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. The instant application having Application No. 1 8 / 225 , 050 has a total of 20 claims pending in the application, there are 3 independent claims and 17 dependent claims, all of which are ready for examination by the examiner. Oath /Declaration The applicant’s oath/declaration has been reviewed by the examiner and is found to conform to the requirements prescribed in 37 C.F.R. 1.63. Drawings The applicant’s drawings submitted are acceptable for examination purposes. Specification The applicant’s specification submitted is acceptable for examination purposes. 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. Claims 1, 6, 8, 13, 14 and 19 are rejected under 35 USC 101 as being drawn to nonstatutory subject matter. The independent claims are rejected under 35 U.S.C. 101 because the claimed invention is drawn to an abstract idea without significantly more. Independent claims 1, 8 and 1 4 are drawn to obtaining data, applying a learning model and applying a neural projection layer . The gathering of data and using a learning model to find invariants in data could have been accomplished through human mental process, and addition of a system with memory and processors, hold as abstract, thus the limitations don’t describe doing significantly more, and the claims as a whole does not provide integration into a practical application. The claims fall within the “Mental Processes” grouping of abstract ideas. Specifically, the limitations as discussed above, as claimed, is a process that covers performance of the limitations in the mind, or with pen and paper, but for the recitation of generic computer components (e.g., computer, storage device) because a user can mentally, or with pen and paper, observe, evaluate and make judgements to perform the claimed limitations. For example, a person can read documents and make judgements to find sections of documents that contain a problem, and provide a report with any findings. A person can further link substantially identical concepts mentally or via taking notes on paper. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements (e.g., computer, storage device) that are recited at a high-level of generality (e.g., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See 2106.05(d)(Il). Accordingly, the 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. Dependent claims 6, 13 and 19 recite generic step of predicting performance of a system with only basic elements stated that can be accomplished through mental processes without significantly more elements. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component is not significantly more than the judicial exception. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1- 4, 6, 8-11, 13-17 and 19 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Wolf et al. (US 20 24 / 0242083 A1). For claim 1 , Wolf et al. teaches: A method comprising: obtaining, using at least one hardware processor, data characterizing a physical system governed by a physical conservation law [ neural network for contrastive learning for conservation, 0103: Wolf ] ; applying, using the at least one hardware processor, contrastive learning to the data to automatically capture system invariants of the physical system [ contrastive learning for anomaly detection, 0103-015: Wolf ] ; and employing, using the at least one hardware processor, a neural projection layer to guarantee that a corresponding dynamic machine learning model preserves the captured system invariants [ anomaly detection with neural network using machine learning model, 0100-0105: Wolf ] . For claim 2 , Wolf et al. teaches: The method of claim 1, further comprising designing, using the at least one hardware processor, a square ratio loss function as a contrastive learning metric, wherein the captured system invariants are contrastively learned as a low-dimensional representation as an invariant quantity for the physical system and wherein the employing operation projects the corresponding dynamic model to an invariant manifold to ensure a conservation property [ contrastive loss function for dimensional representation of the anomaly, 0120-0124: Wolf ] . For claim 3 , Wolf et al. teaches: The method of claim 2, wherein the employing operation imposes conservation of an invariant function for dynamical system trajectory prediction, preserving a conservation quantity during dynamics modeling [ contrastive learning for optimization, 0102-0103: Wolf ] . For claim 4 , Wolf et al. teaches: The method of claim 2, wherein the square ratio loss function is defined as a contrastive loss function: where HΘᶜ (x) is a conservation term [ defined contrastive loss function, 0118-0119: Wolf ] . For claim 6 , Wolf et al. teaches: The method of claim 1, further comprising predicting performance of the physical system using the corresponding dynamic machine learning model [ machine learning model for improving performance, 0067: Wolf ] . Claim 8 is a product of the method taught by claim 1. Wolf et al. teaches the limitations of claim 1 for the reasons stated above . Claim 9 is a product of the method taught by claim 2 . Wolf et al. teaches the limitations of claim 2 for the reasons stated above. Claim 10 is a product of the method taught by claim 3 . Wolf et al. teaches the limitations of claim 3 for the reasons stated above. Claim 11 is a product of the method taught by claim 4 . Wolf et al. teaches the limitations of claim 4 for the reasons stated above. Claim 1 3 is a product of the method taught by claim 6 . Wolf et al. teaches the limitations of claim 6 for the reasons stated above. Claim 1 4 is a system of the method taught by claim 1 . Wolf et al. teaches the limitations of claim 1 for the reasons stated above. Claim 1 5 is a system of the method taught by claim 2 . Wolf et al. teaches the limitations of claim 2 for the reasons stated above. Claim 1 6 is a system of the method taught by claim 3 . Wolf et al. teaches the limitations of claim 3 for the reasons stated above. Claim 1 7 is a system of the method taught by claim 4 . Wolf et al. teaches the limitations of claim 4 for the reasons stated above. Claim 19 is a system of the method taught by claim 6 . Wolf et al. teaches the limitations of claim 6 for the reasons stated above. Allowable Subject Matter Claims 5, 7, 12, 18 and 20 are in condition for allowance. For dependent claims 5, 7, 12, 18 and 20 in combination with the dependent claims , prior art, in current interpretation of the perceived language, teaches obtaining data characterization, applying learning to the data and neural projection for invariants like cited references Wolf et al. (US 2024/0242083 A1) but does not teach an orthogonal projection using a Gram-Schmidt process and employing redesigned physical system using dynamic machine learning model and predicting of the performance . Conclusion The Examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and lin e no(s) in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting the application. When responding to this Office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c). Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJITH M JACOB whose telephone number is (571)270-1763 . The examiner can normally be reached on Monday-Friday: Flexible Hours . If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. 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 the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AJITH JACOB/ Primary Examiner, Art Unit 2161 3/3/2026