DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 2/3/2026, Applicant, on 5/5/2026, amended Claim 1, and added Claims 10-13. Claims 1-13 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant asserts that the claims are not directed at an abstract idea, neither a mental process or a mathematical equation. Examiner disagrees as the claims recite clear abstractions of both Mental Process and a Mathematical Concept/Relationship as per the rejection below. This a mere assertion that the claims are eligible under 101, as the utilization of a computing device does not make a claim eligible, and other than the recited use of this, the amended limitations of the claims are all part of the abstraction. The additional elements of these claims are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. This is utilization of current technologies, “Applying It”, similar to that of Alice, and does not make these limitations eligible under 101, as there is no improvement to any additional element, alone or in combination. Any inventive concept would be contained wholly within the abstraction.
Applicant asserts that the claims are practically integrated, as they solve a technical solution to a technical problem and that the claims as a whole integrate this by reciting the entire amended limitations of the claims and also stating that this make the analysis of data faster, easier, and less complex as described in the Specification. Examiner disagrees as again this a mere assertion that the claims are eligible under 101, as using a computing device to perform the abstract limitations of the claims does not make a claim eligible. The amended limitations of the claims are all part of the abstraction, and the only additional element claimed, a computing device, is recited at a high-level of generality (i.e., as a generic processor 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. This is utilization of current technologies, “Applying It”, similar to that of Alice, and does not make these limitations eligible under 101, as there is no improvement to any additional element, alone or in combination. Any inventive concept would be contained wholly within the abstraction.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Arguments regarding 35 USC §103 – Applicant asserts that the combination of Agrawal and Cheatham does not teach the amended limitations of the claims. Examiner disagrees as generating, by at least one computing device, a training data set based upon one or more principled approaches that provide a gradient of values as in [0032-34] where there the system is 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 as in [0036] and [0053] where 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 as in [0053-55] where there training a neural network algorithm using the trained data to produce parameters, generating at least one neutronics parameter utilizing the trained neural network as in [0025,0033,0062] where the system applying a new data set as input to the trained neural network algorithm for modifying, selecting, and qualifying paraments. Cheatham, a system and method for modeling a nuclear reactor, teaches neutronics parameters being used in modeling as in [0096] and [0098], and sampling of parameters as in [0081] and the training data generated using one or more feature extraction stacks that extract feature values from input data associated with a reactor as in [0180] where inputs from the reactor are stored and used as the [0221] variables used in the training of the model. This teaches the amended limitations of the Claims.
Therefore, the arguments are non-persuasive, the combination of Agrawal and Cheatham teaches the amended limitations of the Claims, and the rejection of the Claims and their dependents are maintained under 35 USC 103.
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 1-13 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, the training data generated using one or more feature extraction stacks that extract feature values from input data associated with a reactor (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.
New Claims 10-13 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 9-10 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 Agrawal 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] and the training data generated using one or more feature extraction stacks that extract feature values from input data associated with a reactor ([0180] inputs from the reactor are stored and used as the [0221] variables used in the training of the model)
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)
Regarding Claim 10, Agrawal teaches wherein the input data is formatted as a defined two- dimensional (2D) array ([0086] the data is used in field programmable gate arrays which are 2D)
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.
Claim 11 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 Official Notice.
Regarding Claim 11, Although Agrawal teaches a 2D array in the method of claim 10 as above, it does not explicitly state this is in a 17x17 format.
Examiner takes official notice that this is a design choice of an array as one can program and change an array as needed on a computing system, and thus this is well-known at the time of the invention, and this would lead to more flexibility in the data system.
Claim 12 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 Israeli (U.S. Publication No. 2020/021,1674).
Regarding Claim 12, Agrawal teaches use of layers in machine modeling, it does not teach a convolutional layer.
Neither does Cheatham.
Israeli teaches one or more convolutional layer being used with a neural network and deep learning as in [0050] and [0072]
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 machine learning modeling of the combination of Agrawal and Cheatham with the convolutional layer of Israeli 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 increase the speed and improve overall efficiency in a nuclear reactor through modeling as taught in [0004-5] of Israeli.
Claim 13 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 Henry (U.S. Publication No. 2021/002,7091).
Regarding Claim 13, the combination of Agrawal teaches the feature extraction stacks as in Claim 1 above, but does not teach specifically temperatures.
Cheatham teaches temperature history of fuel being used as in [0068].
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.
Cheatham does not teach clad or pin stacks.
Henry teaches features of a fuel pin temperature and cladding as in [0293-294].
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 machine learning modeling using temperatures of the combination of Agrawal and Cheatham with the temperature readings of Henry 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 improve the overall reading and efficiency of a nuclear reactor through modeling as taught in [0282-284] of Henry.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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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 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BETH BOSWELL can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 8/1/2026
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