DETAILED ACTION
Claims 1-20 have been examined.
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 .
Claim Rejections - 35 U.S.C. § 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.
The invention, as taught in Claims 1-20, is directed to “mental steps” and “mathematical steps” without significantly more.
The claims recite:
• training dataset
• generating, based on a training dataset, a data-driven model configured to output first model-predicted data associated with at least one process of an industrial plant
• a data-driven model
• first model-predicted data associated with at least one process of an industrial plant
• predicted value for each of one or more target process variables associated with the at least one process
• integrating the data-driven model within a process simulation model
• process simulation model
• operating conditions
Claim 1
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “1. A computer-implemented method for data-driven model predictions within a process simulation system, the computer-implemented method comprising…” Therefore, it is a “computer-implemented method” (or “process”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 1 that recite abstract ideas?
YES. The following limitations in Claim 1 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• a data-driven model
• first model-predicted data associated with at least one process of an industrial plant
• predicted value for each of one or more target process variables associated with the at least one process
• integrating the data-driven model within a process simulation model
• process simulation model
• operating conditions
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) An “execution of the at least one process”
(2) A “deploying the process simulation model”
A “execution of the at least one process” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “execution of the at least one process” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “deploying the process simulation model” is a broad term which is described at a high level. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “deploying the process simulation model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) An “execution of the at least one process”
(2) A “deploying the process simulation model”
A “execution of the at least one process” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Further, Applicant's execution method is well-understood, routine and conventional. Applicant's Specification, paragraph [0110] recites:
[0110] The processes and logic flows described herein can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input information/data and generating output. Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and information/data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive information/data from or transfer information/data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Devices suitable for storing computer program instructions and information/data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “deploying the process simulation model” is a broad term which is described at a high level. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Applicant's “deploying” is well-understood, routine, and conventional because that deploying operation involves generic receiving, generating/calculating, and applying data. Applicant’s Specification, paragraph [0103] recites:
[0103] At block 606, the apparatus 200 includes first principles-driven prediction circuitry 210,data-driven prediction circuitry 212,optional control circuitry 214,communications circuitry 208,input/output circuitry 206,processor 202, and/or the like, or a combination thereof, that deploys the process simulation model for use. In some embodiments, deploying the process simulation model for use includes receiving input data, generating, using the data- driven model, the first model-predicted data, and applying the model-predicted data in one or more of engineering studies or offline optimization operation. In some embodiments, the input data includes process data associated with the at least one process. In some embodiments, the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 1 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 2
Claim 2 recites:
2. The computer-implemented method of claim 1, wherein the process simulation model comprise the data-driven model and a first principles model.
Applicant’s Claim 2 merely teaches pure mathematical data entities. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 2 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 3
Claim 3 recites:
3. The computer-implemented method of claim 2, wherein the first principles model is configured to generate (i.e., calculate)second model-predicted data, wherein the second model-predicted data comprise a predicted value for each of one or more other process variables relative to the one or more target process variables.
Applicant’s Claim 3 merely teaches, in the broadest reasonable interpretation, limitations to pure mathematical calculation. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 3 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 4
Claim 4 recites:
4. The computer-implemented method of claim 1, wherein deploying the process simulation model for use comprises:
receiving input data, wherein the input data comprise process data associated with the at least one process;
generating, using the data-driven model, the first model-predicted data; and
applying the model-predicted data in one or more of (i) engineering studies or (ii) offline optimization operation.
Applicant’s Claim 4 merely teaches, in its broadest reasonable interpretation, limitations to generic mathematical processes of receiving unspecified mathematical data, processing that data in a completely unspecified way, and applying that data in one of two fields of use. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 4 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 5
Claim 5 recites:
5. The computer-implemented method of claim 1, wherein the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.
Applicant’s Claim 5 merely teaches, in its broadest reasonable interpretation, limitations to generic mathematical data applied to one of two fields of use. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 5 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 6
Claim 6 recites:
6. The computer-implemented method of claim 1, wherein the data-driven model is configured to model the at least one process.
Applicant’s Claim 6 merely teaches, in its broadest reasonable interpretation, limitations to generic mathematical data applied to one unspecified field of use. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 6 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 7
Claim 7 recites:
7. The computer-implemented method of claim 1, wherein the data-driven model comprises a neural network model.
Applicant’s Claim 7 merely teaches the mathematical data parameters that describe a generic neural network. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 7 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 8
Claim 8 recites:
8. The computer-implemented method of claim 1, wherein the data-driven model comprises a regression model.
Applicant’s Claim 8 merely teaches the mathematical data parameters that describe a generic, mathematical regression/prediction model. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 8 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 9
Claim 9 recites:
9. The computer-implemented method of claim 1, wherein the data-driven model is previously trained using one or more supervised training techniques.
Applicant’s Claim 9 merely teaches the mathematical data parameters that describe a generic, mathematical supervised learning model. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 9 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 10
Claim 10 recites:
10. The computer-implemented method of claim 2, wherein the data-driven model is trained using the training dataset, wherein the training dataset comprises a plurality of sets of historical process data and corresponding ground truth data for one or more target process variables.
Applicant’s Claim 10 merely teaches the mathematical data parameters that describe a generic, mathematical learning model. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 10 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 11
Claim 11 recites:
11. The computer-implemented method of claim 2, wherein the historical process data in each set of historical process data and corresponding ground truth data comprises one or more of (i) historical predicted values for the one or more target process variables or (ii) historical values for a set of measured process variables.
Applicant’s Claim 11 merely teaches mathematical training values. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 11 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 12
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “12. An apparatus comprising…” Therefore, it is an “apparatus,” which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 12 that recite abstract ideas?
YES. The following limitations in Claim 12 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• a data-driven model
• first model-predicted data associated with at least one process of an industrial plant
• predicted value for each of one or more target process variables associated with the at least one process
• integrating the data-driven model within a process simulation model
• process simulation model
• operating conditions
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) An “execution of the at least one process”
(2) A “deploy the process simulation model”
A “execution of the at least one process” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “execution of the at least one process” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “deploy the process simulation model” is a broad term which is described at a high level. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “deploy the process simulation model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) An “execution of the at least one process”
(2) A “deploy the process simulation model”
A “execution of the at least one process” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Further, Applicant's execution method is well-understood, routine and conventional. Applicant's Specification, paragraph [0110] recites:
[0110] The processes and logic flows described herein can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input information/data and generating output. Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and information/data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive information/data from or transfer information/data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Devices suitable for storing computer program instructions and information/data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “deploy the process simulation model” is a broad term which is described at a high level. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Applicant's “deploy” is well-understood, routine, and conventional because that deploying operation involves generic receiving, generating/calculating, and applying data. Applicant’s Specification, paragraph [0103] recites:
[0103] At block 606, the apparatus 200 includes first principles-driven prediction circuitry 210,data-driven prediction circuitry 212,optional control circuitry 214,communications circuitry 208,input/output circuitry 206,processor 202, and/or the like, or a combination thereof, that deploys the process simulation model for use. In some embodiments, deploying the process simulation model for use includes receiving input data, generating, using the data- driven model, the first model-predicted data, and applying the model-predicted data in one or more of engineering studies or offline optimization operation. In some embodiments, the input data includes process data associated with the at least one process. In some embodiments, the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 12 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 13
Claim 13 recites:
13. The apparatus of claim 12, wherein the process simulation model comprise the data-driven model and a first principles model.
Applicant’s Claim 13 merely teaches pure mathematical data entities. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 13 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 14
Claim 14 recites:
14. The apparatus of claim 13, wherein the first principles model is configured to generate second model-predicted data, wherein the second model-predicted data comprise a predicted value for each of one or more other process variables relative to the one or more target process variables.
Applicant’s Claim 14 merely teaches , in the broadest reasonable interpretation, limitations to pure mathematical calculation. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 14 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 15
Claim 15 recites:
15. The apparatus of claim 12, wherein deploying the process simulation model for use comprises:
receiving input data, wherein the input data comprise process data associated with the at least one process;
generating, using the data-driven model, the first model-predicted data; and
applying the model-predicted data in one or more of (i) engineering studies or (ii) offline optimization operation.
Applicant’s Claim 15 merely teaches , in its broadest reasonable interpretation, limitations to generic mathematical processes of receiving unspecified mathematical data, processing that data in a completely unspecified way, and applying that data in one of two fields of use. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 15 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 16
Claim 16 recites:
16. The apparatus of claim 12, wherein the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.
Applicant’s Claim 16 merely teaches , in its broadest reasonable interpretation, limitations to generic mathematical data applied to one of two fields of use. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 16 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 17
Claim 17 recites:
17. The apparatus of claim 12, wherein the data-driven model is configured to model the at least one process.
Applicant’s Claim 17 merely teaches, in its broadest reasonable interpretation, limitations to generic mathematical data applied to one unspecified field of use. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 17 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 18
Claim 18 recites:
18. The apparatus of claim 12, wherein the data-driven model comprises a neural network model.
Applicant’s Claim 18 merely teaches the mathematical data parameters that describe a generic neural network. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 18 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 19
Claim 19 recites:
19. The apparatus of claim 12, wherein the data-driven model comprises a regression model.
Applicant’s Claim 19 merely teaches the mathematical data parameters that describe a generic, mathematical regression/prediction model. Applicant's Specification, paragraph [0036] recites:
[0036] The term "data-driven model" may refer to a data entity that describes a model that is generated based on empirical data. In some example, the empirical data may be obtained from historical data, simulations, experiments, a combination thereof, and/or the like. A data-driven model may be configured to extract and/or determine correlations and/or patterns in input data to generate corresponding output. In some embodiments, a data-driven model may include a machine learning model. An example of a data-driven model is machine learning soft sensor model. In some embodiments a data-driven model, such as a machine learning soft sensor model is configured, trained, and/or the like to generate model-predicted data (e.g., data-driven model-predicted data) that includes predicted values for one or more target process variables. In some examples, the data-driven model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, reinforcement learning models, and/or the like. In some examples, the data-driven model may include multiple models configured to perform one or more different stages of a prediction process. In some embodiments, the data-driven model includes a neural network, such as a recurrent neural network, deep neural network, and/or the like. In some examples, the data-driven model may include one or more neural networks that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables. In some embodiments, the data-driven model includes a regression model, such as a linear regression model, a partial least square regression model, a support vector regression model, and/or the like. In some examples, the data-driven model may include one or more regression models that are previously trained, using one or more supervised and/or unsupervised machine learning techniques, to generate model-predicted data for one or more target process variables.
It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 19 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 20
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “20. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to…” Therefore, it is a “computer program product,” which is not, itself, limited to a “non-transitory computer-readable storage medium”. Therefore, the answer to the inquiry is: “NO.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 20 that recite abstract ideas?
YES. The following limitations in Claim 20 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• a data-driven model
• first model-predicted data associated with at least one process of an industrial plant
• predicted value for each of one or more target process variables associated with the at least one process
• integrating the data-driven model within a process simulation model
• process simulation model
• operating conditions
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) An “execution of the at least one process”
(2) A “deploy the process simulation model”
A “execution of the at least one process” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “execution of the at least one process” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “deploy the process simulation model” is a broad term which is described at a high level. M.P.E.P. § 2106.04(d)(I) recites:
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
This “deploy the process simulation model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) An “execution of the at least one process”
(2) A “deploy the process simulation model”
A “execution of the at least one process” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Further, Applicant's execution method is well-understood, routine and conventional. Applicant's Specification, paragraph [0110] recites:
[0110] The processes and logic flows described herein can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input information/data and generating output. Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and information/data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive information/data from or transfer information/data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Devices suitable for storing computer program instructions and information/data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “deploy the process simulation model” is a broad term which is described at a high level. M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Applicant's “deploying” is well-understood, routine, and conventional because that deploying operation involves generic receiving, generating/calculating, and applying data. Applicant’s Specification, paragraph [0103] recites:
[0103] At block 606, the apparatus 200 includes first principles-driven prediction circuitry 210,data-driven prediction circuitry 212,optional control circuitry 214,communications circuitry 208,input/output circuitry 206,processor 202, and/or the like, or a combination thereof, that deploys the process simulation model for use. In some embodiments, deploying the process simulation model for use includes receiving input data, generating, using the data- driven model, the first model-predicted data, and applying the model-predicted data in one or more of engineering studies or offline optimization operation. In some embodiments, the input data includes process data associated with the at least one process. In some embodiments, the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 20 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 U.S.C. § 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-8, 10-12, and 15-20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Alhazmi, et al., A Reinforcement Learning-Based Economic Model Predictive Control Framework for Autonomous Operation of Chemical Reactors, Chemical Engineering Journal vol. 428, pp. 1-10, 15 JAN 2022, in its entirety. Specifically:
Claim 1
Claim 1’s “generating, based on a training dataset, a data-driven model configured to output first model-predicted data associated with at least one process of an industrial plant” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the gray box labeled “Process model” (i.e., the claimed “data-driven model”). The “Model parameters” that configure its operation are “generated” by the orange box labelled “Reinforcement Learning”. The claimed “first model-predicted data” are anticipated by the predictions that exit the top and bottom of the “Process model” of Alhazmi, et al.
Claim 1’s “the first model-predicted data comprise a predicted value for each of one or more target process variables associated with the at least one process” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 1’s “integrating the data-driven model within a process simulation model” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the dashed box encompassing the integrated model.
Claim 1’s “the process simulation model is configured to simulate the execution of the at least one process at one or more operating conditions of a plurality of operating conditions” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 1’s “deploying the process simulation model for use” is anticipated by Alhazmi, et al., page 7, right column, last full paragraph, where it recites:
Next, the yield predicted by the model was compared with the yield of the plant when the EMPC-RL scheme was employed. Fig. 5 shows that the error in predicting the yield was less than 4%.
Claim 4
Claim 4’s “receiving input data, wherein the input data comprise process data associated with the at least one process;” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the path at the top named “process state.”
Claim 4’s “generating, using the data-driven model, the first model-predicted data; and” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 4’s “applying the model-predicted data in one or more of (i) engineering studies or (ii) offline optimization operation.” is anticipated by Alhazmi, et al., page 7, right column, last full paragraph, where it recites:
Next, the yield predicted by the model was compared with the yield of the plant when the EMPC-RL scheme was employed. Fig. 5 shows that the error in predicting the yield was less than 4%.
Claim 5
Claim 5’s “5. The computer-implemented method of claim 1, wherein the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.” is anticipated by Alhazmi, et al., Page 8, right column, Remark 2, where it recites:
Remark 2. To ensure asymptotic stability of the nonlinear system of Eq. (1) under the RL-based LEMPC of Algorithm 2, the stability constraint of Eq. (6f) can be enforced for all times which will eventually drive the closed-loop state to a small neighborhood around the origin, due to the stability properties of the Lyapunov-based controller ℎ( ̃ 𝑥(𝑡𝑘)). Nevertheless, activating this constraint for all times will significantly impact the process economics because of the fact that the operating region at which the closed-loop system can maximize process economics has been reduced from 𝛺𝜌𝑒 to 𝛺𝜌𝑠.
Note that the Lyapunov constraint is a function of the predicted data (i.e., the claimed “model-predicted data”.)
Claim 6
Claim 6’s “6. The computer-implemented method of claim 1, wherein the data-driven model is configured to model the at least one process.” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 7
Claim 7’s “7. The computer-implemented method of claim 1, wherein the data-driven model comprises a neural network model.” is anticipated by Alhazmi, et al., page 9, right column, first full paragraph, where it recites:
The Greek letter 𝜃 symbolizes the tuning parameters of a nonlinear dynamic model, while the symbol 𝜙 denotes the set of parameters on which the reinforcement learning policies depend; in the case of neural network policies, the parameters are the weights and biases.
Claim 8
Claim 8’s “8. The computer-implemented method of claim 1, wherein the data-driven model comprises a regression model.” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 10
Claim 10’s “10. The computer-implemented method of claim 2, wherein the data-driven model is trained using the training dataset, wherein the training dataset comprises a plurality of sets of historical process data and corresponding ground truth data for one or more target process variables.” is anticipated by Alhazmi, et al., page 7, left column, second full paragraph, where it recites:
At each time step of the training, the kinetic parameters of the plant (i.e., the claimed “historical process data”) are randomly generated within the range [0.9, 1.1] (i.e., a constraint that is “ground truth data”). Being exposed to this experience, the RL agent attempts to learn an approximate policy. (i.e., the claimed “historical process data and corresponding ground truth data,” since Applicant did not specify how old historical data must be) The learning behavior is shown in Fig. 2, where the average reward the agent receives increases with more learning episodes. It should be noted here that the RL agent is not trained on a particular scenario of how the plant and model mismatch occurs. That is, no correlations among the values of 𝜃1...𝜃6 are assumed. The training is stopped when the change in the average reward with respect to training episodes begins to approach zero.
Claim 11
Claim 11’s “11. The computer-implemented method of claim 2, wherein the historical process data in each set of historical process data and corresponding ground truth data comprises one or more of (i) historical predicted values for the one or more target process variables or (ii) historical values for a set of measured process variables.” is anticipated by Alhazmi, et al., page 7, left column, second full paragraph, where it recites:
At each time step of the training, the kinetic parameters of the plant (i.e., the claimed “historical process data”) are randomly generated within the range [0.9, 1.1] (i.e., a constraint that is “ground truth data”). Being exposed to this experience, the RL agent attempts to learn an approximate policy. (i.e., the claimed “historical process data and corresponding ground truth data,” since Applicant did not specify how old historical data must be) The learning behavior is shown in Fig. 2, where the average reward the agent receives increases with more learning episodes. It should be noted here that the RL agent is not trained on a particular scenario of how the plant and model mismatch occurs. That is, no correlations among the values of 𝜃1...𝜃6 are assumed. The training is stopped when the change in the average reward with respect to training episodes begins to approach zero.
Claim 12
Claim 12’s “generate, based on a training dataset, a data-driven model configured to output first model-predicted data associated with at least one process of an industrial plant” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the gray box labeled “Process model” (i.e., the claimed “data-driven model”). The “Model parameters” that configure its operation are “generated” by the orange box labelled “Reinforcement Learning”. The claimed “first model-predicted data” are anticipated by the predictions that exit the top and bottom of the “Process model” of Alhazmi, et al.
Claim 12’s “the first model-predicted data comprise a predicted value for each of one or more target process variables associated with the at least one process” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 12’s “integrate the data-driven model within a process simulation model” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the dashed box encompassing the integrated model.
Claim 12’s “the process simulation model is configured to simulate the execution of the at least one process at one or more operating conditions of a plurality of operating conditions” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 12’s “deploy the process simulation model for use” is anticipated by Alhazmi, et al., page 7, right column, last full paragraph, where it recites:
Next, the yield predicted by the model was compared with the yield of the plant when the EMPC-RL scheme was employed. Fig. 5 shows that the error in predicting the yield was less than 4%.
Claim 15
Claim 15’s “receiving input data, wherein the input data comprise process data associated with the at least one process;” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the path at the top named “process state.”
Claim 15’s “generating, using the data-driven model, the first model-predicted data; and” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 15’s “applying the model-predicted data in one or more of (i) engineering studies or (ii) offline optimization operation.” is anticipated by Alhazmi, et al., page 7, right column, last full paragraph, where it recites:
Next, the yield predicted by the model was compared with the yield of the plant when the EMPC-RL scheme was employed. Fig. 5 shows that the error in predicting the yield was less than 4%.
Claim 16
Claim 16’s “16. The apparatus of claim 12, wherein the first model-predicted data is implemented as a constraint in one or more of (i) engineering studies or (ii) optimization operation.” is anticipated by Alhazmi, et al., Page 8, right column, Remark 2, where it recites:
Remark 2. To ensure asymptotic stability of the nonlinear system of Eq. (1) under the RL-based LEMPC of Algorithm 2, the stability constraint of Eq. (6f) can be enforced for all times which will eventually drive the closed-loop state to a small neighborhood around the origin, due to the stability properties of the Lyapunov-based controller ℎ( ̃ 𝑥(𝑡𝑘)). Nevertheless, activating this constraint for all times will significantly impact the process economics because of the fact that the operating region at which the closed-loop system can maximize process economics has been reduced from 𝛺𝜌𝑒 to 𝛺𝜌𝑠.
Note that the Lyapunov constraint is a function of the predicted data (i.e., the claimed “model-predicted data”.)
Claim 17
Claim 17’s “17. The apparatus of claim 12, wherein the data-driven model is configured to model the at least one process.” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 18
Claim 18’s “18. The apparatus of claim 12, wherein the data-driven model comprises a neural network model.” is anticipated by Alhazmi, et al., page 9, right column, first full paragraph, where it recites:
The Greek letter 𝜃 symbolizes the tuning parameters of a nonlinear dynamic model, while the symbol 𝜙 denotes the set of parameters on which the reinforcement learning policies depend; in the case of neural network policies, the parameters are the weights and biases.
Claim 19
Claim 19’s “19. The apparatus of claim 12, wherein the data-driven model comprises a regression model.” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 20
Claim 20’s “generate, based on a training dataset, a data-driven model configured to output first model-predicted data associated with at least one process of an industrial plant” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the gray box labeled “Process model” (i.e., the claimed “data-driven model”). The “Model parameters” that configure its operation are “generated” by the orange box labelled “Reinforcement Learning”. The claimed “first model-predicted data” are anticipated by the predictions that exit the top and bottom of the “Process model” of Alhazmi, et al.
Claim 20’s “the first model-predicted data comprise a predicted value for each of one or more target process variables associated with the at least one process” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 20’s “integrate the data-driven model within a process simulation model” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the dashed box encompassing the integrated model.
Claim 20’s “the process simulation model is configured to simulate the execution of the at least one process at one or more operating conditions of a plurality of operating conditions” is anticipated by Alhazmi, et al., page 4, Fig. 1, where it shows the predictions that exit the top and bottom of the gray box labelled “Process model” (i.e., the claimed “data-driven model”).
Claim 20’s “deploy the process simulation model for use” is anticipated by Alhazmi, et al., page 7, right column, last full paragraph, where it recites:
Next, the yield predicted by the model was compared with the yield of the plant when the EMPC-RL scheme was employed. Fig. 5 shows that the error in predicting the yield was less than 4%.
Claims 2-3, 9, and 13-14 are not rejected under art since when reading the claims in light of the specification, as per MPEP § 2111.01, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 2. Specifically:
Claim 2’s "...the process simulation model comprise the data-driven model and a first principles model..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 3. Specifically:
Claim 3’s "...first principles model is configured to generate second model-predicted data...target process variables..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 9. Specifically:
Claim 9’s "...the data-driven model is previously trained using one or more supervised training techniques..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 13. Specifically:
Claim 13’s "...data-driven model and a first principles model..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 14. Specifically:
Claim 14’s "...first principles model is configured to generate second model-predicted data...target process variables…"
Conclusion
Any inquiries concerning this communication or earlier communications from the examiner should be directed to Wilbert L. Starks, Jr., who may be reached Monday through Friday, between 8:00 a.m. and 5:00 p.m. EST. or via telephone at (571) 272-3691 or email: Wilbert.Starks@uspto.gov.
If you need to send an Official facsimile transmission, please send it to (571) 273-8300.
If attempts to reach the examiner are unsuccessful the Examiner’s Supervisor (SPE), Kakali Chaki, may be reached at (571) 272-3719.
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/WILBERT L STARKS/
Primary Examiner, Art Unit 2122
WLS
08 JAN 2026