DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the Application filed on 05/16 /2023. Claims 1- 9 are pending in the case. Claims 1, 8 , and 9 are independent claims. Claim Rejections - 35 U.S.C. § 101 35 U.S.C. § 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-7 are directed towards the statutory category of a machine. Claim 8 is directed towards the statutory category of a process. Claim 9 is directed towards the statutory category of an article of manufacture. With respect to claim 1 : 2A Prong 1 : This claim is directed to a judicial exception. a determination process for determining a range to be satisfied by at least one hyperparameter selected from the group consisting of a plurality of hyperparameters for use in learning by the gradient descent method, the range being determined in accordance with information which has been acquired in the acquisition process ( mathematical concept and/or mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: An information processing apparatus comprising at least one processor, the at least one processor carrying out (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and an acquisition process for acquiring at least one selected from the group consisting of a condition to be satisfied by a loss function, a target error, a condition concerning an initial value of a gradient descent method, and a dimension of a model parameter (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)) . 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: An information processing apparatus comprising at least one processor, the at least one processor carrying out (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and an acquisition process for acquiring at least one selected from the group consisting of a condition to be satisfied by a loss function, a target error, a condition concerning an initial value of a gradient descent method, and a dimension of a model parameter (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer). With respect to claim 2: 2A Prong 1 : This claim is directed to a judicial exception. a setting process for setting a value of the at least one hyperparameter so that the value falls within the range which has been determined in the determination process ( mathematical concept and/or mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 3: 2A Prong 1 : This claim is directed to a judicial exception. 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: a presentation process for presenting the range which has been determined in the determination process (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)) . 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: a presentation process for presenting the range which has been determined in the determination process (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer) . With respect to claim 4: 2A Prong 1 : This claim is directed to a judicial exception. the plurality of hyperparameters include at least one selected from the group consisting of the following: a sample size of training data; a learning rate; and the number of parameter update times ( mathematical concept and/or mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 5: 2A Prong 1 : This claim is directed to a judicial exception. the plurality of hyperparameters include an inverse temperature ( mathematical concept and/or mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 6: 2A Prong 1 : This claim is directed to a judicial exception. the condition to be satisfied by the loss function includes at least one selected from the group consisting of the following: a constant representing an upper bound of a norm at an origin of a gradient of the loss function; a constant representing a Lipschitz constant of the gradient of the loss function; and a constant representing dissipativity of the loss function ( mathematical concept and/or mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 7: 2A Prong 1 : This claim is directed to a judicial exception. the condition concerning the initial value of the gradient descent method includes at least one selected from the group consisting of the following: a constant representing an upper bound of a secondary moment of an initial distribution; and a constant representing an upper bound of a quaternary moment of the initial distribution ( mathematical concept and/or mental process) . 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The remaining claims 8 and 9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claim 1 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more. Claim Rejections - 35 U.S.C. § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 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. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention. Claims 1-6, 8, and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Raginsky et al. ( Raginsky , Maxim, Alexander Rakhlin , and Matus Telgarsky . "Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis." In Conference on Learning Theory , pp. 1674-1703. PMLR, 2017 , hereinafter Raginsky ) in view of Smith ( Smith, Leslie N. "Cyclical Learning Rates for Training Neural Networks." arXiv preprint arXiv:1506.01186 (2015) , hereinafter Smith ). As to independent claim s 1, 8, and 9 , Raginsky teaches : an acquisition process for acquiring at least one selected from the group consisting of a condition to be satisfied by a loss function, a target error, a condition concerning an initial value of a gradient descent method, and a dimension of a model parameter ( Page 5, "impose[s]" loss function regularity assumptions, including (A.1) bound on gradient at origin and (A.2) M-smoothness, and (A.3) dissipativity (i.e., "conditions to be satisfied by a loss function"). Page 6, "for any… ɛ ∈ ." Theorem is parameterized by a user chosen accuracy/target error ɛ and then gives conditions on hyperparameters to achieve the corresponding bound. Page 6, "The probability law μ0 of the initial hypothesis W0." Specifies assumptions on the initial hypothesis W0 via the probability law μ0 and an exponential integrability condition (A.5), and gives an example Gaussian initialization of W0. Page 1, "ω takes values in Rd." d is then used throughout the analysis and bounds ); and a determination process for determining a range to be satisfied by at least one hyperparameter selected from the group consisting of a plurality of hyperparameters for use in learning by the gradient descent method, the range being determined in accordance with information which has been acquired in the acquisition process ( Page 6, "β≥…," "provided k=Ω…," and "η≤…." Explicit admissible constraints (i.e., "ranges") on hyperparameters are given for the learning recursion. Specifically, a condition on β, a required number of iterations k, and an upper bound on η, all as functions of ɛ and other quantities. The hyperparameter constraints are explicitly function of the acquired inputs: ɛ (target error), d (dimension), and constants from loss/initialization conditions (e.g., m, M, etc.) ). Raginsky does not appear to expressly teach an information processing apparatus comprising at least one processor, the at least one processor carrying out . Smith teaches an information processing apparatus comprising at least one processor, the at least one processor carrying out ( Page 1 et seq. "computation" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the stochastic gradient Langevin dynamics (SGLD) learning of Raginsky to include the learning techniques of Smith to set the global learning rates for training neural networks that eliminates the need to perform numerous experiments to find the best values (see Smith at page 1). As to dependent claim 2 , Raginsky further teaches a setting process for setting a value of the at least one hyperparameter so that the value falls within the range which has been determined in the determination process ( Page 6, "β≥…," "provided k=Ω…," and "η≤…." Requires selecting hyperparameter values to satisfy the derived constraints. This reads on setting values so they fall within the determined range/constraint ). As to dependent claim 3 , Smith further teaches a presentation process for presenting the range which has been determined in the determination process ( Abstract, "reasonable bounds." Identifies a range for the learning rate. Page 3, "Next, plot the accuracy versus learning rate. Note the learning rate value when". This presents the range via plotting/visualization ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the stochastic gradient Langevin dynamics (SGLD) learning of Raginsky to include the learning techniques of Smith to set the global learning rates for training neural networks that eliminates the need to perform numerous experiments to find the best values (see Smith at page 1). As to dependent claim 4 , Raginsky further teaches the plurality of hyperparameters include at least one selected from the group consisting of the following: a sample size of training data ( Page 1, "n-tuple Z = (Z1; : : : ;Zn) of i.i.d. samples." n is the dataset/training sample size parameter ); a learning rate ( Page 2, "η > 0 is the step size." This reads on learning rate ); and the number of parameter update times ( Page 6, "provided k=Ω…," and "η≤….". Imposes a requirement on the number of iterations/updates k. This reads on the number of parameter update times ). As to dependent claim 5 , Raginsky further teaches the plurality of hyperparameters include an inverse temperature ( Page 2, " β > 0 is the inverse temperature parameter" ). As to dependent claim 6 , Raginsky further teaches the condition to be satisfied by the loss function includes at least one selected from the group consisting of the following: a constant representing an upper bound of a norm at an origin of a gradient of the loss function ( Page 19, equation A.1 ); a constant representing a Lipschitz constant of the gradient of the loss function ( Page 19, equation A.2 ); and a constant representing dissipativity of the loss function ( Page 19, equation A.3 ). Claims 1-6, 8, and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Raginsky in view of Smith and Farghly et al. ( Farghly , Tyler, and Patrick Rebeschini . "Time-independent Generalization Bounds for SGLD in Non-convex Settings." arXiv preprint arXiv:2111.12876 (2021)., hereinafter Farghly ). As to dependent claim 7 , the rejection of claim 1 is incorporated. Raginsky teaches the condition concerning the initial value of the gradient descent method includes at least one selected from the group consisting of the following: a constant representing an upper bound of a secondary moment of an initial distribution ( Page 6, "The probability law μ0 of the initial hypothesis W0 has a bounded and strictly positive density p0 with respect to the Lebesgue measure on Rd" ). Raginsky does not appear to expressly teach a constant representing an upper bound of a quaternary moment of the initial distribution . Farghly teaches a constant representing an upper bound of a quaternary moment of the initial distribution ( Page 8, "Assumption 4.3. The initial condition μ0 has finite fourth moment δ4 := μ0(|| * ||) < ∞." ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the stochastic gradient Langevin dynamics (SGLD) learning of Raginsky to include the SGLD techniques of Farghly to obtain expected generalization error bounds for SGLD for learning problems under dissipativity and smoothness assumptions (see Farghly at page 2). Citation of Pertinent Prior Art The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nemirovski et al. ( Nemirovski , Arkadi , Anatoli Juditsky , Guanghui Lan, and Alexander Shapiro. "Robust stochastic approximation approach to stochastic programming." SIAM Journal on optimization 19, no. 4 (2009): 1574-1609) teaches optimization problems where the objective function is given in a form of the expectation. A basic difficulty of solving such stochastic optimization problems is that the involved multidimensional integrals (expectations) cannot be computed with high accuracy. Two computational approaches based on Monte Carlo sampling techniques are compared, namely, the stochastic approximation (SA) and the sample average approximation (SAA) methods. Conclusion The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated - interview-request-air-form. /Casey R. Garner/ Primary Examiner, Art Unit 2123