Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION 1. This Office Action is in response to the application filed on 0 9/12/2023 . Claims 1- 20 are pending. Information Disclosure Statement 2. The information disclosure statement (IDS) filed on 0 9/12/2023 complies with the provisions of M.P.E.P. 609. The examiner has considered it. Claim Rejections - 35 USC § 101 3. 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. 4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. C laims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 8 and 15 recite: A method comprising: receiving user data; receiving utility data; generating, via a trained physics-informed neural network (PINN), a utility usage prediction based on the utility data; generating a utility plan based on the user data and the utility usage prediction, wherein the utility plan includes limits or restrictions of a utility usage; and controlling the utility usage based on the utility plan. Step 2A Prong One : The limitations of receiving user data and utility data , generating, via a trained physics-informed neural network (PINN), a utility usag e prediction based on the utility data , generating a utility plan based on the user data and the utility usage prediction, wherein the utility plan includes limits or restrictions of a utility usage; and controlling the utility usage based on the utility plan. , which covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting, “computer method’; nothing in the claim element precludes the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer components 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 (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Step 2A Prong Two : The judicial exception is not integrated into a practical application. Claims 1, 8 and 15 recite the additional element, “ receiving, processing and outputting .” these limitation is a mere generic transmission and presentation of collected and analyzed data (MPEP 2106.05(g)). The limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)). Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation “boosting and outputting”, are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05( qd )/(II) (iv) transferring and/or displaying information, Versata Dev. Group Inc. Dependent Claims 2-7, 9-14 and 16-20 The limitations as recited in dependent claims 2 , 9 and 16 recite , “ wherein the user data includes at least one of .. ” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one. Dependent c laim 3 and claim 10 recite , “ transferring the utility plan ...” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one. Dependent c laims 4- 7, 11-14 and 17-20 , recite “ wherein training the trained PINN comprises ...” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one. Claim Rejections - 35 USC § 101 5 . 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. 6 . Claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because claim 15 directs to “computer-readable medium” in which, when using the broadest reasonable interpretation would include non-statutory subject matter. That is, l anguage such as “physical”, “tangible” and “storage” do not make an otherwise non-statutory computer-readable medium claim statutory, since a data signal per se is considered a physical and tangible medium that temporarily stores data (it’s a transitory storage medium, but it’s still a storage medium). Examiner’s Note 7 . Train ed Physics-Informed Neural Networks (According to Google): “ Train ed Physics-Informed Neural Networks (PINNs) involves optimizing a neural network to approximate solutions to differential equations by incorporating physical laws (PDEs/ODEs) directly into the loss function. It minimizes residuals of these laws alongside data errors, using automatic differentiation for derivatives ”. Utility usage (According to Google): “ Utility usage refers to the quantifiable consumption of essential public services—such as electricity, natural gas, water, and sewage disposal—by residential, commercial, or industrial customers over a specific period. It measures resource consumption to facilitate billing, efficiency planning, and environmental management . ” I s a neural network a Trained Physics-Informed Neural Networks (PINNs) ? (According to Google): “ Yes, a Physics-Informed Neural Network (PINN) is a specialized type of neural network trained by embedding physical laws (often partial differential equations) directly into the loss function. Unlike traditional networks that only learn from data, PINNs use these physical constraints to guide learning, making them highly accurate for scientific modeling, even with sparse data ”. LSTM-RNN neural network (According to Google): “ A Long Short-Term Memory (LSTM) network is a specialized type of Recurrent Neural Network (RNN) designed to process sequential data by overcoming the "vanishing gradient" problem of traditional RNNs. Using unique gating mechanisms, LSTMs learn long-term dependencies, remembering relevant information over long sequences and forgetting irrelevant data . ” LSTM-RNN neural network (According to Google): “ A Long Short-Term Memory (LSTM) neural network is not inherently a Physics-Informed Neural Network (PINN), but it can be trained or structured to become one. A standard LSTM is a data-driven model, while a Physics-informed LSTM model integrates physics-based equations directly into its architecture or loss function . ” A Deep Recurrent Neural Network (According to Google): “ A Deep Recurrent Neural Network (Deep RNN) is a hierarchical neural network architecture designed for sequential data by stacking multiple hidden layers, allowing it to learn complex temporal dynamics better than standard RNNs .” Distributed Ledger Technology (According to Google): “ Distributed Ledger Technology (DLT) is a decentralized digital system for recording, sharing, and synchronizing transactions across a network of multiple computers (nodes) without a central authority. Using consensus protocols, nodes validate data, ensuring records are tamper-proof, secure, and transparent. Blockchain is a popular type of DLT . ” Shaabana et al, US 20200019841, [ Shaabana : Abstract and paragraph 13 (“ Examples of this disclosure provide a neural network based usage prediction system that can generate usage predictions that can inform resource allocation decisions before the usage demand arrives at the system. Additionally, a validation mechanism that can validate the predictions generated by the neural network is also provided. In one implementation, a long short-term memory recurrent neural network (LSTM-RNN) can generate usage predictions based upon historical usage demand of a particular system. The usage prediction can be translated into a resource allocation metric that drives the allocation of CPU, memory, or storage resources within a HCI. The usage predictions can be made for a set of workloads executing on one or more physical host devices within a HCI or virtual data center (VDC). Workloads can include virtual machines or applications that are executed on host devices, or computing devices, that are deployed within a hyper-converged infrastructure. Validation of the usage predictions can be based on actual usage demand that is observed by the set of workloads implemented on the HCI ”)] [ Shaabana : Paragraphs 34-45 (“ he neural network load analyzer 204 can generate a usage prediction for a given set of workloads 233, or a tenant environment 230, by analyzing the usage data 213 of the workloads 233. For example, a CPU usage prediction can be generated by the neural network load analyzer 204, which can inform an administrator how much CPU resources to allocate to a given set of workloads. Similarly, storage, memory, and network usage predictions can be generated by the neural network load analyzer 204 using a neural network trained based upon training data 211 that represents historical usage data of the set of workloads 233. The usage predictions can inform how much storage, memory, and network resources should be respectively allocated to the set of workloads 233 ”)] [ Shaabana : Paragraph s 24 and 40 (“ Examples of this disclosure provide a mechanism to generate usage predictions so that physical resources can be more efficiently allocated to workloads within the networked environment. ” AND “ The usage predictions can be provided to the autoscaler ” , i.e., ‘restriction of a utility usage’)] . Zalmanovitch et al, US 20150181047, [ Zalmanovitch : Abstract and paragraph 46 (“ The data usage plan describes threshold values associated with network connections of computing devices of the user. A web service dynamically generates data usage statistics for the computing devices to represent data consumed by the computing devices under the data usage plan. The schema is updated with the data usage statistics and distributed to the computing devices for presentation to the user ”)] . Davis, II, US 20240345558, [Davis: Abstract and paragraphs 161-162 and 172 -173 (“ the utility service predictive management system 1200 may include a consumer usage predicting module 1238 configured for predicting consumer usage in relation to one or more variables such as, for example, calendar date and weather. In an embodiment, for example, consumer usage predicting module ”)] . Mimaroglu et al, US 20240405548, [ Mimaroglu : Paragraphs 1 , 38 and 39 (“ the present disclosure generally relate to utility metering devices, and more particularly to generating time-series energy usage forecast predictions using machine learning and utility metering devices ” AND “ the design of prediction module 306 can include any suitable machine learning model components (e.g., a neural network, support vector machine, specialized regression model, and the like). For example, a neural network can be implemented along with a given cost function (e.g., for training/gradient calculation). The neural network can include any number of hidden layers (e.g., 0, 1, 2, 3, or many more), and can include feed forward neural networks, recurrent neural networks, convolution neural networks, modular neural networks, and any other suitable type. The different trained machine learning models of prediction module 306 can comprise the same machine learning architecture, similar machine learning architecture, and/or different machine learning architecture. The machine learning model(s) can comprise a deep recurrent neural network (e.g., DeepAR , etc.), for example that utilize a supervised learning algorithm for generating time-series forecasts ” )] . Pillai et al, US 20230177327, [Pillai: Abstract and paragraph 5 (“ Present application provides systems and method implement apply a Physics-Informed Neural Network (PINN) for inversely calculating the effective material parameters of a multi-dimensional metamaterial from its scattered field(s). By employing a loss function based on the Helmholtz wave equation, performance of a metamaterial is modeled by the system the dependance of resonant behavior on the homogenized electric permittivity distribution profile generated by the PINN is demonstrated ”)] . Claim Rejections - 35 USC § 103 8 . 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. 9 . 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 . 10 . Claims 1- 2, 8-9 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Risbeck et al ( US 20240222970 ), in view of Pillai et al ( US 20230177327 ). Claim 1 : Risbeck suggests a method comprising: receiving user data [ Risbeck : Paragraph 1 2 (“ to host a graphical user interface comprising a net energy plot comprising a first line illustrating actual net energy over the first subperiod ”)] . Mimaroglu suggests receiving utility data [ Risbeck : Paragraph 83 (“ The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers ”)] [ Risbeck : Paragraph 131 (“ baseline energy consumption and energy production are predicted ”)] . Risbeck suggests generating, via a neural network, a utility usage prediction based on the utility data [ Risbeck : Paragraph 131 (“ baseline energy consumption and energy production are predicted ”)] [ Risbeck : Paragraphs 111-112 (“ using a neural network trained to predict future energy consumption ”)] . Risbeck suggests generating a utility plan based on the user data and the utility usage prediction, wherein the utility plan includes limits or restrictions of a utility usage [ Risbeck : Paragraph s 7 and 5 (“ Generating the net energy trajectory can include performing an optimization constrained by the net energy goal ” AND “ the first forecasted ranges and the second forecasted ranges are associated with confidence intervals of predictive models for predicting the energy consumption and the energy production ” , i.e., ‘generating a utility plan’ based on “historical energy consumption data” )] . Risbeck suggests controlling the utility usage based on the utility plan [ Risbeck : Paragraph s 7 and 5 (“ Generating the net energy trajectory can include performing an optimization constrained by the net energy goal ” . Pillai suggests generating predicted data using a trained physics-informed neural network (PINN) [Pillai: Abstract and paragraph 5 (“Present application provides systems and method implement apply a Physics-Informed Neural Network (PINN) for inversely calculating the effective material parameters of a multi-dimensional metamaterial from its scattered field(s). By employing a loss function based on the Helmholtz wave equation, performance of a metamaterial is modeled by the system the dependance of resonant behavior on the homogenized electric permittivity distribution profile generated by the PINN is demonstrated”)] . Both references ( Risbeck and Pillai ) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as managing data in neural network . It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Risbeck and Pillai before him/her, to modify the system of Risbeck with the teaching of Pillai in order to optimizing a neural network to approximate solutions to differential equations by incorporating physical laws (PDEs/ODEs) directly into the loss function [ Pillai: Abstract and paragraph 5 ] . Claim 2 : The combined teachings of Risbeck and Pillai suggest wherein the user data includes at least one of: a utility budget, a utility usage analytics request, a utility usage prediction request, or an optimized utility plan request [ Risbeck : Paragraph s 26 and 83 (“ to perform a first predictive optimization and a second predictive optimization ” and “ Demand response layer 414 can be configured to optimize resource usage ” )] ; wherein the utility data includes data from a utility system, a weather system, or a metering system [ Risbeck : Paragraph 83 (“ or other data received from utility providers, ”)] [ Risbeck : Paragraph 110 (“ energy meters ”)] ; wherein the limits or restrictions of the utility plan indicate the utility usage during a time window or during a given time of a day [ Risbeck : Paragraph s 7 and 16 (“ Each net energy target indicates a target difference between cumulative energy consumption and cumulative energy production or offset from a beginning of the time period to a corresponding time step of the plurality of time steps. Providing the strategy can also include generating, for a given time step ” and “ generating first forecasted ranges for amounts of energy consumption or carbon emissions for a plurality of time steps in a time period ” )] ; and wherein the limits or restrictions of the utility plan indicate the utility usage in compliance with the utility budget [ Risbeck : Paragraphs 171-172 (“ a consumption budgets widget 1404, and a planning widget 1406. The summary widget 1402 shows a year-to-date summary of net energy consumption ”)] . Claim 8 : Claim 8 is essentially the same as claim 1 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 9 : Claim 9 is essentially the same as claim 2 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 15 : Claim 15 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 1 6 : Claim 1 6 is essentially the same as claim 2 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . 11 . Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Risbeck et al ( US 20240222970) , in view of Pillai et al ( US 20230177327 ), and further in view of Alhelo (US 20220004902). Claim 3 : The combined teachings of Risbeck , Pillai and Alhelo suggest transferring the utility plan to a user [ Alhelo : Paragraph 19 (“ The system then predicts the appropriate utility usage based on the demanding factors and the information we have for each building ”)] ; and recording, via a distributed ledger technology, user payments associated with the utility plan [ Alhelo : Paragraph 50 (“ In one embodiment the hardware comprises the ability to allow blockchain coding of energy associated with waste versus what is needed for operation. As an example, if the building receives a specific portion of green energy and there is a commitment that green is used optimally, blockchain can provide the answer as to where and how efficiently the green energy has been used ”)] . Three references ( Risbeck , Pillai and Alhelo ) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as managing data in neural network . It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Risbeck , Pillai and Alhelo before him/her, to modify the system of Risbeck and Pillai with the teaching of Alhelo in order to predicting utility usage in a blockchain system [ Alhelo : Paragraphs 19 and 50] . Claim 10 : Claim 10 is essentially the same as claim 3 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Allowable Subject Matter 12 . Claims 4-7 , 11-14 and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 13 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404] . The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.]. If att empts to reach the examiner by telephone are unsuccessful, the examiner’s supervi sor, 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 au tomated information system, contact [ 800-786-9199 (IN USA OR CANADA) or 571-272-1000 ] . Hung Le 03 / 23 /202 6 /HUNG D LE/ Primary Examiner, Art Unit 2161