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 .
Response to Amendment
This Office Action has been issued in response to Applicant’s Communication of amended application S/N 18/300,122 filed on June 8, 202. Claims 1 to 20 are currently pending with the application.
Claim Objections
Claims 1, 10, and 17 are objected to because of the following informalities:
Claim 1 recites the limitation “identify a portion of the evaluated data that satisfy…” in line 10, which contains a typographical error and should read “identify a portion of the evaluated data that satisfies…”. Same rationale applies to claims 10 and 17, since they recite similar limitations.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation “the plant” in line 3. There is insufficient antecedent basis for this limitation in the claim.
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 20 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 evaluating data, identifying a portion of data, associating the identified portion, and determining a dataset.
The limitation of evaluating data, which specifically recites “evaluate data associated with operation of the physical system and data generated by a process simulation model against one or more qualification criteria”, 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, “evaluating”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, comparing data with simulation data. The limitation of identifying a portion of data, which specifically recites “identify a portion of the evaluated data that satisfy the one or more qualification criteria” 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, “identifying”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying part of the previously evaluated data that satisfies a requirement.
The limitation of associating the identified portion, which specifically recites “associate the identified portion with correlation information”, 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, “associating”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, writing in a sheet of paper the previously identified data, in association with other information.
The limitation of determining a dataset, which specifically recites “determining at least one qualifying dataset, wherein the at least one qualifying dataset is based at least on the correlation information, the operational data and the simulation-related data”, 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 qualifying dataset based on the previously associated correlation information, operational data and simulation data. 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 physical system”, “storing at least a portion of the received data in a data repository, wherein the data repository is configured to store at least operational data and simulation-related data”, “executing, by the process simulation system, at least one processing module, wherein the at least one processing module is configured to: automatically evaluate data”, “retrieving, from the data repository”, “training the intelligent model using the at least one qualifying dataset”, “deploying the trained intelligent model for use in connection with the process simulation model”, at least one processor, and at least one non-transitory memory. The limitations “receiving data associated with the operation of a physical system” and “retrieving, from the data repository” amount to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)).
Continuing with the analysis of the additional limitations, the limitation “storing at least a portion of the received data in a data repository, wherein the data repository is configured to store at least operational data and simulation-related data” amounts to data storing steps, and 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”, “executing, by the process simulation system, at least one processing module, wherein the at least one processing module is configured to: automatically evaluate data”, and “deploying the trained intelligent model for use in connection with the process simulation model” 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 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 activity identified above, which include the data gathering and the data storing 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); (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)). The claims 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 nor 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 physical system [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];
storing at least a portion of the received data in a data repository, wherein the data repository is configured to store at least operational data and simulation-related data [Paragraph 0032 teaches storing enterprise data in enterprise data area, therefore, storing operational data, where enterprise data can include actually observed data collected by an enterprise and received from enterprise system of enterprise systems, and can include enterprise datasets, and further, synthetic data can include data other than data collected by an enterprise, and the datasets of synthetic data are stored in synthetic data area];
executing, by the process simulation system, at least one processing module, wherein the at least one processing module is configured to: automatically evaluate data associated with operation of the physical system and data generated by a process simulation model against one or more qualification criteria [Paragraph 0037 teaches parameter examination process can examine enterprise data of an enterprise in order to extract enterprise dataset characterizing parameter values therefrom, and then can compare the extracted enterprise dataset characterizing parameter values to synthetic dataset characterizing parameter values of synthetic data area, therefore, automatically evaluating operational and simulation data against qualification parameters or criteria];
identify a portion of the evaluated data that satisfy the one or more qualification criteria [Paragraph 0037 teaches examination process, based on a comparing of enterprise dataset characterizing parameter values to synthetic dataset characterizing parameter values, can select one or more synthetic dataset from synthetic data area for application to a set of models for training and testing]; and
associate the identified portion with correlation information [Paragraph 0030 teaches synthetic datasets are associated with respective dataset ID for the dataset, as well as a set of extracted parameter values that characterizes respective ones of the synthetic datasets; Paragraph 0039 teaches select a synthetic dataset based on comparing of extracted enterprise dataset characterizing parameter values to multiple sets of extracted synthetic dataset characterizing parameter values, where the datasets are scored to provide an ordered list of ranked synthetic datasets ranked in order of similarity to an enterprise dataset, hence, correlation information];
determining at least one qualifying dataset by retrieving, from the data repository, wherein the at least one qualifying dataset is based at least on the correlation information, the operational data and the simulation-related 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, where the selection is based on the highest ranked synthetic datasets that has been ranked in order of similarity to the enterprise dataset, therefore, based on the correlation information, the operational data and the simulation data];
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; Paragraph 0060 teaches selecting the dataset for training the predictive models]; and
deploying the trained intelligent model for use in connection with the process simulation model [Paragraph 0028 teaches selecting a model for deployment; Paragraph 0047 teaches selecting the model for deployment; Paragraph 0108 teaches identifying potential issues with trained predictive models, and providing analysis of the patterns of existing training data and can recommend what kind of time series data for use in training and testing a predictive model should be prepared, where a simulation module can simulate the represented time series data automatically].
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 data, 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 enterprise 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 simulating 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 at least on data received from the plant and data outputted from a process simulation model [Paragraph 0027 teaches predicting events including time series events subject to data collection by the enterprise; Paragraph 0108 teaches identifying potential issues with trained predictive models, and providing analysis of the patterns of existing training data and can recommend what kind of time series data for use in training and testing a predictive model should be prepared, where a simulation module can simulate the represented time series data automatically].
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 simple 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 simple 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.
Response to Arguments
The following is in response to arguments filed on June 8, 2026. Arguments have been fully and respectfully considered, but are not persuasive.
Claim Rejections - 35 USC § 101
In regards to claim 1, Applicant argues that “amended claim 1 recites “storing at least a portion of the received data in a data repository configured to store at least operational data and simulation-related data,” “executing, by the process simulation system, at least one processing module configured to automatically evaluate data associated with operation of the physical system and data generated by a process simulation model against one or more qualification criteria,” “associating the identified portion with correlation information,” and “determining at least one qualifying dataset by retrieving, from the data repository, the qualifying dataset based at least on the correlation information, the operational data and the simulation-related data” which cannot be practically performed in human mind”, and further, that “the claimed step of executing, by the process simulation system, at least one processing module configured to automatically evaluate data associated with operation of the physical system and data generated by a process simulation model against one or more qualification criteria is not practically performed in the human mind, at least because it requires a processor executing automated evaluation of operational data and simulation-generated data produced by a process simulation model”.
In response to the preceding argument, Examiner respectfully points out that, as further described in the rejections above, the limitations “storing”, “executing”, and “retrieving” are additional limitations, not part of the abstract idea. However, the limitations including the evaluations and determinations, can be performed in the human mind, with the aid of pen and paper, and are therefore, directed to an abstract idea without significantly more. Furthermore, as presently presented, the additional limitations including the execution of the processing module, 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”.
In regards to claim 1, Applicant further argues that “the way the components of claim 1 (e.g., the plurality of processing devices, the controller, the processor with the memory, the communication network, etc.) interact with each other and impact each other, by performing specific steps to achieve a specific outcome, integrates the purported abstract idea into a practical application of integrating intelligent model execution with process simulation execution for operation of a physical system”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that merely invoking computers or machinery as a tool to perform an existing process, does not integrate a judicial exception into a practical application. It is noted that adding a “computer-aided” limitation to a claim covering an abstract concept, without significantly more, is insufficient to render a claim eligible where the claims are silent as to how the computer aids the method, the extent to which a computer aids the method, or the significance of the computer to the performance of the method. In order for a machine to add significantly more, it must “play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly”. (See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); See MPEP 2106.05(f)(II)(v) Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)).
In regards to claim 1, Applicant further argues that “the pending claims provide an improvement to the technical field and an improvement to the functioning of the process simulation system”, where “such claimed elements of improve the functioning of the process simulation system by enabling automated, correlated use of operational data and simulation-generated data to integrate intelligent model execution directly into execution of the process simulation model, thereby improving accuracy, efficiency, and capability of the process simulation system and avoiding isolated or off-boarded machine learning execution, which in turn results in an improvement to the technical field and an improvement to the functioning of the computer itself”, and further, that “the claimed invention integrates intelligent model execution with execution of a process simulation model using correlated operational and simulation-generated data to improve simulation accuracy, efficiency, and functionality, the additional elements of claim 1 integrate the alleged judicial exception into a practical application”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that it is not clear, from the Applicant’s argument, what is the specific improvement in the functioning of a computer, or the improvement to another technology or technical field, that is achieved with the claimed invention. Furthermore, it is also not apparent from the Applicant’s argument, how such improvement correlate with the claim language as presently presented. Based on the preceding argument, it appears that the improvement is related to “using correlated operational and simulation-generated data” to integrate the use of one model into another model. However, it is not clear what the technical improvement is, nor its correlation with the claim limitations, nor how integrating the execution of one model into execution of another model constitutes an improvement in the function of the computer, or technology. Therefore, the claims are directed to an abstract idea without significantly more, under the “Mental Processes” grouping of abstract ideas, as further detailed in the rejections above. 101 Rejections are hereby sustained.
Claim Rejections - 35 USC § 103
In regards to claim 1, Applicant argues that “Patel does not disclose or suggest executing a process simulation system or a process simulation model. Further, Patel does not disclose evaluating data generated by a process simulation model together with operational data”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that Patel discloses a process simulation model, and discloses evaluating data generated by a simulation model with operational data.
Patel [Paragraph 0030] teaches synthetic data can include data other than enterprise collected data including data, e.g., from fictitious events, simulated events, etc., where [Paragraph 0102] teaches that developer system can automatically simulate the data according to the recommended characteristics, and further, [Paragraph 0037] teaches that parameter examination process can examine enterprise data of an enterprise in order to extract enterprise dataset characterizing parameter values therefrom, and then can compare the extracted enterprise dataset characterizing parameter values to synthetic dataset characterizing parameter values of synthetic data area, therefore, automatically evaluating operational and simulation data against qualification parameters or criteria.
In regards to claim 1, Applicant further argues that “Patel does not disclose determining a qualifying dataset based on correlation information between operational data and simulation-related data produced by a process simulation model”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “determining a qualifying dataset based on correlation information between operational data and simulation-related data produced by a process simulation model”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Further, still in response to the preceding argument, the claims as presently presented require “correlation information”, however, does not comprehensively define what such correlation information entails, includes, nor what it is. Examiner respectfully submits that Patel discloses the limitations as required by the claims as presently presented, “determining at least one qualifying dataset by retrieving, from the data repository, wherein the at least one qualifying dataset is based at least on the correlation information, the operational data and the simulation-related data”.
Patel [Paragraph 0060] teaches developer system selects one or more datasets for use in training a set of predictive models, where the selection is based on the highest ranked synthetic datasets that has been ranked in order of similarity to the enterprise dataset, therefore, based on the correlation information, the operational data and the simulation data.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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