modules 508 before creating system project 302 for configuration, control and visualization. This teaches submitting the prompt to a GAI.);
receiving, in response to the prompt, the reply comprising one or more alternate selection suggestions for the configuration option (Fig.5 #518 “Design Feedback”; para 64 , the feedback include recommendation such as rewrite code portion or visualizations), wherein the one or more alternate selection suggestions are representative of previous selections made in the previous industrial design submissions from the plurality of users of the industrial design application (para 88, aggregated project data from previous project is used as training data to rendering appropriate design feedback 518; para 69 using AI for design feedback); and
providing a notification to the user via the user interface, the notification comprising the one or more alternate selection suggestions (Fig.5 “Design Feedback” #518 going to client 514, paras 65 and 85 – provides recommendations and suggestions).
Stump et al. do not clearly specify whether the training module/GAI analyze based on a request for the GAI.
However, in the same field of endeavor, Reissner et al. teach a request for the GAI (Reissner et al. teach sending request for analysis by machine learning engine. See Fig.8 and para 73; “analysis request”, the lifecycle tool 815 can request an analysis by machine learning engine 825; para 29, “use of unconventional and non-routine computer operations to contextually provide notifications and recommendations for component (or alternative component) selection” based on machine learning).
It would have been obvious to a person ordinary skilled in the art, before the effective filling date of the claimed invention, to modify the method for industrial design suggestions taught by Stump et al. and to include the request analysis activities taught by Reissner et al. This would have been obvious because both Stump et al. and Reissner at al. teach industrial automation. Such combination provides more flexibility as to how the analysis can be performed, either machine learning or other methods including manual evaluation (Reissner et al., para 29).
As per claim 2, the combination of Stump et al. and Reissner et al. teach the computer-implemented method of claim 1, wherein the notification further comprises one or more selectable elements corresponding to the one or more alternate selection suggestions, the method further comprising:
receiving, from the user in response to the notification, a user selection of a first of the selectable elements indicating an adoption of a first of the alternate selection suggestions (Stump et al. ,para 57 and Fig.5,user is using recommendation for developing system project 302; para 108, “suggested automation objects to be added to the design project”; para 69 “guide user selection of equipment or devices for a given design goal” ; Reissner et al. ,para 29, “and recommendations for component (or alternative component) selection”) ; and
updating the design of the industrial automation project with the first of the alternate selection suggestions (Stump et al., para 64; “auto-completion” based on recommendations; para 66 “automatically modify”).
As per claim 4, the combination of Stump et al. and Reissner et al. teach the computer-implemented method of claim 1, further comprising:
receiving from a user, via the user interface, a request from a user for a design of the industrial automation project (Stump et al., Fig. 5, #512, design input received via user interface, para 57, a developer can submit design input and based on the design input via configuration, the user interface component renders design feedback, this teaches user requesting for a design feedback); and
providing to the user, via the user interface in response to the request, an initial design for the industrial automation project (Stump et al. ,Fig. 5, #518,design feedback), wherein the industrial design selection from the user comprises a modification of the initial design provided to the user ( Stump et al. ,para 66, user can modify the project generation code by running internal guardrail templates through project generation component 206.This execution enables alternative or additional recommendation which could use to modify the project design, also see para 144).
As per claim 5, the combination of Stump et al. and Reissner et al. teach the computer-implemented method of claim 4, wherein the method further comprises:
receiving from the user, via the user interface, a submission of a finalized design of the industrial automation project; and
providing the finalized design to the GAI model for updating learned common selections among users of the industrial design application (Stump et al. ,para 103 , “When legacy control project data 1102 is converted to a new system project 302 as described above, at least a portion of the resulting system project 320 can be added to the aggregated project data 904 to enhance the set of training data used by the training component 210 to train” ; therefore converted new system project/finalized data is added to the training data. Also see para 85, “Data that can be analyzed for training or learning purposes can include both design data (e.g., system project data) from which common design approaches and relationships between project components can be learned, as well runtime production data collected after these system projects have been commissioned for operation”.
Also see para 89, “aggregated project data 904 and fed to the IDE system's training component 210 as training data. Aggregated project data 904 can be collected in a manner that protects the industrial enterprises' proprietary project information. In some embodiments, IDE system 202 may only feed a user's project data to the training module 210 if the user expressly volunteers to allow their project information to be used to train the IDE system 202.”).
As per claim 6, the combination of Stump et al. and Reissner et al. teach the computer-implemented method of claim 1, wherein the prompt further requests a brief narrative of a potential problem associated with the industrial design selection, and wherein the notification sent to the user further comprises the brief narrative (Stump et al., para 66, display selection of alternative or recommending modifications of the code in order to bring the code into compliance. Also see, Fig.5 #518 “Design Feedback”; para 64 the feedback include recommendation such as rewrite code portion or visualizations).
As per claim 7, the combination of Stump et al. and Reissner et al. teach the computer-implemented method of claim 1, wherein the prompt for the GAI model further comprises an industry and an install location (Stump et al. ,para 69, installation site, e.g., available floor, wall or cabinet space; dimensions of the installation space; etc.; for different industries) for the industrial automation project, and a request to generate suggestions based on common user selections for designs submitted in the industry and the install location (Stump et al. ,para 69,design feedback; para 47; Project generation component 206 generate system project based on a training module generated based on analysis of multiple sets of project data submitted to the industrial IDE, therefore request to use the training module/GAI is understood).
As per claim 8, Stump et al. teach a system for providing industrial design suggestions, the system comprising: one or more processors; and one or more memories operably coupled to the one or more processors and having stored thereon software instructions that, upon execution by the one or more processors, cause the one or more processors to (para 45, one or more processors 218, and memory 220, para 51, software instruction):
Please refer to the analysis of claim 1 above for further clarifications.
As per claim 15, Stump et al. teach a computer-readable storage media device having program instructions stored thereon for providing industrial design suggestions, wherein the program instructions, upon execution by one or more processors, cause the one or more processors to (para 25, computer readable storage media having various instructions):
Please refer to the analysis of claim 1 above for further clarifications.
As per system claim 9 and device claim 16, please refer to the analysis of claim 2 above, as they recite the same limitations.
As per system claim 11 and device claim 18, please refer to the analysis of claim 4 above, as they recite the same limitations.
As per system claim 12 and device claim 19, please refer to the analysis of claim 5 above, as they recite
As per system claim 13 and device claim 20, please refer to the analysis of claim 6 above, as they recite the same limitations.
As per system claim 14, please refer to the analysis of claim 7 above, as they recite the same limitations.
Claims 3,10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Stump et al. (US 20210096827 A1.), and in view of Reissner et al. (US 20200104723 A1.), and further in view of Varro et al. (US 20230281486 A1.).
As per claim 3. Stump et al. teach the computer-implemented method of claim 1, the method further comprising: providing the GAI model with static data during initial training (para 3, the analytical module has been trained based on training analysis performed on aggregated system project data; aggregated system project data teaches static data;)
However, the combination of Stump et al. and Reissner et al. do not teach
the static data comprising one or more of: industrial product literature, industry standard data.
In the same field of endeavor, Varro et al. teach
the static data comprising one or more of: industrial product literature, industry standard data (Varro et al., para 28, teaches static data or training data include policies and standard), and safety requirements data.
It would have been obvious to a person ordinary skilled in the art, before the effective filling date of the claimed invention, to modify the method for industrial automation taught by Stump et al. and Reissner et al. and to include the AI based assistant feature to prevent potential violation for an industrial system into the system. This would have been obvious because the combination of Stump et al., Reissner at al. and Varro et al. teach Industrial automation. By adding Varro et al.’s AI based assistant feature to display indication for potential violations and to provide recommendations to fix the problem, the overall automation system will be compliant with industrial standard (Varro et al., para 28).
As per system claim 10 and device claim 17, please refer to the analysis of claim 3 above, as they recite the same limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please refer to the form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rokeya Alam whose telephone number is (571) 272-0083. The examiner can normally be reached on 7:30am - 4:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Scott Baderman can be reached at telephone number (571-272-3644). The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ROKEYA SHAWALI ALAM/Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
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