DETAILED ACTION
Claims 1-9 are pending. Claims 1-9 are considered in this Office action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/9/2023 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
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.
Alice - Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites limitations of generating a training data set based upon one or more principled approaches that provide a gradient of values (Analyzing the Information, an Evaluation, a Mental Process; a Mathematical Concept/Relationship), generating a neural network using structured or unstructured sampling of a hyperparameter space augmented by probabilistic machine learning (Analyzing the Information, an Evaluation, a Mental Process; a Mathematical Concept/Relationship), training the generated neural network based on the training data set to produce one or more neutronics parameters (Analyzing the Information, an Evaluation, a Mental Process; a Mathematical Concept/Relationship), and generating at least one neutronics parameter utilizing the trained neural network (Analyzing and Transmitting the Information, an Evaluation and Judgment, a Mental Process; a Mathematical Concept/Relationship), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of a Mathematical Concept/Relationship, but for the recitation of generic computer components. That is, other than reciting by at least one computing device, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of a Mathematical Concept/Relationship. For example, generating a training data set based upon one or more principled approaches that provide a gradient of values encompasses a data analyst determining a training data set for use in a model, an observation, evaluation, and judgement, which is also Mathematical Concept/Relationship. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a “Mathematical Concept or Relationship”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computing device is recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the transmitting steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0180] With reference to FIG. 38, shown is a schematic block diagram of a computing device 300 that can be utilized to analyze patient data for diagnosis and/or recommend treatment or prevention using the KNN techniques. In some embodiments, among others, the computing device 300 may represent a mobile device (e.g., a smartphone, tablet, computer, etc.). Each computing device 300 includes at least one processor circuit, for example, having a processor 303 and a memory 306, both of which are coupled to a local interface 309. To this end, each computing device 300 may comprise, for example, at least one server computer or like device. The local interface 309 may comprise, for example, a data bus with an accompanying address/control bus or other bus structure as can be appreciated.”
Which shows that any generic computer can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the computing device, nor the transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-9 contain the identified abstract ideas, further narrowing them, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 3-6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal (U.S. Publication No. 2020/012,5961) in view of Cheatham (U.S. Publication No. 2018/025,4109).
Regarding Claim 1, Agrawal, a mini-machine learning system and method, teaches a method comprising:
generating, by at least one computing device ([0026] computer system), a training data set based upon one or more principled approaches that provide a gradient of values; ([0032-34] selecting (generating) training data using gradient descent)
generating a neural network using structured or unstructured sampling of a hyperparameter space augmented by probabilistic machine learning ([0036] and [0053] hyperparameters are used to generate a trained model comprising neural network and reducing the value of hyper-parameters for a number of layers in the neural network algorithm);
training the generated neural network based on the training data set to produce one or more neutronics parameters ([0053-55] training a neural network algorithm using the trained data to produce parameters) and
generating at least one neutronics parameter utilizing the trained neural network ([0025,0033,0062] and Claim 1 Agarwal teaches applying a new data set as input to the trained neural network algorithm for modifying, selecting, and qualifying paraments).
Although Oracle teaches the hyper-parameters as above, it does not explicitly state neutronics parameters.
Cheatham, a system and method for modeling a nuclear reactor, teaches neutronics parameters being used in modeling as in [0096] and [0098], and
Cheatham also teaches sampling of parameters as in [0081].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the parameters and modeling of Agrawal with the neutronics parameters of Cheatham as they are both analogous art along with the claimed invention which teach modeling solutions and the combination would lead to an improved system which would efficiently model groups and moves as taught in [0010] of Cheatham.
Regarding Claim 3, Agrawal teaches wherein the probabilistic machine learning comprises tree- structured Parzen estimators (TPE) ([0032] Parzen estimators are used in the learning).
Regarding Claim 4, Agrawal teaches wherein the structured or unstructured sampling is random ([0032] random search is also used as part of sampling and optimization)
Regarding Claim 5, Agrawal does not teach a reactor being adjust, but does teach the parameters as in Claim 1 above.
Cheatham teaches adjustment parameters as in [0101] and [0140] where problem states are evaluated by performing N reactor simulations and then the optimal solution is selected and provided to a user interface, in order to adjust reactor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the parameters and modeling of Agrawal with the neutronics parameters of Cheatham as they are both analogous art along with the claimed invention which teach modeling solutions and the combination would lead to an improved system which would efficiently model groups and moves as taught in [0010] of Cheatham.
Regarding Claim 6, Agrawal teaches further comprising testing the trained neural network based upon a defined set of input data associated with a known result ([0017] applying/testing the learning algorithm on the training set data for which the outcomes/results are known).
Regarding Claim 9, Agrawal teaches wherein data of the training data set is augmented by a lower order physical model ([0053-55, 0087] training the neural network with the training data set is implemented on a computer system shown in fig 6, a lower order physical model)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Agrawal (U.S. Publication No. 2020/012,5961) in view of Cheatham (U.S. Publication No. 2018/025,4109) in further view of PWR (NPL - Analysis of uncertainty propagation in scenario studies Surrogate models application to the French historical PWR fleet)
Regarding Claim 2, the combination of Agrawal and Cheatham teaches the structured and unstructured sampling as in Claim 1 above.
Neither teaches a Latin hypercube sampling.
PWR teaches wherein the structured or unstructured sampling comprises Latin hypercube sampling (LHS) (p. 8 1st clmn., the sampling is LHS)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the unstructured and structured sampling of the combination of Agrawal and Cheatham with the sampling using LHS of PWR as they are all analogous art along with the claimed invention which teach modeling solutions and the combination would lead to an improved system which would produce clear input data without sampling artifacts which is known by those in the art.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal (U.S. Publication No. 2020/012,5961) in view of Cheatham (U.S. Publication No. 2018/025,4109) in further view of Behler (NPL - Constructing high-dimensional neural network potentials: A tutorial review).
Regarding Claim 7, the combination of Agrawal and Cheatham teaches the known result as in Claim 1 above.
Neither teaches a symmetric function about the center of the evaluated region.
Behler teaches generating/determining weight parameters as in p.11 3rd para, and
Behler teaches a symmetric unction about the center of the evaluated region as in p.18-21 where a symmetry function is applied to a center of the nonlinear region.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the determination of the known result using parameters of the combination of Agrawal and Cheatham with the inclusion of a symmetric function of Behler as they are all analogous art along with the claimed invention which teach modeling solutions and the combination would lead to an improvement in the parameter usage in the neural network model as taught in p. 20 of Behler.
Regarding Claim 8, the combination of Agrawal and Cheatham teaches the nuclear reactor being modeled as in Claims 1 and 7 above.
Cheatham teaches wherein the evaluated region is a portion of a nuclear reactor core as in [0099] where the model is an evaluation of how well a nuclear reactor core performs.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the parameters and modeling of Agrawal with the neutronics parameters and evaluated region of Cheatham as they are both analogous art along with the claimed invention which teach modeling solutions and the combination would lead to an improved system which would optimize movements of fuel assemblies in a nuclear reactor through modeling as taught in [0008-10]] of Cheatham.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 1/30/2026