DETAILED ACTION
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 .
Response to Amendment
The amendment filed 12/26/2025 has been entered. As directed, claims 1, 6, 8, 9, 13 and 20 have
been amended, claim 17 has been canceled and no claim is added. Thus claims 1-11, 13-16 and 18-20 remain pending in the application. The applicant’s amendments to the claims have overcome each and every objection and rejection under 35 U.S.C 112(a) previously set forth in the Non-final Office Action mailed 10/01/2025. However, new rejection under 35 U.S.C 112(b) has been made in the current office action based on the amendment.
Response to Arguments
With respect to the Applicant’s argued claim interpretation in “Applicant Arguments/Remarks Made in an Amendment,”
Applicant argues:
In the Office Action, the Examiner interprets "objective functions" as recited in the claims as a mathematical expression. (Office Action, page 11). Applicant respectfully disagrees.
Applicant's specification recites:
The objective function 530 may include one or more terms that represent desired performance and/or emissions. For example, the objective function 530 may include terms including performance (e.g., thrust demand, power off-take demand), emissions (e.g., an emission regulation limit), thrust-specific fuel consumption (TSFC), combinations thereof, and the like.
Thrust-specific fuel consumption (TSFC) is the fuel efficiency of an engine design with respect to thrust output. TSFC may be fuel consumption (grams/second) per unit of thrust (kilonewtons, or kN). TSFC is thrust-specific in that the fuel consumption is divided by the thrust.
(paragraphs 0140 and 0141). Applicant's specification provides one example of a thrust-specific fuel consumption (TSFC) value that may be derived. Based on that one example, the Examiner appears to assert that any "objective function" is a mathematical expression. Applicant respectfully disagrees. As set forth in Applicant's specification, the "objective function" may be an emission regulation limit. Therefore, Applicant respectfully disagrees with the Examiner's interpretation.
(see Response filed 12/26/2025 [page 7]).
With respect to Applicant's argument, the argument has been considered but is not persuasive because under the broadest reasonable interpretation (BRI) in light of specification, the claimed “objective functions” reasonably include mathematical formulations. The instant specification discloses that an “objective function” may include terms representing performance and emissions and may be formulated as an optimization problem. Therefore, under broadest reasonable interpretation, the claimed “objective functions” include mathematical formulations and relationships used in optimization, as well as values used within the formulations (see [0140] and [0142]-[0143]). The “objective function” may include an emission regulation limits do not preclude the objective function from being represented mathematically. Rather, the disclosure indicates that terms may use as variables or constraints within a mathematical formulation, and the claim limitations do not exclude mathematical implementation.
Accordingly, when given its broadest reasonable interpretation in light of specification, the recited “objective functions” include mathematical relationships, formulas, or calculations used to evaluate performance and emissions terms. Therefore, the Examiner’s interpretation is proper.
With respect to the Applicant’s argued rejection under 35 U.S.C 101, Step 2A, Prong one in “Applicant Arguments/Remarks Made in an Amendment,”
Applicant argues:
Regarding prong one under step 2A, Applicant respectfully submits that the claims do not recite a mathematical concept. When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), the examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. M.P.E.P. § 2106.04(a)(2). A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. For example, a limitation that is merely based on or involves a mathematical concept described in the specification may not be sufficient to fall into this grouping, provided the mathematical concept itself is not recited in the claim. (M.P.E.P. § 2106.04(a)(2).
In the instant case, the claims do not recite a numerical formula or equation that would be considered as falling within the "mathematical concepts" grouping claims. The claims do not recite a mathematical relationship between variables or numbers expressed in words or using mathematical symbols. Further, the claims do not recite a formula or equation in written text format. The claims also do not recite a mathematical calculation. Accordingly, the claims do not appear to recite a mathematical concept.
(see Response filed 12/26/2025 [pages 8-9]).
With respect to applicant's argument, the examiner respectfully disagrees that “the claims do not appear to recite a mathematical concept.”
The claims do recite a mathematical concept.
In MPEP 2106.04(a)(2)(I)(A): “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.” MPEP 2106.04(a)(2)(I)(C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” (emphasis added). It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).”
The present claims explicitly recite “objective function,” or “user-selected objective function” which is well-known mathematic expression used in linear programming to optimize to find the optimum solution for a given problem. https://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/objective-function. The instant specification explicitly discloses that at least one type of “objective function” can be expressed as mathematical formular. The limitations of “determining, with an emission turning model, one of a plurality of sets of fuel cell operating conditions based on the set of real-time flight conditions and the user-selected objective function,” when given its with broadest reasonable interpretation (BRI) in light of specification, can be considered to recite mathematical concepts. Please refer to the detailed analysis under Step 2A, Prong One provided in the current office action for the complete reasoning. Therefore, the limitation is direct to a “mathematical concept”, similar to the comparison steps in MPEP §2106.04(a)(2)(I).
Therefore, claim 1 is direct to abstract idea, and rejection under 35 U.S.C. § 101 Step 2A, prong one is maintained.
With respect to the Applicant’s argued rejection under 35 U.S.C 101, Step 2A, Prong two in “Applicant Arguments/Remarks Made in an Amendment,”
Applicant argues:
Regarding prong two, assuming arguendo that claim 1 does recite subject matter directed to a judicial exception (i.e., an abstract idea) as determined under prong one of Step 2A, Applicant submits that claim 1 is not "directed to" a judicial exception as the alleged judicial exception is integrated into a practical application with meaningful limitations. Independent claim 1 recites elements that clearly recite improvements to propulsion technology, namely improvements to controlling emissions of a propulsion system based on a set of fuel cell operating conditions based on the set of real-time flight conditions and a user-selected objective function such that a fuel cell assembly operating parameter is used to maintain the temperature of the combustor within an emissions temperature range to reduce emissions in a combustor exhaust of the propulsion system and meet the thrust demand of the propulsion system. The aforementioned elements recite an improvement to controlling emissions in combustor exhaust of a propulsion system.
Applicant respectfully submits that any alleged abstract idea is integrated into a practical application that amounts to significantly more than any alleged abstract idea, and provides a direct technical improvement for controlling combustor exhaust emissions of a propulsion system.
For at least these reasons, Applicant respectfully submits that independent claim 1 is directed to eligible subject matter under 35 U.S.C. § 101. Accordingly, Applicant respectfully requests withdrawal of the rejection of independent claim 1 and its dependent claims.
Independent claim 13 "generating and training an emissions tuning model for real time control of fuel cell assembly using one or more of a neural network model, a machine learning model, a kernel based model, a fuzzy logic, or a deep learning model, based on a selected set of the plurality of emulated flight conditions, the selected set of the plurality of the fuel cell operating conditions, and the determined value for the one or more terms of the objective function." As such, Applicant also respectfully submits that any alleged abstract idea in claim 13 is integrated into a practical application that amounts to significantly more than any alleged abstract idea, and provides a direct technical improvement for controlling a fuel assembly of a propulsion system . Applicant respectfully requests withdrawal of the rejection of independent claim 13 and all claims depending therefrom.
(see Response filed 12/26/2025 [pages 9-10]).
With respect to applicant's argument, the examiner respectfully disagrees that “independent claim 1 is directed to eligible subject matter under 35 U.S.C. § 101.”
In particular, the claim 1 recites the following additional element: "A controller, comprising: a memory and one or more processors, the memory storing instructions that when executed by the one or more processors cause the controller to perform operations including:,” which is mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea with the broad reasonable interpretation, which does not integrate a judicial exception into practical application. See MPEP § 2106.05(f)).
Further, the claim 1 and claim 13 recite the following additional elements: “receiving a set of real-time flight conditions with respect to a propulsion system, the set of real-time flight conditions comprising an emission output, wherein the emission output comprises a temperature of a combustor of the propulsion system; receiving a user-selected objective function, wherein the user-selected objective function is one of a plurality of objective functions, and wherein the user-selected objective function comprises a thrust demand of the propulsion system” and “inputting into a simulator of a propulsion system: one of a plurality of sets of emulated flight conditions with respect to the propulsion system; and one of a plurality of sets of fuel cell operating conditions for a fuel cell assembly of the propulsion system,” are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving data), which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)).
Further, the claim 1 and claim 13 recite the following additional elements: “…, with emissions tuning model, …” and “… by the simulator …,” which is mere adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computing component to perform prediction and data processing function to identifying one of a plurality of sets of fuel cell operating conditions and a value for one or more terms of a plurality of objective functions at high level of generality is simply the act of instructing a computer to perform generic functions, which is merely an instruction to apply a computer to the judicial exception and does not integrate judicial exception into practical application. see MPEP 2106.05(f). it is also merely generally linking the use of the judicial exception to a particular technological environment or field of use. Therefore, limiting an abstract idea to a generic machine learning model and/or simulator does not integrate the exception into a practical application. See MPEP § 2106.05(h).
Further, the claim 1 recites the following additional elements: “controlling a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor according to the determined one of the first set of fuel cell operating conditions or the second set of fuel cell operating conditions to move the emission output into or maintain the emission output within an emissions range to contribute to or meet the thrust demand,” which is merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Under its Broadest reasonable interpretation, this step merely uses a generic controller/computing component based on the previously determined fuel cell operating conditions to adjust operating parameters of a physical system. The additional limitations do not recite any particular technological improvement to the fuel cell assembly, combustor, controller architecture, or control technique, but instead generically uses the results of the determination to perform a control action to achieve a desired outcome (i.e., maintaining emissions within arrange and meeting trust demand) do not integrate judicial exception into practical application. (see MPEP 2106.05(f)).
Additionally, the controlling step is merely a recitation of insignificant extra-solution activity such as post solution (Insignificant application) because it occurs after the abstract determination of operating conditions and simply implements the result by issuing control adjustments. Below are examples of activities that the courts have found to be insignificant extra-solution activity: i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. Therefore, adding a final step of control a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor to a process that only recites determining one of a plurality of sets of fuel cell operating conditions (mental process and mathematical concepts) does not add a meaningful limitation to the process of determining the conditions. Accordingly, the limitation does not integrate the exception into a practical application. See MPEP § 2106.05(g).
The alleged improvement to propulsion technology is not persuasive because the asserted benefits (e.g., improved emissions performance and/or meeting thrust demand) is achieved through the abstract idea of determination of operating conditions using optimization logic, rather than from a technological modification to the propulsion system or its control mechanism. The subsequent control merely applies the determined values to adjust fuel cell assembly parameters. As explained in MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."
Further, the claim 13 recites following additional elements: “generating and training … using one or more of a neural network model, a machine learning model, a kernel based model, a fuzzy logic, or a deep learning model,” which is merely generically links the judicial exception to a particular technological environment or field of use. Therefore, limiting an abstract idea to a machine learning model does not integrate the exception into a practical application. See MPEP § 2106.05(h).
Regarding step 2B, claims 1 and 13: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory,…
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016); iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014); v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); and vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
The additional limitations “with emissions tuning model” and “by the simulator” and “controlling a fuel cell assembly of the propulsion system …” and “generating and training an emissions tuning model …,” do not provide significantly more than the judicial exception. The steps of recited predicting, processing, controlling, and generating steps merely describe generic computer functions that are well-understood, routine, and conventional in the art.
The claim limitations do not recite any specific improvement to how a machine learning algorithm functions, to the operation of computer or controller, or to the technology of emissions control. Instead, the limitations are recited at a high level of generality and are directed to applying known mathematical modeling techniques to input data to obtain an intended result. The limitation " maintain emission output within emissions range and meet thrust demand " is a desirable goal, it is merely the intended result after adjusting parameter in a particular environment, not an improvement to technology field.
Furthermore, as explained by the Supreme Court: in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". (Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014). See also Yu v. Apple Inc.: 1 F.4th 1040 (Fed. Cir. 2021)).
Furthermore, merely applying generic modeling or predictive techniques to a new data environment (e.g., objective function and flight conditions) without disclosing improvement to the techniques does not qualify as significantly more. See Recentive Analytics, Inc. v. Fox Corp., 23-2437 (Fed. Cir. Apr. 18, 2025) (we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101). Emphasis added.
References have found related to controlling steps of claim 1. For Example, Chandler (US20120275899A1) discloses as a consequence, the control of fuel conditions, distribution and injection into the combustion zones has become a critical operating parameter and requires frequent adjustment, … Tuning issues are any situation whereby any operational parameters of a system are in excess of acceptable limits. Examples include emissions excursion outside of allowable limits, combustor dynamics excursion outside of allowable limits, or any other tuning event requiring adjustment of a turbine's operational control elements (see Chandler, Background). Tomlinson (US20040076218A1) discloses the firing temperature is a parameter that is traditionally used to limit the power output of gas turbines in industrial or electric utility service … The emission performance of a gas turbine combustion system operating with premixed fuel and air is sensitive to its operating temperature. If combustion temperature exceeds the rated combustion temperature, then oxides of nitrogen (NOx) emissions will increase. If the actual temperature is lower than the rated temperatures, the carbon monoxide (CO) emissions will increase … The combustion reference temperature is employed for optimum sequencing of the combustion system and, thus, reliable operation and effective control of NOx and CO emissions in the turbine exhaust gas. For gas turbine control purposes, gas turbine firing temperature and combustion′ reference temperature have been conventionally determined using algorithms … Operating off the rated firing and combustion temperatures adversely affects the range of operation of the gas turbine and the exhaust emission levels (see Tomlinson, Background). It was well-known to control operating parameters that affect combustor/combustion temperature to keep emission within acceptable limits while achieving power(thrust) objectives. Therefore, the limitations amount to no more than instructing a generic computer to perform conventional operations, which is insufficient to qualify as “significantly more” under Step 2B.
Therefore, claims 1 and 13 do not integrated judicial exception into a practical application and do not amount to significantly more. Accordingly, independent claims 1 and 13 are directed to patent ineligible subject matter under 35 U.S.C. § 101.
Applicant’s arguments with respect to claim(s) 1-11, 13-16 and 18-20 have been considered but are
moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The newly applied reference Haltiner US20030235732A1 teaches the newly amended limitations of claim 1 “the emission output comprises a temperature of a combustor of the propulsion system; determining one of the plurality of sets of fuel cell operating conditions comprises: determining a first set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has fallen below a lower limit of a temperature range; or determining a second set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has exceeded an upper limit of the temperature range, and controlling a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor according to the determined one of the first set of fuel cell operating conditions or the second set of fuel cell operating conditions to move the emission output into or maintain the emission output within an emissions range.” Therefore, the combination of Rajashekara US20150367950A1 in view of “Conceptual Design of a Hybrid Gas Turbine - Solid Oxide Fuel Cell System for Civil Aviation” by Papagianni, published in 2019 and “Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects” by Zhang, published in 2021 and Haltiner US20030235732A1 teach or suggest the amended limitations of claim 1, and the rejection of claims 1-11 under 35 U.S.C. §103 is maintained.
The combination of Zhang (“Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects,” 2021) in view of Papagianni (“Conceptual Design of a Hybrid Gas Turbine - Solid Oxide Fuel Cell System for Civil Aviation,” 2019) and Rajashekara US20150367950A1 teach or suggest the newly amended limitations of claim 13, and the rejection of claims 13-16 and 18-20 under 35 U.S.C. §103 is maintained.
Claim Interpretation
Claims 1 and 13 recite “objective functions”. In the instant specification, [0140], “the objective
function 530 may include terms including performance (e.g., thrust demand, power off-take demand), emissions (e.g., an emission regulation limit), thrust-specific fuel consumption (TSFC), combinations thereof, and the like.” [0142], “The objective function 530 may include a combination of terms of performance and emissions. For example, the objective function 530 may include thrust demand with an emission regulation limit, thrust and power offtake demand with emission regulation limit, lowest TSFC with emission regulation limit, etc. Weighting coefficients may be used to prioritize the importance of each term, as well as the penalty for violating certain terms.” [0143], “As an example, the objective function 530 may include terms representing lowest TSFC and an emission regulation limit. Here, the objective function 530 may be formulated as a minimization problem. As an example, the objective function 530 may include terms representing lowest TSFC and an emission regulation limit. Here, the objective function 530 may be formulated as a minimization problem. The objective function 530 may be given as:
a*TSFC + b*(E - E-Limit)
where TSFC is thrust specific fuel consumption, E is an actual emission value (such as CO% and NOx%), E-Limit is a regulation limit (e.g., 118 gram per kilo-newton (g/kN) for CO). In this formulation, the objective function 530 includes a first term (TSFC) that represents the performance (e.g., thrust and fuel efficiency) and a second term (E - E-Limit) that represents emissions (e.g., the degree of violation of an emissions regulation).”
The instant specification discloses that an “objective function” may include terms representing performance and emissions and may be formulated as an optimization problem. Therefore, under broadest reasonable interpretation, the claimed “objective functions” encompass mathematical formulations and relationships used in optimization, as well as values used within the formulations.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-16 and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites “… based on the selected set of the plurality of emulated flight conditions and the selected set of the plurality of the fuel cell operating conditions …” There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites “… the plurality of objective functions comprising a thrust demand and an emissions of the propulsion system,” which renders the claim indefinite because it is unclear if the “an emissions of the propulsion system” refers to “one or more emissions of the propulsion system” or “an emission” or “a plurality of emissions of the propulsion system”. For the purpose of substantive examination, the examiner presumes that “an emissions of the propulsion system” is one or more emissions of the propulsion system.
The remaining claims are dependent upon the independent claim 13, and are rejected for the same reason.
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.
The claim(s) 1-11, 13-16 and 18-20 are rejected under 35 USC § 101 because the claimed invention
is directed to judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated
the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance
published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates,
and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-11 are directed to controller and fall within the statutory category of machine;
Yes, Claims 13-16 and 18-20 are directed to method and fall within the statutory category of process.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
The limitation of claim 1: “determining, …, one of a plurality of sets of fuel cell operating conditions based on the set of real-time flight conditions and the user-selected objective function, wherein determining one of the plurality of sets of fuel cell operating conditions comprises:
determining a first set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has fallen below a lower limit of a temperature range; or
determining a second set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has exceeded an upper limit of the temperature range,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing available sets of fuel cell operating conditions, evaluating real-flight condition and a desired objective/goal, and determining whether a temperature condition is approaching or has crossed a threshold, and then mentally identifying/selecting an appropriated set of fuel cell operating conditions based on the evaluated real-flight condition and objective. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
The limitation of claim 13: “determining, …, a value for one or more terms of a plurality of objective functions based on the selected set of the plurality of emulated flight conditions and the selected set of the plurality of the fuel cell operating conditions, the plurality of objective functions comprising a thrust demand and an emissions of the propulsion system,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing and evaluating emulated flight conditions and corresponding candidate fuel cell operating conditions, considering objective criteria such as thrust demand and emissions, and then mentally determining/estimating a value for one or more objective function terms based on the evaluated emulated flight conditions and corresponding candidate fuel cell operating conditions. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
If a claim limitation, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea under step 2A, Prong One (See MPEP 2106.04(a)(2)(III)).
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
As explained in MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea).
MPEP 2106.04(a)(2)(I)(A): A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1: The limitations of “determining, with an emission turning model, one of a plurality of sets of fuel cell operating conditions based on the set of real-time flight conditions and the user-selected objective function,” when given its with broadest reasonable interpretation (BRI) in light of specification, can be considered to recite mathematical concepts, for example, [0142] The objective function 530 may include a combination of terms of performance and emissions. For example, the objective function 530 may include thrust demand with an emission regulation limit, thrust and power offtake demand with emission regulation limit, lowest TSFC with emission regulation limit, etc. Weighting coefficients may be used to prioritize the importance of each term, as well as the penalty for violating certain terms. [0143], “As an example, the objective function 530 may include terms representing lowest TSFC and an emission regulation limit. Here, the objective function 530 may be formulated as a minimization problem. The objective function 530 may be given as:
a*TSFC + b*(E - E-Limit)
where TSFC is thrust specific fuel consumption, E is an actual emission value (such as CO% and NOx%), E-Limit is a regulation limit (e.g., 118 gram per kilo-newton (g/kN) for CO). In this formulation, the objective function 530 includes a first term (TSFC) that represents the performance (e.g., thrust and fuel efficiency) and a second term (E - E-Limit) that represents emissions (e.g., the degree of violation of an emissions regulation).” [0164] The data of the table of Fig. 9 may be used to train a tuning model used in real-time control. The tuning model may include a neural network model, a machine learning model, a kernel based model, a fuzzy logic, a deep learning model, combinations thereof, and the like.” [0165] As used herein, the term "machine learning model" refers to one or more mathematical models configured to find patterns in data and apply the determined pattern to new data sets to form a prediction. [0169] A machine learning model is understood as meaning any variety of mathematical model having at least one non-linear operation (e.g., a non-linear activation layer in the case of a neural network). A machine learning model is trained or optimized via minimization of one or more loss functions (e.g., minimization of cross entropy loss or negative log-likelihood) that are separate from the model itself. [0171] …, the emissions tuning model 540 may be trained using each row of the data of the table of Fig. 9, with the emulated flight conditions 522 and the objective functions 530 as the model input, and the fuel cell operating conditions 524 … as the output.
The claim limitation includes mathematical aspects based on the disclosures of the instant specification. In particular, the specification explains that the objective function may include combinations of terms representing performance and emissions with weighting relationships, and that the objective function may be formulated as a minimization problem and indicated using mathematical formulas ([0142]–[0143]). The specification further explains that the emissions tuning model is a mathematically trained model configured to generate operating condition outputs from input data ([0164] – [0165], [0171]). Therefore, the claimed step of determining one of the plurality of sets of fuel cell operating conditions based on input data reasonably includes evaluating mathematical expressions and generating outputs using mathematical relationships. Examiner note: the claim limitation does not exclude mathematical formulations (e.g., it does not specify that the objective function or tuning model must be implemented using anything other than mathematical expressions), and the specification describes both the objective function and the tuning model in mathematical terms.
Accordingly, the claim limitation is reasonably interpreted under BRI as reciting mathematical concepts, including mathematical relationships, mathematical formulas or equations, and mathematical calculations used to determine operating conditions based on the input data. See MPEP 2106.04(a)(2)(I).
Claim 13: The limitations of “determining, by the simulator, a value for one or more terms of a plurality of objective functions based on the selected set of the plurality of emulated flight conditions and the selected set of the plurality of the fuel cell operating conditions, the plurality of objective functions comprising a thrust demand and an emissions of the propulsion system,” when given its with broadest reasonable interpretation (BRI) in light of the specification can be considered to recite mathematical concepts, for example, [0142] The objective function 530 may include a combination of terms of performance and emissions. For example, the objective function 530 may include thrust demand with an emission regulation limit, thrust and power offtake demand with emission regulation limit, lowest TSFC with emission regulation limit, etc. Weighting coefficients may be used to prioritize the importance of each term, as well as the penalty for violating certain terms. [0143] As an example, the objective function 530 may include terms representing lowest TSFC and an emission regulation limit. Here, the objective function 530 may be formulated as a minimization problem. The objective function 530 may be given as:
a*TSFC + b*(E - E-Limit)
where TSFC is thrust specific fuel consumption, E is an actual emission value (such as CO% and NOx%), E-Limit is a regulation limit (e.g., 118 gram per kilo-newton (g/kN) for CO). In this formulation, the objective function 530 includes a first term (TSFC) that represents the performance (e.g., thrust and fuel efficiency) and a second term (E - E-Limit) that represents emissions (e.g., the degree of violation of an emissions regulation).
The claim limitation includes mathematical aspects based on the disclosures of the instant specification. In particular, the specification explains that the objective function may include combinations of terms representing performance and emissions, such as thrust demand with an emission regulation limit, and that weighting coefficients may be used to prioritize the importance of each term ([0142]). The specification further explains that the objective function may be formulated as a minimization problem and indicated using mathematical formulas, including weighted relationships between variables ([0143]). Therefore, the claimed step of determining a value for one or more terms of a plurality of objective functions based on input data reasonably includes evaluating mathematical expressions involving variables representing thrust demand and emissions. Examiner note: The claim limitation does not exclude mathematical formulations (e.g., it does not specify that thrust demand or emissions must be evaluated using anything other than mathematical expressions), and the specification clearly describes objective functions including thrust demand and emissions terms that may be represented using mathematical relationships and weighting coefficients.
Accordingly, the claimed limitation is reasonably interpreted under BRI as reciting mathematical concepts, including mathematical relationships, mathematical formulas/equations, and mathematical calculations used to determine objective function values based on the input data. See MPEP 2160.04(a)(2)(I).
Claim 13: The limitations of “generating and training an emissions tuning model for real time control of the fuel cell assembly using one or more of a neural network model, a machine learning model, a kernel based model, a fuzzy logic, or a deep learning model, based on the selected set of the plurality of emulated flight conditions, the selected set of the plurality of the fuel cell operating conditions, and the determined value for the one or more terms of the objective function,” when given its with broadest reasonable interpretation (BRI) in light of the specification can be considered to recite mathematical concepts, for example, [0164] The data of the table of Fig. 9 may be used to train a tuning model used in real-time control. The tuning model may include a neural network model, a machine learning model, a kernel based model, a fuzzy logic, a deep learning model, combinations thereof, and the like. [0165] As used herein, the term "machine learning model" refers to one or more mathematical models configured to find patterns in data and apply the determined pattern to new data sets to form a prediction. [0169] A machine learning model is understood as meaning any variety of mathematical model having at least one non-linear operation (e.g., a non-linear activation layer in the case of a neural network). A machine learning model is trained or optimized via minimization of one or more loss functions (e.g., minimization of cross entropy loss or negative log-likelihood) that are separate from the model itself.
The claim limitation includes mathematical aspects based on the disclosures of the instant specification. In particular, the specification explains that a machine learning model refers to one or more mathematical models configured to find patterns in data and apply the determined pattern to new data sets to form a prediction ([0165]), and further explains that a machine learning model is understood as meaning any variety of mathematical model having at least one non-linear operation and the emission model (i.e., machine learning model) is trained or optimized via minimization of one or more loss functions ([0169]).
Accordingly, the claimed limitation is reasonably interpreted under BRI as reciting mathematical concepts, including mathematical relationships, mathematical formulas/equations, and mathematical calculations associated with training and generating a predictive model based on input data. See MPEP 2160.04(a)(2)(I).
Therefore, claims 1 and 13 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1 and 13: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements: "A controller, comprising: a memory and one or more processors, the memory storing instructions that when executed by the one or more processors cause the controller to perform operations including:,” which is mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea with the broad reasonable interpretation, which does not integrate a judicial exception into practical application. See MPEP § 2106.05(f)).
Further, the following additional elements: “receiving a set of real-time flight conditions with respect to a propulsion system, the set of real-time flight conditions comprising an emission output, wherein the emission output comprises a temperature of a combustor of the propulsion system; receiving a user-selected objective function, wherein the user-selected objective function is one of a plurality of objective functions, and wherein the user-selected objective function comprises a thrust demand of the propulsion system” and “inputting into a simulator of a propulsion system: one of a plurality of sets of emulated flight conditions with respect to the propulsion system; and one of a plurality of sets of fuel cell operating conditions for a fuel cell assembly of the propulsion system,” are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving data), which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)).
Further, the following additional elements: “…, with emissions tuning model, …” and “… by the simulator …,” which is mere adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computing component to perform prediction and data processing function to identifying one of a plurality of sets of fuel cell operating conditions and a value for one or more terms of a plurality of objective functions at high level of generality is simply the act of instructing a computer to perform generic functions, which is merely an instruction to apply a computer to the judicial exception and does not integrate judicial exception into practical application. see MPEP 2106.05(f). it is also merely generally linking the use of the judicial exception to a particular technological environment or field of use. Therefore, limiting an abstract idea to a generic machine learning model and/or simulator does not integrate the exception into a practical application. See MPEP § 2106.05(h).
Further, the following additional elements: “controlling a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor according to the determined one of the first set of fuel cell operating conditions or the second set of fuel cell operating conditions to move the emission output into or maintain the emission output within an emissions range to contribute to or meet the thrust demand,” which is merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Under its Broadest reasonable interpretation, this step merely uses a generic controller/computing component based on the previously determined fuel cell operating conditions to adjust operating parameters of a physical system. The additional limitation do not recite any particular technological improvement to the fuel cell assembly, combustor, controller architecture, or control technique, but instead generically uses the results of the determination to perform a control action to achieve a desired outcome (i.e., maintaining emissions within arrange and meeting trust demand) do not integrate judicial exception into practical application. (see MPEP 2106.05(f)).
Additionally, the controlling step is merely a recitation of insignificant extra-solution activity such as post solution (Insignificant application) because it occurs after the abstract determination of operating conditions and simply implements the result by issuing control adjustments. Below are examples of activities that the courts have found to be insignificant extra-solution activity: i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. Therefore, adding a final step of control a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor to a process that only recites determining one of a plurality of sets of fuel cell operating conditions (mental process and mathematical concepts) does not add a meaningful limitation to the process of determining the conditions. Accordingly, the limitation does not integrate the exception into a practical application. See MPEP § 2106.05(g).
Examiner note: The additional limitation do not specify how the controller, the combustor, or the fuel cell assembly is technologically improved to achieve the result. The controller is recited at a high level of generality, performing the conventional function of adjusting parameters based on operation conditions. No specific algorithm, new architecture, or unconventional control configuration is disclosed in the claim. The fuel cell assembly and combustor are also used in their conventional function, the claim limitation does not describe any structural modification or different configuration. The recited improvement (maintain emission output within emissions range and meet thrust demand) is an intended result of using known systems for their expected purpose.
Further, the following additional elements: “generating and training … using one or more of a neural network model, a machine learning model, a kernel based model, a fuzzy logic, or a deep learning model,” which is merely generically links the judicial exception to a particular technological environment or field of use. Therefore, limiting an abstract idea to a machine learning model does not integrate the exception into a practical application. See MPEP § 2106.05(h).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1 and 13 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1 and 13: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory,…
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016); iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014); v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); and vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
The limitations “with emissions tuning model” and “by the simulator” and “controlling a fuel cell assembly of the propulsion system …” and “generating and training an emissions tuning model …,” do not provide significantly more than the judicial exception. The steps of recited predicting, processing, controlling, and generating steps merely describe generic computer functions that are well-understood, routine, and conventional in the art.
The claim limitations do not recite any specific improvement to how a machine learning algorithm functions, to the operation of computer or controller, or to the technology of emissions control. Instead, the limitations are recited at a high level of generality and are directed to applying known mathematical modeling techniques to input data to obtain an intended result. The limitation " maintain emission output within emissions range and meet thrust demand " is a desirable goal, it is merely the intended result after adjusting parameter in a particular environment, not an improvement to technology field.
As explained by the Supreme Court: in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". (Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014). See also Yu v. Apple Inc.: 1 F.4th 1040 (Fed. Cir. 2021)).
Furthermore, merely applying generic modeling or predictive techniques to a new data environment (e.g., objective function and flight conditions) without disclosing improvement to the techniques does not qualify as significantly more. See Recentive Analytics, Inc. v. Fox Corp., 23-2437 (Fed. Cir. Apr. 18, 2025) (we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101). Emphasis added.
For Example, Chandler (US20120275899A1) discloses as a consequence, the control of fuel conditions, distribution and injection into the combustion zones has become a critical operating parameter and requires frequent adjustment, … Tuning issues are any situation whereby any operational parameters of a system are in excess of acceptable limits. Examples include emissions excursion outside of allowable limits, combustor dynamics excursion outside of allowable limits, or any other tuning event requiring adjustment of a turbine's operational control elements (see Chandler, Background). Tomlinson (US20040076218A1) discloses the firing temperature is a parameter that is traditionally used to limit the power output of gas turbines in industrial or electric utility service … The emission performance of a gas turbine combustion system operating with premixed fuel and air is sensitive to its operating temperature. If combustion temperature exceeds the rated combustion temperature, then oxides of nitrogen (NOx) emissions will increase. If the actual temperature is lower than the rated temperatures, the carbon monoxide (CO) emissions will increase … The combustion reference temperature is employed for optimum sequencing of the combustion system and, thus, reliable operation and effective control of NOx and CO emissions in the turbine exhaust gas. For gas turbine control purposes, gas turbine firing temperature and combustion′ reference temperature have been conventionally determined using algorithms … Operating off the rated firing and combustion temperatures adversely affects the range of operation of the gas turbine and the exhaust emission levels (see Tomlinson, Background). It was well-known to control operating parameters that affect combustor/combustion temperature to keep emission within acceptable limits while achieving power(thrust) objectives. Therefore, the limitations amount to no more than instructing a generic computer to perform conventional operations, which is insufficient to qualify as “significantly more” under Step 2B.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1 and 13 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-11, 14-16 and 18-20 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-12, 14-16 and 18-20 are also rejected for incorporating the deficiency of their independent claims 1 and 13.
Claim 2 recites “each of the plurality of sets of fuel cell operating conditions is associated with one of a plurality of sets of emulated flight conditions and one of the plurality of objective functions.” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind. A person, for example, is capable of observing and evaluating emulated flight conditions and a desired objective/goal, and identify a set of fuel cell operating conditions associated with one selected emulated flight condition and the desired objective/goal (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “the plurality of sets of emulated flight conditions include at least one of historical values and modeled values.”
This merely further defines the emulated flight conditions referring to claim 2, and therefore it merely an extension of the mental process. Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “the emissions tuning model is a model that is trained on the plurality of sets of fuel cell operating conditions, the plurality of sets of emulated flight conditions, and the plurality of objective functions.”
This merely specifies the emissions tuning model can be trained by defined data, and therefore it is a mathematical concept (see instant specification [164]-[0165] and [0169[. Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “the tuning model is one of a neural network model, a machine learning model, a kernel based model, a fuzzy logic, and a deep learning model.”
This merely further defines tuning model, and therefore it is amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (e.g., machine learning model, neural network model …etc.), as discussed in MPEP § 2106.05(h). Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “each of the plurality of sets of fuel cell operating conditions is determined to provide, along with one of the plurality of sets of emulated flight conditions, a preferred value for one of the plurality of objective functions.” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind. A person, for example, is capable of observing each set of fuel cell operating conditions, then define an optimized parameter based on the determined fuel cell operating conditions combine with the selected one emulated flight condition for a desired objective/goal (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011), and a mathematical concept ([0142]-[0143]). Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7 recites “the plurality of sets of fuel cell operating conditions are determined offline.”
This merely specifies the determination for the fuel cell operating condition offline, and therefore it is mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computer component to perform a generic offline process at high level of generality is simply the act of instructing a computer to perform generic functions, which is merely an instruction to apply a computer component to the judicial exception, which does not integrate judicial exception into practical application or significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8 recites “the user-selected objective function includes a first term corresponding to the thrust demand.”
This merely further defines the received user-selected objective function referring to claim 1, and therefore it merely a recitation of insignificant extra-solution data gathering (i.e., receiving data) activity, which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)). Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “the user-selected objective function includes a second term corresponding to emissions.”
This merely further defines the received user-selected objective function referring to claim 1, and therefore it merely a recitation of insignificant extra-solution data gathering (i.e., receiving data) activity, which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)). Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claim 10 recites “at least one of the first term and the second term are weighted.”
This merely further defines the received user-selected objective function referring to claims 8 and 9, and therefore it merely a recitation of insignificant extra-solution data gathering (i.e., receiving data) activity, which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)). Therefore, the office finds that the claim 10 is ineligible under 35 USC 101.
Claim 11 recites “the one of a plurality of sets of fuel cell operating conditions corresponds to at least one of a temperature of a fuel cell stack, a hydrogen conversion rate, a fuel utilization, a current drawn from the fuel cell stack, an exhaust gas temperature from the fuel cell stack, and a location in an axial direction for injecting output products from the fuel cell stack to the combustor.”
This merely further defines one of a plurality of sets of fuel cell operating conditions referring to claim 1, and therefore it merely an extension of mental process. Therefore, the office finds that the claim 11 is ineligible under 35 USC 101.
Claim 14-16 and 18-20 recite the similar elements as claims 1-3, 7, 9 and 11, and are rejected for the same reasons under 35 U.S.C. 101. Furthermore, claim 14 is substantially similar to claim 1 with broader limitations. Therefore, the analysis set forth with respect to claim 1 applies equally to claim 14. Claim 19 is substantially similar to claim 9, except that the correspondence of the first term and the second term with respect to thrust demand and emissions is reversed. Therefore, the analysis set forth with respect to claim 9 applies equally to claim 19.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and
103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set
forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Rajashekara
US20150367950A1 in view of “Conceptual Design of a Hybrid Gas Turbine - Solid Oxide Fuel Cell System for Civil Aviation” by Papagianni, published in 2019 and “Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects” by Zhang, published in 2021 and Haltiner US20030235732A1.
Claim 1 (Currently amended), Rajashekara teaches A controller, comprising:
a memory and one or more processors, the memory storing instructions that when executed by the one or more processors cause the controller to perform operations ([0055], “ The controller 500 includes at least one processor 510 (e.g. a microprocessor, microcontroller, digital signal processor, etc.), memory 512, and an input/output (I/O) subsystem 514.” [0059], “Embodiments may also be implemented as instructions stored using one or more machine-readable media, which may be read and executed by one or more processors … a machine-readable medium may include any suitable form of volatile or non-volatile memory.”) including:
receiving a set of real-time flight conditions with respect to a propulsion system, ([0033], “the engine controller 144 and/or the controllers 140, 142, 146, 148 receive electrical signals from a number of different sensors 162, which are installed at various locations on the engine 110 and/or other mechanical components of the system 100, to sense various physical parameters, such as temperature (T), air pressure (P), torque (T), pitch angle (γ), rotational speed (w), electrical current (i), and voltage (v), which represent various aspects of the current operating condition of the system 100.” [0043], “A desired thrust 316 represents the currently desired thrust, as determined from, e.g., sensor input or another control algorithm, during operation of the system 100.” [0003], “Gas turbine engines typically include a compressor, a combustor, and a turbine.” Examiner note: A POSITA would understand that sensors installed at various locations on the engine measure temperatures of engine components, including combustor temperature, because the propulsion system includes a combustor and the sensed temperature parameter represents operating combustion of the propulsion system);
receiving a user-selected objective function, wherein the user-selected objective function is one of a plurality of objective functions, and wherein the user-selected objective function comprises a thrust demand of the propulsion system ([0043], “Referring to FIG. 3, an embodiment 300 of the optimizer 150 is shown. A desired optimization objective is embodied as a maximum efficiency parameter 314 (e.g., the objective of the optimization is to maximize the fuel efficiency of the system 100). A desired thrust 316 represents the currently desired thrust, as determined from, e.g., sensor input or another control algorithm, during operation of the system 100 … the algorithm 312 computes optimal values … based on the maximum efficiency 314 and the desired thrust 316, using, for example, a nonlinear optimal control method.” [0042], “… the optimizer 150 can integrate multiple control effectors pursuant to the desired optimization objective (e.g., maximum efficiency).” [0053], “As an example hypothetical, suppose the optimization objective is efficient propulsion. Suppose further that together, motor efficiency and propulsor (e.g., fan) efficiency provide optimal thrust. Further, suppose that motor efficiency is as function of the motor speed, and the propulsor efficiency is a function of the pitch angle and the motor speed …” [0022], “The optimizer 150 is configured to optimize one or more optimization parameters (e.g., efficiency, performance, reliability, etc.) of the system 100 … to achieve a desired objective, …” Examiner note: the reference discloses an optimization framework operating according to desired optimization objectives ([0022]) and further discloses that optimal operating values are computed based on both a maximum efficiency parameter and a desired thrust parameter ([0043]). Because the desired thrust parameter is selected as an input to the optimization algorithm to determine operating parameters of the propulsion system, the desired thrust corresponds to an objective function comprising a thrust demand of the propulsion system);
determining, with an emissions tuning model, one of a plurality of sets of (0046], “The inputs to the optimization subsystems as depicted in FIG. 4 are the aircraft-required temperature (Treq), the ambient temperature (Tamb), the ambient air pressure (Pamb), the motor torque (Tm), the motor speed (ωm), the generator torque (Tg), the generator speed (ωg), the fan pitch angle (γ), the electrical current (i), and the voltage (v). These inputs may be obtained from, e.g., the sensors 162, the engine controller 144, and/or other components of the aircraft 410.” [0047], “The optimization subsystems 422, 426, 430, 434, 156 use these inputs, as well as their respective local model 424, 428, 432, 436, 438 (examiner note: i.e., emissions tuning model), to determine the optimum value for each set point, to optimize the engine 110 and system 100 for efficiency …” See also [0022], [0033], [0042]-[0043] and [0053] for real-time flight conditions and the user-selected objective function), wherein
controlling ([0032], “The engine controller 144 may control the overall operation of the engine 110. For example, the engine controller 144 may control the rate of fuel flow to the combustion section 118, as well as the airflow through the engine 110 (e.g., by varying the pitch angle of vanes of the fan(s) 112).” [0047], “The optimization subsystems 422, 426, 430, 434, 156 use these inputs, as well as their respective local model 424, 428, 432, 436, 438, to determine the optimum value for each set point, to optimize the engine 110 and system 100 for efficiency.” [0022], “the optimizer 150 can, for example, help improve the thrust specific fuel consumption (SFC) of the engine 110 and/or reduce the engine 110's overall emissions.” [0043], “A desired thrust 316 represents the currently desired thrust, as determined from, e.g., sensor input or another control algorithm, during operation of the system 100 … the algorithm 312 computes optimal values … based on the maximum efficiency 314 and the desired thrust 316, using, for example, a nonlinear optimal control method.” Examiner note: A POSITA would understand that controlling the rate of fuel flow and airflow to the combustion section directly results in increasing or decreasing combustor temperature, since combustion temperature is determined by the fuel-air mixture and operating condition. Further, combustor temperature directly influence emission characteristics and thrust performance in propulsion systems. Therefore, applying determined operating setpoints to control combustor parameters in order to optimize emissions and meet desired thrust corresponds to increasing or decreasing combustor temperature to move emission output into or maintain emission output within an emissions range to contribute to or meet thrust demand).
However, Rajashekara fails to teaches fuel cell operating conditions and a fuel cell assembly of the propulsion system.
Papagianni teaches fuel cell operating conditions and a fuel cell assembly of the propulsion system (Page.1, Abstract, “A conceptual design of a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system is presented … A configuration is designed, where a SOFC and the burner is modeled as one and simulated, …” Page.5, 2.3 SOFC’s operation simulation, “The computational model also simulates the operation of a pressurized SOFC according to the flight’s conditions … The ideal fuel utilization is chosen when the current density is between 0.8 and 0.85 A/cm2 and the voltage also between 0.8 and 0.85 Volt.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara to incorporate the teachings of Papagianni, and apply a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) propulsion configuration, including fuel cell operating conditions and a fuel cell assembly in order to implement the known propulsion system control and optimization framework of Rajashekara in a propulsion architecture that includes fuel cell components. In this case, Rajashekara teaches controlling propulsion system operating parameters, including adjust fuel flow and airflow to achieve desired thrust and emissions performances. Papagianni teaches a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) propulsion system in which the SOFC and associated components operate according to defined fuel cell operating parameters (e.g., current density, voltage, and utilization ranges) under flight conditions. The combinations of teachings would predictably provide benefit of increasing flexibility and emissions management for different propulsion architectures.
However, Rajashekara and Papagianni fail to teach move the emission output into or maintain the emission output within an emissions range.
Zhang teaches move the emission output into or maintain the emission output within an emissions range (page.2643, Table II, Design net thrust demand. Page.2645, “… to minimize the objective function, such as fuel consumption, energy consumption, and emissions of CO2 and NOx in flight while maintaining operating limits, e.g., gas turbine structural and temperature limits.” Examiner note: A POSITA would understand that optimization to minimize emission while maintaining operating limits necessarily involves adjusting controllable operating parameters so that emission output is driven toward desire values and maintained within permissible ranges defined by system constraints. Maintaining operating limits corresponds to maintain parameters, including related emission outputs influenced by combustor temperature, which an acceptable range.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni to incorporate the teachings of Zhang, and apply a nonlinear model predictive control based optimal energy management scheme in order to minimize the objective function (e.g., fuel consumption, energy consumption, and emissions of CO2 and NOx in flight) while maintaining structural and temperature limits. In this case, Rajashekara teaches a hybrid turbo electric aero-propulsion system with real-time control of combustor conditions, including adjusting fuel flow and airflow to increase or decrease combustor temperature. Papagianni teaches a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) propulsion system in which the SOFC and associated components operate according to defined fuel cell operating parameters (e.g., current density, voltage, and utilization ranges) under flight conditions. Zhang teaches minimizing emissions of CO2 and NOx in flight while maintaining operating limits, including gas turbine structural and temperature limits. The combination of teachings would provide the benefit of improving propulsion system efficiency, reduced emission, and compliance with operating limits.
However, Rajashekara and Papagianni and Zhang fail to teach the emission output comprises a temperature of a combustor of the propulsion system;
determining one of the plurality of sets of fuel cell operating conditions comprises:
determining a first set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has fallen below a lower limit of a temperature range; or
determining a second set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has exceeded an upper limit of the temperature range, and
controlling a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor according to the determined one of the first set of fuel cell operating conditions or the second set of fuel cell operating conditions to move the emission output into or maintain the emission output within an emissions range.
Haltiner teaches the emission output comprises a temperature of a combustor of the propulsion system ([0011], “In a control means for the fuel cell system, temperature is monitored in the combustor.” [0010], “Because the combustibles content of the tail gas can vary … the combustion temperature can also vary … If the mixture in the combustor is relatively rich in fuel, as may happen during start-up, the combustion temperature can be high enough to generate undesirable oxides of nitrogen and/or damage the combustor components.” Examiner note: A POSITA would understand that the monitored combustor temperature is an emissions related output signal used by the control means because the reference discloses combustor operation to reduce system emissions and monitoring temperature in the combustor as part of the control);
determining one of the plurality of sets of fuel cell operating conditions comprises:
determining a first set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has fallen below a lower limit of a temperature range; or
determining a second set of fuel cell operating conditions in response to determining that the temperature of the combustor is approaching or has exceeded an upper limit of the temperature range ([0001], “the combustor temperature is controlled within a predetermined range by adjusting the ratio of the mixture: supplying less spent cathode air to make the mixture richer and thus increase the combustor temperature or supplying more fresh air to make the mixture leaner and thus decrease the combustor temperature.” [0011], “… When the temperature becomes unacceptably low, a control valve in the spent cathode air return is adjusted by the control means to divert a portion of the air around the combustor, thus increasing the enriching the fuel/air mixture going through the combustor. When the temperature becomes unacceptably high, a control valve in the combustor fresh air supply is adjusted by the control means to provide more air, thus causing the mixture to become leaner.” Examiner note: A POSITA would understand that the alternative temperature based control responses correspond to different sets of operating conditions selected/determined based on the sensed combustor temperature condition because the reference teaches controlling combustor temperature within a predetermined range and discloses a first response when temperature becomes unacceptably low and a second response when temperature becomes unacceptably high, and “approaching” a limit includes detecting that temperature is trending toward the unacceptable low or unacceptable high condition that triggers the corresponding response); and
controlling a fuel cell assembly of the propulsion system via a fuel cell assembly operating parameter to increase or decrease the temperature of the combustor according to the determined one of the first set of fuel cell operating conditions or the second set of fuel cell operating conditions ([0001] the combustor temperature is controlled within a predetermined range by adjusting the ratio of the mixture: supplying less spent cathode air to make the mixture richer and thus increase the combustor temperature or supplying more fresh air to make the mixture leaner and thus decrease the combustor temperature. [0009] Briefly described, in a solid-oxide fuel cell system having a fuel cell stack assembly for combining oxygen from air with hydrogen and carbon monoxide in a reformed fuel, the fuel cell tail gas contains significant residual amounts of combustibles. A combustor burns the tail gas in the presence of spent cathode air to reduce system emissions … [0011] When the temperature becomes unacceptably low, a control valve in the spent cathode air return is adjusted by the control means to divert a portion of the air around the combustor, thus increasing the enriching the fuel/air mixture going through the combustor. When the temperature becomes unacceptably high, a control valve in the combustor fresh air supply is adjusted by the control means to provide more air, thus causing the mixture to become leaner. Examiner note: A POSITA would understand that adjusting mixture ratio, airflow, and associated control valves constitutes controlling the fuel cell assembly via fuel cell assembly operating parameters, and the alternative control responses for low and high temperature conditions corresponds to selecting a set of operating conditions based on the determined temperature condition).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni and Zhang to incorporate the teachings of Haltiner, and apply temperature based monitoring and control strategies for a fuel cell integrated combustor system in order to provide an additional control approach for regulating combustor operating conditions in hybrid propulsion environments where temperature variation directly affect emissions performance and system stability; the combination of teachings would provide the benefit of improving combustor control flexibility, enhancing efficiency and reducing emissions while satisfying propulsion operating constraints.
Claim 2, Rajashekara fails to teach, but Papagianni teaches The controller of claim 1, each of the plurality of sets of fuel cell operating conditions (page.3., “To solve this problem an innovative hybrid system of a Gas Turbine and a Solid Oxide Fuel Cell (SOFC) is proposed for aviation. The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Page.5, “For the flight’s simulation a computational model was created based on the thermodynamic analysis of the engine’s operation…” Equation 6: produced thrust. Page.6, 2.5.1, “Step change in TET, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara to incorporate the teachings of Papagianni, and apply a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system, including fuel cell assembly operating conditions in order to improve the control of fuel cell operating parameters associated with the combustor.
However, Rajashekara and Papagianni fail to teach operating conditions is associated with one of a plurality of sets of emulated flight conditions and one of the plurality of objective functions.
Zhang teaches operating condition is associated with one of a plurality of sets of emulated flight conditions and one of the plurality of objective functions (Page.2641, “The mission segments are discretized by an energy-based approach with a mission explicitly defined for each step, which involves a large number of iterations between the propulsion system performance module and the aircraft performance module based on a multidisciplinary analysis of aerodynamics, propulsion system performance, and weight [17].” Page.2647, right column, “Table IV lists the flight conditions and thrust of representative steady-state operating points: takeoff, top of climb (TOC), and cruise, to assess engine performance and investigate gas turbine–electrical powertrain …” Page.2642, right column, “In this study, motivated by the above issues, a nonlinear MPC-based EMS using cross-entropy method (CEM) for hybrid electric aircraft is proposed to achieve an optimal solution for power distribution considering the following competing objectives: 1) to prevent battery excessive discharge; 2) to satisfy aircraft maximum takeoff weight (MTOW) constraints; 3) to achieve energy balance; and 4) to minimize the objective function, such as fuel consumption, energy consumption, emissions of carbon emissions [carbon dioxide (CO2)], and nitrogen oxide (NOx ) in flight while maintaining operating limits, e.g., gas turbine structural and temperature limits.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni to incorporate the teachings of Zhang, and apply a nonlinear model predictive control based optimal energy management scheme in order to optimizing control of fuel cell operating conditions under emulated flight conditions while meeting multiple competing performance and emissions objectives.
Claim 3, Rajashekara and Papagianni fail to teach, but Zhang teaches The controller of claim 2, wherein the plurality of sets of emulated flight conditions include at least one of historical values and modeled values (Page.2641, “The mission segments are discretized by an energy-based approach with a mission explicitly defined for each step, which involves a large number of iterations between the propulsion system performance module and the aircraft performance module based on a multidisciplinary analysis of aerodynamics, propulsion system performance, and weight [17].” Page.2647, right column, “Table IV lists the flight conditions and thrust of representative steady-state operating points: takeoff, top of climb (TOC), and cruise, to assess engine performance and investigate gas turbine–electrical powertrain …” - Examiner note: modeled values (iterations between performance models, multidisciplinary analysis) and historical/representative values (steady-state operating points such as takeoff, TOC, and cruise)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni to incorporate the teachings of Zhang, and apply a nonlinear model predictive control based optimal energy management scheme including modeled values and historical values in order to optimizing control of fuel cell operating conditions under emulated flight conditions while meeting multiple competing performance and emissions objectives.
Claim 4, Rajashekara fails to teach, but and Papagianni teaches The controller of claim 2, wherein (page.3., “To solve this problem an innovative hybrid system of a Gas Turbine and a Solid Oxide Fuel Cell (SOFC) is proposed for aviation. The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Page.5, “For the flight’s simulation a computational model was created based on the thermodynamic analysis of the engine’s operation…” Equation 6: produced thrust. Page.6, 2.5.1, “Step change in TET, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara to incorporate the teachings of Papagianni, and apply a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system, including fuel cell assembly operating conditions in order to improve the control of fuel cell operating parameters associated with the combustor.
However, Rajashekara and Papagianni fail to teach the emissions tuning model is a model that is trained on the plurality of sets of operating conditions, the plurality of sets of emulated flight conditions, and the plurality of objective functions.
Zhang teaches the emissions tuning model is a model that is trained on the plurality of sets of operating conditions, the plurality of sets of emulated flight conditions, and the plurality of objective functions (page.2642, right column, “In this study, motivated by the above issues, a nonlinear MPC-based EMS using cross-entropy method (CEM) for hybrid electric aircraft is proposed to achieve an optimal solution for power distribution considering the following competing objectives: 1) to prevent battery excessive discharge; 2) to satisfy aircraft maximum takeoff weight (MTOW) constraints; 3) to achieve energy balance; and 4) to minimize the objective function, such as fuel consumption, energy consumption, emissions of carbon emissions [carbon dioxide (CO2)], and nitrogen oxide (NOx ) in flight while maintaining operating limits, e.g., gas turbine structural and temperature limits.” Page.2641, “The mission segments are discretized by an energy-based approach with a mission explicitly defined for each step, which involves a large number of iterations between the propulsion system performance module and the aircraft performance module based on a multidisciplinary analysis of aerodynamics, propulsion system performance, and weight [17].” Examiner note: the nonlinear MPC-based EMS is trained on multiple sets of operating conditions (fuel consumption, emissions, energy usage) with emulated flight conditions (mission segments, steady-state operating points), while optimizing for a plurality of objective functions (fuel efficiency, emission reduction, structural /temperature limits)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni to incorporate the teachings of Zhang, and apply a nonlinear MPC-based EMS is trained on multiple sets of operating conditions (e.g., fuel consumption, emissions, energy usage) with emulated flight conditions (e.g., mission segments, steady-state operating points), while optimizing for a plurality of objective functions (e.g., fuel efficiency, emission reduction, structural /temperature limits) in order to optimizing control of fuel cell operating conditions while meeting multiple competing performance and emissions objectives.
Claim 5, Rajashekara and Papagianni fail to teach, but Zhang teaches The controller of claim 4, wherein the emissions tuning model is one of a neural network model, a machine learning model, a kernel based model, a fuzzy logic, and a deep learning model (page.2640, Abstract, “First, the artificial neural network (ANN) model is adopted to predict turbofan engine performance; meanwhile, gas turbine–electrical powertrain integration is investigated and analyzed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear MPC with the cross-entropy method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni to incorporate the teachings of Zhang, and apply neural network/machine learning model in order to improve prediction accuracy of turbofan engine and optimization of combustor operating conditions.
Claim 6, Rajashekara fails to teach, but and Papagianni teaches The controller of claim 2, wherein each of the plurality of sets of fuel cell operating conditions (page.3., “To solve this problem an innovative hybrid system of a Gas Turbine and a Solid Oxide Fuel Cell (SOFC) is proposed for aviation. The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Page.5, “For the flight’s simulation a computational model was created based on the thermodynamic analysis of the engine’s operation…” Equation 6: produced thrust. Page.6, 2.5.1, “Step change in TET, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara to incorporate the teachings of Papagianni, and apply a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system, including fuel cell assembly operating conditions in order to improve the control of fuel cell operating parameters associated with the combustor.
However, Rajashekara and Papagianni fails to teach each of the plurality of sets of operating conditions is determined to provide, along with one of the plurality of sets of emulated flight conditions, a preferred value for one of the plurality of objective functions.
Zhang teaches each of the plurality of sets of operating conditions is determined to provide, along with one of the plurality of sets of emulated flight conditions, a preferred value for one of the plurality of objective functions (Page.2641, “The mission segments are discretized by an energy-based approach with a mission explicitly defined for each step, which involves a large number of iterations between the propulsion system performance module and the aircraft performance module based on a multidisciplinary analysis of aerodynamics, propulsion system performance, and weight [17].” – Examiner note: determination of sets of operating conditions by iterating mission segments and system modules. Page.2647, right column, “Table IV lists the flight conditions and thrust of representative steady-state operating points: takeoff, top of climb (TOC), and cruise, to assess engine performance and investigate gas turbine–electrical powertrain …” – Examiner note: i.e., sets of emulated flight conditions. Page.2642, right column, “In this study, motivated by the above issues, a nonlinear MPC-based EMS using cross-entropy method (CEM) for hybrid electric aircraft is proposed to achieve an optimal solution for power distribution considering the following competing objectives: 1) to prevent battery excessive discharge; 2) to satisfy aircraft maximum takeoff weight (MTOW) constraints; 3) to achieve energy balance; and 4) to minimize the objective function, such as fuel consumption, energy consumption, emissions of carbon emissions [carbon dioxide (CO2)], and nitrogen oxide (NOx ) in flight while maintaining operating limits, e.g., gas turbine structural and temperature limits.” – Examiner note: A POSITA would understand that minimizing the objective function (e.g., fuel consumption, emissions, energy usage) corresponds to providing a preferred value for one of the plurality of objective functions).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara and Papagianni to incorporate the teachings of Zhang, and apply operating conditions is determined to provide, along with one of the plurality of sets of emulated flight conditions, a preferred value for one of the plurality of objective functions in order to optimizing control of fuel cell operating conditions while meeting multiple competing performance and emissions objectives.
Claim 7, Rajashekara fails to teach, but and Papagianni teaches The controller of claim 6, wherein the plurality of sets of fuel cell operating conditions are determined offline (page.3., “To solve this problem an innovative hybrid system of a Gas Turbine and a Solid Oxide Fuel Cell (SOFC) is proposed for aviation. The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Page.5, “For the flight’s simulation a computational model was created based on the thermodynamic analysis of the engine’s operation…” Equation 6: produced thrust. Page.6, 2.5.1, “Step change in TET, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.” Examiner note: A POSITA would understand that the simulation used to establish turbine entry temperature and thrust stability can be applied to the SOCF subsystem, corresponds to determining fuel cell operation conditions offline.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara to incorporate the teachings of Papagianni, and apply a simulation model for fuel cell operating conditions in order to improve the control of combustor operating parameters, and allowing sets of fuel cell operating conditions to be determined offline and improve stability of emissions control in hybrid propulsion systems.
Claim 8, Rajashekara teaches The controller of claim 1, wherein the user-selected objective function includes a first term corresponding to the thrust demand ([0043], “Referring to FIG. 3, an embodiment 300 of the optimizer 150 is shown. A desired optimization objective is embodied as a maximum efficiency parameter 314 (e.g., the objective of the optimization is to maximize the fuel efficiency of the system 100). A desired thrust 316 represents the currently desired thrust, as determined from, e.g., sensor input or another control algorithm, during operation of the system 100 … the algorithm 312 computes optimal values … based on the maximum efficiency 314 and the desired thrust 316, using, for example, a nonlinear optimal control method.”).
Claim 9, Rajashekara teaches The controller of claim 8, wherein the user-selected objective function includes a second term corresponding to the emissions ([0022], “… the optimizer 150 can, for example, help improve the thrust specific fuel consumption (SFC) of the engine 110 and/or reduce the engine 110's overall emissions.” [0047], “The optimization subsystems 422, 426, 430, 434, 156 use these inputs, as well as their respective local model 424, 428, 432, 436, 438, to determine the optimum value for each set point, to optimize the engine 110 and system 100 for efficiency.”).
Claim 10, Rajashekara teaches The controller of claim 9, wherein at least one of the first term and the second term are weighted ([0043], “the algorithm 312 computes optimal values for stored energy 318, total load 320, and engine speed 322, based on the maximum efficiency 314 and the desired thrust 316, using, for example, a nonlinear optimal control method.” [0022], “the optimizer 150 can, for example, help improve the thrust specific fuel consumption (SFC) of the engine 110 and/or reduce the engine 110's overall emissions.” Examiner note: A POSITA would understand that optimizing for both emissions and thrust demand requires assigning relative importance (i.e., weighting)).
Claim 11, Rajashekara fails to teach, but Papagianni teaches The controller of claim 1, wherein the one of the plurality of sets of fuel cell operating conditions corresponds to at least one of a temperature of a fuel cell stack, a hydrogen conversion rate, a fuel utilization, a current drawn from the fuel cell stack, an exhaust gas temperature from the fuel cell stack, and a location in an axial direction for injecting output products from the fuel cell stack to the combustor (Abstract, “The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Examiner note: The SOFC generates electricity during operation as current drawn from the fuel cell stack).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rajashekara to incorporate the teachings of Papagianni, and apply SOFC as fuel cell assembly capable of generating electricity to provide current drawn from the fuel cell stack as one of the operating parameters for controlling the propulsion system in order to improve control of combustor operating parameters by utilizing fuel cell data to optimize performance and reduce emissions.
Claim(s) 13-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over “Nonlinear
Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects” by Zhang, published in 2021 in view of “Conceptual Design of a Hybrid Gas Turbine - Solid Oxide Fuel Cell System for Civil Aviation” by Papagianni, published in 2019 and Rajashekara US20150367950A1 .
Claim 13, Zhang teaches A method, comprising:
inputting into a simulator of a propulsion system (Page.2643, A point-Mass Aircraft Model of P-HEA, “A point-mass aircraft model is used in this study for a fast-time aircraft motion simulation environment, …” Page.2645, A. Problem Formulation of Energy Management Strategy, “A nonlinear model of P-HEA with the MIPH propulsion configuration is established to estimate and predict flight dynamics, gas turbine, and electrical powertrain behaviors.” Examiner note: the reference teaches a simulation environment (“fast-time aircraft motion simulation environment”) and a modeled propulsion configuration (“P-HEA with the MIPH propulsion configuration …) corresponds to a simulator of a propulsion system under BRI):
one of a plurality of sets of emulated flight conditions with respect to the propulsion system (Fig.6; Page.2645, “For each segment, the flight altitude h and the Mach number Ma are given with constant velocity constant climb/descent rate in the flight mission profile, as shown in Fig. 6.” Page.2647, right column, “Table IV lists the flight conditions and thrust of representative steady-state operating points: takeoff, top of climb (TOC), and cruise, to assess engine performance …” Examiner note: The reference teaches identify flight conditions (e.g., Mach number, altitude; and Table IV’s operating points), and state that fight conditions are “given” for segments/operating points used in the modeled propulsion system assessment (i.e., flight condition data provided to the simulation workflow)); and
one of a plurality of sets of (page.2645, “The discrete-segment state space is described as follows: x(k + 1) = φ(x(k), u(k)), k ∈ N, where x = [h, Ma, W, D, T, SOC, δ]T is the state vector … and φ is a nonlinear function that assigns the successor state x(k + 1) given x(k)and the control input u(k). u = [hp] is the control input that represents hybridization of power on the turbofan low-power turbine. The constraints of aircraft MTOW constraints and battery SOC operating range are W ≤ WMTOW and SOCmin ≤ SOC ≤ SOCmax.” Examiner note: the reference teaches operating/control conditions for the propulsion configuration (e.g., hybridization control input and SOC constraints/operating range). Under BRI, operating/control conditions as operational parameters/constraints applied to the modeled propulsion architecture is interpreted as operating conditions for assembly of the propulsion system. Therefore, the same dynamic model/simulator advances state using x(k)(includes flight altitude and Mach number as emulated flight conditions) and u(k) (hybridization control as operating conditions) shows both inputs feed the same simulator);
determining, by the simulator, a value for one or more terms of a plurality of objective functions based on the selected set of the plurality of emulated flight conditions and the selected set of the plurality of the (Page.2645-2646, “Given a prior flight mission, the dynamic process of P-HEA … can be described as a flight-segment-related model … Assuming a generalized propulsion system with multiple thrust sources M, block fuel burn … and energy consumption … in the kth flight segment can be described as follows: … Based on the NOx emission index, the emissions in the kth flight segment can be described as follows: … (18) … Based on emission indexes, the emissions in the kth flight segment can be described as follows: … (19) … Therefore, the cost function in the kth segment is defined as the weighted sum of optimization objectives in the following equation: … (20) … are the weight coefficients for performance indexes of block fuel burn, energy consumption, CO2 emissions, and NOx emissions, …” Examiner note: A POSITA would understand that the propulsion system simulation described in the reference determines numerical values for performance metrics including emissions, fuel consumption, and energy consumption for each flight segment, which constitute values of objective function terms. Further, the simulator dynamics are defined as functions of both the system state variables (e.g., fight conditions) and control inputs (operating conditions), such that the determined values are based on the selected emulated flight conditions and selected operating conditions); and
generating and training an emissions tuning model for real time control of(Page.2640, Abstract, “artificial neural network (ANN) model is adopted to predict turbofan engine performance …” Page.2643, B. Modeling of MIPH Propulsion System, “Considering highly nonlinear characteristics of gas turbine operation, the ANN is employed with the backpropagation algorithm to predict turbofan engine performance with high accuracy and computational efficiency … According to the gas turbine subsystem states G = [h, Ma, FN ]T …The input and output parameters of the proposed ANN framework are summarized in Table II.” Page.2644, “Once the ANN framework and neuron transfer functions are determined, weight matrixes and biases are trained and adjusted by the collected dataset of input–output pairs, until the training error Em meets the terminal condition as follows: … the target output vector that consists of Pontake, power on-take for the low-pressure turbine (LPT), ˙W f is the gas turbine fuel flow rate, T4 is the turbine entry temperature, T3 and P3 are burn inlet temperature and pressure, and NCN is the nondimensional rotational speed …” Page.2645, A. Problem Formulation of Energy Management Strategy, “A. Problem Formulation of Energy Management Strategy. At the next sampling segment, only the first control action in the optimal control sequence will be taken, and the optimal control problem will be solved successively by receding horizon optimization.” Examiner note: the reference teaches the ANN model is trained using datasets generated from a propulsion system model under different flight states (e.g., altitude and Mach number) and control inputs u(k) representing propulsion operating conditions, together with corresponding performance outputs such as fuel flow, temperature and related emission parameters. A POSITA would understand that these datasets correspond to selected flight conditions, selected operating conditions, and determined performance values produced by the simulator, and that the trained model is used within the optimization framework to support propulsion system control during operation).
However, Zhang fails to teach fuel cell operating conditions and fuel cell assembly.
Papagianni teaches fuel cell operating conditions and fuel cell assembly (Page.1, Abstract, “A conceptual design of a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system is presented … A configuration is designed, where a SOFC and the burner is modeled as one and simulated, …” Page.5, 2.3 SOFC’s operation simulation, “The computational model also simulates the operation of a pressurized SOFC according to the flight’s conditions … The ideal fuel utilization is chosen when the current density is between 0.8 and 0.85 A/cm2 and the voltage also between 0.8 and 0.85 Volt.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Papagianni, and apply a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) propulsion configuration, including fuel cell operating conditions and a fuel cell assembly in order to extend the propulsion system modeling and optimization framework of Zhang to hybrid propulsion architectures that include fuel cell components. In this case, Zhang teaches a propulsion system simulation and optimization framework using nonlinear model predictive control with multiple operating conditions and emulated flight conditions to determine objective function values related to propulsion system performance, including emissions and energy consumption, and generating and training models based on the conditions for control optimization. Papagianni teaches a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) propulsion system in which the SOFC and associated components are modeled and simulated under flight conditions using fuel cell operating parameters (e.g., current density, voltage, and utilization ranges). The combinations of teachings would predictably provide benefit of improving emission prediction and control performance by including fuel cell operating characteristics within the propulsion system model.
However, Zhang and Papagianni fail to teach objective functions comprising a thrust demand.
Rajashekara teaches objective functions comprising a thrust demand ([0043], “Referring to FIG. 3, an embodiment 300 of the optimizer 150 is shown. A desired optimization objective is embodied as a maximum efficiency parameter 314 (e.g., the objective of the optimization is to maximize the fuel efficiency of the system 100). A desired thrust 316 represents the currently desired thrust, as determined from, e.g., sensor input or another control algorithm, during operation of the system 100 … the algorithm 312 computes optimal values … based on the maximum efficiency 314 and the desired thrust 316, using, for example, a nonlinear optimal control method.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang and Papagianni to incorporate the teachings of Rajashekara, and apply an optimization objective including a thrust demand parameter in order to improve propulsion system optimization by using thrust together with emission and efficiency metrics. In this case, Zhang teaches a propulsion system simulation and optimization framework using nonlinear model predictive control with multiple operating conditions and emulated flight conditions to determine objective function values related to propulsion system performance, including emissions and energy consumption, and generating and training models based on the conditions for control optimization. Papagianni teaches a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) propulsion system in which the SOFC and associated components are modeled and simulated under flight conditions using fuel cell operating parameters (e.g., current density, voltage, and utilization ranges). Rajashekara teaches an optimization framework in which desired thrust is included as an optimization parameter used by an algorithm to compute optimal values for system control. The combinations of teachings would predictably provide benefit of improving propulsion system control accuracy and optimization performance by incorporating thrust demand objective together with emissions and operating condition parameters.
Claim 14, Zhang teaches receiving a set of (page.2645, Section A, “the
nonlinear MPC-EMS is constructed as shown in Fig. 5. The purpose of the EMS is to achieve an optimal solution … For each segment, the flight altitude h and the Mach number Ma are given … The flight segment duration δ is calculated based on the prior flight mission profile …”);
receiving a
one of the plurality of objective functions (page.2645, Section A, “The purpose of the EMS is … minimize the objective function, such as fuel consumption, energy consumption, and emissions
of CO2 and NOx …”);
selecting, with the emissions tuning model, one of the plurality of sets of (Page.2645, Section A, “the nonlinear MPC-EMS is constructed … to achieve an optimal solution for power distribution considering the following competing objectives: … 4) to minimize the objective function, such as fuel consumption, energy consumption, and emissions of CO2 and NOx in flight while maintaining operating limits …” Page.2642, “an ANN gas turbine surrogate model is established, … is obtained by analyzing the pressure-build, corrected core/bypass mass flow, and component efficiency at typical operating conditions. Next, a nonlinear MPC-based EMS using CEM and backward induction algorithm is proposed based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model.”); and
However, Zhang fails to teach receiving a set of real-time flight conditions; receiving a user-selected objective function; fuel cell operating conditions; and controlling a fuel cell assembly operating parameter according to the selected one of the plurality of sets of fuel cell operating conditions.
Papagianni teaches fuel cell operating conditions (page.5, “The computational model also
simulates the operation of a pressurized SOFC according to the flight’s conditions.” See section 2.3, “… current density and voltage are been accounted for every fuel utilization…”); and controlling a fuel cell assembly operating parameter according to the selected one of the plurality of sets of fuel cell operating conditions (Page.6, section 2.5.1, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.” Examiner note: The turbine entry temperature is considered the fuel cell assembly operating parameter, and the step change is applied that maintain thrust and stability indicates control according to selected one of the sets of operating conditions, since the adjustment corresponds to operating conditions (e.g., thrust output, stability) being maintain when the parameter is varied).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Papagianni, and apply fuel cell operating conditions, and control the fuel cell assembly operating parameter according to the selected one of the plurality of sets of fuel cell operating conditions in order to achieve more accurate regulation and stable performance of the hybrid propulsion system, improving efficiency and reducing emissions across a range of conditions.
However, Zhang and Papagianni fail to teach real-time flight conditions and the user-selected objective function.
Rajashekara teaches real-time flight conditions and the user-selected objective function ([0033], “the engine controller 144 and/or the controllers 140, 142, 146, 148 receive electrical signals from a number of different sensors 162, which are installed at various locations on the engine 110 and/or other mechanical components of the system 100, to sense various physical parameters, such as temperature (T), air pressure (P), torque (T), pitch angle (γ), rotational speed (w), electrical current (i), and voltage (v), which represent various aspects of the current operating condition of the system 100.” [0043], “Referring to FIG. 3, an embodiment 300 of the optimizer 150 is shown. A desired optimization objective is embodied as a maximum efficiency parameter 314 (e.g., the objective of the optimization is to maximize the fuel efficiency of the system 100).” [0042], “… the optimizer 150 can integrate multiple control effectors pursuant to the desired optimization objective (e.g., maximum efficiency).” [0053], “As an example hypothetical, suppose the optimization objective is efficient propulsion. Suppose further that together, motor efficiency and propulsor (e.g., fan) efficiency provide optimal thrust. Further, suppose that motor efficiency is as function of the motor speed, and the propulsor efficiency is a function of the pitch angle and the motor speed …” Examiner note: A POSITA would understand that the system requires a selection among multiple optimization objectives (e.g., maximizing fuel efficiency, efficient propulsion, thrust optimization), and the selection would necessarily be made by a user or operator depending on mission needs or desired performance outcomes).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang and Papagianni to incorporate the teachings of Rajashekara, and apply real-flight conditions and user-selected objective function in order to allowing online optimization of propulsion system to perform optimization dynamically during flight, thereby improving adaptability , efficiency and emissions control under actual operating conditions.
Claim 15, Zhang teaches The method of claim 13, wherein each of the plurality of sets of fuel cell operating conditions is associated with one of the plurality of sets of emulated flight conditions and one of the plurality of objective functions (Page.2641, “The mission segments are discretized by an energy-based approach with a mission explicitly defined for each step, which involves a large number of iterations between the propulsion system performance module and the aircraft performance module based on a multidisciplinary analysis of aerodynamics, propulsion system performance, and weight [17].” Page.2647, right column, “Table IV lists the flight conditions and thrust of representative steady-state operating points: takeoff, top of climb (TOC), and cruise, to assess engine performance and investigate gas turbine–electrical powertrain …” Page.2642, right column, “In this study, motivated by the above issues, a nonlinear MPC-based EMS using cross-entropy method (CEM) for hybrid electric aircraft is proposed to achieve an optimal solution for power distribution considering the following competing objectives: 1) to prevent battery excessive discharge; 2) to satisfy aircraft maximum takeoff weight (MTOW) constraints; 3) to achieve energy balance; and 4) to minimize the objective function, such as fuel consumption, energy consumption, emissions of carbon emissions [carbon dioxide (CO2)], and nitrogen oxide (NOx ) in flight while maintaining operating limits, e.g., gas turbine structural and temperature limits.”).
However, Zhang fails to teach fuel cell operating conditions.
Papagianni teaches fuel cell operating conditions (page.3., “To solve this problem an innovative hybrid system of a Gas Turbine and a Solid Oxide Fuel Cell (SOFC) is proposed for aviation. The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Page.5, “For the flight’s simulation a computational model was created based on the thermodynamic analysis of the engine’s operation…” Equation 6: produced thrust. Page.6, 2.5.1, “Step change in TET, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.” see section 2.3, “… current density and voltage are been accounted for every fuel utilization…”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Papagianni, and apply a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system, including fuel cell assembly operating conditions in order to improve the control of fuel cell operating parameters associated with the combustor.
Claim 16, Zhang teaches The method of claim 13, wherein the plurality of sets of emulated flight conditions include at least one of historical values and modeled values (Page.2641, “The mission segments are discretized by an energy-based approach with a mission explicitly defined for each step, which involves a large number of iterations between the propulsion system performance module and the aircraft performance module based on a multidisciplinary analysis of aerodynamics, propulsion system performance, and weight [17].” Page.2647, right column, “Table IV lists the flight conditions and thrust of representative steady-state operating points: takeoff, top of climb (TOC), and cruise, to assess engine performance and investigate gas turbine–electrical powertrain …” examiner note: modeled values (iterations between performance models, multidisciplinary analysis) and historical/representative values (steady-state operating points such as takeoff, TOC, and cruise)).
Claim 18, Zhang fails to teach, but Papagianni teaches The method of claim 13, wherein the plurality of sets of fuel cell operating conditions are determined offline (page.3., “To solve this problem an innovative hybrid system of a Gas Turbine and a Solid Oxide Fuel Cell (SOFC) is proposed for aviation. The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Page.5, “For the flight’s simulation a computational model was created based on the thermodynamic analysis of the engine’s operation…” Equation 6: produced thrust. Page.6, 2.5.1, “Step change in TET, “The step change imposed in the Turbine Entry Temperature (TET) of the model shows that each time the net thrust’s output and the model’s stability is not significantly altered.” Examiner note: A POSITA would understand that the simulation used to establish turbine entry temperature and thrust stability can be applied to the SOCF subsystem, corresponds to determining fuel cell operation conditions offline.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Papagianni, and apply a simulation model for fuel cell operating conditions in order to improve the control of combustor operating parameters, and allowing sets of fuel cell operating conditions to be determined offline and improve stability of emissions control in hybrid propulsion systems.
Claim 19, Zhang teaches The method of claim 13, wherein the one of a plurality of objective functions includes a first term corresponding to emissions (page.2645, left column, “… to minimize the objective function, such as fuel consumption, energy consumption, and emissions of CO2 and NOx in flight while maintaining operating limits, e.g., gas turbine structural and temperature limits.”).
Claim 20, Zhang fails to teach, but Papagianni teaches The method of claim 13, wherein the one of the plurality of sets of fuel cell operating conditions corresponds to at least one of a temperature of a fuel cell stack, a hydrogen conversion rate, a fuel utilization, a current drawn from the fuel cell stack, an exhaust gas temperature from the fuel cell stack, and a location in an axial direction for injecting output products from the fuel cell stack to the combustor (Abstract, “The SOFC can be used before the gas turbine’s burner, reforming hydrocarbon to hydrogen and at the same time generating electricity.” Examiner note: The SOFC generates electricity during operation as current drawn from the fuel cell stack).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Papagianni, and apply SOFC as fuel cell assembly capable of generating electricity to provide current drawn from the fuel cell stack as one of the operating parameters for controlling the propulsion system in order to improve control of combustor operating parameters by utilizing fuel cell data to optimize performance and reduce emissions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chandler US 20130219906 A1, discloses an automated system to sense the operating condition of a combustion system and to make preset adjustments to achieve desired operation of the turbine.
THIS ACTION IS MADE FINAL. 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.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187