DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This Office Action has been issued in response to Applicant’s Communication of application S/N 18/300,122 filed on April 13, 2023 . Claims 1 to 20 are currently pending with the application. Information Disclosure Statement The information disclosure statement (IDS) submitted on 12/26/2024 was filed before the mailing date of the first action on the merits . The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections Claim 9 is ob jected to because of the following informalities: Claim 9 recite s the limitation “ from the plant and/or data outputted… ” in line 3. It is not clear whether the Applicant intends the claim to mean “and” or “or”, therefore, for purposes of clarity and consistency, such deficiencies must be resolved. Appropriate corrections are required. 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. Claims 1 to 2 0 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 10, and 17 recite determining a dataset. The limitation of determining a dataset, which specifically recites “ determining, based on the received data, at least one qualifying dataset determined qualified to train an intelligent model ”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “ by at least one processor ” (claim 10), nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “ by at least one processor ” language, “determining”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying from data, at least one dataset that is suitable to be used to train an intelligent model. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – “ receiving data associated with the operation of a plant ”, “ using at least one specially configured algorithm ”, “ training the intelligent model using the at least one qualifying dataset ”, “ deploying the trained intelligent model for use ”, at least one processor , and at least one non-transitory memory. The limitation “ receiving data associated with the operation of a plant ” amount to data-gathering steps which is considered to be insignificant extra-solution activity ( See MPEP 2106.05(g) ). The limitations “ training the intelligent model using the at least one qualifying dataset ”, and “ deploying the trained intelligent model for use ” are recited at a high-level of generality, with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and is equivalent to merely saying “applying it” . The limitation “ using at least one specially configured algorithm ” and the at least one processor and non-transitory memory in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claims are directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activit y identified above , which include the data gathering steps , is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner ( e.g., at a high level of generality) or as insignificant extra-solution activity ( See MPEP 2106.05(d)(II)( i ) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) ). The claim s are not patent eligible. Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitation of “ storing the received data in a repository associated with the process simulation system ”, which amounts to data storing steps, and which is considered to be insignificant extra-solution activity, ( See MPEP 2106.05(g) ), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity ( See MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Mm., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) ). Therefore, does not amount to significantly more than the abstract idea. Same rationale applies to claim 11, since it recites similar storing limitations. Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitations of “ the received data comprise live data received in near real-time, and wherein determining the at least one qualifying dataset comprises: flagging, based on one or more criteria, portions of the live data that satisfies the one or more criteria; and adopting at least one of the flagged portions of the live data as the at least one qualifying dataset ”, where the determining, including flagging and adopting, can be performed in the human mind with the aid of pen and paper, and therefore, is further elaborating on the abstract idea. The receiving limitation amounts to data-gathering steps which is considered to be insignificant extra-solution activity ( See MPEP 2106.05(g) ), and recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner ( e.g., at a high level of generality) or as insignificant extra-solution activity ( See MPEP 2106.05(d)(II)( i ) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) ) . The claim does not amount to significantly more. Same rationale applies to claims 4, and 5. Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitations of “ the at least one qualifying dataset comprises steady state data determined to correspond to a steady state model ”, which is tying the abstract idea to a field of use by further specifying the target data, and which is simply an attempt to limit the application of the abstract idea to a particular technological environment; merely indicating a field of use or technological environment in which to apply the judicial exception does not meaningfully limit the claim ( See MPEP 2106.05(h) ). Same rationale applies to claim 7. Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites the additional limitation of “ perform one or more pre-processing operations to generate one or more parameters for a process simulation model embodied by the process simulation system ” , which is recited at a high-level of generality , with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result , and is equivalent to merely saying “ applying it”, therefore, does not integrate the judicial exception into a practical application n or amount to significantly more . Same rationale applies to claim 9 Additionally , the claims do not include a requirement of anything other than conventional, generic computer technology for executing the abstract idea, and therefore, do not amount to significantly more than the abstract idea. Same rationale applies to claims 11 to 16, and 18 to 20 since they recite similar limitations. Claims 1 to 20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1 to 5, 8 to 14, and 17 to 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by PATEL et al. (U.S. Publication No. 2022 / 0309391 ) hereinafter Patel . As to claim 1: Patel discloses: A computer-implemented method integrating an intelligent model within a process simulation system, the computer-implemented method comprising: receiving data associated with the operation of a plant [ Paragraph 0032 teaches receiving data collected by the enterprise, where the enterprise datasets include industrial facility monitored events, observed data collected by the enterprise, etc. ; Paragraph 0052 teaches receiving enterprise data ] ; determining, using at least one specially configured algorithm and based on the received data, at least one qualifying dataset determined qualified to train an intelligent model [ Paragraph 0006 teaches examining an enterprise dataset, defined by enterprise collected data, and selecting one or more synthetic dataset in dependence on the examining, in other words, determining one or more synthetic dataset based on the collected enterprise data; Paragraph 0027 teaches features for selecting training data for use in training predictive models ; Paragraph 0060 teaches developer system selects one or more datasets for use in training a set of predictive models ] ; training the intelligent model using the at least one qualifying dataset [ Paragraph 0006 teaches training predictive models using data of the one or more synthetic datasets ; Paragraph 0042 teaches applying the training data to the models ] ; and deploying the trained intelligent model for use [ Paragraph 0028 teaches selecting a model for deployment; Paragraph 0047 teaches selecting the model for deployment ] . As to claim 2 : Patel discloses: storing the received data in a repository associated with the process simulation system [ Paragraph 0032 teaches data repository can store enterprise collected dat a, enterprise datasets and synthetic datasets; Paragraph 0030 teaches data repository stores synthetic data, which includes simulated events ; Paragraph 0052 teaches storing received enterprise data in data repository ] . As to claim 3: Patel discloses: wherein the received data comprise live data received in near real-time [ Paragraph 0050 teaches e nterprise data can include real world, actually occurring, event data of interest to particular enterprise , therefore, live data received in near real-time ] , and wherein determining the at least one qualifying dataset comprises: flagging, based on one or more criteria and using the at least one specially configured algorithm, portions of the live data that satisfies the one or more criteria [ Paragraph 0037 teaches parameter examination process can examine enterprise data of an enterprise in order to extract enterprise dataset characterizing parameter values ] ; and adopting at least one of the flagged portions of the live data as the at least one qualifying dataset [ Paragraph 0040 teaches generating a synthetic dataset using parameter values generated in dependence on enterprise dataset characterizing parameter values extracted from enterprise data ] . As to claim 4 : Patel discloses: flagging, based on one or more criteria and using the at least one specially configured algorithm, a plurality of candidate datasets from historical data [ Paragraph 0037 teaches parameter examination process can examine enterprise data of an enterprise in order to extract enterprise dataset characterizing parameter values ; Paragraph 0029 teaches enterprise data includes historical data ] ; and processing, using statistical model, the plurality of candidate datasets to select the at least one qualifying dataset from the plurality of candidate datasets [ Paragraph 0037 teaches examine enterprise data of an enterprise in order to extract enterprise dataset characterizing parameter values therefrom ; Paragraph 0038 teaches parameter examination process can apply, e.g., spectral analysis, periodogram analysis, and Fourier analysis , for extraction of dataset characterizing parameter values from an enterprise dataset ] . As to claim 5 : Patel discloses: extracting the at least one qualifying dataset for external processing associated with modeling via the process simulation system [ Paragraph 0054 teaches specifying enterprise data to be subject to modeling ] . As to claim 8 : Patel discloses: perform one or more pre-processing operations to generate one or more parameters for a process simulation model embodied by the process simulation system [ Paragraph 0102 teaches automatically simulat ing data according to recommended characteristics , and revising the parameters of the characteristic or adding other characteristics ] . As to claim 9 : Patel discloses: perform one or more post processing operations to generate one or more predictions based on data received from the plant and/or data outputted from a process simulation model [ Paragraph 0027 teaches predicting events including time series events subject to data collection by the enterprise ] . Same rationale applies to claims 10 to 14, and 17 to 20, since they recite similar limitations, and are therefore, similarly rejected. 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. Claims 6, 7 , 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over PATEL et al. (U.S. Publication No. 2022 / 0309391 ) hereinafter Patel , and further in view of YAN et al. (U.S. Publication No. 2019 / 0219994 ) hereinafter Yan . As to claim 6 : Patel discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose the at least one qualifying dataset comprises steady state data determined to correspond to a steady state model. Yan discloses: the at least one qualifying dataset comprises steady state data determined to correspond to a steady state model [ Paragraph 0069 teaches feature discovery and analysis through steady-state; Paragraph 0036 teaches a steady-state model of the industrial asset ] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention , to combine the teachings of the cited references and modify the invention as taught by Patel , by incorporating steady state data determined to correspond to a steady state model , as taught by Yan [Paragraph 0069, 0036], because both applications are directed to analysis and extraction of data for learning models; determining steady state data for a steady state model is a s imple substitution of one known element for another to obtain predictable results . As to claim 7 : Patel discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose the at least one qualifying dataset comprises dynamic data determined to correspond to a dynamic model. Yan discloses: the at least one qualifying dataset comprises dynamic data determined to correspond to a dynamic model [ Paragraph 0066 teaches identifying features for a dynamic model, and extracting dynamic model features ] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention , to combine the teachings of the cited references and modify the invention as taught by Patel , by incorporating s dynamic data determined to correspond to a dynamic model , as taught by Yan [Paragraph 0066], because both applications are directed to analysis and extraction of data for learning models; determining dynamic data for a dynamic model is a s imple substitution of one known element for another to obtain predictable results . Same rationale applies to claims 15 and 16, since they recite similar limitations, and are therefore, similarly rejected. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT RAQUEL PEREZ-ARROYO whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-8969 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday, 8:00am - 5:30pm, Alt Friday, EST . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Sherief Badawi can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-9782 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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