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. Claims 1-12 have been presented for examination based on the application filed on 1/19/2023. Claims 1-12 are rejected under 35 U.S.C. 101 . Claims 1-8, 11-12 are rejected under 35 U.S.C. 102(a)( 1 ) as being anticipated by NPL by Zhao , et al . "A learning-to-infer method for real-time power grid topology identification." arXiv preprint arXiv:1710.07818 (2017) . Claim (s) 9 & 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL by Zhao , et al . "A learning-to-infer method for real-time power grid topology identification." arXiv preprint arXiv:1710.07818 (2017) , in view of De Jongh , Steven, et al. "Physics-informed geometric deep learning for inference tasks in power systems." Electric Power Systems Research 211 (2022): 108362. This action is made Non-Final . ---- This page is left blank after this line ---- 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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea. Claims 1, 11 & 12 : Step 1: the respective claims are drawn to a method , system and article of manufacture respectively, falling under one of the four statutory categories of invention. Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial exception identification. Claim 1 Mapping Under Step 2A Prong 1 1. A method for training a machine learning model, the method comprising: training the machine learning model to process a graph that represents an electrical system to infer, from the graph, one or more unknown electrical values within the electrical system, comprising: obtaining data defining a plurality of graphs, each graph representing a respective electrical system topology; obtaining, for each electrical system topology and from an electrical simulation system, simulation results indicating an electrical behavior of the respective electrical system topology; and training the machine learning model to predict electrical behaviors of electrical systems including by applying data defining each graph as input to the machine learning model to obtain respective output inferences and adjusting parameters of the machine learning model responsive to comparisons between the output inferences with simulation results of corresponding electrical system topologies. Abstract Idea/Mathematical Concept: The training the machine learning model given the level of generality it is claimed is considered to be mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)). The process of infer[ring] the electrical value can be considered as mathematical concept or mental step to infer the unknown electrical values (evaluation /judgement /opinion ) based on mathematical construct ( like graph (nodes, edges) as observation.). See MPEP 2106.04(a)(2)(III)(A)). See Step 2A Prong 2. See Step 2A Prong 2. Abstract Idea/Mathematical Concept: The training the machine learning model given the level of generality it is claimed is considered to be mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)). The process to predict electrical behaviors of electrical systems can be considered as mathematical concept or mental step to infer the unknown electrical values (evaluation /judgement /opinion) based on mathematical construct (like graph (nodes, edges) as observation.). See MPEP 2106.04(a)(2)(III)(A)). Please note this mapping in done in view of latest Memo dated December 5, 2025 related to Ex Parte Desjardins: https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility?MURL=PatentEligibility Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table. In accordance with this step, the judicial exception is not integrated into a practical application. Claim 1 Mapping Under Step 2A Prong 2 1. A method for training a machine learning model, the method comprising: training the machine learning model to process a graph that represents an electrical system to infer, from the graph, one or more unknown electrical values within the electrical system, comprising: obtaining data defining a plurality of graphs, each graph representing a respective electrical system topology; obtaining, for each electrical system topology and from an electrical simulation system, simulation results indicating an electrical behavior of the respective electrical system topology; and training the machine learning model to predict electrical behaviors of electrical systems including by applying data defining each graph as input to the machine learning model to obtain respective output inferences and adjusting parameters of the machine learning model responsive to comparisons between the output inferences with simulation results of corresponding electrical system topologies. See Step 2A Prong 1. Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the this is mere data gathering related to topology/graphs provided as input . Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the this is mere data gathering related simulation results indicating an electrical behavior of the respective electrical system topology , as input. Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution ( adjusting parameters of the machine learning model ) to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result ( responsive to comparisons between the output inferences with simulation results of corresponding electrical system topologies ) , does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Further Under MPEP 2106.05(h) the use of machine learning to infer unknown electrical values is merely field of use at best because the claim intends to infer the unknown values in a abstract construct of graph. Whether that graph represents an electrical grid or lymphatic system or a river delta is mere field of use as the results do not improve the functioning of the claimed application. Further Under MPEP 2106.05( a ) the claim must include the components or steps of the invention that provide the improvement described in the specification . The specification merely alleges improvement due to use of machine learning but does not detail how the machine learning is itself improved or how the application of machine learning improves the electrical grid itself. Hence the claim is not directed to the improvement in the computer as no specific data structure is claimed (in contrast with Enfish ) or is an improvement in the technical field (in contrast with McRO ). Further in view of Memo dated December 5, 2025 related to Ex Parte Desjardins: While the instant claim/ application parallel s Ex Parte Desjardins (i.e. the claimed invention s in both are a method of training a machine learning model ) the instant limitation does not improve the machine learning (in contrast with Desjardins which “ identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. ” – as added to MPEP § 2106.04(d), subsection III per the Memo). Most importantly Importantly , examiner evaluate s the claims as a whole in discerning at least the limitation “ adjusting parameters of the machine learning model responsive to comparisons between the output inferences with simulation results of corresponding electrical system topologies ” does not reflect as improvement in the machine learning by itself and specification ( need not explicitly set forth the improvement ) , but it must describe the invention such that the improvement would be apparent . The improvement is not apparent because the use of machine learning is generic, which is applied to the field of use (electrical system as graph). No details are disclosed how any problems with machine learning are overcome such that it would be apparent to one skilled in the art that an improvement is made to machine learning itself. See MPEP § 2106.04(d)(1) as revised per the Memo above and contrasted with Ex Parte Desjardins . Hence, when the claim is considered as a whole is at best limited to mathematical calculations. Claim 1 does not recite any additional elements like a computer or a processor and appears to be an academic exercise in using a machine learning . Claim 11 recites a system with additional elements of one or more computers, one or more storage devices…coupled with one or more computers performing the operations similar to claim 1. The recitation of generic computer components does not improve on the computer technology (e.g. does not show how as implied in specification [0023] which states "... machine learning models such as graph neural networks are significantly more efficient and easier to parallelize, this can not only improve the accuracy of overall power flow simulations, but also significantly reduce the consumption of computational resources ..." , do improve on parallelism or reduce the consumption of computational resources . Such assertions without details in the specification how such an improvement is brought in instant claimed machine learning application is mere recitation of generic computer components . Claim 12 recites One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for training a machine learning model . Generic recitation of non-transitory computer storage media as additional element does not improve on functioning of the computer. See MPEP 2106.05(a) and (g). Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a one or more storage devices…coupled with one or more computers to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer/processing component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)). (in context of claim 1, 11 & 12). The claims do not improve on the machine learning as a whole and generically apply it to electrical system. The claims 1 , 11 & 1 2 are therefore considered to be patent ineligible. Claims 2 & 3 recite generally extra solution activity and generally an attempt to link the field of use. This type of limitation merely confines the use of the abstract idea to a particular technological environment (adjusting performance/production of well based on simulation) and thus fails to add an inventive concept to the claims. MPEP 2106.05(g) & (h). Claims 4 & 12 recite further executing the algorithm for another time step, and add merely to abstract idea as claimed in claim 1 & 10 respectively. The claims do not disclose any additional limitations that integrate the judicial exception into practical element. Claims 5-9 & 13-17 further add various mathematical calculations pertaining to the algorithm and add merely to abstract idea as claimed in claim 1 & 10 respectively. The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B) . ----- This page is left blank after this line ----- Claim Rejections - 35 USC § 102 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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. (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 1-8, 11-12 are rejected under 35 U.S.C. 102(a)( 1 ) as being anticipated by NPL by Zhao , et al. "A learning-to-infer method for real-time power grid topology identification." arXiv preprint arXiv:1710.07818 (2017) . Regarding Claim s 1 , 11 & 12 Zhao teaches (Claim 1) A method for training a machine learning model ( Zhao : Abstract "... a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount fast and at very little cost" ..." ) , (Claim 1 1 ) A system ( Zhao : Section V.C "... On a laptop with an Intel Core i7 3.1-G H z CPU and 8 GB of RAM, with the 200K training samples, it takes about 14.7 hours ..." ) comprising: one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for training a machine learning model / (Claim 12) One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for training a machine learning model ( Zhao : Section V.C "... On a laptop with an Intel Core i7 3.1-G H z CPU and 8 GB of RAM, with the 200K training samples, it takes about 14.7 hours ..." ) the method /operations comprising: training the machine learning model ( Zhao : Section V.B "... We employ two-layer (i.e., one hidden layer) fully connected neural networks for both the separate training architecture and the feature sharing architecture. Rectified Linear Units ( ReLUs ) are employed as the activation functions in the hidden layer ..." ) ) to process a graph that represents an electrical system ( Zhao : Section I I.A "... a power system with N - buses, and its baseline topology (i.e., the network topology when there is no line outage) with L lines ..." ) to infer, from the graph, one or more unknown electrical values within the electrical system ( Zhao : Section V.A "... We would like our predictor to be able to identify the topology for arbitrary values of power inj ections ..." ) , comprising: obtaining data defining a plurality of graphs, each graph representing a respective electrical system topology ( Zhao : Section V.C "... the generated 300K 30-bus topologies are distinct from each other, so are that of the generated SOOK 118 bus topologies and that of the 2.2M 300 bus topologies .... classifiers trained with the generated data sets ..." ) ; obtaining, for each electrical system topology and from an electrical simulation system, simulation results indicating an electrical behavior of the respective electrical system topology ( Zhao : Section V.A "... we employ the DC power flow model (2) to generate the data sets .... To generate a data set { st,P t,yt , t = 1, ... , T}, we assume the prior distribution p( s,P ) factors as p(s)p(P). As such, we generate the network topologies sand the power injections P independently ..." ) ; and training the machine learning model to predict electrical behaviors of electrical systems including by applying data defining each graph as input to the machine learning model to obtain respective output inferences ( Zhao : Section V.A "We would like our predictor to be able to identify the topology" ) and adjusting parameters of the machine learning model responsive to comparisons between the output inferences with simulation results of corresponding electrical system topologies ( Zhao : Section V.B "... In the output layer we employ hinge loss as the loss function. In training the classifiers, we use stochastic gradient descent (SGD) with momentum update and Nesterov's acceleration ..." ) ; Also see Fig.4 training and validation losses in reference to Section V.C ) . Regarding Claim 2 Zhao teaches t he method of claim 1, wherein the simulation results indicating the electrical behavior of the respective electrical system topology are generated using a ground-truth electrical simulation system ( Zhao : Section I "... the labeled data set for training the variational model can be generated in an arbitrarily large amount …”; and Section V.A and e,g , as stated "... we employ the DC power flow model (2) to generate the data sets ..." ) . Regarding Claim 3 Zhao teaches t he method of claim 1, wherein data defining the plurality of graphs includes one or more unknown electrical values within the electrical system ( Zhao : Section I "... real-time prediction of the network topology based on newly observed instant measurements from the real world ..." ) . Regarding Claim 4 Zhao teaches t he method of claim 1, wherein the electrical system represented by the graph is a real- world electrical power grid, and wherein the electrical system topology is a topology of the real- world electrical power grid ( Zhao : Section I "... real-time prediction of the network topology based on newly observed instant measurements from the real world ..." ; Section V.C "... the testing procedure, i.e., real time topology identification, is pe rf o rm ed extremely fast: In all of our numerical experiments, the testing time per data sample is under a millisecond . The extremely fast testing speed demonstrates that the proposed approach applies very well to real- time tasks , such as failure identification during cascading failures . ..." ) . Regarding Claim 5 Zhao teaches t he method of claim 1, wherein the graph that represents the electrical system comprises a plurality of nodes and a plurality of edges, wherein: ( i ) each node represents a bus in the electrical system and is associated with respective node features ( Zhao : Section II.A "... we also employ smn E {1, O} to denote whether two buses m and n are connected by a line or not ... We denote the real and reactive power injections at all the buses by P ,Q E RN , and the voltage magnitudes and phase angles by V ,8 E RN ..." ) , and (ii) edges in the graph are defined by a nodal admittance matrix that corresponds to a number of buses in the electrical system ( Zhao : Section II.A "... Given the bus admittance matrix Y , the nodal power injections and the nodal voltage ..." ) – here the bus admittance matrix defines the edge of the nodes connecting the two buses m and n connected by a line (edge) , each edge in the graph connects a pair of nodes in the graph, is associated with respective edge features, and represents a conductor in the electrical system that connects a pair of buses represented by the pair of nodes ( Zhao : Section II.A "... "employ smn E {1, O} to denote whether two buses m and n are connected by a line or not. Given a network topology s, the system's bus admittance matrix Y can be determined accordingly with the physical parameters of the system [21 ]: Ymn = smn ( Gmn + jBmn ), where Gmn and Bmn denote conductance and susceptance respectively. Note that, when two buses m and n are not connected, Ymn = smn = O ..." ) . Regarding Claim 6 Zhao teaches t he method of claim 5, wherein obtaining data defining a plurality of graphs, each graph representing a respective electrical system topology comprises, for each graph of the plurality of graphs: obtaining data defining an admittance matrix ( Zhao : Section II.A "... Given a network topology s, the system's bus admittance matrix Y can be determined accordingly with the physical parameters of the system [21]: Y,, rn = s,,,n ( Gmn + jB ,,,,,), where Cmn and Bmn denote conductance and susceptance respectlvely . Note that, when two buses m and n are not connected, Yrnn = Smn = 0. .." ) ; and assigning an edge between a pair of nodes in the graph based on values specified by the admittance matrix ( Zhao : Section II.A "... Given a network topology s, the system's bus admittance matrix Y can be determined accordingly with the physical parameters of the system [21]: Y,, rn = s,,,n ( Gmn + j B mn ), where G mn and Bmn denote conductance and susceptance respectively . Note that, when two buses m and n are not connected, Y m n = Smn = 0 . .." ) - assignment of edge is based on value of Ymn (admittance matrix), when Ymn = 0 , the busses are not connected) . Regarding Claim 7 Zhao teaches t he method of claim 6, wherein one or more of the nodes in the graph represent different bus types in the electrical system, and wherein the bus types include: a swing bus ( Zhao : Section II.A teaching swing bus as slack bus - "... Typically, apart from a slack bus , most buses are "PC) buses" at which the real and reactive power in j ections are controlled inputs, and the remaining buses are "PV buses" at which the real power injection and voltage magnitude are controlled inputs [21]. We refer the readers to [21] for more details of solving AC power flow equations. ..." ) , a generator ( Zhao : Section V.A "... With each pair of generated st and pt, we consider two types of measurements that constitute y: nodal voltage ph;ise ;ingle measurements and nodal power injection measurements. For these, a) we generate IID Gaussian voltage phase angle measurement noises with a standard deviation of 0.01 degree, the state-of-the-art PMU accuracy 1271, and b) we assume power injections are measured accurately. Here, we consider that measurements of voltage phase angles and power injections are collected at all the buses. ..." – power injections are mapped to busses with generator/load type ; Also see Section V.C “power injection”) , and a load ( Zhao : Section V.A - power injections are mapped to busses with generator/load type ; Also see Section V.C “load” ) . Regarding Claim 8 Zhao teaches t he method of claim 6, wherein the node features associated with each node that represents a respective bus in the electrical system include one or more of: a voltage magnitude, a voltage angle, an active power, and a real power ( Zhao : Section II.A "... voltage magnitudes and phase angles ... Given the bus admittance matrix Y , the nodal power injections and the nodal voltages ..." ) , and wherein the edge features associated with each edge include a current associated with the conductor in the electrical system represented by the edge ( Zhao : Section II.A “conductance” ) . ---- This page is left blank after this line ---- Claim Rejections - 35 USC § 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, 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 . 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 is advised of the obligation under 37 CFR 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. Claim(s) 9 & 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL by Zhao , et al . "A learning-to-infer method for real-time power grid topology identification." arXiv preprint arXiv:1710.07818 (2017) , in view of De Jongh, Steven, et al. "Physics-informed geometric deep learning for inference tasks in power systems." Electric Power Systems Research 211 (2022): 108362. Regarding Claim 9 Zhao teaches t he method of claim 1, wherein the machine learning model is a graph neural network ( Zhao : Section V.B "... two-layer (i.e., one hidden layer) fully connected neural networks for both the separate training architecture and the feature sharing architecture. Rectified Linear Units ( ReLUs ) are employed as the activation functions in the hidden layer ..." ) , and wherein training the machine learning model to predict the electrical behaviors of the electrical system by applying the data defining each graph as the input to the machine learning model to obtain the respective output inferences comprises, for each graph representing the respective electrical system topology: updating the graph at each of one or more update iterations ( Zhao : Section V.B "... In the output layer we employ hinge loss as the loss functio n. In training the classifiers, we use stochastic gradient descent (SGD) with momentum update and Nesterov '~ acceleration [28]. While this optimization algorithm works sufficiently well for our experiments, we note that other algorithms may further accelerate the training procedure [29]. ..." ) , comprising, at each update iteration: processing data defining the graph using the graph neural network in accordance with a set of graph neural network parameters to update a current node representation of each node in the graph and a current edge representation of each edge in the graph . Zhao does not explicitly teach comprising, at each update iteration: processing data defining the graph using the graph neural network in accordance with a set of graph neural network parameters to update a current node representation of each node in the graph and a current edge representation of each edge in the graph . Jongh teaches comprising, at each update iteration: processing data defining the graph using the graph neural network in accordance with a set of graph neural network parameters to update a current node representation of each node in the graph ( Jongh : Section 2.3 "... Furthermore, 𝑈 is theupdate function which updates the node features of node 𝑗 [nodes represent the grid or bus] , given the messages received and the previous node features. ..."; ); Section 3.2 and a current edge representation of each edge in the graph ( Jongh : Section 3.2 "... As input to the respective GNN architectures the nodal matrices 𝐆 and 𝐁 can be supplied as edge weights. ..." "... The randomly initiated weights of the GNN get updated using back propagation for each batch in the training data. ..." ) . It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Jongh to Zhao to further detail how the geometric deep learning techniques (like in Zhao Section I "... The proposed approach is also not restricted to specific models and learning methods, but can exploit any powerful models such as deep neural networks ..." ) are applied to learn /infer from approximate models for power system estimation and calculation tasks ( Jongh : Abstract) . The motivation to combine would have been that Jongh and Zhao are analogous art to the instant claim in the field of modeling electrical/power grid/network as a neural network to infer power characteristics of the electrical/power grid/network ( Jongh : Abstract Section 2.3, Section 3; Zhao : Abstract & Section III) . Regarding Claim 10 Jongh teaches The method of claim 9, further comprising: after the updating ( Jongh : Section 3.2 as above) , processing the respective current node representation for each node in the graph to generate a respective final feature corresponding to each node in the graph ( Jongh : Section 3.2 as output nodal features ) , and processing the current edge representation for each edge in the graph to generate a respective final feature corresponding to each edge in the graph ( Jongh : Section 3.2 "... As input to the respective GNN architectures the nodal matrices 𝐆 and 𝐁 can be supplied as edge weights. ..." "... The randomly initiated weights of the GNN get updated using back propagation for each batch in the training data. ..." ) ; and based on the respective final feature corresponding to each node in the graph and the respective final feature corresponding to each edge in the graph, generating the respective output inferences that represent one or more unknown electrical values within the electrical system ( Jongh : Section 4 ) . Conclusion All claims are rejected. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Examiner’s Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. ---- This page is left blank after this line ---- Communication Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT AKASH SAXENA whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-8351 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon-Fri, 7AM-3:30PM . 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT RYAN PITARO can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-4071 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. FILLIN "Examiner Stamp" \* MERGEFORMAT AKASH SAXENA Primary Examiner Art Unit 2188 /AKASH SAXENA/ Primary Examiner, Art Unit 2188 Monday, March 16, 2026