DETAILED ACTION
Notice of 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 .
Claims 1-20 are pending and are rejected.
Priority
Foreign priority:
Acknowledgment is made of applicant’s claim for foreign priority to application no. DE10 2023 200 194.2 filled on 01/11/2023. The certified copy has been received.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/24/2025 and 04/24/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Drawings
Drawings filled on 01/08/2024 are acceptable for the examination purpose.
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 filling 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 5-6, 10, 12, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZEHETLEITNER et al. (US20200096013A1) [hereinafter ZEHETLEITNER] and further in view of Amari (US20200123927A1) [hereinafter Amari].
Regarding claim 1:
ZEHETLEITNER discloses, A method for the automated, measurement data-based configuration of an electronic controller for a hydraulic system, the method comprising the following steps: [¶5: an excitation signal is applied to the target speed and the resulting actual system pressure is measured. The dynamics of the hydraulic system is determined from the actual speed and/or the target speed and the actual system pressure, and the control parameters are also calculated from the determined dynamics];
defining measurement parameters of the hydraulic system, [¶5: to determine the dynamics of the hydraulic system from…the actual system pressure];
executing of a predefined measurement routine on the hydraulic system, [¶37: The hydraulic system 1 is excited by applying an excitation signal n1 to the target speed n_soll….
¶12: excitation signal applied to the initial speed is usually a wide-band signal. Particularly suitable is a combination of step-like excitation, harmonic signals with increasing frequency, and pulse-shaped signals. Therefore, a square wave signal, a harmonic signal, preferably with increasing frequency, pulses, or a mixed signal is advantageously used as the excitation signal.];
automatically acquiring measurement data of the measurement parameters of the hydraulic system during the predefined measurement routine, [¶37: The hydraulic system 1 is excited by applying an excitation signal n1 to the target speed n_soll….During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder];
automatically identifying a behavior of the hydraulic system on the basis of the acquired measurement data using a computer-based model structure, [¶37: resulting progression over time of the actual system pressure p_ist is shown in FIG. 4b.. the progression over time of the actual system pressure p_ist and the actual speed n_ist or the target speed n_soll, …a transfer function Gp/n(z) for describing the system dynamics is estimated…
¶13: The identified transfer function, which describes the system dynamics, thus represents the relationship between the system input, i.e. the actual speed and/or the target speed, and the system output, i.e. the actual system pressure. Therefore, advantageously, the dynamics of the hydraulic system can be described by a transfer function.];
automatically extracting system equations of the hydraulic system from the computer-based model structure, [¶37: a transfer function Gp/n(z) for describing the system dynamics is estimated…a time-discrete (z-range) transfer function, e.g. of the fifth order…
¶14: The transfer function, i.e. the identification of the controller loop, can be determined or approximated using known methods such as the Fast Fourier Transform (FFT) or the method of least squares (LSQ),…The Fast Fourier Transform splits a signal into its frequency components. Applied to the input and output of the present system, i.e. here the actual speed or the target speed and the actual system pressure, the transmission behavior in the frequency range can be concluded therefrom, which can be used for the further design of the control parameters…
¶38: optimization problem is modeled with the error squares (deviation between the measured values and the values calculated from the parameters of the transfer function Gp/n(z)) as the objective function (error is minimized) in order to determine the parameters of the transfer function Gp/n(z).];
automatically synthesizing the electronic controller based on the extracted system equations, and [¶37: During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder…From the progression over time of the actual system pressure p_ist and the actual speed n_ist or the target speed n_soll, which can also be measured or provided by the servo drive 3, a transfer function Gp/n(z) for describing the system dynamics is estimated…
¶40: Based on this transfer function Gp/n(z), a Pl controller having a gain factor Kp and an integration time constant TI is parameterized as a control parameter, using a known frequency characteristic method…Requirements for the closed loop are thus translated to requirements in the frequency range and the control parameters are calculated there.], but doesn’t explicitly disclose, and
Amari discloses, automatically embedding the synthesized electronic controller in an electronic control unit of the hydraulic system for controlling at least one control variable in the hydraulic system. [¶4: obtaining information about a current state…of each effector of the plurality of effectors; wherein the current state includes at least one of an effector position and an effector fluid flow rate. The method also includes updating model data information in an adaptive model based control (MBC) based upon the obtained information, generating at least one control command for at least one effector of the plurality of effectors based upon the adaptive model based control, and commanding the at least one effector of the plurality of effectors based upon the generated at least one control command].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the capability of automatically embedding the synthesized electronic controller in an electronic control unit of the hydraulic system for controlling at least one control variable in the hydraulic system in order to have modified/synthesized controller parameters for a controller of hydraulic system to ensure no constraints are violated while optimally satisfying a given control objective including the control commands for the controllers by developing the best possible solution to meet the system requirements taught by Amari with the method taught by ZEHETLEITNER as discussed above in order to have reasonable expectation of success such as to have modified/synthesized controller parameters for a controller of hydraulic system to ensure no constraints are violated while optimally satisfying a given control objective including the control commands for the controllers by developing the best possible solution to meet the system requirements [Amari, ¶58: The control method 500 can then modify all of the control commands e.g., 151) to ensure that none of the constraints 154 are violated while optimally satisfying a given control objective (including the control commands for each actuator 114 from the engine controller 106 as depicted at process step 520. This means that the control can develop the best possible solution to meet the system requirements].
Regarding claim 2:
ZEHETLEITNER and Amari disclose, The method according to claim 1, and
ZEHETLEITNER further discloses, wherein executing of the predefined measurement routine is an automated execution. [¶5: a control unit is provided which is configured to apply an excitation signal to the target speed and to measure the resulting actual system pressure of the hydraulic system].
Regarding claim 5:
ZEHETLEITNER and Amari disclose, The method according to claim 1, and
ZEHETLEITNER further discloses, wherein the synthesized electronic controller is a robust controller. [¶7: a compensating filter for the control unit can additionally be parameterized from the determined dynamics….a compensation filter can thus also be parameterized automatically…The dynamics of the hydraulic system that has already been identified can be investigated following undesired behavior in certain frequency ranges (e.g. strong resonance behavior) and further amplified or attenuated. Both significant noise reduction and a higher dynamic range is thus possible.
¶8: Compensation filters are used to suppress undesired behavior in the frequency range, for example to suppress resonance behavior at a certain frequency or, vice versa, to raise the system gain at this frequency in the event of anti-resonance behavior.].
Regarding claim 6:
ZEHETLEITNER and Amari disclose, The method according to claim 5, and
Amari further discloses, wherein the robust controller is a controller of type H-infinite, a controller of type H2, a controller of type Backstepping or a controller of type Model-Predictive-Control. [¶34: The control may be implemented utilizing a number of model predictive algorithms, including but not limited to model predictive control (MPC)…
¶48: The predictive engine models 200 and predictive actuator model 150 in the control system 100 may be adapted…to modify states, variables, quality parameters, scalars, adders, constraints, limits or any other adaptable parameter].
Regarding claim 10:
ZEHETLEITNER and Amari disclose, The method according to claim 1, and
ZEHETLEITNER further discloses, wherein the measurement parameters include…parameters available for measurement in the hydraulic system. [¶37: The hydraulic system 1 is excited by applying an excitation signal n1 to the target speed n_soll….During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder], and
Amari further discloses, the measurement parameters include all parameters available for measurement in the hydraulic system. [¶46: In operation, the sensor 108 monitors an engine operating parameter, such as temperature, pressure, position, and the like, and provides data corresponding to the parameter to the controller 106].
Regarding claim 12:
ZEHETLEITNER and Amari disclose, The method according to claim 1, and
ZEHETLEITNER further discloses, wherein the automated steps of the process are performed by the electronic control unit of the hydraulic system. [¶36: By means of the operating unit 5, the actual system pressure p_ist is detected in the differential hydraulic cylinder 7 and the target speed n_soll for the servo drive 4 is calculated by means of the control unit 6, in order to set a desired target system pressure p_soll in the differential hydraulic cylinder 7. Of course, the actual system pressure p_ist could also be detected and processed directly using the control unit 6…
¶37: The hydraulic system 1 is excited by applying an excitation signal n1 to the target speed n_soll….During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder….From the progression over time of the actual system pressure p_ist and the actual speed n_ist or the target speed n_soll,…a transfer function Gp/n(z) for describing the system dynamics is estimated],
Regarding claim 16:
ZEHETLEITNER and Amari disclose, the method according to claim 1, and further disclose, automated, measurement data-based configuring of an electronic controller for the hydraulic system using the method according to claim 1 as described above in claim 1, and
Amari further discloses, A method of controlling at least one control variable in a hydraulic system, the method comprising the following steps: [¶4: commanding the at least one effector of the plurality of effectors based upon the generated at least one control command.];
automated, measurement data-based configuring of an electronic controller for the hydraulic system [¶4: obtaining information about a current state…of each effector of the plurality of effectors; wherein the current state includes at least one of an effector position and an effector fluid flow rate. The method also includes updating model data information in an adaptive model based control (MBC) based upon the obtained information, generating at least one control command for at least one effector of the plurality of effectors based upon the adaptive model based control,];
controlling the at least one control variable in the hydraulic system by the synthesized electronic controller. [¶4: commanding the at least one effector of the plurality of effectors based upon the generated at least one control command.];
Regarding claim 17:
ZEHETLEITNER and Amari disclose, The method according to claim 16, and
Amari further discloses, wherein the at least one control variable comprises a pressure, a volume flow, a displacement or a position. [¶44: the sensor 108 monitors an engine operating parameter, such as temperature, pressure, position,…provides data corresponding to the parameter to the controller 106…If the difference between the measured data of the sensor 108 and the reference data of the actuator model 104 is outside of a threshold value, the controller 106 may take various steps to address the difference…
¶58: The flow control manager 150 also receives one or more feedback signals 131 corresponding to the total fluid flow, and/or the flow associated with each actuator 114 e.g., 114 a, 114 b, . . . 114 n. In an embodiment, an average aggregate fluid flow provided by the pump 130 is employed. The flow control manager 150 employs model predictive control techniques to formulate and adjust the actuator commands 151, e.g., 151 a, 151 b, . . . 151 n for each of the actuators 114 e.g., 114 a, 114 b, . . . 114 n.].
Regarding claim 18:
ZEHETLEITNER and Amari disclose, The method according to claim 16, and
ZEHETLEITNER further discloses, A hydraulic system, comprising: at least one hydraulic consumer, [¶28: FIG. 3 shows a hydraulic system comprising a differential hydraulic cylinder as a hydraulic load,]
at least one electrically actuated valve for actuating the at least one hydraulic consumer; [¶35: the hydraulic load 8 is designed as a differential hydraulic cylinder 7, which is connected to a switching valve 2. The switching valve 2 is further connected both to a servo drive 3 (consisting of a motor 31 and a pump 32)];
at least one hydraulic sensor; and [¶34: An oscillation of the actual system pressure p_ist is primarily visible in the actual pressure signal, which is measured by a sensor…
¶37: During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder]
Amari further discloses, an electronic control unit, wherein the electronic control unit is configured to perform the automated steps of the method for controlling at least one control variable in the hydraulic system according to claim 16. [¶4: commanding the at least one effector of the plurality of effectors based upon the generated at least one control command…
¶15: a controller operably connected to the plurality of sensors, the plurality of effectors, and the pump. The controller executes a method for an adaptive model-based control for controlling each effector].
Regarding claim 19:
ZEHETLEITNER and Amari disclose, The method according to claim 18, and
Amari further discloses, wherein each hydraulic consumer of the hydraulic system is controlled via at least one electrically actuated valve of the hydraulic system. [¶45: The controller 106 is communicatively coupled to the actuator 114 to provide commands to control the engine 20….the actuator 114 may include an electrohydraulic servovalve 116 and hydraulic actuator 118 as may be employed with an electrohydraulic actuator… the actuator 114 could include electric actuators, actuator controllers and electromechanical actuators,…
¶55: the actuator 114 may be comprised of an electrohydraulic type actuators having, in some configurations, a electrohydraulic servovalve… the engine controller 106 may provide command signals…to the servovalves 116 a, 116 b, . . . 116 n. The servovalves 116 may receive control commands 119 from the engine controller 106 as well as feedback signals from the hydraulic actuator 118 and provide control loop closure for the position of the actuator 118.].
Regarding claim 20:
ZEHETLEITNER and Amari disclose, The method according to claim 19, and
Amari further discloses, wherein the at least one hydraulic sensor is assigned to each electrically actuated valve of the hydraulic system. [¶44: system 100 includes engine 20, an actuator 114 and a sensor 108 that is communicatively coupled with a processor or controller 106. Sensor 108 is any of a variety of sensor…including…pressure, flow, speed and position sensors,… sensor 108 is a position sensor associated with one or more of the actuators, and may optionally include a flow sensor associated with fluid delivered by a pump…
¶46: the sensor 108 measures the position of the actuator 114, while another measure the flow of a fluid (e.g., fuel) to provide data to the controller 106 regarding the motion of the actuator 114.
¶56: Position feedback signals 115 from the hydraulic actuator 118 and fluid flow rate signal 131 from the pump 130 are provided the flow control manager for control loop closure for the servovalve(s) 116 and hydraulic actuator(s) 118.].
Claim(s) 3-4, 11 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZEHETLEITNER and Amari and further in view of Somarowthu et al. (US20220408643A1) [hereinafter Somarowthu].
Regarding claim 3:
ZEHETLEITNER and Amari disclose, The method according to claim 1, but they do not explicitly disclose, and
Somarowthu discloses, wherein the computer-based model structure comprises at least one artificial neural network for function approximation. [¶66: controller 16, which may include or be coupled to artificial intelligence engine 218. Utilizing engine 218 allows controller 16 to perform relatively high level classifications based on the received signal(s). Engine 218 may employ any suitable artificial intelligence and/or machine learning techniques].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the computer-based model structure comprises at least one artificial neural network for function approximation in order to achieve enhanced operation with optimized control parameters by the controller using the artificial intelligence to process the input data taught by Somarowthu with the method taught by ZEHETLEITNER and Amari as discussed above in order to have reasonable expectation of success such as to achieve enhanced operation with optimized control parameters by the controller using the artificial intelligence to process the input data [Somarowthu, ¶67: sensors described above may be used to provide additional inputs to AI engine 218 for enhanced operation and classification].
Regarding claim 4:
ZEHETLEITNER, Amari and Somarowthu disclose, The method according to claim 3, and
Somarowthu further discloses, wherein the computer-based model structure comprises an ANARX structure, an LSTM structure, an ARMA structure or an RNN structure. [¶66: Utilizing engine 218 allows controller 16 to perform relatively high level classifications based on the received signal(s). Engine 218 may employ any suitable artificial intelligence and/or machine learning techniques…Examples of suitable artificial intelligence techniques include, without limitation,…LSTMs and Recurrent Neural Networks (RNNSs),…Reinforcement Learning or Reward-based machine learning].
Regarding claim 11:
ZEHETLEITNER and Amari disclose, The method according to claim 1, and
Somarowthu further discloses, wherein the automated steps of the method are carried out by an external device which is connected to the hydraulic system for this purpose via an electronic data communication interface. [¶78: agricultural machine 10 communicates with elements in a remote server architecture 2…remote server architecture 2 can provide computation, software, data access, and storage services…remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols…
¶79: FIG. 8…AI engine 274 can be located at a remote server location 4. Therefore, agricultural machine 10 accesses those systems through remote server location 4…
¶70: Next, at block 306, controller 16 determines one or more parameters relative to the currently harvest operation of the combine harvester 10…controller 16 may utilize the sensor readings provided by one or more Terahertz-based sensors. Controller 16 may determine the above-noted parameters using a suitable artificial intelligence classifier, such as AI engine 218 (shown in FIG. 6 ). Additionally, various additional types of sensor data input 118 may be provided to AI engine 218 and considered by the controller 16 during block 306.].
Regarding claim 14:
ZEHETLEITNER and Amari disclose, the method according to claim 1, and further disclose, perform the automated steps of the method according to claim 1 as described above in claim 1, and
Somarowthu further discloses, A device for the automated, measurement data-based configuration of an electronic controller for a hydraulic system, the device comprising: [¶78: agricultural machine 10 communicates with elements in a remote server architecture 2…remote server architecture 2 can provide computation, software, data access, and storage services…remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols… The computing resources in a remote server environment.
¶79: FIG. 8…AI engine 274 can be located at a remote server location 4. Therefore, agricultural machine 10 accesses those systems through remote server location 4];
an electronic data communication interface for establishing a two-way data connection with the hydraulic system; [¶78: where agricultural machine 10 communicates with elements in a remote server architecture 2….remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols…
¶78: data store 272 and AI engine 274 can be located at a remote server location 4. Therefore, agricultural machine 10 accesses those systems through remote server location 4.].
an electronic computing unit configured to perform the automated steps… [¶78: remote server architecture 2 can provide computation, software, data access, and storage services…The computing resources in a remote server environment…
¶79: FIG. 8…AI engine 274 can be located at a remote server location 4. Therefore, agricultural machine 10 accesses those systems through remote server location 4…
¶67: any of the sensors described above may be used to provide additional inputs to AI engine 218 for enhanced operation and classification…
¶66: the detector(s) 212, 214 are operably coupled to controller 16, which may include or be coupled to artificial intelligence engine 218. Utilizing engine 218 allows controller 16 to perform relatively high level classifications based on the received signal(s).].
Regarding claim 15:
ZEHETLEITNER, Amari and Somarowthu disclose, The device according to claim 14, and
Somarowthu further discloses, the device further comprising the hydraulic system. [¶78: agricultural machine 10 communicates with elements in a remote server architecture 2…remote server architecture 2 can provide computation, software, data access, and storage services…
¶38: harvester 10 includes…wheels 20 are powered by a non-illustrated engine and drivetrain including, for example, an electronically-controlled hydraulic transmission…
¶79: FIG. 8…AI engine 274 can be located at a remote server location 4. Therefore, agricultural machine 10 accesses those systems through remote server location 4].
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZEHETLEITNER and Amari and further in view of Urdaneta et al. (US20230313639A1) [hereinafter Urdaneta].
Regarding claim 7:
ZEHETLEITNER and Amari disclose, The method according to claim 1, and
ZEHETLEITNER further discloses, wherein the measurement parameters comprise at least one hydraulic parameter of the hydraulic system [¶37: During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder];
the step of automatically acquiring measurement data comprises: automatically acquiring measurement data from at least one hydraulic sensor of the hydraulic system and [¶37: During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder…
¶34: An oscillation of the actual system pressure p_ist is primarily visible in the actual pressure signal, which is measured by a sensor. Since the actual system pressure is directly related to the actual speed via the system dynamics, this will then also oscillate.], but they do not explicitly disclose, and
Urdaneta discloses, wherein the measurement parameters comprise…at least one actuating current of an electrically actuated valve and automatically acquiring measurement data of at least one actuating current of an electrically actuated valve of the hydraulic system. [¶29: sensors 48 may comprise a current sensor 56 to monitor current associated with operation of motor 34…
¶31: The processor 66 also may receive voltage data, current data, and position data from the various other sensors 48, e.g. current sensor 56…
¶19: the downhole motor may be controlled to ensure the valve is actuated according to a target position…
¶20: hydraulic fluid pressure may be estimated as a function of motor current and this data may be used to assess valve piston position].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the measurement parameters such as actuating current of an electrically actuated valve and automatically acquiring measurement data of at least one actuating current of an electrically actuated valve of the hydraulic system in order to reliably control the actuation of the valve in the hydraulic system by using the measured actuating current in the electronic controller taught by Urdaneta with the method taught by ZEHETLEITNER and Amari as discussed above in order to have reasonable expectation of success such as to reliably control the actuation of the valve in the hydraulic system by using the measured actuating current in the electronic controller [Urdaneta, ¶24: The electronic control enabled by motor controller 38 working in cooperation with motor 34 and pump 36 to hydraulically actuate valve 24 provides a reliable, downhole electro-hydraulically actuated valve system].
Regarding claim 8:
ZEHETLEITNER, Amari and Urdaneta disclose, The method according to claim 7, and
ZEHETLEITNER further discloses, wherein the at least one hydraulic parameter is a pressure or a volume flow and the at least one hydraulic sensor is a pressure sensor or a volume flow sensor. [Examiner notes that only one of the elements (a pressure, a pressure sensor or a volume flow, a volume flow sensor) separated by or is given the patentable weight since only one of them is required by the claim.
Accordingly, ZEHETLEITNER discloses, wherein the at least one hydraulic parameter is a pressure and the at least one hydraulic sensor is a pressure sensor, as described below:
¶37: During this excitation, the actual system pressure p_ist is measured in the differential hydraulic cylinder…
¶34: An oscillation of the actual system pressure p_ist is primarily visible in the actual pressure signal, which is measured by a sensor].
Regarding claim 9:
ZEHETLEITNER, Amari and Urdaneta disclose, The method according to claim 7, and
Amari further discloses, wherein the measurement parameters comprise at least one displacement or position and [Examiner notes that only one of the elements (displacement or position) separated by or is given the patentable weight since only one of them is required by the claim.
Accordingly, Amari discloses, wherein the measurement parameters comprise at least one position, as described below:
¶46: the sensor 108 monitors an engine operating parameter, such as…position, and the like, and provides data corresponding to the parameter to the controller 106,];
the step of automatically acquiring measurement data comprises: automated acquisition of measurement data from at least one displacement sensor or position sensor. [Examiner notes that only one of the elements (displacement sensor or position sensor) separated by or is given the patentable weight since only one of them is required by the claim.
Accordingly, Amari discloses, the step of automatically acquiring measurement data comprises: automated acquisition of measurement data from at least one…position sensor, as described below:
¶44: Sensor 108 is any of a variety of sensor employed in the engine including temperature, pressure, flow, speed and position sensors, …
¶46: the sensor 108 monitors an engine operating parameter, such as…position, and the like, and provides data corresponding to the parameter to the controller 106].
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZEHETLEITNER and Amari and further in view of Trifol a (US20240076953A1) [hereinafter Trifol].
Regarding claim 13:
ZEHETLEITNER and Amari disclose, The method according to claim 1, but they do not explicitly disclose, and
Trifol discloses, wherein the execution of predefined measurement routine and the acquisition of the measurement data are performed virtually using a simulation model of the hydraulic system. [¶61: one or more digital twins with métier (DTM) can be implemented,…DTM can be a model or models replicating virtually one or more pieces of equipment at the well site. Such a DTM may be used to run simulations and may be trained to determine an optimal behavior of the equipment and/or the system 300 based on current sensed parameters, etc. For example, consider a DTM of a separator that has been trained (e.g., via machine learning)…
¶131: a machine learning model can be a neural network model, which may be developed based on data from a sensors data database (e.g., operational data), which can include various crew actions (e.g., valves, operation, set point changes, pressure, temperature, flowrate changes, etc.). For example, a machine learning model can be trained and tested on such data.].
Therefore, it would have been obvious to one of ordinary skill in the art before the filling date of the claimed invention to have combined the execution of predefined measurement routine and the acquisition of the measurement data are performed virtually using a simulation model of the hydraulic system in order to have optimized control of hydraulic system by using the trained model that is improved via simulation/training by considering most of the data while minimizing the level of processing taught by Trifol with the method taught by ZEHETLEITNER and Amari as discussed above in order to have optimized control of hydraulic system by using the trained model that is improved via simulation/training by considering most of the data while minimizing the level of processing [Trifol, ¶131: where a database is continuously increasing in volume of data, a model can be improved (e.g., additionally trained, retrained, etc.), which may provide the model with an ability to learn more complex patterns over time… ¶142: An optimal decision tree may be defined as a tree that accounts for most of the data, while minimizing the number of levels (e.g., questions).].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892 Notice of Reference Cited document.
BAO et al. (CN114109949B): Digital twin optimal control system and method for valve port independent control electrohydraulic system
Page 5, ¶4: the parameter detection unit is used for monitoring key parameters of the hydraulic valve and the oil circuit system; the decision control unit is used for receiving the simulation data and the optimized control instruction of the digital twin system, synchronously fusing the acquired data of the local sensor and then sending a control signal to the electric control system.
Hernandez (US11513479B1): Automatic system identification and controller synthesis for embedded systems
Col. 1, lines 59-67 and col. 2, lines 1-2: performing model reduction to generate a model numerically suitable for controller synthesis by removing inconsequential states that cause controller optimization methods to fail….performing control synthesis using the generated model or reduced models…to generate a candidate controller design to be used during system operation…checking for controller robustness using the identified model to ensure stability of the system while maximizing closed-loop bandwidth and performance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED SHAFAYET whose telephone number is (571)272-8239. The examiner can normally be reached M-F 8:30 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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