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 .
DETAILED ACTION
Claims 1-20 are pending. Claims 1-20 have been examined and rejected.
Response to Arguments
Applicant’s arguments filed 7/6/2026 amount to mere allegations of patentability. They are respectfully not persuasive, as they do not properly account for the full combination of references, instead considering the relied upon references piecemeal. The previous grounds of rejection are maintained, with new citations provided to address the newly added limitations from the amended language. New claims 16-20 are further rejected over the newly relied upon Guzek (Guzek, M., Białek, J., Królikowski, B., Bielecki, A., Świrski, K., & Wojdan, K. (2019, April). Advanced algorithms for operational optimization and predictive maintenance of large district heating systems. In 2019 IEEE 6th International Conference on Energy Smart Systems (ESS) (pp. 165-170). IEEE.) reference.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a creation unit configured to,” “an identification unit configured to,” “an output unit configured to,” “a correlation analysis unit configured to,” “a selection unit configured to,” “a collection unit configured to,” “an update unit configured to,” “a simulation unit configured to” in claim 1, “a preprocessor configured to” in claim 4. These units may be implemented by hardware, software, and/or a combination thereof as described in the specification ¶ 0028. Hence, these units will be interpreted as such.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Emiko et al. (JP 2009-169771) in view of Fruit et al. (JP 4789277), provided by the Applicant in a filed IDS, Jan et al. (WO 2019197279 A1), and Aversano et al. (Application of Reduced-Order Models Based on PCA & Kriging For the Development Of Digital Twins Of Reacting Flow Applications, Computers and chemical Engineering 121, 2019, pp. 422-441).
As per claim 1, Emiko teaches a computing system having an operating system for a simulation device that performs a simulation based on a digital twin service, comprising:
a creation unit configured to create a simulation model based on model design data (¶ 0010; Emiko teaches a tracking model section for generating a tracking model on basis of actual measurement data of an actual plant; this teaching reads onto this limitation);
an identification unit configured to identify at least one intrinsic parameter of the created simulation model and at least one input value related to the intrinsic parameter (¶ 0004, 0008; Emiko teaches a parameter obtained by conventional parameter adjustment to perform simulation; this teaching reads onto this limitation);
an output unit configured to output an estimated output value is produced by the simulation model depending on the input value based on the intrinsic parameter (¶ 0002-0003; Emiko teaches obtaining simulation results; this teaching reads onto an output unit to output an estimated output value which is produced from the simulation model depending on the input value based on the intrinsic parameter);
a selection unit configured to select at least one representative intrinsic parameter from among the at least one intrinsic parameter (¶ 0005-0007; Emiko teaches collecting data to generate a tracking model and performing parameter adjustment calculation based on the entire plant; this teaching inherently means there existing a selection unit to select at least one representative intrinsic parameter from among the at least one intrinsic parameter);
a collection unit configured to collect real data related to the representative intrinsic parameter from a real model constructed based on the model design data (¶ 0005; Emiko teaches a unit to collect real data related to the representative intrinsic parameter from a real model constructed based on the model design data);
an update unit configured to update the at least one representative intrinsic parameter based on the collected real data and the estimated output value (¶ 0010; Emiko teaches a tracking model unit to adjust a parameter and update a tracking model based on the basis of actual measurement data of an actual plant); and
a simulation unit configured to execute a simulation by applying the updated representative intrinsic parameter to the simulation model (¶ 0004; Emiko teaches an online simulator to perform simulation based on updated model and adjusted parameter); wherein
the collection unit is further configured to:
collect real data corresponding to the execution of the simulation (¶ 0004-¶ 0006; Emiko teaches a unit collect real data related to the representative intrinsic parameter from a real model constructed based on the model design data for simulation), and
enable comparison between the collected real data corresponding to the simulation and the estimated output value (¶ 0006; Emiko teaches performing as a reference of adjustment calculation sum of the weighted errors of the measurement data and simulation data; calculation of errors between the measurement data and simulation data indicates comparison between the collected real collected data corresponding to the simulation and the estimated output value); and
the update unit is further configured to:
repeatedly update the at least one representative intrinsic parameter until an error between the collected real data corresponding to the simulation and the estimated output value to minimize error (¶ 0023-0024; Emiko teaches repeatedly updating until between the collected real sensor data corresponding to the simulation and the estimated output value is minimized).
Emiko does not teach:
a correlation analysis unit configured to analyze a correlation between the intrinsic parameter and the estimated output value;
a selection unit configured to select at least one representative intrinsic parameter from among the at least one intrinsic parameter depending on the analyzed correlation; and
a collection unit configured to collect real sensor data; and
repeatedly update the at least one representative intrinsic parameter until an error between the collected real data corresponding to the simulation and the estimated output value satisfies a predetermined reference;
a surrogate model creation unit configured to:
create a plurality of surrogate models corresponding to a plurality of preset scenarios based on the simulation model updated by applying the at least one updated representative intrinsic parameter,
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set, and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data.
However, Fruit teaches:
a correlation analysis unit configured to analyze a correlation between the intrinsic parameter and the estimated output value (¶ 0010; this paragraph teaches exactly this limitation);
a selection unit configured to select at least one representative intrinsic parameter from among the at least one intrinsic parameter depending on the analyzed correlation (¶ 0010; this paragraph teaches exactly this limitation); and
a collection unit configured to collect real sensor data (¶ 0037; Fruit teaches using sensors to obtain data in a plant).
Emiko and Fruit are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko and Fruit. One of ordinary skill in the art would have been motivated to make such a combination because Fruit’s teachings would have provided a simulation system which corrects simulation models on the basis of actual data and performs simulation in real time in parallel with an operation of an actual plant (Fruit, ¶ 0006).
Emiko and Fruit do not teach:
repeatedly update the at least one representative intrinsic parameter until an error between the collected real sensor data corresponding to the simulation and the estimated output value satisfies a predetermined reference;
a surrogate model creation unit configured to:
create a plurality of surrogate models corresponding to a plurality of preset scenarios based on the simulation model updated by applying the at least one updated representative intrinsic parameter,
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set, and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data.
However, Jan teaches:
repeatedly update the at least one representative intrinsic parameter until an error between the collected real sensor data corresponding to the simulation and the estimated output value satisfies a predetermined reference (p. 3 ¶ 2-4); and
a surrogate model creation unit configured to:
create a plurality of surrogate models corresponding to a plurality of preset scenarios based on the simulation model updated by applying the at least one updated representative intrinsic parameter (p. 2 last paragraph – p. 3 ¶ 1; Jan teaches creating several different simulation models by setting the simulation model according to different scenarios; this teaching reads onto this limitation).
wherein the surrogate model creation unit is further configured to: construct the training data by inputting, into the simulation model updated after the error satisfies the predetermined reference, (p. 3 ¶ 2-4)
Emiko, Fruit, and Jan are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko, Fruit, and Jan. One of ordinary skill in the art would have been motivated to make such a combination because Jan’s teachings would have used a simulation model of a machine tool together with a control module for fault diagnosis (Jan, p. 2 ¶ 3 from the bottom).
Emiko, Fruit, and Jan do not teach:
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set, and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data.
However, Aversano teaches:
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters; the set of input parameters and correspondingly generated outputs used for training a surrogate model are constructed training data; a note that Aversano in these paragraphs also teach limitation “create a plurality of surrogate models …” as recited in this claim),
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data, and (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters).
input values corresponding to each of the plurality of preset scenarios and obtaining output values from the updated simulation model, and (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters; the set of input parameters and correspondingly generated outputs used for training a surrogate model are constructed training data; a note that Aversano in these paragraphs also teach limitation “create a plurality of surrogate models …” as recited in this claim)
train each of the plurality of surrogate models using training data corresponding to a respective one of the plurality of preset scenarios. (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters)
Emiko, Fruit, Jan, and Aversano are analogous art because they are in the same field of modeling an operation to simulate its behaviors. 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 Emiko, Fruit, Jan, and Aversano. One of ordinary skill in the art would have been motivated to make such a combination because Aversano’s teachings would have led to a surrogated model that would have allowed performing parameter exploration with reduced computational cost (Aversano, p. 422 Abstract).
As per claim 8, these limitations have already been discussed in claim 1. They are, hence, rejected for the same reasons.
As per claim 15, Emiko teaches a computer-readable recording medium having stored thereon computer-executable instructions that, in response to execution, cause a computing device to create a simulation model based on model design data, wherein the computer-executable instructions cause the computing device to (¶ 0030, 0010; Emiko teaches processor, simulator to perform simulation; this teaching implies a computer-readable recording medium having stored thereon computer-executable instructions that, in response to execution, cause a computing device to create a simulation model based on model design data, wherein the computer-executable instructions):
identify at least one intrinsic parameter of the created simulation model and at least one input value related to the intrinsic parameter; (¶ 0004, 0008; Emiko teaches a parameter obtained by conventional parameter adjustment to perform simulation; this teaching reads onto this limitation);
output an estimated output value which is produced, depending on the input value, from the simulation model based on the intrinsic parameter (¶ 0002-0003; Emiko teaches obtaining simulation results; this teaching reads onto an output unit to output an estimated output value which is produced from the simulation model depending on the input value based on the intrinsic parameter);
select at least one representative intrinsic parameter from among the at least one identified intrinsic parameter (¶ 0005-0007; Emiko teaches collecting data to generate a tracking model and performing parameter adjustment calculation based on the entire plant; this teaching reads onto this limitation),
collect real data related to the representative intrinsic parameter from a real model constructed based on the model design data (¶ 0005; Emiko teaches a unit to collect real data related to the representative intrinsic parameter from a real model constructed based on the model design data);
update the at least one representative intrinsic parameter based on the collected real data and the estimated output value (¶ 0010; Emiko teaches a tracking model unit to adjust a parameter and update a tracking model based on the basis of actual measurement data of an actual plant), and
execute a simulation by applying the updated at least one representative intrinsic parameter to the simulation model (¶ 0004; Emiko teaches an online simulator to perform simulation based on updated model and adjusted parameter);
collect real data corresponding to the execution of the simulation (¶ 0004-¶ 0006; Emiko teaches a unit collect real data related to the representative intrinsic parameter from a real model constructed based on the model design data for simulation);
compare the collected real data corresponding to execution of the simulation and the estimated output value (¶ 0006; Emiko teaches performing as a reference of adjustment calculation sum of the weighted errors of the measurement data and simulation data; calculation of errors between the measurement data and simulation data indicates comparison between the collected real collected data corresponding to the simulation and the estimated output value); and
repeat the updating of the at least one representative intrinsic parameter until an error between the collected real data and the estimated output value to minimize error (¶ 0023-0024; Emiko teaches repeatedly updating until between the collected real data corresponding to the simulation and the estimated output value is minimized).
Emiko does not teach:
collect real sensor data; and
update the collected real sensor data;
repeat the updating of the at least one representative intrinsic parameter until an error between the collected real sensor data and the estimated output value satisfies a predetermined reference;
create a plurality of surrogate models corresponding to a plurality of preset scenarios based on the simulation model updated by applying the at least one updated representative intrinsic parameter,
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set, and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data.
However, Fruit teaches:
analyze a correlation between the intrinsic parameter and the estimated output value (¶ 0010; this paragraph teaches exactly this limitation);
select at least one representative intrinsic parameter from among the at least one identified intrinsic parameter depending on the analyzed correlation (¶ 0010; this paragraph teaches exactly this limitation); and
collect real sensor data (¶ 0037; Fruit teaches using sensors to obtain data in a plant).
Emiko and Fruit are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko and Fruit to collect real sensor data and update the collected real sensor data. One of ordinary skill in the art would have been motivated to make such a combination because Fruit’s teachings would have provided a simulation system which corrects simulation models on the basis of actual data and performs simulation in real time in parallel with an operation of an actual plant (Fruit, ¶ 0006).
Emiko and Fruit do not teach:
repeat the updating of the at least one representative intrinsic parameter until an error between the collected real sensor data and the estimated output value satisfies a predetermined reference;
create a plurality of surrogate models corresponding to a plurality of preset scenarios based on the simulation model updated by applying the at least one updated representative intrinsic parameter,
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set, and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data.
However, Jan teaches:
repeat the updating of the at least one representative intrinsic parameter until an error between the collected real sensor data and the estimated output value satisfies a predetermined reference (p. 3 ¶ 2-4); and
create a plurality of surrogate models corresponding to a plurality of preset scenarios based on the simulation model updated by applying the at least one updated representative intrinsic parameter (p. 2 last paragraph – p. 3 ¶ 1; Jan teaches creating several different simulation models by setting the simulation model according to different scenarios; this teaching reads onto this limitation).
wherein the computer-executable instructions further cause the computing device to construct the training data by inputting, into the simulation model updated after the error satisfies the predetermined reference, (p. 3 ¶ 2-4)
Emiko, Fruit, and Jan are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko, Fruit, and Jan. One of ordinary skill in the art would have been motivated to make such a combination because Jan’s teachings would have used a simulation model of a machine tool together with a control module for fault diagnosis (Jan, p. 2 ¶ 3 from the bottom).
Emiko, Fruit, and Jan are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko, Fruit, and Jan. One of ordinary skill in the art would have been motivated to make such a combination because Jan’s teachings would have used a simulation model of a machine tool together with a control module for fault diagnosis (Jan, p. 2 ¶ 3 from the bottom).
Emiko, Fruit, and Jan do not teach:
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set, and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data.
However, Aversano teaches:
construct training data for training the surrogate model by mapping input values to output values depending on the input values through a data set (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters; the set of input parameters and correspondingly generated outputs used for training a surrogate model are constructed training data; a note that Aversano in these paragraphs also teach limitation “create a plurality of surrogate models …” as recited in this claim), and
train the surrogate model based on the input values and output values included in the training data constructed from the updated simulation model, by inputting an input value included in the training data into the surrogate model and training the surrogate model so that an output value of the surrogate model corresponds to an output value included in the training data (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters).
input values corresponding to each of the plurality of preset scenarios and obtaining output values from the updated simulation model, and (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters; the set of input parameters and correspondingly generated outputs used for training a surrogate model are constructed training data; a note that Aversano in these paragraphs also teach limitation “create a plurality of surrogate models …” as recited in this claim)
train each of the plurality of surrogate models using training data corresponding to a respective one of the plurality of preset scenarios. (p. 422 right col. ¶ 2 – p. 423 left col. ¶ 2; Aversano teaches developing accurate surrogate models by training created surrogate models using available data comprising outputs generated by a set of input parameters)
Emiko, Fruit, Jan, and Aversano are analogous art because they are in the same field of modeling an operation to simulate its behaviors. 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 Emiko, Fruit, Jan, and Aversano. One of ordinary skill in the art would have been motivated to make such a combination because Aversano’s teachings would have led to a surrogated model that would have allowed performing parameter exploration with reduced computational cost (Aversano, p. 422 Abstract).
Claims 2, 7, 9, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Emiko et al. (JP 2009-169771) in view of Fruit et al. (JP 4789277) and Jan et al. as applied to claims 1 and 8 above, and further in view of Hans-George et al. (EP 3704354).
As per claim 2, Emiko, Fruit, and Jan in combination teach the simulation device of Claim 1,
Emiko, Fruit, and Jan do not teach:
wherein the output unit is further configured to set a range of variation of the at least one intrinsic parameter and output an estimated output value which is produced, depending on the input value, from the simulation model to which the set range of variation of the intrinsic parameter is applied.
However, Hans-George teaches:
to set a range of variation of the at least one intrinsic parameter and output an estimated output value which is produced, depending on the input value, from the simulation model to which the set range of variation of the intrinsic parameter is applied (p. 2 ¶ 2 from bottom; Han-George teaches setting a range of values for parameter to perform simulation).
Emiko, Fruit, Jan, and Han-George are analogous art because they are in the same field of modeling a design to simulate its behaviors. 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 Emiko, Fruit, Jan, and Han-George. One of ordinary skill in the art would have been motivated to make such a combination because Han-George’s teachings would have provided a method for determining the actual value of the operating parameter (Han-George, last paragraph).
As per claim 7, Emiko, Fruit, and Jan in combination teach the simulation device of Claim 1,
Emilo further teaches:
inputting the input value into the simulation model to which the updated representative intrinsic parameter is applied (¶ 0010; Emiko teaches adjusting, corresponding to updating, a parameter in simulation model).
Emiko, Fruit, and Jan do not teach:
wherein when any one of preset scenarios is selected, the simulation unit is further configured to perform a simulation according to the selected scenario by inputting the input value into the simulation model to which the updated representative intrinsic parameter is applied.
However, Hans-George teaches:
when any one of preset scenarios is selected, the simulation unit is further configured to perform a simulation according to the selected scenario by inputting the input value into the simulation model to which the updated representative intrinsic parameter is applied (p. 3 ¶ 7; Hans-George teaches simulation being carried out to determine operating parameters tailored to the application scenario).
Emiko, Fruit, Jan, and Han-George are analogous art because they are in the same field of modeling a design to simulate its behaviors. 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 Emiko, Fruit, Jan, and Han-George. One of ordinary skill in the art would have been motivated to make such a combination because Han-George’s teachings would have provided a method for determining the actual value of the operating parameter (Han-George, last paragraph).
As per claim 9, these limitations have already been discussed in claim 2. They are, hence, rejected for the same reasons.
As per claim 14, these limitations have already been discussed in claim 7. They are, hence, rejected for the same reasons.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Emiko et al. (JP 2009-169771) in view of Fruit et al. (JP 4789277) and Jan et al. as applied to claims 1 and 8 above, and further in view of Phadke et al. (US 2015/0242547).
As per claim 3, Emiko, Fruit, and Jan in combination teach the simulation device of Claim 1,
Emiko, Fruit, and Jan do not teach:
wherein the correlation analysis unit is further configured to derive a distribution map of the estimated output value in relation to each intrinsic parameter depending on the input value for each intrinsic parameter, derive a correlation between the intrinsic parameter and the estimated output value by analyzing the derived distribution map.
However, Phadke teaches:
the correlation analysis unit is further configured to derive a distribution map of the estimated output value in relation to each intrinsic parameter depending on the input value for each intrinsic parameter (¶ 0022, 0069-0070; in paragraph 0022, Phadke teaches determining mean, variance, and other summary statistics associated with various outputs for given distributions of input parameters; this teaching corresponds to deriving a distribution map of the estimated output value in relation to each intrinsic parameter depending on the input value for each intrinsic parameter), derive a correlation between the intrinsic parameter and the estimated output value by analyzing the derived distribution map (Phadke further teaches, see ¶ 0069-0070, computing correlations between input parameter effects and interactions for single and multiple outputs using standard statistical processes including mean, variance, distributions as taught in ¶ 0022; this teaching reads onto this limitation).
Emiko, Fruit, Jan, and Phadke are analogous art because they are in the same field of generating a model and charactering it. 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 Emiko, Fruit, Jan, and Phadke. One of ordinary skill in the art would have been motivated to make such a combination because Phadke’s teachings would have generated a mathemathical model to closely approximate a system under study (Phadke, ¶ 0001).
As per claim 10, these limitations have already been discussed in claim 4. They are, hence, rejected for the same reasons.
Claims 4-6 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Emiko et al. (JP 2009-169771) in view of Fruit et al. (JP 4789277) and Jan et al. as applied to claims 1 and 8 above, and further in view of Goyal et al. (US 2024/0371082).
As per claim 4, Emiko, Fruit, and Jan in combination teach the simulation device of Claim 1,
Emiko further teaches comprising:
the collected real data to be applied to the simulation model in association with the at least one representative intrinsic parameter (¶ 0010; Emiko teaches a tracking model unit to adjust a parameter and update a tracking model based on the basis of actual measurement data of an actual plant),
wherein the update unit is further configured to update the at least one representative intrinsic parameter based on the real data (¶ 0010; Emiko teaches a tracking model unit to adjust a parameter and update a tracking model based on the basis of actual measurement data of an actual plant).
Emiko does not teach:
a preprocessor configured to preprocess the collected real sensor data; and
the preprocessed real sensor data.
However, Fruit teaches:
the collected real sensor data (¶ 0037; Fruit teaches using sensors to obtain data in a plant).
Emiko and Fruit are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko and Fruit. One of ordinary skill in the art would have been motivated to make such a combination because Fruit’s teachings would have provided a simulation system which corrects simulation models on the basis of actual data and performs simulation in real time in parallel with an operation of an actual plant (Fruit, ¶ 0006).
Emiko, Fruit, and Jan do not teach:
a preprocessor configured to preprocess the collected real sensor data; and
the preprocessed real sensor data.
However, Goyal teaches:
a preprocessor configured to preprocess the collected real sensor data (¶ 0047; Goyal teaches preprocessing sensor data for usage); and
the preprocessed real sensor data (¶ 0047; Goyal teaches preprocessing sensor data for usage).
Emiko, Fruit, Jan, and Goyal are analogous art because they are in the same field of modeling a plant operation to simulate its behaviors. 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 Emiko, Fruit, Jan, and Goyal. One of ordinary skill in the art would have been motivated to make such a combination because Goyal’s teachings would have helped removed distortion such as noise (Fruit, ¶ 0006).
As per claim 5, Emiko, Fruit, Jan, and Goyal in combination teach the simulation device of Claim 4, Emiko further teaches wherein the update unit is further configured to recalculate and update the at least one representative intrinsic parameter to minimize an error between the preprocessed real sensor data and the estimated output value (¶ 0011; Emiko teaches adjusting, corresponding to updating, a parameter so that a difference between predicted data and actual measured data becomes minimum; the difference corresponds to error; the step of adjusting a parameter as taught by Emiko corresponds to minimizing an error between the preprocessed real sensor data and the estimated output value).
As per claim 6, Emiko, Fruit, Jan, and Goyal in combination teach the simulation device of Claim 5, Emiko further teaches wherein the output unit is further configured to output the estimated output value which is produced, depending on the input value, from the simulation model to which the updated representative intrinsic parameter is applied (¶ 0002-0003; Emiko teaches obtaining simulation results; this teaching reads onto an output unit to output an estimated output value which is produced from the simulation model depending on the input value based on the intrinsic parameter).
As per claim 11, these limitations have already been discussed in claim 4. They are, hence, rejected for the same reasons.
As per claim 12, these limitations have already been discussed in claim 5. They are, hence, rejected for the same reasons.
As per claim 13, these limitations have already been discussed in claim 6. They are, hence, rejected for the same reasons.
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Emiko et al. (JP 2009-169771) in view of Fruit et al. (JP 4789277) and Jan et al. as applied to claims 1 and 8 above, and further in view of Guzek (Guzek, M., Białek, J., Królikowski, B., Bielecki, A., Świrski, K., & Wojdan, K. (2019, April). Advanced algorithms for operational optimization and predictive maintenance of large district heating systems. In 2019 IEEE 6th International Conference on Energy Smart Systems (ESS) (pp. 165-170). IEEE.).
Regarding Claim 16:
The prior combination of references set forth for claim 1 discloses the features of claim 1. It does not teach in particular, but Guzek teaches:
wherein the selection unit is configured to select, as the at least one representative intrinsic parameter, each intrinsic parameter whose degree of correlation with the estimated output value satisfies a predetermined criterion. (Section II.A., In order to select appropriate features for the model, an advanced method was used to exclude parameters, which were either irrelevant or highly cross-correlated. Since time delays between a system input to the various outputs might vary, Pearson correlation analysis accommodated a range of time delays from 10 to 300 s. This identified a suitable set of features with relevant time delays.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the Pearson correlation-based feature selection of Guzek to the combined system of Emiko as modified above, in order to “identif[y] a suitable set of features” for the digital twin modeling (Guzek, Section II.A.).
Regarding Claim 17:
The prior combination of references set forth for claim 1 discloses the features of claim 1. It does not teach in particular, but Guzek teaches:
wherein the selection unit is configured to select, as the at least one representative intrinsic parameter, each intrinsic parameter whose degree of correlation with the estimated output value satisfies a predetermined criterion. (Section II.A., In order to select appropriate features for the model, an advanced method was used to exclude parameters, which were either irrelevant or highly cross-correlated. Since time delays between a system input to the various outputs might vary, Pearson correlation analysis accommodated a range of time delays from 10 to 300 s. This identified a suitable set of features with relevant time delays.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the Pearson correlation-based feature selection of Guzek to the combined system of Emiko as modified above, in order to “identif[y] a suitable set of features” for the digital twin modeling (Guzek, Section II.A.).
Regarding Claim 18:
The prior combination of references set forth for claim 1 discloses the features of claim 1. It does not teach in particular, but Guzek teaches:
wherein the collection unit is configured to collect, as the real sensor data, a measured quantity that is different from the representative intrinsic parameter and has a predetermined engineering relationship with the representative intrinsic parameter, and to derive the representative intrinsic parameter from the measured quantity using the predetermined engineering relationship. (Section II.C., it is important to balance measurements - usually an integral part of the optimizer. Correction of measured values is calculated so that the system is balanced and trusted measurements are subject only to minor adjustment.; Section III.B., Therefore, initially, a hybrid approach was adopted, devising models partially as a black boxes and completing them by extending the model form to embrace its physical dependencies and subsequently identifying the coefficients.; Section III.D., An approach to fault detection tested in our study relied on digital twin modelling. Three major meter variables were modelled at hourly resolution: supply temperature, flow and heat consumed, performed using a gradient boosting model in the XGBoost implementation, including a full year of weather and calendar data as inputs. The model parameters were calculated independently for each substation to reflect actual measured values more accurately. Using these models, similar measurements were predicted for the next year of data. Substations suspected to be faulty were characterized by large discrepancies between real values and those from the model.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the Pearson correlation-based feature selection of Guzek to the combined system of Emiko as modified above, in order to “identif[y] a suitable set of features” for the digital twin modeling (Guzek, Section II.A.).
Regarding Claim 19:
The prior combination of references set forth for claim 1 discloses the features of claim Emiko further teaches, when the teachings of Guzek as set forth above for claim 18 are included in the combination:
wherein the update unit is configured to recalculate and update the representative intrinsic parameter to minimize an error between the measured quantity and a corresponding quantity produced by the simulation model. (¶6, “minimizes the sum of the weighted errors …”)
Regarding Claim 20:
The prior combination of references set forth for claim 8 discloses the features of claim 8. It does not teach in particular, but Guzek teaches:
The simulation method of Claim 8, wherein collecting the real sensor data comprises collecting a measured quantity different from the representative intrinsic parameter and having a predetermined engineering relationship with the representative intrinsic parameter, and deriving the representative intrinsic parameter from the measured quantity using the engineering relationship. ( Section II.C., it is important to balance measurements - usually an integral part of the optimizer. Correction of measured values is calculated so that the system is balanced and trusted measurements are subject only to minor adjustment.; Section III.B., Therefore, initially, a hybrid approach was adopted, devising models partially as a black boxes and completing them by extending the model form to embrace its physical dependencies and subsequently identifying the coefficients.; Section III.D., An approach to fault detection tested in our study relied on digital twin modelling. Three major meter variables were modelled at hourly resolution: supply temperature, flow and heat consumed, performed using a gradient boosting model in the XGBoost implementation, including a full year of weather and calendar data as inputs. The model parameters were calculated independently for each substation to reflect actual measured values more accurately. Using these models, similar measurements were predicted for the next year of data. Substations suspected to be faulty were characterized by large discrepancies between real values and those from the model.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the Pearson correlation-based feature selection of Guzek to the combined system of Emiko as modified above, in order to “identif[y] a suitable set of features” for the digital twin modeling (Guzek, Section II.A.).
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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/BIJAN MAPAR/ Primary Examiner, Art Unit 2189
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