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. This action is responsive to the Application filed on 11/1/2024. A filing date 11/1/2024 is acknowledged. A National Stage entry of PCT/US2023/021116 and international filing data 5/5/2023 is acknowledged. The sought benefit of provisional application 63338563 (which was filed on 5/5/2022) is acknowledged. Claims 21-40 are pending in this application. Claims 21, 35 are independent claims.
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.
3. Claims 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Devesh Bharadwaj et al (US Publication 20230213922 A1, hereinafter Bharadwaj), and in view of Swetha Subramanian et al (US Publication 20230156031 A1, hereinafter Subramanian).
As for independent claim 21, Bharadwaj discloses: A system for facilitating facility operations ([0004], a system of optimizing operation of a plant; [0061], A model 135 can include a digital twin of one or more processes operated across multiple facilities 10, such as for example filtration processes where one plant 10 runs one part of a process and another plant 10 runs another part of the process), the system comprising: one or more physical processors ([0004], The system can include a data processing system having at least one processor coupled with memory) configured by machine-readable instructions to: obtain historical operation information for a facility ([0129], data processing system 100 loads historical data) based on a digital twin of the facility ([0129], data processing system 100 continues to process the digital twin functionality to keep updating the model 135 based on streamed updated data; [0151], method 650 includes ACT 652 data processing system inputs historical data of physical measurements into a model 135. At ACT 654, virtual data generator 160 determines virtual instruments and virtual instruments data 170. At ACT 656, simulator 145 generates an estimate of future plant performance based on the simulated model of the historical data), the digital twin of the facility defining relationships between components of the facility and a system of record for the facility ([0041], A data processing system 100 can include a system for creating and running a digital twin … creating the model 135 by providing and establishing sets of relationships between different parts of the modeled system); train a machine learning model using the historical operation information for the facility ([0075], The model generator 130 can generate or train the model 135 using an artificial intelligence (“AI”) model, including for example a machine learning (“ML”) function or technique. The model generator 130 can include an AI or ML function and use any type of machine learning technique, including, for example, supervised learning, unsupervised learning, or reinforcement learning), wherein the trained machine learning model facilitates one or more operations at the facility by outputting descriptive information ([0050], Topology data 114 can also include a description of one asset 12 that is a sub-asset of another asset 12; [0063], Asset layer 122 can include any digital description, depiction, representation or modeling of assets 12. Asset layer 122 can include descriptions, depictions, representations or modeling using any asset data 112), predictive information ([0041], optimize the process by predicting future performance of the process or the assets, determine when each of the assets should be serviced or replaced, or determine the optimal settings for the assets), and/or prescriptive information on the one or more operations at the facility ([0099], An alert generator 155 can generate an alert to indicate that one or more assets 12 can be reconfigured and can identify and recommend optimal services for the assets; [0205], The optimizer can find such optimal solutions within constraints of recommended settings or limits of one or more assets); and store the trained machine learning model in a storage medium ([0044], A plant database 110 can include a database management system for facilitating storing, accessing and using of the stored data).
Bharadwaj discloses optimizing facility operation using digital twin including providing recommendation information on facility operation, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to recognize that the recommendation information regarding facility operation is prescriptive information, in addition, in an analogous art of optimizing industrial plans operation using digital twin models, Subramanian discloses: prescriptive information on the one or more operations at the facility (Subramanian: [0079], Prescriptive maintenance includes determining an optimal maintenance option and when it should be performed based on actual conditions rather than time-based maintenance schedule. According to various embodiments, prescriptive analysis selects the right solution based on the company's capital, operational, and/or other requirements. Process optimization is determining optimal conditions via adjusting set-points and schedules).
Bharadwaj and Subramanian are analogous arts because they are in the same field of endeavor, optimizing industrial plans operation using digital twin models. 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 Bharadwaj using the teachings of Subramanian to include providing prescriptive information on facility operation. It would provide Bharadwaj’s system with enhanced capabilities of selecting the right solution based on the plant’s operational and/or other requirements as suggested by Subramanian ([0079]).
As for claim 22, Bharadwaj-Subramanian discloses: obtain facility scenario information, the facility scenario information defining a scenario of a given operation at the facility (Bharadwaj: [0029], in the scenarios where the assets (e.g. equipment at the plant) has been worn out or degraded and so it does not operate as designed; [0058], Measurements 119 can include multiple measurements from an instrument 18 taken based on particular process events, such as events occurring during the process at plant 10, such as daily start or end of a production, plant maintenance, asset service times, asset testing, asset maintenance, or similar; Subramanian: [0138], the heterogeneous cyber assets being monitored are listed and the users may be able to build scenario based visuals that can aid them to analyze the data and troubleshoot cyber risks); and input the facility scenario information into the trained machine learning model, wherein the trained machine learning model outputs the descriptive information, the predictive information, and/or the prescriptive information on the given operation at the facility (Bharadwaj: [0284], use trained machine learning models to identify optimized performance indicators 210 and their corresponding state parameters 230 and set-points 220 to identify the most optimal set-points to use for the assets (e.g. set-point settings 250). COE 280 can include the functionality to train machine learning models using the data from plant database 110. For example, a ML function of the COE 280 can be trained using data from instruments 18 and virtual instruments 165 to compare and correlate plant 10 operation performance between its various inputs and outputs. For example, a COE 280 can include a machine learning function that correlates asset set-points 220 with performance indicators 210 based on the past or present data from plant database 110. COE 280 can include the functionality to train a machine learning functionality to correlate different state parameters 230 with different asset set-points 220 in order to determine environments or situations in the past in which the plant 10 achieved a particular performance).
As for claim 23, Bharadwaj-Subramanian discloses: wherein the trained machine learning model performs a classification task (Subramanian: [0192], widgets that may indicate risk classifications (e.g., endpoint security, network security, patches, backup).
As for claim 24, Bharadwaj-Subramanian discloses: wherein the trained machine learning model performs a regression task (Bharadwaj: [0075], The model generator 130 can include an AI or ML function and use any type of machine learning technique, including, for example, supervised learning, unsupervised learning, or reinforcement learning. The model generator 130 can use functions such as linear regression, logistic regression, a decision tree, support vector machine, Naïve Bayes, k-nearest neighbor, k-means, random forest, dimensionality reduction function, or gradient boosting functions).
As for claim 25, Bharadwaj-Subramanian discloses: wherein the one or more physical processors are further configured by the machine-readable instructions to provide visualization of the descriptive information, the predictive information, and/or the prescriptive information on the one or more operations at the facility (Bharadwaj: [0202], the shape of the graph of the sensor readings from the RO membrane instruments; [0257], Experiment results can be plotted into a graph; Subramanian: Abstract, providing spatially-efficient and comprehensive visualization of cyber-risk data associated with a plurality of assets) .
As for claim 26, Bharadwaj-Subramanian discloses: wherein one or more automated operations at the facility are performed based on the prescriptive information on the one or more operations at the facility (Subramanian: [0079], Prescriptive maintenance includes determining an optimal maintenance option and when it should be performed based on actual conditions rather than time-based maintenance schedule. According to various embodiments, prescriptive analysis selects the right solution based on the company's capital, operational, and/or other requirements. Process optimization is determining optimal conditions via adjusting set-points and schedules).
As for claim 27, Bharadwaj-Subramanian discloses: wherein the digital twin outputs the historical operation information for the facility based on the relationship between the components of the facility and the system of record for the facility (Bharadwaj: [0041], creating the model 135 by providing and establishing sets of relationships between different parts of the modeled system; [0286], The correlation, interpolation or extrapolation can be applied in accordance with constraints 225 limiting the range of set-points 220 and in for the data in which state parameters 230 of the historical data match the state parameters 230 of the current state at the plant 10 within an acceptable tolerance range; Subramanian: [0141], an analysis container 702 may include current and historic Key Performance Indicators (KPIs) that provide metrics on how cyber risks are managed in relation to an asset).
As for claim 28, Bharadwaj-Subramanian discloses: wherein the machine learning model includes a sequence model (Bharadwaj: [0110], The model generator 130 can include bioreactor system models, such as conventional activated sludge, membrane bioreactor, sequential batch reactor and moving-bed bioreactor models).
As for claim 29, Bharadwaj-Subramanian discloses: the historical operation information for the facility includes process control information, alarm information, bypass information, safety information, and operator action information; and the process control information, the alarm information, the bypass information, the safety information, and the operator action information related to an event are correlated for the machine learning model by the digital twin (Bharadwaj: [0028], By providing the one or more settings for the one or more set-points to adjust the performance of the plant, the solution can provide the operators with the adjustment instructions instantaneously or at any period of time, thus allowing for a quick response and adjustment. This technical solution can produce results on a frequent basis and can be configured to produce new results periodically, such as every few minutes, hours, or days. This can allow the plant operators to keep the plant running with optimal set-points continuously adjusting for the changes to the plant's operating conditions; Subramanian: [0077], enables operators to quickly initiate maintenance measures when irregularities occur).
As for claim 30, Bharadwaj-Subramanian discloses: wherein the process control information, the alarm information, the bypass information, the safety information, and the operator action information related to the event are correlated based on a piping and instrumentation diagram (Bharadwaj: [0130], using the plant's piping and instrumentation diagrams (P&IDs), process flow diagrams (“FDs), plant operation procedures and equipment data sheets) and a cause and effect chart (Bharadwaj: [0277], more accurately observe the correlation or cause and effect between the sensor and the performance indicator; Subramanian: [0076], new cause and effect relationships are learned).
As for claim 31, Bharadwaj-Subramanian discloses: wherein correlation of the process control information, the alarm information, the bypass information, the safety information, and the operator action information related to the event based on the piping and instrumentation diagram includes: generation of a graph model for the facility based on the piping and instrumentation diagram; and the correlation of the process control information, the alarm information, the bypass information, the safety information, and the operator action information related to the event being performed based on the graph model for the facility (Subramanian: Abstract, configuring one or more visual nodes of a hierarchical asset graph for the plurality of assets based at least in part on one or more asset groupings. The one or more visual nodes correspond to the particular assets and are configured to visually indicate the cyber-risk data that satisfies the visual configuration thresholds. The method further includes providing the hierarchical asset graph for display).
As for claim 32, Bharadwaj-Subramanian discloses: wherein the graph model for the facility includes nodes for physical components of the facility and control components of the facility (Subramanian: Abstract, configuring one or more visual nodes of a hierarchical asset graph for the plurality of assets based at least in part on one or more asset groupings. The one or more visual nodes correspond to the particular assets and are configured to visually indicate the cyber-risk data that satisfies the visual configuration thresholds. The method further includes providing the hierarchical asset graph for display).
As for claim 33, Bharadwaj-Subramanian discloses: wherein the graph model for the facility includes different types of edges between the nodes to represent physical connection and logical connection between corresponding components of the facility (Subramanian: [0005], A visual edge connects two particular visual nodes and visually indicates a relationship between the respective assets or the respective asset groupings corresponding to each of the two particular visual nodes).
As for claim 34, Bharadwaj-Subramanian discloses: wherein: the physical connection between components of the facility includes a process line between components of the facility; and the logical connection between components of the facility includes electrical connection and/or input/output connection between components of the facility (Subramanian: [0005], A visual edge connects two particular visual nodes and visually indicates a relationship between the respective assets or the respective asset groupings corresponding to each of the two particular visual nodes; [0161], the hierarchical asset graph 1110 comprises a plurality of visual nodes 1202 and visual edges 1204, and the visual nodes 1202 are connected via visual edges 1204 in a hierarchical manner).
As per claim 35, it recites features that are substantially same as those features claimed by claim 21, thus the rationales for rejecting claim 21 are incorporated herein.
As per claim 36, it recites features that are substantially same as those features claimed by claim 22, thus the rationales for rejecting claim 22 are incorporated herein.
As per claim 37, it recites features that are substantially same as those features claimed by claim 29, thus the rationales for rejecting claim 29 are incorporated herein.
As per claim 38, it recites features that are substantially same as those features claimed by claim 30, thus the rationales for rejecting claim 30 are incorporated herein.
As per claim 39, it recites features that are substantially same as those features claimed by claim 31, thus the rationales for rejecting claim 31 are incorporated herein.
As per claim 40, it recites features that are substantially same as those features claimed by claim 34, thus the rationales for rejecting claim 34 are incorporated herein.
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to 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.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Hershey (US Publication 20170286572) DIGITAL TWIN OF TWINNED PHYSICAL SYSTEM
Blevins (US Publication 20150261215) DETERMINING ASSOCIATIONS AND ALIGNMENTS OF PROCESS ELEMENTS AND MEASUREMENTS IN A PROCESS
Wolfe (US Publication 20230152788) DIGITAL MODEL BASED PLANT OPERATION AND OPTIMIZATION
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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
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