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 .
Priority
Acknowledgment is made of applicant's claim for domestic benefit based on provisional application 62/540,749 filed on August 3, 2017. However, the provisional application does not teach transitioning from using a regression model to perform model-driven operations to an artificial intelligence model. This element is taught in application 17/179,832 with a filling date of 02/19/2021.
Claims 1 – 26 and 33 – 40 where cancelled following an election/restriction requirement filed on 10/24/2025.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 27 – 32 and 41 - 54 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 14 of U.S. Patent No. 11,774,923. Although the claims at issue are not identical, they are not patentably distinct from each other because they are simple changes of a statutory category. A person of ordinary skill in the art would conclude that the invention defined in the claims at issue would have been an obvious variation of the invention defined in the claims of the U.S. Patent. Comparisons of selected claims 27 - 32 in the instant application are shown in the following table. A mapping of claims to those disclosed in the ‘923 Patent is as shown in the table; and claims 41 - 54 could also be mapped to similar claims in the patent.
Claims in instant App: 18/234,116
Claims in US Patent 11,774,923
27. (Currently Amended) A method for initiating and automatically improving model-driven operations in a low-data scenario,
the method comprising: creating a regression model using simulated or generated plant data prior to initiating the model-driven operations;
using the regression model to initiate and perform the model-driven operations during an operational stage;
collecting operational data during the operational stage;
creating a first artificial intelligence model using the operational data; and
transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network intelligence model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold.
1. A method for initiating and automatically improving model-driven operations in a low-data scenario,
the method comprising: creating a regression model using pre-operation data prior to initiating the model-driven operations;
using the regression model to initiate and perform the model-driven operations during an operational stage;
collecting operational data during the operational stage;
creating a first artificial neural network model using the operational data; determining, independent of the first artificial neural network model, whether the operational data satisfies a first sufficiency threshold;
transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network model to perform the model-driven operations responsive to the operational data satisfying the first sufficiency threshold.
28. The method of Claim 27, further comprising:
creating a second artificial intelligence model using the operational data; and
transitioning from using the first artificial intelligence model to perform the model-driven operations to using the second artificial intelligence model to perform the model-driven operations responsive to the operational data satisfying a second sufficiency threshold.
2. The method of claim 1, further comprising
creating a second artificial neural network model using the operational data; and transitioning from using the first artificial neural network model to perform the model-driven operations to using the second artificial neural network model to perform the model-driven operations responsive to the operational data satisfying a second sufficiency threshold.
29. The method of Claim 27, wherein the first sufficiency threshold is satisfied when at least a threshold quantity of the operational data is collected.
3. The method of claim 1, wherein the first sufficiency threshold is satisfied when at least a threshold quantity of the operational data is collected.
30. The method of Claim 27, wherein transitioning from using the regression model to using the first artificial intelligence model is further responsive to satisfying a criterion indicative of similarity between the operational data and new operational data.
4. The method of claim 1, wherein transitioning from using the regression model to using the first artificial neural network model is further responsive to satisfying a criterion indicative of similarity between the operational data and new operational data.
31. The method of Claim 30, wherein the criterion indicative of similarity between the operational data and the new operational data is based on a distance between the new operational data and a cluster of the operational data.
5. The method of claim 4, wherein the criterion indicative of similarity between the operational data and the new operational data is based on a distance between the new operational data and a cluster of the operational data.
32. The method of Claim 27, wherein transitioning from using the regression model and using the first artificial intelligence model comprises calculating a combined output using both the regression model and the first artificial intelligence model and using the combined output to perform the model-driven operations, wherein the first
artificial intelligence model is a neural network model.
6. The method of claim 1, wherein transitioning from using the regression model and using the first artificial neural network model comprises calculating a combined output using both the regression model and the first artificial neural network model and using the combined output to perform the model-driven operations.
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.
Claim 27 -30 and 42 - 45 are is rejected under 35 U.S.C. 103 as being unpatentable over Cheng (US PG Pub. No. 20170068886), herein “Cheng,” in view of Nomura et al. (US Patent No. 5,311,421), herein “Nomura.”
Regarding claim 27,
Cheng teaches a method for initiating and automatically improving model-driven operations in a low-data scenario, the method comprising: (Par. 0008: “correction formulas/curves are obtained based on a fixed set of data at the time of manufacture and/or installation. Over time, the unit process characteristics may change slightly, and the electrical energy load and turbine steam inlet pressure relationship needs to be re-calibrated from time-to-time, perhaps at various operating points. A multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load has been used in real-time with the steam turbine power generation process to better track this relationship and how the relationship changes over time. It works well in most conditions, but in certain conditions the actual electrical energy load is off from the electrical energy load set-point by as much as 2 MW. This difference results from an inaccurate electrical energy load and turbine steam inlet pressure relationship obtained by the linear multivariate regression method.” Par. 0009: “A control scheme uses a feedforward neural network model to perform control of a steam turbine power generation process and system in sliding pressure mode in a more efficient and accurate manner than a control scheme that uses only a multivariate linear regression model…”)
using the regression model to initiate and perform the model-driven operations during an operational stage; (Par. 0008: A multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load has been used in real-time with the steam turbine power generation process to better track this relationship and how the relationship changes over time. It works well in most conditions, but in certain conditions the actual electrical energy load is off from the electrical energy load set-point by as much as 2 MW. This difference results from an inaccurate electrical energy load and turbine steam inlet pressure relationship obtained by the linear multivariate regression method.” Par. 0025: “…the first multivariate linear regression model with the minimum root-mean-square error, and operatively coupling the selected model to a control system of the power generation process to produce a pressure set-point control system output, wherein an input of the selected model includes the set-point indicating the desired output of the electrical energy generation unit and the pressure set-point control system output is coupled to an input of the control system.”)
collecting operational data during the operational stage; creating a first artificial neural network model using the operational data; (Par. 0021: “…training a neural network model of the relationship between the output of the electrical energy generating unit and the pressure at the turbine system inlet.” Par. 0013: “In addition, the model adaptation unit may train a new feedforward neural network model of the relationship between the turbine steam inlet pressure and the electrical energy load using process data from the power generation system as training data.”)
transitioning from using the regression model to perform the model-driven operations to using the first artificial intelligence model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold (“trained”). (Par. 0013: “…the model adaptation unit may select one of the new feedforward neural network model and the multivariate linear regression model, the feedforward neural network model operatively coupled to the control system, the previous multivariate linear regression model and the design model having the minimum root-mean-square error.” Par. 0077: “…it can be easily observed that the feedforward neural network model has the smallest fitting error for all models, such as average error, root-mean-square error (RMSE), maximum and minimum absolute errors.” Par. 0080: “As it relates to the model adaptation routine 300 of FIG. 4, a comparison of the root-mean-square errors at block 318 (at least as it pertains to the newly-trained multivariate linear regression model, the newly-trained feedforward neural network model and the manufacturer-supplied correction function) would result in the selection of the newly-trained feedforward neural network model for the set-point model unit 220. This would likely be the case, given that the newly-trained feedforward neural network model had a year's worth of training data…” Par. 0014: “The feedforward neural network model may produce a pressure set-point control system output from an electrical energy load set-point for the control system.” Par. 0056: “After a sufficient number of training cycles, the neural network approaches a state where the errors are small enough such that the neural network is considered "trained".” Par. 0018 and Par. 0074. See also Par. 0048, 0055, 0057, and 0060 – that teach an “artificial” neural network.)
Cheng does not teach using simulated or pre-operational data for creating a regression model. However, Nomura does teach creating a regression model using simulated or generated plant data prior to initiating the model-driven operations; (Examiner’s Note – Col. 3 line 40 – Col. 5, line 6 teach the element of first creating a regression model and then controlling the thermal power plant using a trained neural network model. See Col. 3, lines 40 - 66: “The linear regression model (ii) has simulated a part of characteristics of a thermal power plant, which is represented by equation (80), by using a mathematical formula which estimates an output variable of the thermal power plant by linear connection of time-series signals of an input/output variable of the thermal power plant. A feature of the linear regression model resides in that a relatively large and complex model can be realized. It is however difficult for the linear regression model to simulate a non-linear characteristic which varies depending on the load level. Regarding the conventional techniques described above, it is difficult to realize, with the physical model (i), a model capable of simulating the entirety thermal power plant. There is hence no choice other than using a model which can simulate only a part of a thermal power plant, such as a secondary superheater. This inherently poses a limitation on the prediction of the behavior or state of a thermal power plant, which is a multi-input/multi-output system, in the near future, thereby also posing a limitation on the improvement of controllability. The linear regression model (ii) permits simulation of most of a thermal power plant. It is however necessary to identify a model parameter responsive to each change in load because the thermal power plant has non-linear characteristics that varies depending on the load level.” Col. 4, line 61 – Col. 5, line 6: “The neural network can perform learning, for example, by obtaining time responses with respect to various characteristics of a model of a combined controlling- controlled system while using the model of the combined controlling-controlled system and using, as information being available from the time responses and containing the characteristic of an input/output variable of the model of the combined controlling-controlled system, for example, time-series signals or signal waveform patterns indicative of the characteristics as learning input data and also by determining an optimal control parameter corresponding to each characteristic of the model of the combined controlling-controlled system and using the parameter as learning teacher data.” See also: Col. 6, lines 63 – 68: “…the non-linear regression model which can output an estimated value of the output variable of the plant by inputting time-series signals of the input/output variable of the plant and subjecting these signals to non-linear conversion…” Examiner’s Note – Cheng teaches a portion of this element in paragraph 0013 wherein: “The model adaptation unit may also train a multivariate linear regression model of the relationship between the turbine steam inlet pressure and the electrical energy load using the training data.” Nomura also teaches controlling the plant using the neural network to control the plant in Col. 10, lines 47 – 53: “…the last layer (the m.sup.th layer in this embodiment) of the neural network is an output layer. Each output from the output layer is an output signal from the neural network. In this embodiment, each output signal from the neural network is the control parameter C of the controller.” See also Col. 5, lines 18 – 45.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method of controlling a steam turbine-based power generation system by using a regression and neural network where the neural network is used when it has been trained over a time period in Cheng with using simulation data to create a regression model and then using a neural network to control the thermal power plant as in Nomura in order to “…control system having a controller adapted to control a controlled system to bring a controlled variable into conformity with a desired value, which comprises a tuning system for the controller. The tuning system in turn comprises a neural network having a plurality of units connectable to each other and capable of obtaining an output signal from an input signal in accordance with the state of connection among the units…” (Col. 4, lines 40 – 48)
Regarding claim 28,
Cheng and Nomura teach the limitations of claim 27 which claim 28 depends.
Cheng also teaches creating a second artificial intelligence model using the operational data; and transitioning from using the first artificial intelligence model to perform the model-driven operations to using the second artificial intelligence model to perform the model-driven operations responsive to the operational data satisfying a second sufficiency threshold. (Par. 0024: “The method executes a control routine that determines a control signal for use in controlling the operation of the steam turbine power generation unit based on a pressure set-point control system output predicted by a first neural network model of a relationship between an output of the electrical energy generation unit and pressure at a turbine system inlet of the steam turbine power generation unit in response to the set-point indicating the desired output to develop the predicted pressure set-point control system output, and measures an actual output of the electrical energy generation unit in response to the set-point indicating a desired output of the electrical energy generation unit during a steady-state operation of the power generation process. The method may then adapt a second neural network model of the relationship between the output of the electrical energy generation unit and pressure at the inlet of the steam turbine power generation unit if a difference between the actual output of the electrical energy generation unit and the set-point indicating a desired output of the electrical energy generation unit is greater than a predetermined threshold.” Par. 0080: “As it relates to the model adaptation routine 300 of FIG. 4, a comparison of the root-mean-square errors at block 318 (at least as it pertains to the newly-trained multivariate linear regression model, the newly-trained feedforward neural network model and the manufacturer-supplied correction function) would result in the selection of the newly-trained feedforward neural network model for the set-point model unit 220. This would likely be the case, given that the newly-trained feedforward neural network model had a year's worth of training data, unless for some reason either the previously-trained (i.e., current) neural network model and/or the previously-trained (i.e., current) multivariate linear regression model had a smaller RMSE.” Par. 0025: “Moreover, the method may include selecting one of the second neural network model and the first multivariate linear regression model with the minimum root-mean-square error, and operatively coupling the selected model to a control system of the power generation process to produce a pressure set-point control system output…” See also Par. 0074.)
Regarding claim 29,
Cheng and Nomura teach the limitations of claim 27 which claim 29 depends.
Cheng also teaches that wherein the first sufficiency threshold is satisfied when at least a threshold quantity of the operational data is collected. (Par. 0056: “For example, when training the neural network model 400 at step 306 of FIG. 4, the value of the output of each neuron may be compared with the actual, correct value to determine an error, and the error is fed back through the neural network. The learning algorithm adjusts the weights of the connections to reduce the value of the error, and after a sufficient number of training cycles, the neural network approaches a state where the errors are small enough such that the neural network is considered “trained”.”)
Regarding claim 30,
Cheng and Nomura teach the limitations of claim 27 which claim 30 depends.
Cheng also teaches wherein transitioning from using the regression model to using the first artificial intelligence model is further responsive to satisfying
a criterion indicative of similarity between the operational data and new operational data. (Par. 0051: “Beginning at block 302, in order to train and test the models, the model adaptation routine 300 collects data from the process 222, which may be from the data collection 230 of the control scheme 200. The newly-acquired process data may be combined or otherwise mixed together with older process data in order to form a new data set. The combined data set may be divided into two subsets—one subset for training new models, and another subset for testing both new and current models to identify the model that best approximates the relationship between the turbine steam inlet pressure and the actual electrical energy load.” Par. 0052: “At blocks 304 and 306, respectively, the model adaptation routine 300 trains a new multivariate linear regression model and a new neural network model using the subset of process data for training. Generally speaking, however, a new neural network model of the relationship between the turbine steam inlet pressure and the actual electrical energy load is considered to be the most accurate (and therefore best), as demonstrated further below. However, there are situations in which another model may more accurately describe this relationship, and therefore produces a better turbine steam inlet pressure set-point (SP.sub.P) for input to the control system 218. As such, the model adaptation routine 300 trains not only the new neural network model 306, but also the new multivariate linear regression model 304. In addition, the model adaptation routine 300 tests the accuracy of not only the new neural network model and the new multivariate linear regression model, but also the current (previous) neural network model, the current (previous) multivariate linear regression model and the manufacturer-supplied correction functions.”)
Regarding claims 42 - 45, they are directed to a system or apparatuses to implement the method of steps set forth in claims 27 – 30. Cheng and Nomura teach the claimed method of steps in claims 27 - 30 Therefore, Cheng and Nomura teach the system or apparatuses, to implement the claimed method of steps, in claims 42 – 45.
Regarding claims 49 - 52, they are directed to a memory device with instructions to implement the method of steps set forth in claims 27 – 30. Cheng and Nomura teach the claimed method of steps in claims 27 - 30 Therefore, Cheng and Nomura teach the memory device with instructions, to implement the claimed method of steps, in claims 49 – 52.
Claims 32, 48, and 54, they are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Nomura in further view of Anderson et al. (US Patent No. 20150178865), herein “Anderson.”
Regarding claim 32,
Cheng and Nomura teach the limitations of claim 27 which claim 32 depends. They do not teach using a combination of regression and neural network models. However, Anderson does teach wherein transitioning from using the regression model and using the first artificial intelligence model comprises calculating a combined output using both the regression model and the first artificial intelligence model and using the combined output to perform the model-driven operations, wherein the first artificial intelligence model is a neural network model.. (Par. 0036: “Controlling and managing one or more buildings, like other cyber-physical systems, can be a multistage, time-variable, stochastic optimization endeavor. Adaptive Stochastic Control (ASC) using, for example, approximate dynamic programming (ADP) can offer the capability of achieving autonomous control using computational learning systems to manage the building systems. Additionally, as used herein, the term "Adaptive Stochastic Control" can include a number of decision techniques, such as methods based on a rule based system, neural network, fuzzy logic control, model predictive control, stochastic programming, linear programming, integer programming, mixed integer nonlinear programming, machine learning classifier, logistic regression, or the like, and/or any combination thereof. See also Par. 0040, 0046, 0047, 0064, and 0099.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method of controlling a steam turbine-based power generation system by using a regression and neural network where the neural network is used when it has been trained over a time period in Cheng with using simulation data to create a regression model and then using a neural network to control the thermal power plant as in Nomura with using a combination of models to control operability of the HVAC system as in Anderson in order to maintain appropriate and cost-effective building conditions and to reduce costs of operation while maintaining quality of comfort for tenants, and in some circumstances to comply with mandates or incentives from local, state, and federal governmental regulation. (Par. 0099 and Par. 0005)
Regarding claim 48, it is directed to a system or apparatuses to implement the method of steps set forth in claim 32, Cheng, Nomura, and Anderson teach the claimed method of steps in claim 32, Therefore, Cheng, Nomura, and Anderson teach the system or apparatuses, to implement the claimed method of steps, in claim 48.
Regarding claim 54, it is directed to a memory device having instructions to implement the method of steps set forth in claim 32, Cheng, Nomura, and Anderson teach the claimed method of steps in claim 32, Therefore, Cheng, Nomura, and Anderson teach the memory device having instructions, to implement the claimed method of steps, in claim 54.
Allowable Subject Matter
Regarding claims 31, 41, 46, and 53, they objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the non-statutory double patenting rejections above. Reasons for allowance will be held in abeyance pending final recitation of the claims. For claim 31, the prior art does not disclose the base claim limitations including the elements of claims 27 and 30 and also wherein the criterion indicative of similarity between the operational data and the new operational data is based on a distance between the new operational data and a cluster of the operational data. Claim 41 depends on claim 31 and therefore is also objected to. Claims 46 and 53 disclose a system and memory device having instructions, respectively, and have limitations that parallel those in claim 31 and thus are also objected to.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Georgescu (US PG Pub. No. 20150371151) may also teach the element of creating a regression model prior to initiating the model-drive operations in paragraphs 0039, 0043, 0046, and 0053. Ex: Par. 0039: “To perform the estimation, the energy data rectification server 140 (e.g., the historical data estimator 142 and/or the data forecaster 144) may first model the behavior of the physical data by generating a regression model.”
Levinson et al. (US PG Pub. No. 20030059837) may also teach the elements as
taught in Nomura above in claim 7. Levinson teaches commercially manufacturing a
compound and uses a regression model from initial or experimental data as taught in Par. 0193: "The method also comprises additional optional steps of: displaying clusters
in a multivariate display 906, generating a classifier to assign a solid form label to an
input comprising experimental parameters and/or results 909, generating a regression
model 910 to estimate one or more expected property based on an input comprising
experimental parameters and/or results, selecting a combination of experimental
parameters variable by an automated experimentation apparatus 901, generating a
plurality of sets of values of the experimental parameters, providing one or more of the
sets to a classifier and/or regression model as input, and based on the output of the
classifier and/or regression model, selecting combinations a plurality of sets of values of
experimental parameters corresponding to experiments to be performed 902, providing
selected sets of values of experimental parameters to an automated experimentation
apparatus 903, and determining Raman spectra for experiments that produce solid
forms 904. The method further optionally also comprises the step of: providing one or
more individual experimental result sets as input to a classifier and/or regression
model."
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST..
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116