CTFR 18/588,554 CTFR 87720 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. The following is a Final Office action in response to applicant's amendment and response received 5/7/2026, responding to the 2/13/2026 non-final/final office action provided in rejection of claims 1-20. Claims 1-9, 11-12, 14-15, and 17-20 have been amended. Claim 16 has been cancelled and claim 21 added. Claims 1-15 and 17-21 are pending and are addressed in this office action. New grounds of rejection are presented in view of the newly presented limitation(s). Examiner notes (A). IDS submitted on 02/18/2026 considered by the Examiner. (B). Limitations have been provided with the Bold fonts in order to distinguish from the cited part of the reference (Italic). (C). Examiner has cited particular columns, line numbers, references, or figures in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses to fully consider the reference in entirety, as potentially teaching all or part of the claimed invention. See MPEP §§ 2141.02 and 2123. The examiner requests, in response to this Office action, 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 line number(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). Response to Amendments and Arguments In light of the amendment of claims, the Previous Action's objections and rejections of those claims under 35 U.S.C. § 112 are hereby withdrawn. With respect to rejection under 35 U.S.C. § 112 rejections, applicant’s arguments have been considered but moot in view of new ground rejections. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-23-aia AIA 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. Claims 1, 5, 8-9, 17 and 19 are rejected under 35 U.S.C. 103 as being obvious over Martiel et al (US 20250036992 A1) in view of Alam et al (US 12387130 B1) and Singh et al. (US 20240354615 A1). As to claim 1, Martiel discloses a computer-implemented method for quantum source code generation, said method comprising: receiving a quantum source code (“program”) input with a specification of constraints of a quantum unit on which the quantum source code input is to be run (abstract, method for generating a program to be executed using a quantum computer for producing as output a target quantum state based on an initial quantum state used as input is proposed, which comprises: determining an approximated quantum state which is an approximation of the target quantum state; determining a quantum circuit which, based on the initial quantum state received used as input, produces as output an output quantum state that corresponds to the approximated quantum state; and generating the program based on the determined circuit) , wherein the constraints comprise available quantum qubits (0012, … the input quantum circuit may be described by a sequence of gates acting on one or more qubits, and the method may further comprise: determining a representation of the initial quantum state, and iteratively updating the representation by successively applying a gate of the sequence of gates to the representation, … ) and quantum parameters (par. 0017, … for determining the quantum circuit: determining parameters of a parameterized circuit represented by a tensor network, TN. In such embodiments, the method may further comprise, for determining the parameters of the parameterized circuit: … ) including configuration, properties (par. 0108, … FIG. 3 is provided by way of example only. Numerous other architectures, operating environments, and configurations are possible. Other embodiments of the node may include fewer or greater number of components, and may incorporate some or all of the functionality described with respect to the processing node components shown in FIG. 3. Accordingly, although the control engine 31, quantum state approximation engine 32, quantum circuit synthesis engine 33, quantum program generation engine 34, input interface 35, output interface 36, and memory 37 are illustrated as part of the system 30, no restrictions are placed on the location and control of components 30-37. In particular, in other embodiments, components 30-37 may be part of different entities or computing systems.) , and defaults of the available quantum qubits (par. 0041, … The quantum circuit may be described as the decomposition of a unitary operator U (that corresponds to the quantum circuit) into a sequence of one or more quantum gates U.sub.1, . . . , U.sub.n, and operating on an initial quantum state (e.g. |0custom-character) comprising one or two qubits. …) ; creating a variation of the quantum source code input by analyzing the quantum source code input against trained models (par. 0047, … advantageously allows determining [i.e. analyzing] a quantum circuit based on which a program to be executed using a quantum computer can be generated by approximating a target quantum state … . Further, par. 0072, FIG. 1, in one or more embodiments the generating of a program that is executable on a quantum computer may be performed through generating a quantum circuit U that corresponds to the program, assuming an initial quantum state on which the quantum circuit is applied.) , Martiel does not explicitly disclose the following limitations but, Alam discloses creating a variation of the quantum source code input by analyzing the quantum source code input against trained models (col. 2, ll. 18-29, classical artificial intelligence (AI) systems are used to generate [i.e. create] quantum programs [i.e. code] that can be executed on quantum computers. For example, a problem or a class of problems to be solved by a quantum program (e.g., an optimization problem or another type of problem) can be formulated and provided as an input to the AI-based quantum program synthesis process. In some cases, a statistical model is developed through a training process, and the statistical model can be used to synthesize quantum programs for specific problems (e.g., specific problems in a class of problems that the statistical model has trained on)) , wherein the variation includes adjustment of the quantum unit constraints depending on the specification of constraints the quantum unit (col. 22, ll. 59-67, FIG. 7, at 364, a change [i.e. adjustment] of basis operation is appended to the updated version of the quantum program in each iteration. The change of basis operation is determined by the Hamiltonian associated with the problem that the quantum program is being synthesized to solve, and therefore, the same change of basis operation can be appended to the quantum program in each iteration for the same problem. When a new problem is defined, the change of basis operation can be updated accordingly) ; Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include creating a variation of the quantum source code input against trained models, wherein the variation includes adjustment of the quantum unit constraints depending on the specification of constraints the quantum unit, as disclosed by Alam, because such inclusion will suggest a form for what the initial layer should look like in the case where the input has certain symmetries and make the change of basis operation is appended to the updated version of the quantum program in each iteration. (see col. 15, ll. 64-65 and col. 22, ll. 59-61 of Alam).. Singh discloses providing a recommendation based on the variation to assist in quantum source code generation (par. 0038, … systems in computing environment 100 in understanding the functionality of quantum programs, in recommending or suggesting modifications to program code for optimal (e.g., minimum) qubit consumption, and/or in automatically modifying program code based on qubit optimization requirements. In some examples, intelligent quantum DevOps computing platform 120 may build and/or train one or more machine learning models. … ) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include providing a recommendation based on the variation to assist in quantum source code generation, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). As to claim 5, Singh discloses the computer-implemented method wherein the variation includes adjusting the quantum source code in real time depending on a real time behavior of the quantum unit (par. 0047, … modify/update [i.e. adjust] the quantum program based on the one or more code change recommendations. In some embodiments, the digital computing device (e.g., digital computing device 110) may receive, in real-time, as the user is writing or creating a program in an editor and compilation is occurring in parallel, the suggestions for modifying or optimizing the quantum program. For instance, a program might be consuming 10 qubits, but intelligent quantum DevOps computing platform 120 might suggest that a programmer apply alternate logic or introduce alternate components into the program such that fewer qubits are consumed (e.g., 8 qubits versus 10 qubits). For instance, the digital computing device (e.g., digital computing device 110) may display and/or otherwise present one or more graphical user interfaces similar to graphical user interface 300, which is illustrated in FIG. 3. As seen in FIG. 3, graphical user interface 300 may include text and/or other information associated with providing code change recommendations … ) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein the variation includes adjusting the quantum source code in real time depending on a real time behavior of the quantum unit, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). As to claim 8, Alam discloses the computer-implemented method wherein the variation includes one or more of the group consisting of: error or syntax corrections, performance improvements, quality enhancements, constraints, and configuration of available quantum units (col. 3, ll. 4-10, the techniques and systems described here provide technical advantages and improvements over existing approaches. For example, the quantum program synthesis techniques described here can provide an automated process for generating quantum programs to find solutions to specific problems (e.g., optimization problems or other types of problems). Further, col. 9, ll. 12-14, the measured state information is used in the execution of a quantum algorithm, a quantum error correction procedure, col. 45, ll. 57-62, The quantum programs synthesized by the artificial intelligence system represent computed solutions to these computational problems. Moreover, to solve these problems in a practically efficient manner, constraints can be imposed on the policy network) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include error or syntax corrections, performance improvements, quality enhancements, constraints, and configuration of available quantum units, as disclosed by Alam, because such inclusion for the purpose to generate quantum programs for quantum state preparation is defined. A dynamic programming process is used to improve the policy. An initial state and a target state of the one or more qubits is identified. The policy is used to generate a quantum program to produce the target state from the initial state (see abstract of Alam). As to claim 9, Alam discloses the computer-implemented method further comprising: validating a variation by submitting the variation and the quantum source code input to a quantum unit having the adjustment of the quantum unit constraints and evaluating outcomes (col. 2, ll. 18-29, classical artificial intelligence (AI) systems are used to generate quantum programs that can be executed on quantum computers. For example, a problem or a class of problems to be solved by a quantum program (e.g., an optimization problem or another type of problem) can be formulated and provided as an input to the AI-based quantum program synthesis process. In some cases, a statistical model is developed through a training process, and the statistical model can be used to synthesize quantum programs for specific problems (e.g., specific problems in a class of problems that the statistical model has trained on. Further, col. 25, ll. 15-7, simulate the behavior [i.e. evaluate] of the quantum program. The simulated behavior of the quantum program is then evaluated. Further, col. 30, ll. 43-47, the adjusted conditional probabilities in the presence of such noisy channels may be further used to determine a policy and thus a quantum program to prepare the target stat. Further, col. 41, ll. 24-30, a dynamic programming process can be used to train and improve a policy, for example, to determine an optimal policy, which include one or more low-level unitary operations in the discrete action space that can maximize the expected return for every high-level unitary operation in the discretized state space. For example, a policy can be trained and improved using parameters) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include validating a variation by submitting the variation and the quantum source code input to a quantum unit having the specification and evaluating the outcomes, as disclosed by Alam, because such inclusion for the purpose to generate quantum programs for quantum state preparation is defined. A dynamic programming process is used to improve the policy. An initial state and a target state of the one or more qubits is identified. The policy is used to generate a quantum program to produce the target state from the initial state (see abstract of Alam). As to claim 17 , Martiel, Alam and Singh discloses a computer system for quantum source code generation, the computer system comprising: a processor (Martiel par. 0107, The control engine 31 includes a processor, which may be any suitable microprocessor … ) ; a memory (Martiel par. 0101, … quantum circuit synthesis engine 33, quantum program generation engine 34, input interface 35, output interface 36, and memory 37 … ) ; one or more computer program instructions stored on the memory, the computer program instructions executable by the processor to perform one or more operations, the operations comprising (Martiel pars. 0101 and 0107) : For remaining limitations see remarks regarding claim 1. As to claim 19, Alam discloses the computer system further comprising program instructions to: validate the variation by submitting the variation and the quantum source code input to a quantum unit having the adjustment of the quantum unit constraints (col. 2, ll. 18-29, classical artificial intelligence (AI) systems are used to generate quantum programs that can be executed on quantum computers. For example, a problem or a class of problems to be solved by a quantum program (e.g., an optimization problem or another type of problem) can be formulated and provided as an input to the AI-based quantum program synthesis process. In some cases, a statistical model is developed through a training process, and the statistical model can be used to synthesize quantum programs for specific problems (e.g., specific problems in a class of problems that the statistical model has trained on. Further, col. 25, ll. 15-7, simulate the behavior of the quantum program. The simulated behavior of the quantum program is then evaluated. Further, col. 30, ll. 43-47, the adjusted conditional probabilities in the presence of such noisy channels may be further used to determine a policy and thus a quantum program to prepare the target stat. Further, col. 41, ll. 24-30, a dynamic programming process can be used to train and improve a policy, for example, to determine an optimal policy, which include one or more low-level unitary operations in the discrete action space that can maximize the expected return for every high-level unitary operation in the discretized state space. For example, a policy can be trained and improved using parameters) and evaluating outcomes (par. 0115, … the agent may check to see if the Hamiltonian expectation value is exactly one, which occurs when the quantum program gives the optimal bitstring with 100% certainty (e.g., each bitstring sampled is the MaxCut). Other, less onerous conditions may be used. If the reward does satisfy the “solved” criteria at 356, then the agent returns the results at 360. The results returned by the agent may include the version of the quantum program produced by the final iteration of the process 300). Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include validate a variation by submitting the variation and the quantum source code input to a quantum unit having the specification and evaluating the outcomes, as disclosed by Alam, because such inclusion for the purpose to generate quantum programs for quantum state preparation is defined. A dynamic programming process is used to improve the policy. An initial state and a target state of the one or more qubits is identified. The policy is used to generate a quantum program to produce the target state from the initial state. (see abstract of Alam) Claim 2 is rejected under 35 U.S.C. 103 as being obvious over Martiel al. , Alam et al. and Singh et al. as applied to claim 1, and further in view of Sun et al. (US 20240354379 A1) and Marinescu et al. (US 20230186145 A1, hereinafter Marinescu). The filing date of Sun et al. is based on provisional application no. 63/460,280 filing date of April 18, 2023. The Provisional application supports the subject matter cited herein. As to claim 2, Martiel as modified by Alam and Sigh does not explicitly disclose the following limitation but, Sun discloses the computer-implemented method wherein analyzing against the trained models includes using a language model to generate a variation with the language model trained on datasets that contain custom variations utilizing quantum computing static features (par. 0032, … an automated software 210, a generative artificial intelligence 212 (e.g., language model, such as a generative pre-trained transformer), and a trained guardrail model 214 (e.g., another language model that may be smaller than the generative artificial intelligence 212). The automated software 210 operationally generates a conversation with a user, turn by turn, or performs other automated tasks. The generative artificial intelligence 212 (e.g., large language model) operationally generates training data sets for the trained guardrail model 214) ; Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein analyzing against trained models includes using a language model to generate a variation with the language model trained on datasets, as disclosed by Sun, for the purpose of generating training sets, generating, with an automated software (see abstract of Sun). Further, Marinescu discloses the computer-implemented method wherein analyzing against trained models includes using a language model to generate a variation with the language model trained on datasets that contain custom variations utilizing quantum computing static features (par. 0029, … general-purpose computer, special-purpose computer, quantum computing device (e.g., a quantum computer)… , Further, par. 0041, The model generation component 208 can employ the input dataset 207 as input to build (e.g., generate) an optimization model 213 (also herein referred to as a non-mixed influence model) for use in providing static and/or dynamic probabilistic predictions [i.e. features]. … . Further, par. 0097, the processes described herein can be performed by one or more specialized [i.e. custom] computers (e.g., a specialized processing unit, a specialized classical computer, a specialized quantum computer, a specialized hybrid classical/quantum system and/or another type of specialized computer) to execute defined tasks related to the one or more technologies describe above … ). Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein analyzing against trained models includes using a language model and language model trained on datasets that contain custom variations utilizing quantum computing static features, as disclosed by Marinescu, for the purpose of analyzes an input dataset comprising a constraint in a natural language form, and an augmentation component that generates an influence mapping comprising a constraint variable based on the constraint input. (see abstract of Marinescu). Claims 3-4 are rejected under 35 U.S.C. 103 as being obvious over Martiel et al. , Alam et al. and Singh et al. as applied to claim 1, and further in view of Sun et al. (US 20240354379 A1). As to claim 3, Martiel as modified by Alam and Singh does not explicitly disclose the following limitations but, Sun discloses the computer-implemented wherein analyzing against the trained models includes using a language model trained on different datasets for different types of quantum units or users (par. 0090, … principal component analysis, and generative models like autoencoders. [0093] Reinforcement learning involves training a model to make decisions in a dynamic environment by receiving feedback in the form of rewards or penalties. Examples of reinforcement learning algorithms include Q-learning and policy gradient methods … . Further, par. 0106, … Features 808 may also be of different types, such as numeric features, strings, and graphs, and may include one or more of content 812, concepts 814, attributes 816, historical data 818, and/or user data 820). Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented wherein analyzing against trained models includes using a language model trained on different datasets for different types of users, as disclosed by Sun, for the purpose of training a guardrail machine learning model using the generated training sets (see abstract of Sun). As to claim 4, Martiel discloses the computer-implemented method wherein analyzing against trained models includes using machine learning to adapt source code based on machine learning decisions on which qubits or gates to use and/ or t he quantum parameters for the constraints quantum unit specification (par. 0012, … determining [i.e. deciding] a representation of the initial quantum state, and iteratively updating the representation by successively applying a gate of the sequence of gates to the representation, and determining a representation of the approximated quantum state based on the updated representation of the initial quantum state. … ) . Sun discloses the computer-implemented method wherein analyzing against the trained models includes using machine learning to adapt source code based on machine learning decisions on which qubits or gates to use and/or the quantum parameters for the constraints (pars. 0089-0090, FIG. 8 is a flowchart depicting a machine-learning pipeline 800, according to some examples. The machine-learning pipeline 800 may be used to generate a trained model, for example the trained machine-learning program 802 of FIG. 8, to perform operations associated with searches and query responses. … machine learning may involve using computer algorithms to automatically learn patterns and relationships in data, potentially without the need for explicit programming. Machine learning algorithms can be divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning. [0091] Supervised learning involves training a model using labeled data to predict an output for new, unseen inputs. … ). Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein analyzing against trained models includes using machine learning to adapt source code based on machine learning decisions, as disclosed by Sun, for the purpose of training a guardrail machine learning model using the generated training sets (see abstract of Sun). Claim 6 is rejected under 35 U.S.C. 103 as being obvious over Martiel et al. , Alam et al. and Singh et al. applied to claim 5, and further in view of Andrey et al. (WO 2009002901 A2, herein Andrey). As to claim 6, Singh discloses the computer-implemented method wherein the variation includes modifying backend properties of the quantum unit that are modifiable at any time (abstract, intelligent provisioning of quantum programs to quantum hardware are provided. In some aspects, a quantum program of a plurality of quantum programs to be executed on target quantum hardware may be received from a digital computing device. Further, .par. 0047, … modify/update [i.e. adjust] the quantum program based on the one or more code change recommendations. In some embodiments, the digital computing device (e.g., digital computing device 110) may receive, in real-time, as the user is writing or creating a program in an editor and compilation is occurring in parallel, the suggestions for modifying or optimizing the quantum program. For instance, a program might be consuming 10 qubits, but intelligent quantum DevOps computing platform 120 might suggest that a programmer apply alternate logic or introduce alternate components into the program such that fewer qubits are consumed (e.g., 8 qubits versus 10 qubits). For instance, the digital computing device (e.g., digital computing device 110) may display and/or otherwise present one or more graphical user interfaces similar to graphical user interface 300, which is illustrated in FIG. 3. As seen in FIG. 3, graphical user interface 300 may include text and/or other information associated with providing code change recommendations … ) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein the variation includes modifying of the quantum unit that can be modified at any time, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). Martiel as modified by Alam and Singh does not explicitly disclose the following limitation but, Andrey disclose the computer-implemented method wherein the variation includes modifying backend properties of the quantum unit that are modifiable at any time (abstract, backend data may be received defining data constructs in a backend system. Then entity data may be received defining data constructs in an entity model. User selectable elements may then be received defining a process associating the backend data with the entity data. Further, par. 0012, … translate those into pertinent functionality to be performed on the backend; iii) ability to customize a solutions provided for a specific design/implementation of a conceptual model and a specific type/version of the backend; iv) ability to customize at both a design time and/or runtime of a solution; and v) ability to package the solutions, ship, or deploy them independent of the backends. Further, par. 0033, capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems). Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein the variation includes modifying backend properties of the that can be modified at any time, as disclosed by Andrey, for the purpose of enabling mapping between an entity model and a backend (see par. 0012 of Andrey). Claims 7 and 18 are rejected under 35 U.S.C. 103 as being obvious over Martiel et al. , Alam et al. and Singh et al., applied to claims 1 and 17 in the above, and further in view of Matsuura et al. (US 20240193451 A1, herein Matsuura). As to claim 7, Martiel as modified by Alam and Singh does not explicitly disclose the following limitation but, Matsuura discloses the computer-implemented method wherein the quantum source code input is a high-level abstraction (par. 0060, … the input to each of these components comprises a set of codewords generated by the quantum engine functional units 204E and the output is an analog waveform which manipulates the state of the qubits of the quantum processor 207 … ) : Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein the quantum source code input is quantum assembly level programming, as disclosed by Matsuura, for the purpose hold qubit indices to address logical qubits #2 and #3, respectively, in this particular example. The mapping of the relevant portions of the QASM code 601 to the hybrid processor program code (see par. 0064 of Matsuura). As to claim 18, Martiel does not explicitly discloses the computer system further comprising program instructions to: Alam discloses apply different quantum unit specification constraints as information for the training (Col. 4, ll. 21-30, the other[i.e. different] computing resources 107, and the server 108 can receive the output data from the computational tasks performed by the quantum processor units 103A, 103B and the other computing resources 107. In some implementations, the server 108 includes a personal computing device, a computer cluster, one or more servers, databases, networks, or other types of classical or quantum computing equipment. The server 108 may include additional or different features, and may operate as described with respect to FIG. 1 or in another manner. Further, col. 7, ll. 41-54, quantum information by applying control [i.e. constraints] signals to the qubits in the quantum processor cell 102A. The control signals can be configured to encode information in the qubits, to process the information by performing quantum logic gates or other types of operations, or to extract information from the qubits. In some examples, the operations can be expressed as single-qubit logic gates, two-qubit logic gates, or other types of quantum logic gates that operate on one or more qubits. A sequence of quantum logic operations can be applied to the qubits to perform a quantum algorithm. The quantum algorithm may correspond to a computational task, a hardware test, a quantum error correction procedure, a quantum state distillation procedure, or a combination of these and other types of operations) and Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include apply different quantum unit specification constraints as information for the training, as disclosed by Alam, because such inclusion will suggest a form for what the initial layer should look like in the case where the input has certain symmetries and make the change of basis operation is appended to the updated version of the quantum program in each iteration. (see col. 15, ll. 64-65 and col. 22, ll. 59-61 of Alam) . Singh discloses train the trained models system to provide a recommended variation of the quantum source code input (par. 0047, FIG. 2C, at step 209, the digital computing device (e.g., digital computing device 110) may receive the one or more code change recommendations and prompt a user of the digital computing device (e.g., digital computing device 110) to modify/update the quantum program based on the one or more code change recommendations. In some embodiments, the digital computing device (e.g., digital computing device 110) may receive, in real-time, as the user is writing or creating a program in an editor and compilation is occurring in parallel, the suggestions for modifying or optimizing the quantum program … IG. 3. As seen in FIG. 3, graphical user interface 300 may include text and/or other information associated with providing code change recommendations, including one or more user-selectable options that allow a user to select from one or more recommendations for minimizing qubit consumption (e.g., “Alert! Qubit consumption may be improved. [Recommendation 1 . . . ] [Recommendation 2 . . . ] [Recommendation 3 . . . ]”)) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include train the modeling system to provide a recommended variation of a quantum source code input, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). Matsuura input original quantum source code and custom variations of the original quantum source code (par. 0136, … FIG. 18). In addition to LLVM's built-in passes (such as dead code elimination, instruction combining, loop unrolling, constant folding), these embodiments include custom passes for extracting and filtering quantum functions such as Product of Pauli Rotations (PoPR) synthesis. Further, par. 0147, the input source program ]i.e. original quantum source code), looks for the invocations of functions annotated with the quantum kernel attribute, and replaces them with equivalent Quantum Runtime library API calls using the mapping from the integration header generator 1806. … ) ;; Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include input original quantum source code and custom variations of the original quantum source code, as disclosed by Matsuura, for the purpose hold qubit indices to address logical qubits #2 and #3, respectively, in this particular example. The mapping of the relevant portions of the QASM code 601 to the hybrid processor program code (see par. 0064 of Matsuura). Claim 10 is rejected under 35 U.S.C. 103 as being obvious over Martiel et al. , Alam et al. and Singh et al., applied to claim 9, and further in view of Marinescu et al. (US 20230186145 A1, herein Marinescu). As to claim 10, Martiel as modified by Alam and Sigh does not explicitly disclose the following limitation but, Marinescu discloses the computer-implemented method wherein, when there is an improvement by the variation, including the variation in a training dataset for the trained models (par. 0018, a system, computer-implemented method and/or computer program product that can account for one or more deficiencies of existing techniques for optimization model (e.g., an ML model) generation and/or augmentation … the constraint can employ natural language processing (NLP) to translate the constraint to a mathematical form. This formal form of the constraint can be employed by an inference engine to augment an optimization model and to output an output policy in accordance with the desired constraint. Training of the optimization model can be facilitated after each iteration to continually improve upon natural language text to corresponding formal constraint conversion) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein, when there is an improvement by the variation, including the variation in a training dataset for the trained models, as disclosed by Matsuura, for the purpose of training of the optimization model can be facilitated after each iteration to continually improve upon natural language text to corresponding formal constraint conversion (see par. 0018 of Marinescu). Claims 11 and 13 are rejected under 35 U.S.C. 103 as being obvious over Matsuura et al., in view of Alam et al., Singh et al. and Marinescu et al. As to claim 11, Matsuura discloses a computer-implemented method for training a modeling system for quantum source code generation, said method comprising: inputting original quantum source code and custom variations of the original quantum source code (par. 0136, … FIG. 18, In addition to LLVM's built-in passes (such as dead code elimination, instruction combining, loop unrolling, constant folding), these embodiments include custom passes for extracting and filtering quantum functions such as Product of Pauli Rotations (PoPR) synthesis. Further, par. 0147, the input source program ]i.e. original quantum source code), looks for the invocations of functions annotated with the quantum kernel attribute, and replaces them with equivalent Quantum Runtime library API calls using the mapping from the integration header generator 1806. … ) ; wherein the quantum unit specification constraints include: a backend configuration of static information of a backend of a quantum unit that comprises a name of the backend (FIG. 18 illustrates the stages in the quantum device compilation workflow broken down into multiple steps. At 1802, the frontend parses and translates a hybrid quantum-classical source file 1501 which includes both quantum and classical functions to extract quantum-specific logic 1803. One or more optimization passes are then performed 1804 to identify the most efficient order of quantum operations. The optimized code is provided to backend code generation logic 1805 which generates object code to be executed by a qubit control processor 1450 and an integration header generator 1806 generates the quantum integration header 1515 [i.e. name]) , a version (par. 0144, … A new class is defined for a quantum machine which is inherited from the TargetMachine class. In this phase, relocatable machine code is generated—referred to as a quantum object file (identified by a .qo extension). The backend can optionally generate an assembly file for a human-readable version of the machine code) , a number of qubits employed in the backend (par. 0112, … as well as the total number of quantum bits (qubits) available. …) , gates allowed in the backend (par. 0134, … program code sequence below. This avoids any ambiguity as to the behavior of a quantum gate, and allows for easily changing the name for a future standard. … ) , measurement levels, and a measurement map (par. 0047, … The quantum engine also executes instructions to take a measurement of specified sets of qubits and store the results. In these embodiments, a quantum/classical interface provides connectivity between the quantum engine of the classical processor and the quantum processor. … ) ; backend defaults defining a basic current configuration of the backend of the quantum unit comprising an estimated frequency of qubits, an estimated frequency of measurements (par. 0080, … a measurement unit 1015 reads/estimates the quantum state of one or more of the qubits Q0-Q15 and provides the measurement results to a decoding/error correction unit 1016 which decodes the measurements using error correction decoding techniques. For example, the decoding/error correction unit 1016 decodes a multi-qubit measurement from the quantum …) , a kernel measurement, and a discriminator (par. 0127, Once a quantum kernel is called by the program, the host processor uses a blocking call to issue the corresponding QBB 1601-1602 and transfers the control to the quantum hardware 1265. …) ; and backend properties defining optimized gates , coupling maps, and qubits of the backend of the quantum unit (par. 0116, … In LLVM, this is managed by transformation passes that translate the IR into an optimized IR. The backend is associated with a specific target machine (e.g., a particular X86 processor model), and performs code generation by mapping the IR to the target instruction set. … . Further, par. 0131, FIG. 18 illustrates the stages in the quantum device compilation workflow broken down into multiple steps. At 1802, the frontend parses and translates a hybrid quantum-classical source file 1501 which includes both quantum and classical functions to extract quantum-specific logic 1803. One or more optimization passes are then performed 1804 to identify the most efficient order of quantum operations. The optimized code is provided to backend code generation logic 1805 which generates object code to be executed by a qubit control processor 1450) ; Matsuura does not explicitly disclose the following limitations but, Alam discloses applying different quantum unit specification constraints as information for the training (Col. 4, ll. 21-30, the other[i.e. different] computing resources 107, and the server 108 can receive the output data from the computational tasks performed by the quantum processor units 103A, 103B and the other computing resources 107. In some implementations, the server 108 includes a personal computing device, a computer cluster, one or more servers, databases, networks, or other types of classical or quantum computing equipment. The server 108 may include additional or different features, and may operate as described with respect to FIG. 1 or in another manner. Further, col. 7, ll. 41-54, quantum information by applying control [i.e. constraints] signals to the qubits in the quantum processor cell 102A. The control signals can be configured to encode information in the qubits, to process the information by performing quantum logic gates or other types of operations, or to extract information from the qubits. In some examples, the operations can be expressed as single-qubit logic gates, two-qubit logic gates, or other types of quantum logic gates that operate on one or more qubits. A sequence of quantum logic operations can be applied to the qubits to perform a quantum algorithm. The quantum algorithm may correspond to a computational task, a hardware test, a quantum error correction procedure, a quantum state distillation procedure, or a combination of these and other types of operations) , backend properties defining optimized gates (col. 3, ll. 13-15, the quantum logic gates may include parametric gates that can be further optimized for an individual quantum resource). a buffer of the backend to store information (col. 9, ll. 4-11, the controllers 106A include memory [i.e. buffer] or other components that store quantum state information, for example, based on qubit readout operations performed by the quantum processor unit 103A. For instance, the states of one or more qubits in the quantum processor cell 102A can be measured by qubit readout operations, and the measured state information can be stored in a cache or other type of memory system) , a command definition (col. 9, ll. 54-57, the controllers 106A may execute a quantum processor unit (QPU) driver software, which may include machine code compiled from any type of programming language (e.g., Python, C++, etc.) or instructions [i.e. command]) , Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include applying different quantum unit specification constraints as information for the training, and backend properties defining optimized gates and a buffer of the backend to store information a command definition, as disclosed by Alam, because such inclusion will suggest a form for what the initial layer should look like in the case where the input has certain symmetries and make the change of basis operation is appended to the updated version of the quantum program in each iteration. (see col. 15, ll. 64-65 and col. 22, ll. 59-61 of Alam). Sings discloses training the modeling system to provide a recommended variation of a quantum source code input (par. 0047, FIG. 2C, at step 209, the digital computing device (e.g., digital computing device 110) may receive the one or more code change recommendations and prompt a user of the digital computing device (e.g., digital computing device 110) to modify/update the quantum program based on the one or more code change recommendations. In some embodiments, the digital computing device (e.g., digital computing device 110) may receive, in real-time, as the user is writing or creating a program in an editor and compilation is occurring in parallel, the suggestions for modifying or optimizing the quantum program … IG. 3. As seen in FIG. 3, graphical user interface 300 may include text and/or other information associated with providing code change recommendations, including one or more user-selectable options that allow a user to select from one or more recommendations for minimizing qubit consumption (e.g., “Alert! Qubit consumption may be improved. [Recommendation 1 . . . ] [Recommendation 2 . . . ] [Recommendation 3 . . . ]”)) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include training the modeling system to provide a recommended variation of a quantum source code input, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0006 of Singh). Marinescu discloses a backend configuration of static information of a backend of a quantum unit (par. 0029, … general-purpose computer, special-purpose computer, quantum computing device (e.g., a quantum computer)… , Further, par. 0041, The model generation component 208 can employ the input dataset 207 as input to build (e.g., generate) an optimization model 213 (also herein referred to as a non-mixed influence model) for use in providing static and/or dynamic probabilistic predictions [i.e. features]. … . Further, par. 0097, the processes described herein can be performed by one or more specialized [i.e. custom] computers (e.g., a specialized processing unit, a specialized classical computer, a specialized quantum computer, a specialized hybrid classical/quantum system and/or another type of specialized computer) to execute defined tasks related to the one or more technologies describe above … ); Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include configuration of static information of the quantum unit, as disclosed by Marinescu, for the purpose of analyzes an input dataset comprising a constraint in a natural language form, and an augmentation component that generates an influence mapping comprising a constraint variable based on the constraint input. (see abstract of Marinescu). As to claim 13, Singh discloses the computer-implemented method further comprising: training a machine learning model in the modeling system to adapt a quantum source code based on a configuration of a quantum unit’s specification constraints (par. 0021, intelligent orchestration of quantum programs to external quantum hardware that leverages NFT technology. In particular, one or more aspects of the disclosure may leverage artificial intelligence and/or machine learning (AI/ML) algorithms or models, including abstract syntax trees, to determine contextual logic about a quantum program. Additional aspects of the disclosure may leverage AI/ML algorithms or models, including natural language processing (NLP) to gain insight into code and provide recommendations for modifying quantum program code in a manner that consumes an optimal (e.g., minimum) number of qubits. Additional aspects of the disclosure may, based on understanding contextual and functional logic of a quantum program) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include training a machine learning model in the modeling system to adapt a quantum source code based on a configuration of a quantum unit’s specification constraints, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). Claims 12 and 14-15 are rejected under 35 U.S.C. 103 as being obvious over Matsuura et al., Alam et al., and Singh et al. and Marinescu et al as applied to claim 11 in the above and further in view of Sun et al. (US 20240354379 A1, hereinafter Sun). As to claim 12, Matsuura as modified by Alam and Sigh does not explicitly disclose the following limitation but, Sun discloses the computer-implemented method further comprising: training a language model in the modeling system to learn how quantum source code is composed, to learn the differences between the different versions of the quantum source code (par. 0032, … an automated software 210, a generative artificial intelligence 212 (e.g., language model, such as a generative pre-trained transformer), and a trained guardrail model 214 (e.g., another language model that may be smaller than the generative artificial intelligence 212). The automated software 210 operationally generates a conversation with a user, turn by turn, or performs other automated tasks. The generative artificial intelligence 212 (e.g., large language model) operationally generates training data sets for the trained guardrail model 214) ; Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include the computer-implemented method wherein analyzing against trained models includes using a language model to generate a variation with the language model trained on datasets, as disclosed by Sun, for the purpose of generating training sets, generating, with an automated software (see abstract of Sun). Singh discloses to learn to recognize when to propose variations in the quantum source code to enhance the quantum source code (par. 0006, … optimizing [i.e. enhance] the quantum program may include generating and sending, to the digital computing device, one or more code change recommendations [i.e. propose] for minimizing qubit consumption, and causing the digital computing device to prompt a user of the digital computing device to update the quantum program based on the one or more code change recommendations) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include to learn to recognize when to propose variations in the quantum source code to enhance it, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). As to claim 14, Singh discloses the computer-implemented method further comprising: running automatic processes of evaluating the recommended variations of the modeling system against quantum units to check whether code functionality (par. 0038, … computing environment 100 in understanding the functionality of quantum programs, in recommending or suggesting modifications to program code for optimal (e.g., minimum) qubit consumption, and/or in automatically modifying program code based on qubit optimization requirements … ) and performance are enhanced (par. 0047, … including one or more user-selectable options that allow a user to select from one or more recommendations for minimizing qubit consumption (e.g., “Alert! Qubit consumption may be improved. [Recommendation 1 . . . ] [Recommendation 2 . . . ] [Recommendation 3 . . . ]”)) ; Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include running automatic processes of evaluating the recommended variations of the modeling system against quantum units to check whether the code functionality and performance are enhanced, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). Sun discloses providing feedback to the modeling system (par. 0102, model evaluation 708: This phase may include evaluating the performance of a trained model (e.g., the trained machine-learning program 802) on a separate testing dataset. This phase can help determine if the model is overfitting or underfitting and determine whether the model is suitable for deployment. [0103] Prediction 710: This phase involves using a trained model (e.g., trained machine-learning program 802) to generate predictions on new, unseen data. [0104] Validation, refinement or retraining 712: This phase may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback. … ) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include providing feedback to the modeling system, as disclosed by Sun, for the purpose of generating training sets, generating, with an automated software (see abstract of Sun). As to claim 15, Singh discloses the computer-implemented method further comprising: running automatic code analysis to evaluate when syntaxis, style, or of the quantum source code security is improved with a recommended variation (par. 0038, … computing environment 100 in understanding the functionality of quantum programs, in recommending or suggesting modifications to program code for optimal (e.g., minimum) qubit consumption, and/or in automatically modifying program code based on qubit optimization requirements … . Further, par. 0047, … including one or more user-selectable options that allow a user to select from one or more recommendations for minimizing qubit consumption (e.g., “Alert! Qubit consumption may be improved. [Recommendation 1 . . . ] [Recommendation 2 . . . ] [Recommendation 3 . . . ]”)) ; Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include running automatic code analysis to evaluate when the quantum source code syntaxis, style, or security is improved with a recommended variation, as disclosed by Singh, for the purpose of recommending update the quantum program based on the one or more code change recommendations. (see par. 0005 of Singh). Sun discloses providing feedback to the modeling system (par. 0102, model evaluation 708: This phase may include evaluating the performance of a trained model (e.g., the trained machine-learning program 802) on a separate testing dataset. This phase can help determine if the model is overfitting or underfitting and determine whether the model is suitable for deployment. [0103] Prediction 710: This phase involves using a trained model (e.g., trained machine-learning program 802) to generate predictions on new, unseen data. [0104] Validation, refinement or retraining 712: This phase may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback. … ) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Matsuura to include providing feedback to the modeling system, as disclosed by Sun, for the purpose of generating training sets, generating, with an automated software (see abstract of Sun). Claim 20 is rejected under 35 U.S.C. 103 as being obvious over Martiel et al. , Alam et al. and Singh et al. and Matsuura et al., as applied to claim 18 in the above and further in view Sun et al. (US 20240354379 A1, hereinafter Sun). As to claim 20, Martiel as modified by Alam, Sigh, and Matsuura does not explicitly disclose the following limitation but, Sun discloses the computer system further comprising program instructions to: provide feedback of the quantum source code input and the recommended variation to the trained models (par. 0102, model evaluation 708: This phase may include evaluating the performance of a trained model (e.g., the trained machine-learning program 802) on a separate testing dataset. This phase can help determine if the model is overfitting or underfitting and determine whether the model is suitable for deployment. [0103] Prediction 710: This phase involves using a trained model (e.g., trained machine-learning program 802) to generate predictions on new, unseen data. [0104] Validation, refinement or retraining 712: This phase may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback. … ) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include provide feedback of a quantum source code input and recommended variation to the training system, as disclosed by Sun, for the purpose of generating training sets, generating, with an automated software (see abstract of Sun). Claim 21 is rejected under 35 U.S.C. 103 as being obvious over Martiel et al. , Alam et al. and Singh et al., as applied to claim 1 in the above and further in view Izaac et al. (US 20240028943 A1, hereinafter Izaac). As to claim 21, Martiel a s modified by Alam and Sigh does not explicitly disclose the following limitation but, Izaac discloses the computer-implemented method wherein the quantum source code input comprises pulse-level programming (par. 0116, the code generation block 250 can also include various operations defined to optimize the representation of the quantum program when the quantum program is ultimately compiled into code that is executable by the quantum hardware/simulator and/or classical computing elements. For example, the code generation block 250 can include one or more of hardware target optimizations 252, operator fusion 254, optimizations specific to hardware topologies & gates 256, and optimizations that utilize pulse-level representations 258, as well as other general quantum compilation operations) . Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Martiel to include the computer-implemented method wherein the quantum source code input comprises pulse-level programming, as disclosed by Izaac, for the purpose of including one or more of hardware target optimizations, operator fusion, optimizations specific to hardware topologies & gates 256, and optimizations that utilize pulse-level representations (see par. 0116 of Izaac). Conclusion 07-40 AIA 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 MOHAMMAD H KABIR whose telephone number is (571)270-1341. The examiner can normally be reached M-F, 8:00 am - 5:00 pm. 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, Sam Sough can be reached at 571-272-6799. 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. /Mohammad Kabir/ Examiner, Art Unit 2192 /S. Sough/SPE, Art Unit 2192 Application/Control Number: 18/588,554 Page 2 Art Unit: 2192 Application/Control Number: 18/588,554 Page 3 Art Unit: 2192 Application/Control Number: 18/588,554 Page 5 Art Unit: 2192 Application/Control Number: 18/588,554 Page 6 Art Unit: 2192 Application/Control Number: 18/588,554 Page 7 Art Unit: 2192 Application/Control Number: 18/588,554 Page 8 Art Unit: 2192 Application/Control Number: 18/588,554 Page 9 Art Unit: 2192 Application/Control Number: 18/588,554 Page 10 Art Unit: 2192 Application/Control Number: 18/588,554 Page 11 Art Unit: 2192 Application/Control Number: 18/588,554 Page 12 Art Unit: 2192 Application/Control Number: 18/588,554 Page 13 Art Unit: 2192 Application/Control Number: 18/588,554 Page 14 Art Unit: 2192 Application/Control Number: 18/588,554 Page 15 Art Unit: 2192 Application/Control Number: 18/588,554 Page 16 Art Unit: 2192 Application/Control Number: 18/588,554 Page 17 Art Unit: 2192 Application/Control Number: 18/588,554 Page 18 Art Unit: 2192 Application/Control Number: 18/588,554 Page 19 Art Unit: 2192 Application/Control Number: 18/588,554 Page 20 Art Unit: 2192 Application/Control Number: 18/588,554 Page 21 Art Unit: 2192 Application/Control Number: 18/588,554 Page 22 Art Unit: 2192 Application/Control Number: 18/588,554 Page 24 Art Unit: 2192 Application/Control Number: 18/588,554 Page 25 Art Unit: 2192 Application/Control Number: 18/588,554 Page 26 Art Unit: 2192 Application/Control Number: 18/588,554 Page 27 Art Unit: 2192 Application/Control Number: 18/588,554 Page 28 Art Unit: 2192 Application/Control Number: 18/588,554 Page 29 Art Unit: 2192 Application/Control Number: 18/588,554 Page 30 Art Unit: 2192 Application/Control Number: 18/588,554 Page 31 Art Unit: 2192 Application/Control Number: 18/588,554 Page 32 Art Unit: 2192 Application/Control Number: 18/588,554 Page 33 Art Unit: 2192 Application/Control Number: 18/588,554 Page 34 Art Unit: 2192 Application/Control Number: 18/588,554 Page 35 Art Unit: 2192
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