DETAILED ACTION Status of Claims Claim(s) 1-20 are pending and are examined herein. Claim(s) 1-20 are rejected under 35 U.S.C. § § 101 and 102/ 103. 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. Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claim(s) 1-5, 9, 11-15, and 19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-2, 4-5, 11-12, and 14-15 of copending application No. 18/345,125 in view of Rao et al. (Pub. No. US 12079689 B1). Although the claims at issue are not identical, they are not patentably distinct from each other. Corresponding claims/features and the rejection based on anticipation/obviousness analysis is described below. Claims of the Present Application Filed on 06/30/2023 Claims of Copending Application # 18/345,125 Filed on 06/30/2023 A method comprising: predicting, using a machine learning model , runtime characteristics concerning a quantum computing function; predicting, using the machine learning model , resources needed to perform the quantum computing function; selecting an execution environment for the quantum computing function; and executing the quantum computing function in the execution environment. 2. The method as recited in claim 1, wherein the quantum computing function comprises a circuit cutting process. 3. The method as recited in claim 1, wherein the quantum computing function comprises a quantum circuit execution. 4. The method as recited in claim 1, wherein telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself . 5. The method as recited in claim 1, wherein metadata and runtime characteristics relating to the quantum computing function are collected from the execution environment while the quantum computing function is being executed. 9. The method as recited in claim 1, wherein the quantum computing function comprises a circuit cutting process, and the predicting of the runtime characteristics comprises predicting that the circuit cutting process can be performed. Examiner’s Note : as shown in the correspondence table, the bolded limitations of the claims of the instant application are not explicitly recited in the claims of the copending application. To the extent these features are not inherently or implicitly disclosed by the claims of the copending application, they would have been obvious in view of Rao. The obviousness-type double patenting rejection analysis is further described below. 1. A method, comprising: obtaining telemetry data from resources available for executing a quantum job; performing a cutting operation to cut a quantum circuit included in the quantum job into quantum subcircuits; performing a runtime prediction for each of the quantum subcircuits to generate runtime characteristics for each of the quantum subcircuits; generating an execution plan by optimizing use of the resources based on the telemetry data and the runtime characteristics; and executing each of the quantum subcircuits according to the execution plan . 2. The method of claim 1, further comprising determining whether to proceed with cutting the quantum subcircuits based on criteria associated with executing the quantum circuit and performing the runtime prediction on the quantum circuit to generate corresponding runtime characteristics for the quantum circuit . 4. The method of claim 1, wherein the runtime prediction is configured to predict an amount of resources and an execution time for executing each of the quantum subcircuits . 5. The method of claim 4, wherein the runtime prediction is configured to predict a success rate, resource consumption, and execution time associated with the cutting operation . 11 . A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: predicting, using a machine learning model , runtime characteristics concerning a quantum computing function; predicting, using the machine learning model , resources needed to perform the quantum computing function; selecting an execution environment for the quantum computing function; and executing the quantum computing function in the execution environment. 12. The non-transitory storage medium as recited in claim 11, wherein the quantum computing function comprises a circuit cutting process. 13. The non-transitory storage medium as recited in claim 11, wherein the quantum computing function comprises a quantum circuit execution. 14 . The non-transitory storage medium as recited in claim 11, wherein telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself . 15. The non-transitory storage medium as recited in claim 11, wherein metadata and runtime characteristics relating to the quantum computing function are collected from the execution environment while the quantum computing function is being executed. 19. The non-transitory storage medium as recited in claim 11, wherein the quantum computing function comprises a circuit cutting process, and the predicting of the runtime characteristics comprises predicting that the circuit cutting process can be performed. Examiner’s Note : as shown in the correspondence table, the bolded limitations of the claims of the instant application are not explicitly recited in the claims of the copending application. To the extent these features are not inherently or implicitly disclosed by the claims of the copending application, they would have been obvious in view of Rao. The obviousness-type double patenting rejection analysis is further described below. 11. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: obtaining telemetry data from resources available for executing a quantum job; performing a cutting operation to cut a quantum circuit included in the quantum job into quantum subcircuits; performing a runtime prediction for each of the quantum subcircuits to generate runtime characteristics for each of the quantum subcircuits; generating an execution plan by optimizing use of the resources based on the telemetry data and the runtime characteristics; and executing each of the quantum subcircuits according to the execution plan . 12. The non-transitory storage medium of claim 11, further comprising determining whether to proceed with cutting the quantum subcircuits based on criteria associated with executing the quantum circuit and performing the runtime prediction on the quantum circuit to generate corresponding runtime characteristics for the quantum circuit . 14. The non-transitory storage medium of claim 11, wherein the runtime prediction is configured to predict an amount of resources and an execution time for executing each of the quantum subcircuits . 15. The non-transitory storage medium of claim 14, wherein the runtime prediction is configured to predict a success rate, resource consumption, and execution time associated with the cutting operation. With respect to instant claim 1 : Claim 4 of the copending application, which incorporates all limitations of its parent claim 1 by dependency, in view of Rao, discloses all limitations of claim 1 of the present application. Specifically, the claims of the copending application discloses the following: Performing a runtime prediction for ach quantum subcircuit to generate runtime characteristics ( i.e., predicting runtime characteristics concerning a quantum computing function ); Predicting an amount of resources and an execution time for executing each of the quantum subcircuits ( i.e., predicting resources needed to perform the quantum computing function ); and Generating an execution plan by optimizing use of resources based on the telemetry data and the runtime characteristics ( i.e., implicitly discloses selecting an execution environment for the quantum computing function ); and executing each of the quantum subcircuits according to the execution plan. ( i.e., executing the quantum computing function in the execution environment ). The differences between the claims of the copending application and the claims of the present application are the following: ( i ) define the prediction of runtime and resources as being performed using a machine learning model; and (ii) define the generated execution plan as selecting an execution environment. These features would have been obvious in view of Rao, which particularly describes determining a type of computing device (e.g., quantum, classical, or hybrid) based on a machine learning model’s output, and executing the quantum computational task on the selected device. Further, Rao teaches using the ML model to determine/predict performance factors including processing time, error rates, and resource utilization. The device-type selection based on ML model output to perform quantum computational task. These directly correspond to the recitations of using a machine learning model to predict (i.e., runtime and resources) and selecting an execution environment (i.e., computing devices). The corresponding citations and sections described by Rao: [Col. 1, Lines 25-35] “The method may include determining, by the formulation circuitry, a type of computing device needed for based on the intermediate output... the type of computing device may identify one of a hybrid computing device, a quantum computing device, and a classical computing device.” [Col. 11-13, Lines 60-65 and 5-10] “Such an intermediate output may be based on one or more factors determined via the trained machine learning model or classifier. In other words, the trained machine learning model may take into account factors relating to a request or problem and determine the best type or combination of types of computing devices to apply to the request. Such factors may include the length of potential processing time via each of the one or more types of computing devices, ... The intermediate output may indicate a type of computing device to be utilized for a particular computational or statistical problem.” [Col. 16, Lines 5-15] “Such set up or initialization may include locating and selecting computing devices of the determined type(s). Once the computing devices are located and selected, the FED task initialization instructions 342 may transmit all or a portion of the parsed data to selected computing devices.” [Col. 20, Lines 10-20] “The type of computing device may include a quantum computing device, a classical computing device, or a combination thereof (e.g., a hybrid computing device). Such a determination may be made based on the intermediate output of the machine learning model. Other factors may be taken into consideration within the machine learning model, such as whether a quantum computing device provides sufficient speedup of algorithm execution. The quantum computing device may not be chosen if the benefits are outweighed by the cost.” Further See [Col. 21]. Accordingly, a person of ordinary skill in the art would have been motivated to combine the orchestration decision framework of claims 1 and 6 of the copending application with the device-type selection teaching of Rao, as both references are directed to the same technical field of quantum/hybrid computing orchestration using machine learning models. Doing so would enable identification of the proper, suitable, or efficient types of computing devices to use to for a given project (Rao [Col. 7, Lines 10-15]). With respect to instant claims 2-5 : Claim 2 further recites that the quantum computing function comprises a circuit cutting process. Claim 1 of the copending application recites performing a cutting operation to cut a quantum circuit into quantum subcircuits, which anticipate the limitation of instant claim 2. Claim 3 further recites that the quantum computing function comprises a quantum circuit execution. Claim 1-3 of the copending application define a method operating and executing quantum circuit, which anticipate the limitation of instant claim 3. Claim 4 further recites that telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself. Claim 1 of copending application discloses obtaining telemetry data from resources available for executing a quantum job, which corresponds to the recitation of telemetry concerning execution of quantum computing function. Rao additionally discloses retraining the machine learning model based on output and evaluation data (Fig. 6, Col. 3 and Col. 17, Lines 45-50), which corresponds to using telemetry to retrain the machine learning model. Accordingly, the claims of copending application in view of Rao teaches the limitation of claim 4. Claim 5 further recites: “metadata and runtime characteristics relating to the quantum computing function are collected from the execution environment while the quantum computing function is being executed.” Claim of copending application recites: obtaining telemetry data from resources available for executing the quantum job and generating runtime characteristics for each quantum subcircuit during execution, which correspond to collecting metadata and runtime characteristics during execution of quantum function. Accordingly, the claims of copending application in view of Rao teaches the limitation of claim 5. Claim 9, further recites: “wherein the quantum computing function comprises a circuit cutting process, and the predicting of the runtime characteristics comprises predicting that the circuit cutting process can be performed.” Claims of the copending application discloses performing cutting operation to cut a quantum circuit into subcircuits and determining whether to proceed with cutting the quantum subcircuits based on criteria associated with executing the quantum circuit. This reads on the claimed predicting that the circuit cutting process can be performed. Accordingly, claim 9 is not patentably distinct from the corresponding claims 1-2 of the copending application. With respect to instant claim 11 : Claim 11 of the instant application is rejected under obviousness-type non-statutory double patenting over the claims of copending application in view of Rao. Claim 11 of the instant application recites limitations substantially similar to those of claims 1, differing only in that claim 11 is directed to a non-transitory storage medium rather than a method. Accordingly, the same reasoning and rationale applied above with respect to claim 1 is equally applicable to claim 11 using the corresponding non-transitory storage medium claim 11 of the copending application of Rao . With respect to instant claims 12-15 and 19 : Claims 12-15 and 19 of the instant application are rejected under obviousness-type non-statutory double patenting over the claims 11-12 and 14-15 of reference application in view of Rao. Claims 12-15 and 19 of the instant application recite limitations substantially similar to those recited in claims 2-5 and 9, respectively, except that they are directed to a non-transitory storage medium rather than a method. Therefore, the same reasoning and rationale applied above with respect to claims 2-5 and 9 are equally applicable to claims 12-15 and 19. 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. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis. Under Step 1 analysis , Claims 1-10 recite a method (representing a process); and Claims 11- 20 recite a non-transitory storage medium (representing an article of manufacture); Therefore, each set of the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter). Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, and hence is not patent-eligible subject matter. Regarding Claim 1 , Step 2A Prong 1 : The claim recites an abstract idea enumerated in the 2019 PEG. predicting, using a machine learning model, runtime characteristics concerning a quantum computing function; (An abstract idea of a mental process. Examiner’s note: the “ predicting ” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). The broader recitation of predicting runtime characteristics concerning a quantum computing function falls under the mental process category. For example, an individual (e.g., a data scientist) can mentally predict the execution time required to perform a quantum computing function. This is a decision-making process that can be performed in the human mind.) predicting, using the machine learning model, resources needed to perform the quantum computing function; (An abstract idea of a mental process. Examiner’s note: the “ predicting ” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2) (III). The broader recitation of predicting resources needed to perform the quantum computing function falls under the mental process category of abstract idea. For example, a person looks at a quantum algorithm, counts how many qubits it needs, estimates how long it will take to run, and decides if the available hardware component can handle it. This is an evaluation and judgment process that could be performed mentally without a computer .) selecting an execution environment for the quantum computing function; (An abstract idea of a mental process. Examiner’s note: the “ selecting ” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. This involves an evaluation and decision-making process that can be performed in the human mind. For example, a person considers the determined runtime and resources requirements of a quantum function and decides whether to run it on a particular device or cloud computing environment. This is a mental process. See MPEP § 2106.04(a)(2)(I) & (III) .) Step 2A Prong 2 : Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application. Additional Elements Analysis : The claim recite the additional element such as: “ using a machine learning model” (This amounts to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). In other words, the claim invokes computer and/or other machinery in its ordinary capacity merely as a tool to perform the abstract idea.) “ executing the quantum computing function in the execution environment.” (This amounts to no more linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). This step ties the abstract idea of prediction and selection to an execution in a particular field of technology (e.g., quantum computing environment). However, merely linking the judicial exception to a field of use does not by itself integrate the abstract idea into a practical application.) Step 2B : Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept). Additional Elements Analysis : As explained above, the claimed additional elements merely represents generic computer component (i.e., conventional models) configured to perform the abstract ideas and generally linking the abstract idea into a technological environment. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. Therefore, claim 1 does not recite patent-eligible subject matter. Regarding Claim 2 , Step 2A Prong 1 : Claim 2, which incorporates the rejection of claim 1 , recites further limitation such as: wherein the quantum computing function comprises a circuit cutting process. (That is part of the abstract idea recited in claim 1. This limitation describes a type of quantum algorithm (i.e., circuit cutting process), which is an abstract method. It does not introduce any technical improvement, and therefore, falls within the judicial exception of an algorithmic or mathematical concept.) Step 2A Prong 2 : The claim does not recite additional element that integrates the judicial exception into a practical application. Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, claim 2 is ineligible. Regarding Claim 3 , Step 2A Prong 1 : Claim 3, which incorporates the rejection of claim 1 , recites further limitation such as: wherein the quantum computing function comprises a quantum circuit execution. (That is part of the abstract idea recited in claim 1. This limitation merely specifies the type of quantum function for which the prediction of runtime and resources requirements and selection are performed, and therefore falls within the same abstract idea recited in claim 1. The claim does not introduce any technical implementation of the abstract idea that would be considered as additional element and evaluated under Step 2A, Prong 2.) Step 2A Prong 2 : The claim does not recite additional element that integrates the judicial exception into a practical application. Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, claim 3 is ineligible. Regarding Claim 4 , Step 2A Prong 1 : Claim 4, which incorporates the rejection of claim 1 , doesn’t recite an abstract idea. Step 2A Prong 2 : The judicial exception is not integrated into a practical application. wherein telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself. (The claim introduces two additional elements including: collecting telemetry data (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This represents a generic computer function (i.e., data gathering in conjunction with the abstract idea).); and using the telemetry data to retrain the machine learning model (This amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). This represents high-level training a model using the obtained data, which amounts to no more than invoking computer or other machinery in their ordinary capacity as a tool to perform an existing process.) The additional elements do not integrate the abstract idea into a practical application; they merely perform routine data gathering and generic model training. Step 2B : the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Collecting telemetry data represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. Further, the high-level recitation of model retraining amounts to generic and conventional computer component and does not recite an inventive concept. See MPEP § 2106.05(d). Therefore, claim 4 is ineligible. Regarding Claim 5 , Step 2A Prong 1 : Claim 5, which incorporates the rejection of claim 1 , doesn’t recite an abstract idea. Step 2A Prong 2 : The judicial exception is not integrated into a practical application. wherein metadata and runtime characteristics relating to the quantum computing function are collected from the execution environment while the quantum computing function is being executed. (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This describes obtaining information from a system during execution (i.e., collecting metadata and runtime characteristics). This does not integrate the abstract idea into a practical application; it is a routine operation for obtaining data. This additional element does not transform the abstract idea into a practical application and is considered generic data gathering operation.) Step 2B : the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Collecting metadata of execution represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d). Therefore, claim 5 is ineligible. Regarding Claim 6 , Step 2A Prong 1 : Claim 6, which incorporates the rejection of claim 1 , recites further limitation such as: wherein the predicting of the runtime characteristics and/or the predicting of the resources, by the machine learning model, are performed based on inputs comprising any one or more of hybrid algorithm, service level objective, simulation engine, and available hardware. (That is part of the abstract idea recited in claim 1. The claim only specifies inputs to the model. The claim merely restates the concept of performing predictions of runtime/resources, which is a mental or algorithmic process that can be mentally performed. Merely specifying the input used by the model does not provide any technical improvement that would transform the abstract idea into a practical application.) Step 2A Prong 2 : The judicial exception is not integrated into a practical application. The claim does not introduce new additional elements beyond the machine learning model already recited in claim 1. The machine learning model amounts to merely invoking generic computer component as a tool to perform the abstract idea. The inputs (hybrid algorithm, SLO, simulation engine, hardware) merely define what information is used and do not constitute an inventive or unconventional technical element. Accordingly, claim 6 does not provide additional elements sufficient to integrate the abstract idea into a practical application. Step 2B : the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, the additional elements identified above do not provide significantly more than the abstract idea. The merely define the type of input used to make the prediction and the use of the machine learning model does amount to inventive concept as it merely invokes generic computer component or other machinery in their ordinary capacity as a tool to perform the abstract idea. Therefore, claim 6 is ineligible. Regarding Claim 7 , Step 2A Prong 1 : Claim 7, which incorporates the rejection of claim 1 , recites further limitation such as: wherein the resources are used to retrain the machine learning model when demand for those resources permits. (The newly introduced limitation in claim 7 recites an abstract idea because it is directed to evaluation and conditional model update. The limitation involves deciding when to retain the machine learning model based on resources availability. The claim does not specify the technical aspect of this conditional determination to retrain the model when demands for those resources permits. This high-level recitation represents a decision-making process about retraining based on available resources, but does not define how it is technically accomplished.) Step 2A Prong 2 : The judicial exception is not integrated into a practical application. As noted above, claim 7 does not introduce new additional elements beyond what was already recited in claim 1 (machine learning model, computing environment). No hardware configuration or technological implementation is specified. This claim does not recite additional elements sufficient to integrate the abstract idea into a practical application. Step 2B : the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, claim 7 is ineligible. Regarding Claim 8 , Step 2A Prong 1 : Claim 8, which incorporates the rejection of claim 1 , recites further limitation such as: wherein the quantum computing function comprises a circuit cutting process, and the runtime characteristics concerning the circuit cutting process comprise a prediction as to how many sub-circuits can be created from a circuit that is a subject of the circuit cutting process. (This limitation forms part of the abstract idea of claim 1. The claim merely specifies that the predicted runtime characteristics relate to a circuit cutting process and include estimating the number of sub-circuits that would result from partitioning a quantum circuit. This step is directed to evaluating or estimating circuit partitioning, which is a mental process and/or mathematical process. For example, a person could conceptually examine a quantum circuit diagram, determine where the circuit could be partitioned, and estimate the number of resulting sub-circuits. Furthermore, the claim does not specify any particular technical implementation of the circuit cutting process, such as whether the circuit cutting process represents a specific physical configuration or a logical decomposition. Instead, the limitation broadly recites predicting the outcome of such partitioning. Accordingly, the limitation represents an evaluation and estimation step that would fall within the abstract idea processes.) Step 2A Prong 2 : The claim does not recite additional element that integrates the judicial exception into a practical application. Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, claim 8 is ineligible. Regarding Claim 9 , Step 2A Prong 1 : Claim 9, which incorporates the rejection of claim 1 , recites further limitation such as: wherein the quantum computing function comprises a circuit cutting process, and the predicting of the runtime characteristics comprises predicting that the circuit cutting process can be performed. (This limitation recites predicting whether a circuit cutting process can be performed, which is an evaluation or possibility determination regarding a computational operation. This represents decision-making process, which can be conceptually performed by examining the circuit diagram and determining whether it can be partitioned. Thus, this limitation is directed to evaluation and judgement regarding circuit cutting process, which falls within the mental process grouping of abstract idea. See MPEP § 2106.04(a)(2)(III). ) Step 2A Prong 2 : The claim does not recite additional element that integrates the judicial exception into a practical application. Step 2B : The claim does not recite additional elements that amount to significantly more than the judicial exception. Therefore, claim 9 is ineligible. Regarding Claim 10 , Step 2A Prong 1 : Claim 10, which incorporates the rejection of claim 1 , doesn’t recite an abstract idea. Step 2A Prong 2 : The judicial exception is not integrated into a practical application. wherein the machine learning model is trained in real-time as telemetry is received concerning execution of another quantum computing function. (The telemetry aspect amounts to data gathering associated with the abstract idea of predicting runtime and resources. This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). The real-time training of the machine learning model to perform the abstract idea amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application.) Step 2B : the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, the additional elements identified above do not provide significantly more than the abstract idea. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. Furthermore, collecting telemetry data represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d). Therefore, claim 10 is ineligible. Regarding Claim 11 , The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 11. The only difference is that claim 1 is drawn to a method, and claim 11 is drawn to a non-transitory storage medium. The recitation of “a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instructions to apply the abstract idea using generic computer components. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f). Therefore, claim 11 is ineligible. Regarding Claim 12 , The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 2, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible. Regarding Claim 13 , The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible. Regarding Claim 14 , The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible. Regarding Claim 15 , The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible. Regarding Claim 16 , The claim recites similar limitations as corresponding claim 6. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 6, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible. Regarding Claim 17 , The claim recites similar limitations as corresponding claim 7. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 7, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible. Regarding Claim 18 , The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 8, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible. Regarding Claim 19 , The claim recites similar limitations as corresponding claim 9. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 9, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible. Regarding Claim 20 , The claim recites similar limitations as corresponding claim 10. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 10, as described above, is equally applicable to claim 20. Therefore, claim 20 is ineligible. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(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 . Claim(s) 1, 3, 6, 11, 13, and 1 6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Durazzo et al., (Pub. No.: US 20210406151 A1). Regarding Claim 1, Durazzo discloses the following: A method, comprising: ( Durazzo , [0008] “Embodiments of the present invention generally relate to quantum computing. More particularly, at least some embodiments of the invention relate to systems, hardware, software, computer-readable media, and methods for determining, for one or more particular tasks, whether real QPUs (quantum Processing Units) or simulation should be used to carry out the task, and for allocating computing resources accordingly.” [0043] “Attention is directed now to FIG. 2, where methods are disclosed for the implementation and use of a quantum computing platform that may comprise both real QPUs and quantum simulation clusters, where one example method is denoted generally at 300.”) predicting, using a machine learning model, runtime characteristics concerning a quantum computing function; ( Durazzo , [0032] “the estimator of the runtime cluster 125, may, based on information provided to it by the quantum middleware 133, estimate one or more runtime statistics for one or more quantum computing services, that is, the services provided as a result of execution of one or more quantum circuits. Such runtime statistics may include, but are not limited to, the execution time and memory space consumption for the quantum computing services. Such estimates may be based on historical information for the same, or similar, quantum computing services.” [0036] “an estimator may alternatively employ a top-down approach which may involve the use of machine learning (ML). In one embodiment of the top-down approach, the estimator may make one or more predictions based on previously defined ML models. These ML models may be trained by historical data from experimentations. Any embodiment of an estimator, including the aforementioned examples, may provide the quantum simulation cluster 175 with at least two predictions or inputs, namely, execution time for the quantum code, and memory space requirements for execution of the quantum code.”) [ Examiner’s Note : Durazzo explicitly disclose the use of a machine learning model (the top-down ML-based estimator) to predict runtime characteristics, specifically execution time and memory space requirements of a quantum computing function (i.e., a quantum computing algorithm/service).] predicting, using the machine learning model, resources needed to perform the quantum computing function; ( Durazzo , [0036] “Another input that may be generated by an estimator is processing requirements for execution of the quantum code.” [0041] “For example, some embodiments comprise an estimator that may be operable to predict runtime statistics for a quantum circuit, such as execution time and memory space required. Some embodiments may comprise a cluster orchestration engine that may be operable to dynamically allocate, and release, resources based on estimated resources required for quantum circuits, as determined by an estimator for example.” [0018] “.. embodiments of the invention may determine the resources required by the algorithm prior its execution, so that those resources can be allocated accurately.”) [ Examiner’s Note : the ML-based estimator predict processing requirements (i.e., the resources needed) for execution of the quantum code. Under BRI, the “ resources needed to perform the quantum computing function ” covers the predicted processing requirements, memory space, and computational resources.] selecting an execution environment for the quantum computing function; ( Durazzo , [0046] “Based on the outcome of the estimating process 304, a recommendation may then be generated 306 as to whether, for example, one or more QPUs should be used to execution the quantum circuit or, alternatively, whether a quantum simulation should be employed for execution of the quantum circuit. In some embodiments, the recommendation that is generated 306 may indicate that execution of the quantum circuit should be split between one or more QPUs and a quantum simulation process.” [0037] “Using inputs, such as the two inputs from an estimator for example, a cluster orchestration module 185 of the quantum simulation cluster 175 may determine how to best execute the quantum algorithm based on available resource and user-chosen service plan... possible choices of processing resources may include, but are not limited to, QPUs, GPUs, and CPUs.”) and executing the quantum computing function in the execution environment. ( Durazzo , [0048] “if adequate resources will be available to execute the quantum circuit, those resources may be allocated 310 for execution of the quantum circuit. Using the allocated resources, the quantum circuit may then be executed 312.” [0039] “Once the simulation cluster is in place, the simulation cluster may then execute the quantum circuit.”) Regarding Claim 3, Durazzo teaches the elements of claim 1 as outlined above, and further teaches: wherein the quantum computing function comprises a quantum circuit execution. ( Durazzo , [0028] “At execution time, the runtime environment may interpret programming instructions which require, or would at least benefit form, the use of quantum computing. The programming instructions may be compiled, such as by a runtime compiler of the runtime cluster 125, into a binary, or digital, version of a quantum circuit.” [0038] “In the case when the resources required for execution of quantum circuit fit within the total available resources, but some resources are consumed by another quantum circuit, the pending quantum circuit may enter a queue and return with a waiting-time estimation based on the jobs in front of it.”) Regarding Claim 6, Durazzo teaches the elements of claim 1 as outlined above, and further teaches : wherein the predicting of the runtime characteristics and/or the predicting of the resources, by the machine learning model, are performed based on inputs comprising any one or more of hybrid algorithm, service level objective, simulation engine, and available hardware. ( Durazzo , [0036] -[ 0037] “Any embodiment of an estimator, including the aforementioned examples, may provide the quantum simulation cluster 175 with at least two predictions or inputs, namely, execution time for the quantum code, and memory space requirements for execution of the quantum code. Another input that may be generated by an estimator is processing requirements for execution of the quantum code. Using inputs, such as the two inputs from an estimator for example, a cluster orchestration module 185 of the quantum simulation cluster 175 may det