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
This Office Action has been issued in response to amendment filed 12/18/2025. Applicant's arguments have been carefully and fully considered; and they are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made. Accordingly, this action has been made FINAL.
Claim Status
Claim 1 has been amended. Claims 1-23 remain pending and are ready for examination.
Rejections not based on Prior Art
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: A state variable acquisition unit configured to acquire, an evaluation data acquisition unit configured to acquire, an updating unit configured to update, the learning unit being configurated to learn in claim 1-23.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Rejections based on Prior Art
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) 1-3 and 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo et al. (JP 2001165163 A -hereinafter Kyo -Note: IDS reference filed 08/31/2021) in view of Ueyama et al. (US5879113A -hereinafter Ueyama) in view of Okubo et al. (US6515442B1 -hereinafter Okubo).
Regarding Claim 1, Kyo teaches:
A machine learning device that learns a control condition for a magnetic bearing device that includes a magnetic bearing having a plurality of electromagnets that apply an electromagnetic force to a shaft, the machine learning device comprising: (see [0004]; Kyo: “Conventionally, as a controller of this magnetic bearing control device, a controller such as a PID (proportional integral differential) controller or a phase compensator based on an analog control method has been often used.” See [0001]: “The present invention relates to a magnetic bearing that supports a rotating shaft by the magnetic force of an electromagnet.”)
a learning unit; (see [0020]; Kyo: “FIG. 3 is a configuration diagram of a fuzzy neural network configured based on the fuzzy rule L.”)
a state variable acquisition unit configured to acquire a state variable including at least one parameter correlating with a position of the shaft; (see [0013]; Kyo: “a gap sensor 4 for detecting the position of the rotating shaft 1 is attached to the electromagnet. The gap sensor 4 of the radial magnetic bearing 2 detects the radial position of the rotating shaft 1, and the gap sensor 4 of the thrust magnetic bearing 3 detects the axial position of the rotating shaft 1. As the gap sensor, for example, an inductance type displacement sensor that detects displacement by changing the inductance can be used.”)
an evaluation data acquisition unit configured to acquire evaluation data including at least one parameter selected from among a measured value of the position of the shaft, a target value of the position of the shaft, and a parameter correlating with a deviation from the target value; and (see [0014]; Kyo: “the controller 5 is based on the input from the sensor 4.” See [0015]: “The analog displacement signal detected by the gap sensor 4 is input to the A / D converter 6 and converted into a digital signal.”)
an updating unit configured to update a learning state of the learning unit by using the evaluation data (see [0023]: “In this way, when the steps of inputting the data obtained in the experiment, comparing the calculated value and the actual value, and correcting the coupling load wi (i = 1,2, ..., 11) are repeated a predetermined number of times, the optimum coupling load is obtained. wi (i = 1,2, ..., 11) can be obtained. When the optimum coupling load wi (i = 1,2, ..., 11) is obtained, each parameter of the fuzzy rule L is determined based on this, and the movement of the rotating shaft / magnetic bearing system is determined by the fuzzy rule L. Can describe characteristics.”),
the learning unit being configured to learn the control condition in accordance with an output of the updating unit, (see [0041]; Kyo: “Furthermore, since an accurate model of the rotating shaft I magnetic bearing system can be obtained by a fuzzy neural network, control based on an accurate model of the rotating shaft I magnetic bearing system becomes possible, and it is easy to obtain input/ output data obtained by experiments.”)
However, Kyo does not explicitly teach: the evaluation data being a shaft position deviation that is a deviation of a detected value of the position of the shaft relative to the target value of the position of the shaft, and when the shaft position deviation is in a predetermined range, the evaluation data acquisition unit being configured to regard the shaft position deviation as a constant value in the predetermined range and acquire the constant value as the evaluation data.
Ueyama from the same or similar field of endeavor teaches:
the evaluation data being a shaft position deviation that is a deviation of a detected value of the position of the shaft relative to the target value of the position of the shaft, (see column 1, lines 26-32; Ueyama: “magnetic force controlling means for finding the deviation between the position in the radial direction of the spindle obtained on the basis of an output signal from the position sensor and the target position in the radial direction of the spindle with respect to the main body (the amount of the shift in the position in the radial direction from the target position of the spindle)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kyo to include Ueyama’s features of the evaluation data being a shaft position deviation that is a deviation of a detected value of the position of the shaft relative to the target value of the position of the shaft. Doing so would prevent insufficient cutting in the initial stage of working and excessive cutting in the final stages of working. (Ueyama, column 7, lines 41-43)
However, it does not explicitly teach: when the shaft position deviation is in a predetermined range, the evaluation data acquisition unit being configured to regard the shaft position deviation as a constant value in the predetermined range and acquire the constant value as the evaluation data.
Okubo from the same or similar field of endeavor teaches: when the shaft position deviation is in a predetermined range, the evaluation data acquisition unit being configured to regard the shaft position deviation as a constant value in the predetermined range and acquire the constant value as the evaluation data. (see column 3, lines 56-60; Okubo: “when the absolute value of the position deviation signal changes to be equal to the reference value or below, the torque corrective gain is changed from zero to a constant value.”) [The torque corrective gain reads on ‘the evaluation data]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kyo and Ueyama to include Okubo’s features of when the shaft position deviation is in a predetermined range, the evaluation data acquisition unit being configured to regard the shaft position deviation as a constant value in the predetermined range and acquire the constant value as the evaluation data. Doing so would make the damping effect effective and suppress the low-frequency disturbance, and thereby realize positioning with high accuracy in a short time. (Okubo, column 9, lines 65-68)
Regarding Claim 2, the combination of Kyo, Ueyam, and Okubo a teaches all the limitations of claim 1 above, Kyo further teaches wherein
the state variable includes at least an output value of a displacement sensor that outputs a signal according to the position of the shaft, and (see [0013]; Kyo: “As the gap sensor, for example, an inductance type displacement sensor that detects displacement by changing the inductance can be used.”)
the learning unit is configured to learn, as the control condition, at least one of a voltage value of the electromagnets and a current value of the electromagnets. (see [0020]; Kyo: “In this fuzzy neural network, the input/ output data obtained from the experiment, that is, the displacement of the rotating shaft at a specific time point, the first order differential value with respect to the displacement of the rotating shaft, the exciting voltage or exciting current of the electromagnet, and after a unit time has elapsed. Learning is performed by the displacement of the rotation axis of.”)
Regarding Claim 3, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above, Kyo further teaches wherein
the state variable includes at least
a current value and a voltage value of the electromagnets or (see [0017]; Kyo: “the exciting voltage or exciting current of the electromagnet is u,”)
a current value and a magnetic flux of the electromagnets, and
the learning unit is configured to learn, as the control condition, at least one of
the voltage value of the electromagnets and
the current value of the electromagnets. (see [0021]; Kyo: “First, the displacement of the rotating shaft x1 at a specific time point obtained in the experiment, the first-order differential value x2 with respect to the displacement of the rotating shaft x1, and the exciting voltage or exciting current u of the electromagnet are input to the fuzzy neural network of FIG.”)
Regarding Claim 22, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above, Kyo further teaches wherein the learning unit is configured to output the control condition, based on a trained model obtained as a result of learning. (see [0011]; Kyo: “As a result, an accurate fuzzy model of the rotating shaft I magnetic bearing system can be obtained by a fuzzy neural network, and sliding mode control based on this accurate fuzzy model of the rotating shaft/ magnetic bearing system becomes possible.”)
Regarding Claim 23, the combination of Kyo, Ueyam, and Okubo teaches a magnetic bearing device including the machine learning device according to claim 22. (see [0001]; Kyo: “The present invention relates to a magnetic bearing that supports a rotating shaft by the magnetic force of an electromagnet”. See [0016]: “In this embodiment, this modeling is performed using a fuzzy neural network.”)
Claim(s) 4-6 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Tamai et al. (US20200141415A1 -hereinafter Tamai).
Regarding Claim 4, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above, Kyo further teaches wherein
the state variable includes at least an output value of a displacement sensor that outputs a signal according to the position of the shaft, (see [0013]; Kyo: “As the gap sensor, for example, an inductance type displacement sensor that detects displacement by changing the inductance can be used.”)
and the learning unit is configured to learn, as the control condition, the position of the shaft. (see [0003]: “It is composed of a controller that controls the exciting voltage or exciting current (that is, magnetic attraction force) of the electromagnet so as to support the position of.” See [0020]: “In this fuzzy neural network, the input/ output data obtained from the experiment, that is, the displacement of the rotating shaft at a specific time point, the first order differential value with respect to the displacement of the rotating shaft, the exciting voltage or exciting current of the electromagnet, and after a unit time has elapsed. Learning is performed by the displacement of the rotation axis of.”)
However, it does not explicitly teach: the evaluation data includes at least a true value of the position of the shaft,
Tamai from the same or similar field of endeavor teaches the evaluation data includes at least a true value of the position of the shaft, (see [0030]; Tamai: “Each of the magnetic bearings 17A to 17C includes electromagnets and displacement sensors, and the levitation position of the rotor shaft 15 is detected by the displacement sensors.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Tamai’s features of including at least a true value of the position of the shaft. Doing so would prevent erroneous determination upon abnormality detection. (Tamai, [0013])
Regarding Claim 5, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above, Kyo further teaches wherein
the state variable includes at least
a current value and a voltage value of the electromagnets or (see [0017]; Kyo: “the exciting voltage or exciting current of the electromagnet is u,”)
a current value and a magnetic flux of the electromagnets,
and the learning unit is configured to learn, as the control condition, the position of the shaft. (see [0003]: “It is composed of a controller that controls the exciting voltage or exciting current (that is, magnetic attraction force) of the electromagnet so as to support the position of.” See [0020]: “In this fuzzy neural network, the input/ output data obtained from the experiment, that is, the displacement of the rotating shaft at a specific time point, the first order differential value with respect to the displacement of the rotating shaft, the exciting voltage or exciting current of the electromagnet, and after a unit time has elapsed. Learning is performed by the displacement of the rotation axis of.”)
However, it does not explicitly teach: the evaluation data includes at least a true value of the position of the shaft,
Tamai from the same or similar field of endeavor teaches the evaluation data includes at least a true value of the position of the shaft, (see [0030]; Tamai: “Each of the magnetic bearings 17A to 17C includes electromagnets and displacement sensors, and the levitation position of the rotor shaft 15 is detected by the displacement sensors.”)
The same motivation to combine Kyo, Ueyama, Okubo, and Tamai a set forth for Claim 4 equally applies to Claim 5.
Regarding Claim 6, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above, Kyo further teaches wherein
the state variable includes at least a detected value of the position of the shaft…, and (see [0003]; Kyo: “a sensor that detects the floating position of the rotating shaft, and a signal from the sensor.”)
the learning unit is configured to learn, as the control condition, at least one of a voltage value of the electromagnets and a current value of the electromagnets. (see [0021]; Kyo: “First, the displacement of the rotating shaft x1 at a specific time point obtained in the experiment, the first-order differential value x2 with respect to the displacement of the rotating shaft x1, and the exciting voltage or exciting current u of the electromagnet are input to the fuzzy neural network of FIG.”)
Tamai from the same or similar field of endeavor teaches …a command value of the position of the shaft, (see [0061]; Tamai: “Hereinafter, a case where displacement signals of displacement sensors X2 a, X2 b, Y2 a, Y2 b configured to detect the levitation position in a radial direction are used as the signals indicating the rotation state will be described.”)
The same motivation to combine Kyo, Ueyama, Okubo, and Tamai a set forth for Claim 4 equally applies to Claim 6.
Regarding Claim 11, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the state variable further includes a number of rotations of the shaft.
Tamai from the same or similar field of endeavor teaches wherein the state variable further includes a number of rotations of the shaft. (see [0030]; Tamai: “The number of rotations of the rotor shaft 15 is detected by a rotation number sensor 18.”)
The same motivation to combine Kyo, Ueyama, Okubo, and Tamai a set forth for Claim 4 equally applies to Claim 11.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Shinzen et al. (JP2005057847A -hereinafter Shinzen).
Regarding Claim 7, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the updating unit is configured to cause the learning unit to further perform learning so as to make a current value usable to drive the magnetic bearing less than or equal to a predetermined allowable value.
Shinzen from the same or similar field of endeavor teaches wherein the updating unit is configured to cause the learning unit to further perform learning so as to make a current value usable to drive the magnetic bearing less than or equal to a predetermined allowable value. (see [0022]; Shinzen: “an inverter for driving an induction motor with a magnetic bearing …when the speed command is in a predetermined low speed range, the voltage adjustment is performed so that the output voltage of the AC power generation means is lower than a value based on the speed command.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Shinzen’s features of causing the learning unit to further perform learning so as to make a current value usable to drive the magnetic bearing less than or equal to a predetermined allowable value. Doing so would achieve accurately grasp the rotation speed. (Shinzen, [0016])
Claim(s) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Someya (US20050052146A1 -hereinafter Someya).
Regarding Claim 8, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein
the evaluation data further includes a parameter correlating with a temperature of an inverter that drives the magnetic bearing, and
the updating unit is configured to cause the learning unit to further perform learning so as to make the temperature of the inverter lower than or equal to a predetermined allowable value.
Someya from the same or similar field of endeavor teaches wherein
the evaluation data further includes a parameter correlating with a temperature of an inverter that drives the magnetic bearing, and (see [0101]; Someya: “the motor control system includes a control device 500, in which also the inverter circuits 222 are equipped with an inverter temperature sensor 524.” See [0012]: “Mounted at the center of this rotary member 103 is a rotor shaft 113, which is floatingly supported and position-controlled by, for example, a 5-axis control magnetic bearing.”)
the updating unit is configured to cause the learning unit to further perform learning so as to make the temperature of the inverter lower than or equal to a predetermined allowable value. (see [0135]; Someya: “the comparator 512 sets the upper limit value of the motor current Im based on the temperatures detected by the motor temperature sensor 424 and the inverter temperature sensor 524.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Someya’s features of the evaluation data further includes a parameter correlating with a temperature of an inverter that drives the magnetic bearing, and the updating unit is configured to cause the learning unit to further perform learning so as to make the temperature of the inverter lower than or equal to a predetermined allowable value. Doing so would achieve a reduction in the service life of the material. (Someya, [0066])
Regarding Claim 19, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the state variable further includes a parameter correlating with a temperature of the displacement sensor.
Someya from the same or similar field of endeavor teaches wherein the state variable further includes a parameter correlating with a temperature of the displacement sensor. (see [0013]; Someya: “The upper radial sensor 107 detects a radial displacement of the rotor shaft 113, and transmits a displacement signal to the control device 200.” See [0021]: “Further, there is provided a motor 121, which is a so-called brush-less motor. The motor 121 is equipped with an RPM detecting sensor, a motor current detecting sensor, a motor temperature detecting sensor, etc. described below, and, on the basis of detection signals from these sensors, the RPM, etc. of the rotor shaft 113 are controlled by the control device 200.”)
The same motivation to combine Kyo, Ueyama, Okubo, and Someya a set forth for Claim 8 equally applies to Claim 19.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Kita et al. (WO 9938249 A1 -hereinafter Kita -Note: As the translation attached).
Regarding Claim 9, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the state variable further includes a detected current value of the electromagnets in a case in which the magnetic bearing is driven by a voltage type inverter, and a detected voltage value of the electromagnets in a case in which the magnetic bearing is driven by a current-type inverter.
Kita from the same or similar field of endeavor teaches wherein the state variable further includes
a detected current value of the electromagnets in a case in which the magnetic bearing is driven by a voltage type inverter, and (see page 29, fourth paragraph; Kita: “The output voltage of the DC voltage source 100 is appropriately connected to the windings 102 of each phase of the SR motor by the transistor of the voltage source inverter 101, and a constant voltage is applied. As a result, a current corresponding to the constant voltage and the winding impedance flows through the corresponding winding 102. This current is detected by the current detector 103 and the inverter control unit 110”)
a detected voltage value of the electromagnets in a case in which the magnetic bearing is driven by a current-type inverter. (see page 29, paragraph 8; Kita: “The output current of the DC current source 200 is appropriately connected to the windings 102 of each phase of the SR motor by a transistor of the current source inverter 201, and a predetermined current is supplied. As a result, a voltage corresponding to the predetermined current and the winding impedance is generated in the corresponding winding 102. This voltage is detected, and the detected voltage value is calculated by the inverter control unit 210.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Kita’s features of a detected current value of the electromagnets in a case in which the magnetic bearing is driven by a voltage type inverter, and a detected voltage value of the electromagnets in a case in which the magnetic bearing is driven by a current-type inverter. Doing so would achieve maximum efficiency control and a significant cost reduction. (Kita, page 9, paragraphs 8-9)
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Kita in view of Fujita et al. (US20190386595A1 -hreinafter Fujita).
Regarding Claim 10, the combination of Kyo, Ueyama, Okubo, and Kita teaches all the limitations of claim 9 above; however, it does not explicitly teach wherein the updating unit is configured to cause the learning unit to further perform learning in order to reduce a value correlating with responsivity of control of the current value.
Fujita from the same or similar field of endeavor teaches the updating unit is configured to cause the learning unit to further perform learning in order to reduce a value correlating with responsivity of control of the current value. (see [0032]; Fujita: “The machine learning apparatus 100 learns the correction parameter that enables setting of a correction condition which reduces an error included in the drive data of the motor drive system 99.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyama, Okubo, and Kita to include Fujita’s features of the updating unit is configured to cause the learning unit to further perform learning in order to reduce a value correlating with responsivity of control of the current value. Doing so would easily adjust a correction parameter for determining a correction condition of a command value given to a motor drive system. (Fujita, [0006])
Claim(s) 12-14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Ueda et al. (US20030213256A1 -hereinafter Ueda).
Regarding Claim 12, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach wherein the state variable further includes at least one parameter correlating with an operation condition of a refrigeration apparatus, the refrigeration apparatus includes a refrigerant circuit in which a compressor, a condenser, an expansion mechanism, and an evaporator are coupled, and the operation condition includes a range of a refrigerating capacity of the refrigeration apparatus and a range of a temperature of a medium that is usable for heat exchange with refrigerant circulating through the refrigerant circuit and that flows into the condenser.
Ueda from the same or similar field of endeavor teaches
the state variable further includes at least one parameter correlating with an operation condition of a refrigeration apparatus, (see [0027]; Ueda: “the pressure ratio between the refrigerant discharge pressure and the refrigerant suction pressure, varies according to the operating condition,”)
the refrigeration apparatus includes a refrigerant circuit in which a compressor, a condenser, an expansion mechanism, and an evaporator are coupled, and (see [0016]; Ueda: “in the air conditioner 50, a closed circuit for circulating the refrigerant is formed by the evaporator 53, the condenser 55, the gas flow path Gp and the liquid flow path Lp which are placed between the evaporator 53 and the condenser 55, the compressor 56 placed in the gas flow path Gp, and the expansion valve 57 placed in the liquid flow path Lp.”)
the operation condition includes
a range of a refrigerating capacity of the refrigeration apparatus and (see [0064]; Ueda: “The compressor driving unit 101 a includes a volume circulation rate instruction unit 7 which calculates refrigerating capacity required of the refrigeration cycle apparatus (i.e., the amount of heat exchange to be carried out per unit time) on the basis of the temperature information outputted from the temperature detectors 3 and 5 and the temperature instruction unit 6, and outputs an instruction signal (refrigerant circulation rate information) indicating a volume circulation rate Vco of the refrigerant in accordance with the calculated refrigerating capacity (i.e., the volume of the refrigerant to be discharged from or sucked into the linear compressor 1 a per unit time)”)
a range of a temperature of a medium that is usable for heat exchange with refrigerant circulating through the refrigerant circuit and that flows into the condenser. (see [0089]; Ueda: “the refrigeration cycle apparatus 101 using the linear compressor 1 a is provided with the volume circulation rate instruction unit 7 for calculating the volume circulation rate Vco of the refrigerant in accordance with the refrigeration capacity required of the refrigeration cycle apparatus on the basis of the ambient temperature of the indoor heat exchanger (evaporator) 53 a, the target temperature set on the evaporator 53 a by the user, and the ambient temperature of the outdoor heat exchanger (condenser) 55 a; and the volume circulation rate detector 8 a for detecting the volume circulation rate Vcd of the refrigerant that is actually circulating in the refrigerant circulation path of the refrigeration cycle apparatus.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Ueda’s features of the updating unit is configured to cause the learning unit to further perform learning in order to reduce a value correlating with responsivity of control of the current value. Doing so would efficiently carry out heat exchange and keep a comfortable room temperature. (Ueda, [0002] and [0229])
Regarding Claim 13, the combination of Kyo, Kawasaki, Okubo, and Ueda teaches all the limitations of claim 12 above, Kyo further teaches wherein
the state variable further includes at least one parameter correlating with the electromagnetic force applied to the shaft, and (see [0003]; Kyo: “applying a magnetic attraction force to the rotating shaft,”)
However, it does not explicitly teach: the parameter correlating with the electromagnetic force includes at least one of a parameter correlating with a refrigerant load of the refrigeration apparatus and a parameter correlating with a physical characteristic of the refrigeration apparatus.
Ueda from the same or similar field of endeavor teaches
the parameter correlating with the electromagnetic force includes at least one of (see [0005]; Ueda: “The piston 72 reciprocates along its axis direction due to an electromagnetic force generated between the electromagnet 74 and the magnet 73, and elasticity of the spring 81.”) a parameter correlating with a refrigerant load of the refrigeration apparatus (see [0060]; Ueda: “the compressor driving unit 101 a has temperature detectors 3 and 5 for detecting the state of load applied to the refrigeration cycle apparatus 101.”) and a parameter correlating with a physical characteristic of the refrigeration apparatus.
The same motivation to combine Kyo, Ueyama, Okubo, and Ueda a set forth for Claim 12 equally applies to Claim 13.
Regarding Claim 14, the combination of Kyo, Ueyama, Okubo, and Ueda teaches all the limitations of claim 12 above, Kyo further teaches wherein
the state variable further includes at least one parameter correlating with a characteristic of the magnetic bearing, (see [0016]; Kyo: “First, a fuzzy model defined by the if-then format and describing the dynamic characteristics of the rotating shaft I magnetic bearing system is prepared.”)
However, it does not explicitly teach: and the parameter correlating with the characteristic of the magnetic bearing includes at least one of a parameter correlating with an inductance of coils of the electromagnets and a parameter correlating with a resistance of the coils of the electromagnets.
Ueda from the same or similar field of endeavor teaches and the parameter correlating with the characteristic of the magnetic bearing includes at least one of a parameter correlating with an inductance of coils of the electromagnets and a parameter correlating with a resistance of the coils of the electromagnets. (see [0220]; Ueda: “A cryogenic freezer 109 according to the ninth embodiment has a freezing chamber, and cools the inside of the chamber at a cryogenic temperature (lower than −50° C.). Objects to be cooled by the cryogenic freezer include superconducting elements (electromagnetic circuit elements such as resistors, coils, magnets), electronic elements such as low-temperature reference parts for infrared sensors, medical objects such as blood and viscera, and frozen foods such as frozen tunas.”)
The same motivation to combine Kyo, Ueyama, Okubo, and Ueda a set forth for Claim 12 equally applies to Claim 14.
Regarding Claim 18, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 17 above; however, it does not explicitly teach: the state variable further includes at least one of
at least one parameter correlating with an operation condition of a refrigeration apparatus and at least one parameter correlating with adiabatic efficiency of an impeller coupled to the shaft, the refrigeration apparatus includes a refrigerant circuit in which the compressor, a condenser, an expansion mechanism, and an evaporator are coupled, the refrigeration apparatus includes a refrigerant circuit in which the compressor, a condenser, an expansion mechanism, and an evaporator are coupled, the operation condition includes
a range of a refrigerating capacity of the refrigeration apparatus and a range of a temperature of a medium that is usable for heat exchange with refrigerant circulating through the refrigerant circuit and that flows into the condenser, and the parameter correlating with the adiabatic efficiency of the impeller includes at least one of a parameter correlating with a pressure of the refrigerant and a parameter correlating with a temperature of the refrigerant.
Ueda from the same or similar field of endeavor teaches
the state variable further includes at least one of
at least one parameter correlating with an operation condition of a refrigeration apparatus and (see [0027]; Ueda: “the pressure ratio between the refrigerant discharge pressure and the refrigerant suction pressure, varies according to the operating condition,”)
at least one parameter correlating with adiabatic efficiency of an impeller coupled to the shaft, (see [0109]; Ueda: “When it is assumed that there is no leakage of the refrigerant when the refrigerant is compressed in the linear compressor 1 a, the state change of the refrigerant is an adiabatic change.”)
the refrigeration apparatus includes a refrigerant circuit in which the compressor, a condenser, an expansion mechanism, and an evaporator are coupled, (see [0016]; Ueda: “in the air conditioner 50, a closed circuit for circulating the refrigerant is formed by the evaporator 53, the condenser 55, the gas flow path Gp and the liquid flow path Lp which are placed between the evaporator 53 and the condenser 55, the compressor 56 placed in the gas flow path Gp, and the expansion valve 57 placed in the liquid flow path Lp.”)
the operation condition includes
a range of a refrigerating capacity of the refrigeration apparatus and (see [0064]; Ueda: “The compressor driving unit 101 a includes a volume circulation rate instruction unit 7 which calculates refrigerating capacity required of the refrigeration cycle apparatus (i.e., the amount of heat exchange to be carried out per unit time) on the basis of the temperature information outputted from the temperature detectors 3 and 5 and the temperature instruction unit 6, and outputs an instruction signal (refrigerant circulation rate information) indicating a volume circulation rate Vco of the refrigerant in accordance with the calculated refrigerating capacity (i.e., the volume of the refrigerant to be discharged from or sucked into the linear compressor 1 a per unit time)”)
a range of a temperature of a medium that is usable for heat exchange with refrigerant circulating through the refrigerant circuit and that flows into the condenser, and (see [0089]; Ueda: “the refrigeration cycle apparatus 101 using the linear compressor 1 a is provided with the volume circulation rate instruction unit 7 for calculating the volume circulation rate Vco of the refrigerant in accordance with the refrigeration capacity required of the refrigeration cycle apparatus on the basis of the ambient temperature of the indoor heat exchanger (evaporator) 53 a, the target temperature set on the evaporator 53 a by the user, and the ambient temperature of the outdoor heat exchanger (condenser) 55 a; and the volume circulation rate detector 8 a for detecting the volume circulation rate Vcd of the refrigerant that is actually circulating in the refrigerant circulation path of the refrigeration cycle apparatus.”)
the parameter correlating with the adiabatic efficiency of the impeller includes at least one of a parameter correlating with a pressure of the refrigerant (see [0109]; Ueda: “Assuming that the pressure of the refrigerant is P, the volume is V, and the ratio of specific heat is γ, formula (1) holds as follows.”) and a parameter correlating with a temperature of the refrigerant.
The same motivation to combine Kyo, Ueyama, Okubo, and Ueda a set forth for Claim 12 equally applies to Claim 18.
Claim(s) 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Cella et al. (US20190121338A1 -hereinafter Cella).
Regarding Claim 15, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach:
the evaluation data further includes a parameter correlating with power consumption of the magnetic bearing,
the updating unit is configured to cause the learning unit to further perform learning in order to reduce the power consumption, and
wherein the parameter correlating with the power consumption includes at least two of a current value usable to drive the magnetic bearing, a voltage value usable to drive the magnetic bearing, and a resistance of coils of the electromagnets.
Cella from the same or similar field of endeavor teaches
the evaluation data further includes a parameter correlating with power consumption of the magnetic bearing, (see [0777]; Cella: “There are many different types of bearings such as …magnetic bearings
the updating unit is configured to cause the learning unit to further perform learning in order to reduce the power consumption, and (see [0022]; Cella: “reducing an energy consumption of communication operations of the one of the analog sensor inputs;”)
wherein the parameter correlating with the power consumption includes at least two of a current value usable to drive the magnetic bearing, a voltage value usable to drive the magnetic bearing, and a resistance of coils of the electromagnets. (see [0800]; Cella: “Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 9206 may comprise one or more of, without limitation, a vibration sensor, an optical vibration sensor, a thermometer, a hygrometer, a voltage sensor, a current sensor, an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Cella’s features of the evaluation data further includes a parameter correlating with power consumption of the magnetic bearing, the updating unit is configured to cause the learning unit to further perform learning in order to reduce the power consumption, and wherein the parameter correlating with the power consumption includes at least two of a current value usable to drive the magnetic bearing, a voltage value usable to drive the magnetic bearing, and a resistance of coils of the electromagnets. Doing so would provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments. (Cella, [0012])
Regarding Claim 17, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 2 above; however, it does not explicitly teach: the evaluation data further includes at least one parameter correlating with input energy supplied to a compressor, and the updating unit is configured to cause the learning unit to further perform learning in order to reduce the input energy.
Cella from the same or similar field of endeavor teaches
the evaluation data further includes at least one parameter correlating with input energy supplied to a compressor, and (see [0022]; Cella: “communicating a digital signal representative of the one of the analog sensor inputs at the one of the plurality of output channels;”)
the updating unit is configured to cause the learning unit to further perform learning in order to reduce the input energy. (see [0022]; Cella: “reducing an energy consumption of communication operations of the one of the analog sensor inputs;”)
The same motivation to combine Kyo, Ueyama, Okubo, and Cella a set forth for Claim 15 equally applies to Claim 17.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Shinzen in view of Ueda.
Regarding Claim 16, the combination of Kyo, Ueyama, Okubo, and Shinzen teaches all the limitations of claim 7 above; however, it does not explicitly teach: the state variable further includes at least one parameter correlating with an operation condition of a refrigeration apparatus, the refrigeration apparatus includes refrigerant circuit in which a compressor, a condenser, an expansion mechanism, and an evaporator are coupled, and the operation condition includes a range of a refrigerating capacity of the refrigeration apparatus and a range of a temperature of a medium that is usable for heat exchange with refrigerant circulating through the refrigerant circuit and that flows into the condenser.
Ueda from the same or similar field of endeavor teaches
the state variable further includes at least one parameter correlating with an operation condition of a refrigeration apparatus, (see [0027]; Ueda: “the pressure ratio between the refrigerant discharge pressure and the refrigerant suction pressure, varies according to the operating condition,”)
the refrigeration apparatus includes refrigerant circuit in which a compressor, a condenser, an expansion mechanism, and an evaporator are coupled, and the operation condition includes (see [0016]; Ueda: “in the air conditioner 50, a closed circuit for circulating the refrigerant is formed by the evaporator 53, the condenser 55, the gas flow path Gp and the liquid flow path Lp which are placed between the evaporator 53 and the condenser 55, the compressor 56 placed in the gas flow path Gp, and the expansion valve 57 placed in the liquid flow path Lp.”)
a range of a refrigerating capacity of the refrigeration apparatus and (see [0064]; Ueda: “The compressor driving unit 101 a includes a volume circulation rate instruction unit 7 which calculates refrigerating capacity required of the refrigeration cycle apparatus (i.e., the amount of heat exchange to be carried out per unit time) on the basis of the temperature information outputted from the temperature detectors 3 and 5 and the temperature instruction unit 6, and outputs an instruction signal (refrigerant circulation rate information) indicating a volume circulation rate Vco of the refrigerant in accordance with the calculated refrigerating capacity (i.e., the volume of the refrigerant to be discharged from or sucked into the linear compressor 1 a per unit time)”)
a range of a temperature of a medium that is usable for heat exchange with refrigerant circulating through the refrigerant circuit and that flows into the condenser. (see [0089]; Ueda: “the refrigeration cycle apparatus 101 using the linear compressor 1 a is provided with the volume circulation rate instruction unit 7 for calculating the volume circulation rate Vco of the refrigerant in accordance with the refrigeration capacity required of the refrigeration cycle apparatus on the basis of the ambient temperature of the indoor heat exchanger (evaporator) 53 a, the target temperature set on the evaporator 53 a by the user, and the ambient temperature of the outdoor heat exchanger (condenser) 55 a; and the volume circulation rate detector 8 a for detecting the volume circulation rate Vcd of the refrigerant that is actually circulating in the refrigerant circulation path of the refrigeration cycle apparatus.”)
The same motivation to combine Kyo, Ueyama, Okubo, Shinzen, and Ueda a set forth for Claim 12 equally applies to Claim 16.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Tsutsumi (US20170063261A1 -hereinafter Tsutsumi).
Regarding Claim 20, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above; however, it does not explicitly teach wherein
the updating unit is further configured to calculate a reward, based on the evaluation data, and
the learning unit is configured to perform learning by using the reward.
Tsutsumi from the same or similar field of endeavor teaches wherein
the updating unit is further configured to calculate a reward, based on the evaluation data, and (see [0013]; Tsutsumi: “The learning unit may include a reward computation unit that computes a reward based on the state variable, and a function update unit that updates a function for changing the excitation current command increment and the excitation start timing adjusting amount based on the reward.”)
the learning unit is configured to perform learning by using the reward. (see [0046]; Tsutsumi: “The Reinforcement Learning is a learning in which an agent (agent of action) in a certain environment observes a current state and determines an action to be taken. The agent obtains a reword from the environment by selecting actions, and learns a measure for obtaining the maximum reward through a series of actions.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Kyo, Ueyam, and Okubo to include Tsutsumi’s features of the updating unit is further configured to calculate a reward, based on the evaluation data, and the learning unit is configured to perform learning by using the reward. Doing so would achieve quick increase and change of a magnetic flux corresponding to a variation in load and operation condition. (Tsutsumi, [0007])
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kyo in view of Ueyama in view of Okubo in view of Fujita.
Regarding Claim 21, the combination of Kyo, Ueyam, and Okubo teaches all the limitations of claim 1 above, Kyo further teaches wherein
the learning unit is configured
to change a parameter of a function in accordance with the output of the updating unit a plurality of number of times and (see [0023]; Kyo: “In this way, when the steps of inputting the data obtained in the experiment, comparing the calculated value and the actual value, and correcting the coupling load wi (i = 1,2, ..., 11) are repeated a predetermined number of times, the optimum coupling load is obtained. wi (i = 1,2, ..., 11) can be obtained.”)
to output, for each function whose parameter is changed, the control condition from the state variable, (see [0023]; Kyo: “When the optimum coupling load wi (i = 1,2, ..., 11) is obtained, each parameter of the fuzzy rule L is determined based on this, and the movement of the rotating shaft / magnetic bearing system is determined by the fuzzy rule L. Can describe characteristics.”)
and the learning unit is configured to perform learning, based on the training data accumulated in the accumulation unit. (see [0011]; Kyo: “an accurate fuzzy model of the rotating shaft I magnetic bearing system can be obtained by a fuzzy neural network.”)
However, it does not explicitly teach the updating unit includes an accumulation unit and an assessment unit, the assessment unit is configured to assess the evaluation data and to output an assessment result, the accumulation unit is configured to create, based on the assessment result, training data from the state variable and the evaluation data, and to accumulate the training data,
Fujita from the same or similar field of endeavor teaches
the updating unit includes an accumulation unit and an assessment unit, (see [0033]; Fujita: “The machine learning apparatus 100 includes a state observation unit 101 and a learning unit 102.”)
the assessment unit is configured to assess the evaluation data and to output an assessment result, (see [0033]; Fujita: “The state observation unit 101 creates and outputs a training data set for each correction function on the basis of the state variables.”)
the accumulation unit is configured to create, based on the assessment result, training data from the state variable and the evaluation data, and to accumulate the training data, (see [0033]; Fujita: “The learning unit 102 learns the correction parameter for each feature, that is, for each correction function, using the training data set created on the basis of the state variables by the state observation unit 101, and outputs the learning result Fr.”)
The same motivation to combine Kyo, Ueyama, Okubo, and Fujita a set forth for Claim 10 equally applies to Claim 21.
Response to Arguments
Applicant's arguments with respect to the claim rejection(s) of the independent claim(s) have been fully considered and are persuasive because of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
With respect to applicant’s argument located on page 16 of the Amendment:
“However, the present invention is characterized in that, when the shaft position deviation is within a predetermined range (e.g., within a range close to zero), the shaft position deviation is considered to be a predetermined value within that range (e.g., zero), and that predetermined value is used as training data (i.e. evaluation data) in supervised learning. Claim 1 of the present application does not state that the shaft position control conditions (e.g., the voltage applied to the magnetic bearing coil) are set so that the shaft position deviation falls within the predetermined range. In contrast, in reference (Y), the feed speed of the cutting tool (the cutting tool position control conditions) is set so that the "radial deviation" falls within a predetermined allowable range Q. Therefore, the present invention and the invention in reference (Y) use the shaft position deviation (radial deviation) differently. For these reasons withdrawal of this rejection of claim 1 is respectfully requested.”
Examiner notes that the argument is persuasive, causing the new grounds of rejection. A new reference, namely Okubo, has been relied upon to reject the limitations incorporated in the amendment.
With respect to applicant’s argument located on page 17 of the Amendment:
“Applicant believes that the present invention defined in amended claim 1 is not obvious over references (A) and (Y). Furthermore, references (A) and (Y) differ in technical fields or subject matter from cited references other than references (A) and (Y), and neither of cited references other than references (A) and (Y) suggest magnetic bearing devices or machine learning.”
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Kyo discloses determining the movement of magnetic bearing system by using fuzzy neural network while Ueyama discloses feeding the radial deviation which is the amount of the shift in the position in the radial direction from the target position in the radial direction of the rotating shaft to the electromagnet driver. The advantage of incorporating Kyo into the teachings of Ueyama is outlined in the non-final office action dated 9/18/2025. Therefore, the combination of Kyo and Ueyama still read the claimed limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sakawaki et al. (US9689398B2) discloses outputting, as a position deviation value (P2), a difference value between the drive shaft position (P1) detected by the gap sensor and a target position (P0) (a target position in the radial direction of the drive shaft) indicated by an external position command.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/V.N.T./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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