DETAILED ACTION
This action is in response to the Applicant Response filed 12 June 2026 for application 18/231,657 filed 08 August 2023.
Claim(s) 1, 9, 16-17 is/are currently amended.
Claim(s) 1-20 is/are pending.
Claim(s) 1-20 is/are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
Claim Objections
Claim(s) 2, 10 is/are objected to because of the following informalities:
Claim 2, lines 7-8, performance of each the different prediction models should read “performance of each of the different prediction models”
Claim 10, lines 7-8, performance of each the different prediction models should read “performance of each of the different prediction models”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 9, 16 recite each of the different prediction models is based upon production experience from industry experts. However, the specification does not provide support for prediction models based on industry experts. The specification discloses the following:
[0021] Some conventional continuous production processes generally rely on industry experts with production experience for adjustment of control parameters of production equipment. Specifically, these industry experts manually make real-time adjustments to the control parameters based on feedback to improve energy efficiency and ensure quality of production. In some other conventional production processes, specific optimization models may be leveraged to convert production experience of industry experts into machine learning models, thereby reducing consumption of human resources (i.e., reducing reliance on industry experts) and improving timeliness and accuracy of forecasts. However, these conventional optimization models cannot easily, accurately, and fully capture dependencies between prediction models as well as relationships (i.e., connections) between production optimization goals (i.e., objectives) and control parameters of production equipment. Further, these conventional optimization models require large usage limitations on time range and types of production processes. For example, these conventional optimization models may utilize brute force search to determine which control parameters of production equipment cause prediction models to output target predictions that incur penalty values when compared against production optimization goals.
Not only does this fail to teach that the claimed invention provides for each of the prediction models being based upon production experience from industry experts, it, at best, discloses the opposite by distinguishing itself from conventional models which are based on production experience from industry experts. Therefore, there is no support in the original description for the inclusion of the amendment to the claims and the claims fail to comply with the written description requirement. Correction or clarification is required.
Claims 2-8, 10-15, 17-20 are rejected under 35 U.S.C 112(a) due to their dependence, either directly or indirectly, on claims 1, 9, 16.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of generating an objective optimization model based on each target prediction output related to the production equipment from each of the different prediction models, wherein the objective optimization model comprises a deep neural network to assist in aiding the continuous production process, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating a loss function corresponding to the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of optimizing a plurality of weights for a plurality of parameters of the different prediction models using backpropagation of the deep neural network and the loss function, resulting in a plurality of optimized weights for the parameters of the different prediction models, the backpropagation process correlating results of indicators of the continuous production process with production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating parameter values.
The limitation of optimizing the continuous production process ... while maintaining relationships between control parameters of production equipment and production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-implemented. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – prediction models, objective optimization model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites training different prediction models based on the input data, wherein each of the different prediction models is trained to output a target prediction relating to the production equipment and each of the different prediction models is based upon production experience from industry experts which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites receiving input data comprising a plurality of datasets, each of the datasets in the plurality of datasets including one or more variables relating to a production equipment involved in the continuous production process, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-implemented amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
prediction models, objective optimization model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of splitting the datasets between a first group of datasets for training and a second group of datasets for testing, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of defining a problem type for each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of selecting a machine-learning algorithm for training each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of selecting one or more statistical measures for evaluating performance of each the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – machine-learning algorithm. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
machine-learning algorithm amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 3 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the datasets include different variables with different time ranges that correspond to the different prediction models.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of providing, as output, the optimized weights for the parameters of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of providing, as output, the different prediction models with fixed parameters based on the optimized weights, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of generating an objective function corresponding to the objective optimization model, wherein the objective function represents one or more production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating constraints corresponding to the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of training the objective optimization model to minimize a difference quantified by the loss function, wherein the difference is between an expected objective value and a predicted objective value, the expected objective value is based on the objective function, and the predicted objective value is output from the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 8 carries out the method of claim 1 but for the recitation of additional element(s) of wherein an input layer of the deep neural network propagates initial weight matrices representing configurations of the different prediction models.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein an input layer of the deep neural network propagates initial weight matrices representing configurations of the different prediction models which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of generating an objective optimization model based on each target prediction output related to the production equipment from each of the different prediction models, wherein the objective optimization model comprises a deep neural network to assist in aiding the continuous production process, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating a loss function corresponding to the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of optimizing a plurality of weights for a plurality of parameters of the different prediction models using backpropagation of the deep neural network and the loss function, resulting in a plurality of optimized weights for the parameters of the different prediction models, the backpropagation process correlating results of indicators of the continuous production process with production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating parameter values.
The limitation of optimizing the continuous production process ... while maintaining relationships between control parameters of production equipment and production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – system, at least one processor, processor-readable memory device, instructions. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – prediction models, objective optimization model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites training different prediction models based on the input data, wherein each of the different prediction models is trained to output a target prediction relating to the production equipment and each of the different prediction models is based upon production experience from industry experts which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites receiving input data comprising a plurality of datasets, each of the datasets in the plurality of datasets including one or more variables relating to a production equipment involved in the continuous production process, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
system, at least one processor, processor-readable memory device, instructions amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
prediction models, objective optimization model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of splitting the datasets between a first group of datasets for training and a second group of datasets for testing, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of defining a problem type for each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of selecting a machine-learning algorithm for training each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of selecting one or more statistical measures for evaluating performance of each the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – machine-learning algorithm. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
machine-learning algorithm amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 11 carries out the system of claim 9 but for the recitation of additional element(s) of wherein the datasets include different variables with different time ranges that correspond to the different prediction models.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of providing, as output, the optimized weights for the parameters of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of providing, as output, the different prediction models with fixed parameters based on the optimized weights, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of generating an objective function corresponding to the objective optimization model, wherein the objective function represents one or more production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating constraints corresponding to the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for optimization of a continuous production process, the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of training the objective optimization model to minimize a difference quantified by the loss function, wherein the difference is between an expected objective value and a predicted objective value, the expected objective value is based on the objective function, and the predicted objective value is output from the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating a loss.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for optimization of a continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of generate an objective optimization model based on each target prediction output related to the production equipment from each of the different prediction models, wherein the objective optimization model comprises a deep neural network to assist in aiding the continuous production process, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate a loss function corresponding to the objective optimization model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of optimize a plurality of weights for a plurality of parameters of the different prediction models using backpropagation of the deep neural network and the loss function, resulting in a plurality of optimized weights for the parameters of the different prediction models, the backpropagation process correlating results of indicators of the continuous production process with production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating parameter values.
The limitation of optimize the continuous production process ... while maintaining relationships between control parameters of production equipment and production optimization goals, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer program product, computer readable storage medium, program instructions, processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – prediction models, objective optimization model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites train different prediction models based on the input data, wherein each of the different prediction models is trained to output a target prediction relating to the production equipment and each of the different prediction models is based upon production experience from industry experts which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites receive input data comprising a plurality of datasets, each of the datasets in the plurality of datasets including one or more variables relating to a production equipment involved in the continuous production process, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer program product, computer readable storage medium, program instructions, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
prediction models, objective optimization model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for optimization of a continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of split the datasets between a first group of datasets for training and a second group of datasets for testing, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of define a problem type for each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of select a machine-learning algorithm for training each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of select one or more statistical measures for evaluating performance of each of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – machine-learning algorithm. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
machine-learning algorithm amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for optimization of a continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 8 carries out the computer program product of claim 16 but for the recitation of additional element(s) of wherein the datasets include different variables with different time ranges that correspond to the different prediction models.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for optimization of a continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of provide, as output, the optimized weights for the parameters of the different prediction models, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a computer program product, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer program product for optimization of a continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner.
The limitation of provide, as output, the different prediction models with fixed parameters based on the optimized weights, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shamsian et al. (Personalized Federated Learning Using Hypertnetworks, hereinafter referred to as “Shamsian”) in view of Li et al. (Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT, hereinafter referred to as "Li") and further in view of Applicant Provided Prior Art through Applicant’s Own Admission (hereinafter referred to as “Applicant”).
Regarding claim 1 (Currently Amended), Shamsian teaches a computer-implemented method (Shamsian, section 3.2 - teaches servers and clients performing the method; Shamsian, section 5.2 - discusses communication, storage and computational resources of devices) for optimization of a continuous production process (Shamsian, section 3.1 – teaches optimizing a plurality of models [In combination with Li below, the models are interpreted to optimize a production process]) … , the computer-implemented method comprising:
receiving input data comprising a plurality of datasets, each of the datasets in the plurality of datasets including one or more variables (Shamsian, section 3.1 – teaches a plurality of clients each with its own personal private dataset) …;
training different prediction models based on the input data, wherein each of the different prediction models is trained to output a target prediction (Shamsian, section 3.1 – teaches each client training its own personal model defined by its private parameters, herein the model takes an input and provides a target output) …;
generating an objective optimization model based on each target prediction output related to the production equipment from each of the different prediction models (Shamsian, section 3.2 – teaches a hypernetwork [optimization model] which determines parameter values for each client model based on local model parameter adjustments caused from training the local model [In combination with Li below, the models are interpreted to optimize a production process related to production equipment]), wherein the objective optimization model comprises a deep neural network to assist in aiding the continuous production process (Shamsian, section 3.2 – teaches the hypernetwork is a deep neural network [In combination with Li below, the models are interpreted to optimize a production process]);
generating a loss function corresponding to the objective optimization model (Shamsian, section 3.2 – teaches an objective function for the hypernetwork);
optimizing a plurality of weights for a plurality of parameters of the different prediction models (Shamsian, section 3.2 – teaches a hypernetwork [optimization model] which determines parameter values for each client model) using backpropagation of the deep neural network and the loss function, resulting in a plurality of optimized weights for the parameters of the different prediction models, the backpropagation process correlating results of indicators of the continuous production process with production optimization goals (Shamsian, section 3.2 – teaches updating the hypernetwork based on backpropagation and a loss function in order to obtain optimized weights for the client models [In combination with Li below, the models are interpreted to optimize a production process]).
While Shamsian teaches optimizing weights for a plurality of prediction models based on an optimization model, Shamsian does not explicitly teach a continuous production process.
Li teaches a computer-implemented method for optimization of a continuous production process the continuous production process (Li, section III - teaches local devices using real-time streaming industrial sensor data to train local prediction models) …, the computer-implemented method comprising:
receiving input data comprising a plurality of datasets, each of the datasets in the plurality of datasets including one or more variables relating to a production equipment involved in the continuous production process (Li, section III - teaches local devices using real-time streaming industrial sensor data to train local prediction models);
training different prediction models based on the input data, wherein each of the different prediction models is trained to output a target prediction relating to the production equipment (Li, section III - teaches local devices using real-time streaming industrial sensor data to train local prediction models) …;
optimizing the continuous production process using the different prediction models while maintaining relationships between control parameters of production equipment and production optimization goals (Li, section III - teaches local devices using real-time streaming industrial sensor data to train local prediction models).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Shamsian with the teachings of Li in order to improving model performance in industrial environments in the field of optimizing a plurality of prediction models (Li, Abstract – “Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning (ML) models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this article, we propose FEDGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-independent and identically distributed (non-j) data. Taking advantage of naturally clustered factory devices, FEDGS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FEDGS has better convergence performance than FedAvg and give a relaxed condition under which FEDGS is more communication efficient. The extensive experiments show that FEDGS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.”).
However, Shamsian in view of Li does not explicitly teach the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner; … each of the different prediction models is based upon production experience from industry experts.
Applicant teaches
the continuous production process designed to manufacture, produce, or process products or material in a consistent, constant, and uninterrupted manner (Applicant, Background, [0002] - teaches in manufacturing, a continuous production process (or system) is a type of production method (or production system) used by manufacturing, production, or processing companies to manufacture, produce, or process a very large volume of products or materials in a consistent, constant, and uninterrupted manner);
.. each of the different prediction models is based upon production experience from industry experts (Applicant, [0021] – teaches convention models in conventional continuous production processes relay on industry experts).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Shamsian in view of Li with the teachings of Applicant in order to identify prior art in the field of optimizing a plurality of prediction models (Applicant, [0021] - Some conventional continuous production processes generally rely on industry experts with production experience for adjustment of control parameters of production equipment. Specifically, these industry experts manually make real-time adjustments to the control parameters based on feedback to improve energy efficiency and ensure quality of production. In some other conventional production processes, specific optimization models may be leveraged to convert production experience of industry experts into machine learning models, thereby reducing consumption of human resources (i.e., reducing reliance on industry experts) and improving timeliness and accuracy of forecasts. However, these conventional optimization models cannot easily, accurately, and fully capture dependencies between prediction models as well as relationships (i.e., connections) between production optimization goals (i.e., objectives) and control parameters of production equipment. Further, these conventional optimization models require large usage limitations on time range and types of production processes. For example, these conventional optimization models may utilize brute force search to determine which control parameters of production equipment cause prediction models to output target predictions that incur penalty values when compared against production optimization goals.).
Regarding claim 2, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 1 as noted above. Shamsian further teaches
splitting the datasets between a first group of datasets for training and a second group of datasets for testing (Shamsian, Appendix B – teaches 70/15/15 split of the dataset including training and test sets);
defining a problem type for each of the different prediction models (Shamsian, section 3.1 – teaches personalized machine learning models based on personalized private data);
selecting a machine-learning algorithm for training each of the different prediction models (Shamsian, section 3.1 – teaches personalized machine learning models based on personalized private data); and
selecting one or more statistical measures for evaluating performance of each the different prediction models (Shamsian, section 3.1 – teaches a local optimization function for each of the client models).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 3, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 1 as noted above. Li further teaches wherein the datasets include different variables with different time ranges that correspond to the different prediction models (Li, section III - teaches local devices using real-time streaming industrial sensor data to train local prediction models).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Shamsian, Li and Applicant in order to include variable data to improving model performance in industrial environments (Li, Abstract).
Regarding claim 4, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 1 as noted above. Shamsian further teaches providing, as output, the optimized weights for the parameters of the different prediction models (Shamsian, section 3.2 – teaches outputting the parameters [weights] for each client model using the hypernetwork).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 5, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 1 as noted above. Shamsian further teaches providing, as output, the different prediction models with fixed parameters based on the optimized weights (Shamsian, section 3.2 – teaches outputting the parameters [weights] for each client model using the hypernetwork and sending the weights to the client to generate the updated client models).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 6, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 1 as noted above. Shamsian further teaches
generating an objective function corresponding to the objective optimization model, wherein the objective function represents one or more production optimization goals (Shamsian, section 3.2 – teaches an objective function of the hypernetwork used to optimize the client model weights [production optimization goals]); and
generating constraints corresponding to the objective optimization model (Shamsian, section 3.2 – teaches constraints of local update steps before server-client communication, descriptor type, size of hyper network, number of rounds, learning rate; see also Shamsian, Algorithm 1, section 5).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 7, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 6 as noted above. Shamsian further teaches training the objective optimization model to minimize a difference quantified by the loss function, wherein the difference is between an expected objective value and a predicted objective value, the expected objective value is based on the objective function, and the predicted objective value is output from the objective optimization model (Shamsian, section 3.2 – teaches the hyperparameter objective function based on the difference between the output value and an expected value).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 6 above.
Regarding claim 8, Shamsian in view of Li and further in view of Applicant teaches all of the limitations of the method of claim 1 as noted above. Shamsian further teaches wherein an input layer of the deep neural network propagates initial weight matrices representing configurations of the different prediction models (Shamsian, section 3.2 – teaches transmitting the change in local model parameters to the hypernetwork).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 9, it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Shamsian further teaches a system for optimization of a continuous production process, comprising:
at least one processor (Shamsian, section 3.2 - teaches servers and clients performing the method; Shamsian, section 5.2 - discusses communication, storage and computational resources of devices); and
a processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including (Shamsian, section 3.2 - teaches servers and clients performing the method; Shamsian, section 5.2 - discusses communication, storage and computational resources of devices) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 2.
Regarding claim 11, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 3.
Regarding claim 12, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 4.
Regarding claim 13, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 5.
Regarding claim 14, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 6.
Regarding claim 15, the rejection of claim 14 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 7.
Regarding claim 16, it is the computer program product embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Shamsian further teaches a computer program product for optimization of a continuous production process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to (Shamsian, section 3.2 - teaches servers and clients performing the method; Shamsian, section 5.2 - discusses communication, storage and computational resources of devices) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Shamsian, Li and Applicant for the same reasons as disclosed in claim 1 above.
Regarding claim 17, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 2.
Regarding claim 18, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 3.
Regarding claim 19, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 4.
Regarding claim 20, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Shamsian in view of Li and further in view of Applicant for the reasons set forth in the rejection of claim 5.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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