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 .
Status of the Application
2. Claim 1-20 have been examined in this application. This communication is the first action on the merits.
Drawings
3. The drawings filed on 9/29/23 are acceptable for examination proceedings.
Claim Rejections - 35 USC § 112
4. 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.
5. The term “may be” in claim 6 and 18 is lead to indefiniteness because it fails to provide a person skilled in the art with "reasonable certainty" about the invention's scope.
6. Claim 5 recites the limitation " the feedforward NN",
Claim 5, and 17 recites the limitation “the power system model”,
Claim 11 recites the limitation “the reduction of capacitance”. There is insufficient antecedent basis for this limitation in the claim.
Dependent claim 6-10 and 18-20 are also rejected under 35 U.S.C 112(b) due to their direct/indirect dependency over the claim 5, and 17.
Claim Rejections - 35 USC § 103
7. 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.
8. Claim 1-2, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Baig (Pub: 2021/0394916) in view of Wang (NPL: Model Predictive Control Using Artificial Neural Network for Power Converters, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 69, NO. 4, APRIL 2022).
9. Regarding claim 1, Baig teaches a method of performing power regulation in an aircraft power system, the method comprising: generating, for each of a plurality of power loads, a respective control inputs configured to regulate a power system (e.g., At block 304, power management controller 190 can receive an engine propulsion power demand for the aircraft engine 20. At block 306, the power management controller 190 can perform a model predictive control to dynamically adjust one or more electric power flows 120-124 through a bidirectional power converter 114 based on the engine propulsion power demand) (Para. [0048], also refer to Para. [0039]-[0040], Fig. 2, 5);
[training a neural network (NN) to learn different control inputs] for controlling power regulation for each of the plurality of power loads [and to mimic a model predictive control (MPC) to establish an NN-based MPC (NNMPC)] (e.g., A plurality of aircraft electrical subsystems 140 can be operably coupled to the bidirectional power converter 114, and the power management controller 190 can be configured to extract power from or provide power to the aircraft electrical subsystems 140. The aircraft electrical subsystems 140 can include one or more of an engine subsystem 144, an aircraft low-voltage DC subsystem 146, an aircraft high-voltage DC subsystem 148, and an aircraft AC subsystem 150. In embodiments, model predictive control can be based on one or more aircraft subsystem power demands of the aircraft electrical subsystems 140. The power management controller 190 can be operable to monitor a state of charge of the hybrid energy storage system 102, a power request of an engine subsystem control 154 of the engine subsystem 144, an aircraft subsystem control 154, and the one or more controllers 115, 116, 130 of the electric motor 110, the first generator 111, and the second generator 132) (Para. [0049]);
utilizing the [NN]MPC to obtain at least one [learned] control input for power regulation for a current system state and power load measurement in real-time (e.g., In embodiments, model predictive control can be based on one or more aircraft subsystem power demands of the aircraft electrical subsystems 140. The power management controller 190 can be operable to monitor a state of charge of the hybrid energy storage system 102, a power request of an engine subsystem control 154 of the engine subsystem 144, an aircraft subsystem control 154, and the one or more controllers 115, 116, 130 of the electric motor 110, the first generator 111, and the second generator 132) (Para. [0049]);
and performing an output action that regulates the power system based on the at least one [learned] control input obtained by the [NN]MPC (e.g., In the example of FIG. 4 with continued reference to FIGS. 1-3, a control system 200 of the power system 100 can include the power management controller 190 includes a model-based control, such as a model predictive control, operable to output one or more electric power flow control signals. Predictive power management can include dynamically reading status of each of the power source and predicting what loads will be required to proactively optimize power flows, such as closing contactors predictively to enable an optimal power flow with reduced delays. As an example, a delay between requesting power and delivering power can exist where contactors or other components have a switching or settling time delay. By predictively closing contactors or commanding other such state changes prior to a needed delivery of power, system responsiveness can be enhanced) (Para. [0040]).
Baig does not specifically teach training a neural network (NN) to learn different control inputs, and to mimic a model predictive control (MPC) to establish an NN-based MPC (NNMPC).
Wang disclose teach training a neural network (NN) to learn different control inputs, and to mimic a model predictive control (MPC) to establish an NN-based MPC (NNMPC) (e.g., This article introduces a general ANN-MPC approach to address the computational challenge in adopting MPC in highly complex power converters. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop (PHIL) simulation environment. An ANN is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The trained neural network is capable of producing good approximation and offering comparable control performance of the conventional MPC controller. What is more, the structure of ANN-MPC in this article is very intuitive and simple, making it suitable for controlling multicell power converters. The authors recently reported the ANN-MPC concept in a single-phase five-level (5L) FC converter in [21]. In this article, the general principle of ANN-MPC are more extensively discussed as a common solution for highly complex multilevel and/or multiphase or high-order power converters. Furthermore, the ANN-MPC can be used to approximate both FCS-MPC and CCS-MPC. This article theoretically analyzes the basic ANN-MPC concept, ANN structure, offline training method, and online operation of ANNMPC as a general method for various power converters. The concept of ANN-MPC is validated by the case studies in this article) (Page. 3690, Ln. 21-45).
Because Wang is also directed to ANN-MPC controlling approach for power electronic applications such as converters, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Baig and Wang before him/her, to modify the teachings of Baig to include the an artificial neural network (termed ANN-MPC) teaching of Wang in order to provide the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time (Wang: Abstract).
10. Regarding claim 2, the combination of Baig and Wang teaches the method of claim 1, wherein Baig further teaches the power system includes a direct current (DC) bus (e.g., The aircraft electrical subsystems 140 can include one or more of an engine subsystem 144, an aircraft low-voltage DC subsystem 146, an aircraft high-voltage DC subsystem 148, and an aircraft AC subsystem 150. The power management controller 190 is operable to monitor a state of charge of the hybrid energy storage system 102, a power request of an engine subsystem control 152 of the secondary power unit 134, an engine subsystem control 154 of the engine subsystem 144, an aircraft subsystem control 156, and the one or more controllers 115, 116, 130 of the electric motor 115, the first generator 111, and the second generator 132) (Para. [0035], Fig. 2-5), and wherein the power regulation includes regulating a DC voltage of the DC bus (e.g., In embodiments, the power management controller 190 can provide a means for controlling one or more electric power flows of the hybrid energy storage system 102 to/from the one or more electric motor 110, first generator 111, second generator 132, and aircraft electrical subsystems 140 based on a modeled electric power demand of an engine load of the aircraft engine that may be at a current time step or predicted at one or more future time steps, for example. The power management controller 190 (also referred to as controller 190) is operable to detect one or more conditions of the super/ultra-capacitor 104 and the battery system 106 and configure the one or more electric power flows between the hybrid energy storage system 102 and other elements of the power system 100) (Para. [0037]).
11. Regarding claim 12, Claim 12 recites a power system that implement the method of claim 1, with substantially the same limitations, respectively. Therefore the rejection applied to claim 1, also applies to claim 12 respectively.
Wherein Baig further teaches A power system comprising: at least one power source configured to output at least one type of electrical power (e.g., a first generator 111, and a second generator 132, where the hybrid energy storage system 102) (Para. [0048], Refer to Fig. 2);
at least one power converter in signal communication with the at least one power source, the at least one power converter configured to convert the at least one type of electrical power into a converted power (e.g., In the example of FIGS. 2 and 3, the hybrid energy storage system 102 is operably coupled to a bidirectional power converter 114 (e.g., a bidirectional DC-to-DC converter) which is operably coupled to a motor controller 115 (also referred to as power conditioning electronics 115) and a first generator controller 116 (also referred to as power conditioning electronics 116)) (Para. [0035], Fig. 2);
a power bus in signal communication with the at least one power converter to receive the converted power and deliver the converted power to a power load connected to the power bus (e.g., The super/ultra-capacitor 104 can be operatively coupled to the battery system 106 through a direct current (DC)-to-DC converter 108. The DC-to-DC converter 108 can convert a voltage level of the battery system 106 to match a voltage level of the super/ultra-capacitor 104 to support charging of the super/ultra-capacitor 104 by the battery system 106. In alternate embodiments, the DC-to-DC converter 108 can be omitted where regulation between the super/ultra-capacitor 104 and the battery system 106 is not needed) (Para. [0033], also refer to Para. [0036]).
12. Regarding claim 13, as to claim 13, applicant is directed to citation of claim 2 above.
Allowable Subject Matter
Claim 3, and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 4-11 and 15-20 are also objected due to their direct/indirect dependency over the claim 3, and 14, respectively and when 35 U.S.C 112(b) rejection is overcome for claim 5-11 and 17-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li (Pat: 11527955) disclose the use artificial neural networks to control DC/DC converters and integrate ANN control with droop mechanism for control of a standalone DC microgrid (Col. 2, Ln. 9-11).
Chen (NPL: IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 8, NO. 3) disclose A Backpropagation Neural Network-Based Explicit Model Predictive Control for DC–DC Converters With High Switching Frequency.
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/JIGNESHKUMAR C PATEL/ Primary Examiner, Art Unit 2116