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
2. The action is responsive to the communications filed on 6/2/2026. Claims 1, 4-6, 8, 11-13, 15, 18-20 are pending in the case. Claims 1, 4, 5, 8, 11, 12, 15, 18, 19 are amended. Claims 2-3, 7, 9-10, 14, 16-17 are cancelled. Claims 1, 8, 15 are independent claims. Claims 1, 4-6, 8, 11-13, 15, 18-20 are rejected.
Summary of claims
3. Claims 1, 4-6, 8, 11-13, 15, 18-20 are pending,
Claims 1, 4, 5, 8, 11, 12, 15, 18, 19 are amended,
Claims 2-3, 7, 9-10, 14, 16-17 are cancelled,
Claims 1, 8, 15 are independent claims,
Claims 1, 4-6, 8, 11-13, 15, 18-20 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 6/2/2026, with respect to the rejection(s) of claim(s) 1, 4-6, 8, 11-13, 15, 18-20 under 103 have been fully considered and are not persuasive.
Applicant argued Zarur and Greunke did not teach “derivation of a detailed problem definition”, the comparison is made with definition of plant facility and not problem definition. Examiner respectfully disagrees and submits that Zarur discloses supervisory program 2206 may be designed to alter control of the automation system 2208 to mitigate predicted future performance problems identified based on the predictive analysis of the BIDT data 2216 in view of digital twin 1506 ([0179]), AI fields 2604 can also define the type or class of analytic problem to be solved relative to the key variable (e.g., modeling, clustering, optimization, minimization, etc.). The type of analytic problem can be determined by the AI engine component 514 based on a number of factors, including but not limited to the type of industrial asset or process modeled by the digital twin 1506 and the identity of the key variable ([0204]), For example, a digital twin 1506 may initially be used to solve the problem of minimizing fuel usage by its modeled industrial asset (e.g., within the context of a supervisory control system as illustrated in FIG. 22). If it is decided to prioritize minimization of emissions rather than fuel usage, the AI field 2604 defining the problem statement can be modified programmatically to reflect this new analytic problem, thereby conveying the new operational goal to the analytic system ([0205]), please note in this example, “solve the problem of minimizing fuel usage” is derivation of a detailed problem definition. Further, Zarur discloses development of the digital twin 1506 can involve AI contextualization of the smart objects 322 by AI engine component 514 to codify discovered relationships between modeled variables of the industrial system (e.g., measured telemetry values, KPIs, device statuses, power statistics, etc.) and to specify types of analytics to be performed or analytical problems to be solved on selected variables (e.g., optimization, minimization, clustering, etc.) ([0212]), that is, Zarur’s system uses digital twins to identify solutions to detailed problems, such as “minimizing furl usage” based on the type of the problem “minimizing”.
Applicant argued Zarur and Greunke did not teach “derives plant view of interest considering the detailed problem definition, which assets do they model and to what extent, are they relevant for the problem at hand or they model the plant assets to full extent irrespective of the problem to be solved”. Examiner respectfully disagrees and submits that Zarur discloses allows the user to easily compare test results for the different designs relative to a selected performance metric of interest ([0257]), a digital twin 1506 may initially be used to solve the problem of minimizing fuel usage by its modeled industrial asset (e.g., within the context of a supervisory control system as illustrated in FIG. 22). If it is decided to prioritize minimization of emissions rather than fuel usage, the AI field 2604 defining the problem statement can be modified programmatically to reflect this new analytic problem, thereby conveying the new operational goal to the analytic system ([0205]), that is, Zarur’s system uses digital twin based on the type of the problem, e.g., “minimizing fuel usage,” or “minimizing emissions”.
Applicant argued Zarur and Greunke did not teach “picking suitable digital twin models from potential variants for solving current problem”. Examiner respectfully disagrees and submits that Zarur discloses if the AI engine component 514 ascertains or infers that the digital twin 1506 models an industrial system that corresponds to this category of production line, the smart object 322 representing product throughput can be selected as the key variable. In some embodiments that use this approach for identifying key variables, AI engine component 514 can reference an industry knowledgebase that defines key variables for respective different types of industrial systems (e.g., machines, production lines, processes, verticals, etc.), and identify key variables by correlating the type of industrial system being modeled with the industry knowledgebase. In other scenarios, one or more of the key variables may be explicitly identified or defined by a user. In general, there may be more than one key variable or KPI for a given digital twin 1506. Other types of key variables can include, but are not limited to, energy consumption, energy efficiency, cycle time, asset availability, capacity utilization, or other such variables modeled by smart objects 322 that make up the digital twin 1506 ([0195]), the smart objects 322 can serve as a standardized interface to the digital twin 1506 through which other analytics systems can learn a desired or beneficial type of analytic to be performed on the digital twin 1506 (e.g., minimization of energy consumption, maximization of product throughput, maximization of efficiency, selection of a subset of available industrial assets to be deployed in order to achieve a desired result given defined constraints, etc.) ([0204]), development of the digital twin 1506 can involve AI contextualization of the smart objects 322 by AI engine component 514 to codify discovered relationships between modeled variables of the industrial system (e.g., measured telemetry values, KPIs, device statuses, power statistics, etc.) and to specify types of analytics to be performed or analytical problems to be solved on selected variables (e.g., optimization, minimization, clustering, etc.). It may then be decided that a copy of the resulting digital twin 1506 will be used at another plant facility to in connection with performing analytics on or performing supervisory control of a similar industrial system ([0212]), that is, Zarur’s system may identify digital twin based on analytical problems to be solved on selected variables, and the picked digital twin will be used at another plant facility to solve similar problem.
Applicant argued Zarur and Greunke did not teach “the digital twin update/adaptation piece mentions the detail of reformulation of problem definition and invoking a separate model tuning workflow”. Examiner respectfully disagrees and submits that Zarur discloses Program generation component 2914 can be configured to translate the automation model created based on the design input to an industrial control program file that is executable on an industrial controller (e.g., a PLC or another type of controller) to facilitate monitoring and control of the physical LSM transport system. The control program file can comprise an executable control program, associated data tag and smart object definitions, and other controller configuration settings. HMI generation component 2916 can be configured to translate the design input submitted by the user to a visualization application (e.g., an HMI application) that can be executed on a visualization terminal device to render visualization displays that allow an operator to view operational and status data for the physical LSM transport system, and to interact with selected portions of the system (e.g., by setting setpoints or speeds, starting or stopping the system, etc.) ([0226]), the simulation component 2908 can execute simulations of the model 3002 for multiple permutations of these configuration settings within the user-specified ranges. The user may also specify steps within the defined ranges that are to be tested (e.g., station velocities in increments of 500 mm/s), and simulation component 2908 will execute simulations for permutations of the design parameters in sequential increments according to the specified ranges and steps. Alternatively, the simulation component 2908 may select suitable ranges or steps of the design parameters to be tested based on known operating ranges for the track components as defined in the track component definitions 2932. In addition to sequential simulated testing, the simulation component 2908 can also perform randomized tests using randomly selected values of the configuration settings (e.g., numbers of movers, station velocities and accelerations, etc.) ([0250]), that is, Zarur’s system may set ranges of the design settings based on different problem types.
Applicant argued Zarur, Greunke and Alesiani are silent about the technical implementation of generating a plurality of negative images for the query image, as amended in claim 1. However, “the technical implementation of generating a plurality of negative images for the query image” is not part of claim 1.
Applicant argued Zarur, Greunke and Alesiani are silent about the steps performed for knowledge-based engineering of digital twin for plant monitoring and optimization. Examiner respectfully disagrees and submits that Zarur discloses simulate the resulting object-driven model to predict performance metrics or identify an optimal set of track design parameters that satisfy user-defined design criteria. The system can also translate the design parameters encoded in the model into an executable controller code that can be executed on an industrial controller to monitor and control the physical transport system (Abstract), AI engine component 514 can identify a key variable based in part on industry knowledge of the type of industrial asset or process being modeled by the digital twin 1506. For example, it may be known that product throughput is a primary concern of a certain type of industrial production line. Accordingly, if the AI engine component 514 ascertains or infers that the digital twin 1506 models an industrial system that corresponds to this category of production line, the smart object 322 representing product throughput can be selected as the key variable. In some embodiments that use this approach for identifying key variables, AI engine component 514 can reference an industry knowledgebase that defines key variables for respective different types of industrial systems (e.g., machines, production lines, processes, verticals, etc.), and identify key variables by correlating the type of industrial system being modeled with the industry knowledgebase ([0195]), that is, Zarur’s system may perform industry knowledge base for plant monitoring and optimization using digital twins.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
5. Claims 1-4, 6-11, 13-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ashraf Zarur et al (US Publication 20220027529 A1, hereinafter Zarur), and in view of Larry Greunke (US Publication 20230242044 A1, hereinafter Greunke).
As for independent claim 1, Zarur discloses: A processor implemented method, comprising: receiving, via one or more hardware processors (Zarur: [0065], a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry which is operated by a software or a firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application), a high-level problem statement as input (Zarur: [0089], Input data that can be received via user interface component 314 can include, but is not limited to, user-defined control programs or routines, data tag definitions, BIDT metadata configuration data, or other such data; [0193], AI engine component 514 can modify some or all of the smart objects 322 that make up the digital twin 1506 to add AI contextualization metadata in the form of additional fields or properties. These additional fields or properties can encode learned relationships between modeled variables (e.g., process measurements, device outputs or statuses, etc.) that are not already defined in the digital twin 1506, as well as parameters defining the type of analytic problem to be solved by analytic engines that leverage the digital twin 1506; [0204], Encapsulating these problem statements in the structure of the smart objects 322 themselves can enable programmatic access to the nature of data analysis to be carried out by analytic engines); identifying, via the one or more hardware processors, one or more problem types associated with the high-level problem statement (Zarur: [0204], AI fields 2604 can also define the type or class of analytic problem to be solved relative to the key variable (e.g., modeling, clustering, optimization, minimization, etc.). The type of analytic problem can be determined by the AI engine component 514 based on a number of factors, including but not limited to the type of industrial asset or process modeled by the digital twin 1506 and the identity of the key variable; [0212], specify types of analytics to be performed or analytical problems to be solved on selected variables (e.g., optimization, minimization, clustering, etc.)); deriving, via the one or more hardware processors, one or more detailed technical problem definitions (Zarur: [0134], an explicit definition of the plant facility and production area within which the asset represented by asset model 1302 resides) from the high-level problem statement, based on the one or more identified problem types associated with the high-level problem statement (Zarur: [0212], specify types of analytics to be performed or analytical problems to be solved on selected variables (e.g., optimization, minimization, clustering, etc.); [0201], the search topology extracted from the structure of smart objects 322 and the asset and mechanical model definitions of the digital twin 1506 can significantly constrain the parameters of optimization problems solved by the AI engine component 514, significantly reducing the time and expertise required to analyze the digital twin 1506 to identify key variables and their correlations to other variables), by executing one or more associated problem type specific problem definition workflows (Zarur: [0151], assign selected smart objects 322 from the smart object definitions for inclusion in one or both of the asset model 422 or the mechanical model 1504), using a plant domain knowledge and a problem knowledge (Zarur: [0195], AI engine component 514 can identify a key variable based in part on industry knowledge of the type of industrial asset or process being modeled by the digital twin 1506), wherein one of the one or more detailed technical problem definitions is of an optimization problem type (Zarur: [0205], For example, a digital twin 1506 may initially be used to solve the problem of minimizing fuel usage by its modeled industrial asset (e.g., within the context of a supervisory control system as illustrated in FIG. 22). If it is decided to prioritize minimization of emissions rather than fuel usage, the AI field 2604 defining the problem statement can be modified programmatically to reflect this new analytic problem, thereby conveying the new operational goal to the analytic system, please note in this example, “solve the problem of minimizing fuel usage” is derivation of a detailed problem definition), wherein defining the optimization problem type comprises executing a workflow for optimization problem definition using the plant domain knowledge and an optimization problem type knowledge, wherein the workflow for optimization problem definition comprises one or more knowledge-guided steps for selecting one or more objectives, identifying one or more relevant Key Performance Indicators (KPs), identifying one or more KPIs influencing variables, identifying one or more manipulated and disturbance variables, and identifying one or more constraints ([0194], AI engine component 514 can apply correlation and/or causation analysis on the digital twin 1506 to identify one or more smart objects 322 representing important or key variables of the industrial asset or process being modeled by the digital twin 1506. The key variables may represent key performance indicators (KPIs) of the modeled asset or process, or other aspects of the modeled system of key importance. In general, key variables represent an overall performance quality of the industrial asset; [0195], there may be more than one key variable or KPI for a given digital twin 1506. Other types of key variables can include, but are not limited to, energy consumption, energy efficiency, cycle time, asset availability, capacity utilization, or other such variables modeled by smart objects 322 that make up the digital twin 1506; [0209], Simulated result data 2704 can also include other calculated indicators of asset performance (e.g., KPIs, efficiencies, cycle times, etc.) based on the data relationships modeled by the digital twin 1506 and its smart objects 322); and
identifying, via the one or more hardware processors, a plant view of interest for the detailed technical problem definition (Zarur: [0077], On the plant floor level, industrial assets 206—e.g., industrial machines, production lines, industrial robots, etc.—carry out respective tasks in connection with manufacture, packaging, or handling of a product; control of an industrial process; or other such industrial functions. These industrial assets 206 are directly monitored and controlled by industrial devices 204; [0218], This inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events; [0257], allows the user to easily compare test results for the different designs relative to a selected performance metric of interest), using a plant configuration knowledge (Zarur: [0195], AI engine component 514 can reference an industry knowledgebase that defines key variables for respective different types of industrial systems (e.g., machines, production lines, processes, verticals, etc.), and identify key variables by correlating the type of industrial system being modeled with the industry knowledgebase).
Zarur discloses using user-defined data as input, but does not clearly discloses a high-level problem statement, in an analogous art of optimizing a plant process using digital twin, Greunke discloses: a high-level problem statement as input (Greunke: [0236], highLevelForDoingTask: A high-level description of the task being performed; [0251], xAPIStatement: An xAPI (Experience API) statement that describes the user's interaction with the action for tracking and analytics);
Zarur and Greunke are analogous arts because they are in the same field of endeavor, optimizing a plant process using digital twin. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Zarur using the teachings of Greunke to include a high-level description statement. It would provide Zarur’s method with enhanced capabilities of accurately and efficiently provide solutions to optimize a plant process.
As for claim 2, Zarur-Greunke discloses: comprising identifying the plant data of interest using the plant view of interest (Zarur: [0218], This inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events; [0257], allows the user to easily compare test results for the different designs relative to a selected performance metric of interest), by: receiving a real-time plant data as input, wherein the real-time plant data comprises values of a plurality of operational parameters (Zarur: [0155], the digital twin 1506 can be fed with live (real-time) data from the smart objects 322 during operation of the industrial asset); comparing the received real-time plant data with a plurality of operational parameters identified in the plant view of interest (Zarur: [0210], AI engine component 514 can compare selected simulated results with their corresponding real values generated by the physical industrial asset, and determine whether the simulated results—generated based on relationships modeled in the digital twin 1506 and its smart objects 322—are within a defined tolerance of their corresponding actual asset outputs. This comparison can be performed for individual aspects and relationships of the modeled asset so that deviations between the model and its corresponding physical asset can be identified at a highly granular level, and appropriate modifications made to the digital twin 1506); and determining the plant data of interest based on matches found for the received real-time plant data with the plurality of operational parameters (Zarur: [0248], a partial listing of track design parameters that can be varied across different simulation sessions for the purpose of comparative analysis. Operating parameters that can be varied across different simulation sessions can include, but are not limited to, the number of active movers 3104 on the track, the acceleration and deceleration rates of each defined station 3120, the velocities of each defined station 3120, or other such characteristics. In the list depicted in FIG. 35, each row represents a different system configuration, or test case, for which a simulation can be executed).
As for claim 3, Zarur-Greunke discloses: comprising building a digital twin for the derived one or more technical problem definitions, using the plant domain knowledge, the problem knowledge and the plant data of interest, using one or more knowledge-guided workflows, comprising: generating a plurality of digital twin models comprising physics-based and data-based digital twin models, by executing one or more knowledge guided workflows (Zarur: [0084], Smart objects that include BIDT metadata can also serve as a foundation for building automation models or digital twins of industrial systems that can be simulated prior to deployment of those systems. In an example implementation, a testing and development platform for designing and simulating track-guided linear synchronous motor (LSM) transport systems can generate a digital model (e.g., an automation model, a digital twin, or another type of model) that represents an LSM transport system in accordance with design input submitted by a user); and generating an integrated digital twin by combining the plurality of digital twin models (Zarur: [0185], The contextualized data infrastructure of the smart objects 322 can also allow digital twins 1506 to be readily integrated with artificial intelligence (AI) tools and systems; Greunke: [0419], Integrate the blockchain-based system with existing tools and technologies used for managing discrepancies, procedures, tasks, and digital twins, ensuring seamless data exchange and interoperability. [0424] 6) Test the system thoroughly to ensure that it meets performance, security, and usability requirements. Perform simulations and real-world tests to validate the effectiveness of the system).
As for claim 4, Zarur-Greunke discloses: wherein the integrated digital twin model is used to solve the high-level problem statement, wherein solving the high-level problem statement comprising: identifying a right composition of one or more digital twin models to use, from the plurality of digital twin models in the integrated digital twin model, based on a digital twin model knowledge, and the plant view of interest at an instance (Zarur: [0195], if the AI engine component 514 ascertains or infers that the digital twin 1506 models an industrial system that corresponds to this category of production line, the smart object 322 representing product throughput can be selected as the key variable. In some embodiments that use this approach for identifying key variables, AI engine component 514 can reference an industry knowledgebase that defines key variables for respective different types of industrial systems (e.g., machines, production lines, processes, verticals, etc.), and identify key variables by correlating the type of industrial system being modeled with the industry knowledgebase); identifying a right solution configuration for solving the high-level problem statement, using a solution space knowledge, and the current plant view of interest (Zarur: [0185], This supplemental data can fill in gaps in the data available directly from the plant floor devices, machines, and processes, yielding a more comprehensive AI-analysis solution; [0292], any of the automation model 3002, the digital twin 4802, or the predictive models 3904 can be deployed in local process control solutions, cloud infrastructures, or containerized infrastructures in industrial controllers); and solving the high-level problem statement using the right composition of one or more digital twin models and the right solution configuration (Zarur: [0204], the smart objects 322 can serve as a standardized interface to the digital twin 1506 through which other analytics systems can learn a desired or beneficial type of analytic to be performed on the digital twin 1506 (e.g., minimization of energy consumption, maximization of product throughput, maximization of efficiency, selection of a subset of available industrial assets to be deployed in order to achieve a desired result given defined constraints, etc.).
As for claim 6, Zarur-Greunke discloses: wherein the domain knowledge comprises plant knowledge, process knowledge, product knowledge, material knowledge, and phenomenon knowledge, at a plurality of abstraction levels (Zarur: [0195], AI engine component 514 can identify a key variable based in part on industry knowledge of the type of industrial asset or process being modeled by the digital twin 1506).
As for claim 7, Zarur-Greunke discloses: wherein one of the one or more detailed technical problem definitions is of an optimization problem type, wherein defining the optimization problem type comprises executing a workflow for optimization problem definition using the plant domain knowledge and an optimization problem type knowledge (Zarur: [0195], AI engine component 514 can identify a key variable based in part on industry knowledge of the type of industrial asset or process being modeled by the digital twin 1506), and wherein the workflow for optimization problem definition comprises one or more knowledge-guided steps for selecting one or more objectives, identifying one or more relevant Key Performance Indicators (KPIs), identifying one or more KPIs influencing variables, identifying one or more manipulated and disturbance variables, and identifying one or more constraints (Zarau: [0194], AI engine component 514 can apply correlation and/or causation analysis on the digital twin 1506 to identify one or more smart objects 322 representing important or key variables of the industrial asset or process being modeled by the digital twin 1506. The key variables may represent key performance indicators (KPIs) of the modeled asset or process, or other aspects of the modeled system of key importance. In general, key variables represent an overall performance quality of the industrial asset; [0209], Simulated result data 2704 can also include other calculated indicators of asset performance (e.g., KPIs, efficiencies, cycle times, etc.) based on the data relationships modeled by the digital twin 1506 and its smart objects; [0212], development of the digital twin 1506 can involve AI contextualization of the smart objects 322 by AI engine component 514 to codify discovered relationships between modeled variables of the industrial system (e.g., measured telemetry values, KPIs, device statuses, power statistics, etc.) and to specify types of analytics to be performed or analytical problems to be solved on selected variables (e.g., optimization, minimization, clustering, etc.)).
As for claim 8, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As for claim 9, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As for claim 10, it recites features that are substantially same as those features claimed by claim 3, thus the rationales for rejecting claim 3 are incorporated herein.
As for claim 11, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As for claim 13, it recites features that are substantially same as those features claimed by claim 6, thus the rationales for rejecting claim 7 are incorporated herein.
As for claim 14, it recites features that are substantially same as those features claimed by claim 7, thus the rationales for rejecting claim 7 are incorporated herein.
As for claim 15, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As for claim 16, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As for claim 17, it recites features that are substantially same as those features claimed by claim 3, thus the rationales for rejecting claim 3 are incorporated herein.
As for claim 18, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As for claim 20, it recites features that are substantially same as those features claimed by claim 6, thus the rationales for rejecting claim 6 are incorporated herein.
4. Claims 5, 12, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zarur and Greunke as applied on claims 1, 8, 15 and further in view of Francesco Alesiani (US Publication 20220277859 A1, hereinafter Alesiani).
As for claim 5, Zarur-Greunke discloses: wherein the digital twin model is updated (Zarur: [0212], This approach to validating and, if necessary, updating the digital twin 1506 to ensure a high level of fidelity can be useful when the digital twin 1506—or a replica thereof—is transferred for use with a similar, but not identical, industrial asset to that for which the digital twin 1506 was developed; [0214], When performing modifications 2706 on the digital twin 1506 to address discovered inconsistencies between the digital twin 1506 and the modeled asset, AI engine component 514 can apply similar techniques to those described above for discovering updated relationships between key variables and other modeled variables of the digital twin 1506. These updated relationships can be identified based on analysis of the digital twin's outputs when time-series process data 2702 from the new industrial system is applied) if a measured performance of the digital twin model is below a threshold (Zarur: [0246], Based on results of the simulation—e.g., based on evaluation of the predicted performance metrics—the user may modify the transport system design to yield an updated model 3002 and re-run the simulation; [0247], the simulation component 2908 can conclude the simulation session when all metrics variances are within a defined threshold (which may be a user-defined threshold), wherein updating the digital twin model comprises: identifying one or more causes for a performance degradation of the digital twin model, based on the plant domain knowledge (Zarur: [0242], This simulation technique can allow the user to view potential mover throughput problems, such as instances of station starvation or congestion at certain segments of the transport system at certain times. The user may also recognize possible causes of throughput problems based on visual examination of the animated simulation; [0291], Analytics component 2912 can also analyze past and present BIDT data 4908 together with the system properties encoded in the automation model 3002 or digital twin 4802 in order to identify root causes of operational problems, such as deviations from expected transport system behavior); … and updating the digital twin model by identifying a mode based on the identified one or more causes and reformulated one or more detailed problem definitions and invoking an associated model tuning workflow (Zarur: [0291], Analytics component 2912 can also analyze past and present BIDT data 4908 together with the system properties encoded in the automation model 3002 or digital twin 4802 in order to identify root causes of operational problems, such as deviations from expected transport system behavior; Greunke: [0395], reconstruct the digital twin to match the desired state); but Zarur-Greunke does not clearly disclose reformulating the input, in another analogous art of optimizing a plant process using digital twin, Alesiani discloses: reformulating the one or more detailed problem definitions (Alesiani: [0055], determining the solution for the bilevel problem is based on using Karush-Kuhn-Tucker (KKT) reformulation of the bilevel problem and/or alternating direction method of multipliers (ADMM));
Zarur and Greunke and Alesiani are analogous arts because they are in the same field of endeavor, optimizing a plant process using digital twin. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Zarur using the teachings of Alesiani to include reformulating the problem. It would provide Zarur’s method with enhanced capabilities of accurately and efficiently provide solutions to optimize a plant process.
As for claim 12, it recites features that are substantially same as those features claimed by claim 5, thus the rationales for rejecting claim 5 are incorporated herein.
As for claim 19, it recites features that are substantially same as those features claimed by claim 5, thus the rationales for rejecting claim 5 are incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
Prosecution Insights