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 .
Drawings
Drawings have been reviewed and accepted.
Specification
The specification filed on 09/16/24 has been entered. Specification has been reviewed and accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted filed on 09/16/24 has been received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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: “control parameter adjustment device” in claims 1-4, and 6-8.
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.
In accord with the description provided with respect to the elements illustrated in Fig. 1 and Fig.5 in the specification, each element recited in the claims included in the control parameter adjustment device are described without a corresponding structure. According to paragraph [0066] of the published specification, the control parameter adjustment device 100 is realized, for example, as, in a computer device that includes a processor (not shown), a memory (not shown), and various input and output interfaces (not shown), the processor executes a program stored in the memory. So each element recited in claims 1-4 and 6-8 (except for “calculator” as it is recognized to be a hardware element) needs to be construed as a computer including a processor running an algorithm performing the corresponding function recited, in accord with Paragraph [0066] and corresponding paragraphs of the specification offering support for the function claimed.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yamuguchi (US20180164756A1).
Regarding claim 1, Yamaguchi teaches A control parameter adjustment device that adjusts each of control parameters of an adjustment target servomotor included in an adjustment target apparatus ([0069] The servo gain setting section 32 sets the servo gain of a servo motor provided in the machine 3 in response to a request from the high-order apparatus 2. For the setting of servo gain, current loop gain, speed loop gain, position loop gain, pressure control proportional/integral gain, or the like may be set, [0070] The machine learning device 20 that performs machine learning performs the action of adjustment of the servo gain of the machine 3 when machining by the machine 3 is started, and performs the learning of the action of adjustment when the machining by the machine 3 with the servo gain adjusted by the action of adjustment is completed), the control parameter adjustment device comprising: a first obtainer that obtains an adjustment target mechanical characteristic of the adjustment target apparatus ([0066] The machine information output section 30 acquires information on the machine 3 such as temperature and rigidity of the machine 3, a type of a workpiece to be machined, a tool and a jig used in machining, a type of a mold, and types of a resin and clay used in machining and outputs the acquired information to the high-order apparatus 2, [0067] The machine information output section 30 acquires information on the machine 3 from a setting memory (not shown) of the machine 3, a sensor (not shown) provided in each section of the machine 3, or the like and outputs the acquired information on the machine 3 in response to a request from the high-order apparatus 2); a second obtainer that obtains a plurality of mechanical characteristics and a plurality of adjustment histories respectively corresponding to a plurality of adjusted apparatuses each including an adjusted servomotor of which each of control parameters has been adjusted ([0007] plurality of controllers to perform machine learning so that a priority factor is optimized based on information (position deflection, cycle time, a motor load, consumption power, a speed fluctuation rate, or the like) on each axis and information (characteristics of a motor, rigidity of the machine, a type of a workpiece, viscosity of a resin, a used tool, a jig, weight and a shape of a mold, a type and viscosity of a resin, or the like) on the operating conditions of each machine collected from the controller of the machine, [0097] In order to exchange state data and learning data between a plurality of high-order apparatuses 2 as described above, communication may be performed via a management apparatus (not shown), the high-order apparatuses 2 may directly communicate with each other, or a cloud may be used), the plurality of adjustment histories each being a history of adjusting each of the control parameters of the adjusted servomotor of a corresponding one of the adjusted apparatuses ([0053] by the application of learning to the adjustment of servo gain used to control a machine by each controller, additional learning under new machining preconditions is performed based on the learning of the adjustment of past servo gain, [0072] The state data observed by the state observation section 21 may be data acquired by the latest machining operation of the machine 3 or may be data acquired by the past machining operation of the machine 3, [0032] the agent acquires the mapping of an observed state st, an action at, and a reward rt+1 as reference information for determining an amount of a reward that he/she is allowed to obtain in the future. For example, when the number of states that the agent is allowed to take at each time is m and the number of actions that the agent is allowed to take is n, the agent obtains a two-dimensional arrangement of m×n, in which rewards rt+1 corresponding to pairs of states at and actions at are stored, by repeatedly performing action, [0054] a plurality of agents is connected to each other via a network or the like, and information on states s, actions a, rewards r, or the like is shared between the agents and applied to each learning, whereby each of the agents performs dispersed reinforcement learning in consideration of the environments of the other agents to be able to perform efficient learning); a control parameter adjuster that adjusts each of the control parameters of the adjustment target servomotor with use of an optimization algorithm in which at least one of a search range or a search initial value of each of the control parameters of the adjustment target servomotor is initially set ([0024] machining machine, it is necessary to consider the fact that results may be obtained as data only after the machining machine actually operates, i.e., searching of optimum actions is performed by a trial and error approach, [0074] The learning section 23 performs the machine learning (reinforcement learning) of the action of adjustment of servo gain with respect to machine information and machining information for each priority factor based on state data observed by the state observation section 21, determination data acquired by the determination data acquisition section 22, and a reward calculated by the reward calculation section 26 … A value function used in the learning is determined according to an applied learning algorithm. For example, when Q-learning is used, it is only necessary to update an action value function Q(st, at) according to the above Formula 2 to advance the learning, [0007] an estimated initial value of optimum gain is set in a controller according to the operating conditions of each machine and the latest data of a value function, and a reward based on information on each axis obtained from the controller is calculated and used in the leaning of a machine learning device. In the control system of the present invention, a plurality of value functions used in machine learning may be stored in advance according to at least a priority factor, and an optimum one of the value functions may be selectively used from a data base according to a situation.), and outputs each of the control parameters adjusted; and an initial setter that initially sets at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor into the optimization algorithm, based on the adjustment target mechanical characteristic, the plurality of mechanical characteristics, and the plurality of adjustment histories (Fig. 1, and Fig. 6, [0009] since a value function is updated by the operating conditions of a separate machine and an estimated result of optimum gain and the shared value function may be used in the learning of another machine, the optimum gain may be automatically and efficiently estimated and set, [0007] an estimated initial value of optimum gain is set in a controller according to the operating conditions of each machine and the latest data of a value function, and a reward based on information on each axis obtained from the controller is calculated and used in the leaning of a machine learning device, [0053] additional learning under new machining preconditions is performed based on the learning of the adjustment of past servo gain, [0084] the gain changing section 28 instructs the servo gain setting section 32 of the machine 3 to adjust servo gain based on the action of adjustment of the servo gain of the machine 3 determined by the decision making section 27) .
Regarding claim 9, Yamaguchi teaches A control parameter adjustment method of adjusting each of control parameters of an adjustment target servomotor included in an adjustment target apparatus ([0069] The servo gain setting section 32 sets the servo gain of a servo motor provided in the machine 3 in response to a request from the high-order apparatus 2. For the setting of servo gain, current loop gain, speed loop gain, position loop gain, pressure control proportional/integral gain, or the like may be set, [0083] The decision making section 27 determines the action of adjustment of the servo gain of the machine 3 based on a learning result learned by the learning section 23 (and stored in the value function storage section 40) and state data observed by the state observation section 21. The determination of the action of adjustment of the servo gain here is equivalent to an action a used in machine learning. The action of adjustment of the servo gain may be performed in such a way that the selection of a type (current loop gain, speed loop gain, position loop gain, or pressure control proportional/integral gain) of the gain to be adjusted and an adjustment degree of the type of the selected gain are combined together) , the control parameter adjustment method comprising: obtaining an adjustment target mechanical characteristic of the adjustment target apparatus ([0066] The machine information output section 30 acquires information on the machine 3 such as temperature and rigidity of the machine 3, a type of a workpiece to be machined, a tool and a jig used in machining, a type of a mold, and types of a resin and clay used in machining and outputs the acquired information to the high-order apparatus 2. The machining information output section 31 acquires information on machining such as a position deflection rate and a fluctuation rate of an axis, machining cycle time, a maximum motor load value, consumption power, and a speed and a fluctuation rate of a motor and outputs the acquired information to the high-order apparatus 2. The servo gain setting section 32 sets servo gain such as current loop gain, speed loop gain, position loop gain, and pressure control proportional/integral gain) ; obtaining a plurality of mechanical characteristics and a plurality of adjustment histories respectively corresponding to a plurality of adjusted apparatuses each including an adjusted servomotor of which each of control parameters has been adjusted ([0007] operating conditions of each machine collected from the controller of the machine. In the control system of the present invention, an estimated initial value of optimum gain is set in a controller according to the operating conditions of each machine and the latest data of a value function, and a reward based on information on each axis obtained from the controller is calculated and used in the leaning of a machine learning device, [0096] that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range. Thus, the learning may be more efficiently performed), the plurality of adjustment histories each being a history of adjusting each of the control parameters of the adjusted servomotor of a corresponding one of the adjusted apparatuses ([0053] Accordingly, as in the present invention, by the application of learning to the adjustment of servo gain used to control a machine by each controller, additional learning under new machining preconditions is performed based on the learning of the adjustment of past servo gain. Thus, it becomes possible to perform the learning of the adjustment of servo gain in a short period of time, [0096] the machine learning may be more efficiently performed. For example, parallel learning is advanced between a plurality of the high-order apparatuses 2 in such a way that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range) ; adjusting each of the control parameters of the adjustment target servomotor with use of an optimization algorithm in which at least one of a search range or a search initial value of each of the control parameters of the adjustment target servomotor is initially set, and outputs each of the control parameters adjusted; and setting at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor into the optimization algorithm, based on the adjustment target mechanical characteristic, the plurality of mechanical characteristics, and the plurality of adjustment histories ([0023] The “reinforcement learning” is a method by which to learn not only determinations or classifications but also actions to perform learning of optimum actions in consideration of interactions given to environments by actions, i.e., learning to maximize rewards that will be obtained in the future, [0088] Step SA03. The decision making section 27 selects an action at (action of adjustment of the servo gain of the machine 3) based on a past learning result and the state st specified in step SA02, [0007] In the control system of the present invention, an estimated initial value of optimum gain is set in a controller according to the operating conditions of each machine and the latest data of a value function, and a reward based on information on each axis obtained from the controller is calculated and used in the leaning of a machine learning device, [0084] Then, the gain changing section 28 instructs the servo gain setting section 32 of the machine 3 to adjust servo gain based on the action of adjustment of the servo gain of the machine 3 determined by the decision making section 27, [0096] that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range, [0009] since a value function is updated by the operating conditions of a separate machine and an estimated result of optimum gain and the shared value function may be used in the learning of another machine, the optimum gain may be automatically and efficiently estimated and set) .
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) 2, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Yamuguchi (US20180164756A1), and in view of Murai et al. (US20180224824A1, herein Murai).
Regarding claim 2, Yamaguchi teaches The control parameter adjustment device according to claim 1,
Yamaguchi does not teach wherein the initial setter initially sets, into the optimization algorithm, both the search range and the search initial value of each of the control parameters of the adjustment target servomotor, and the control parameter adjuster adjusts each of the control parameters of the adjustment target servomotor with use of the optimization algorithm in which both the search range and the search initial value of each of the control parameters of the adjustment target servomotor are initially set.
Murai teaches wherein the initial setter initially sets, into the optimization algorithm, both the search range and the search initial value of each of the control parameters of the adjustment target servomotor, and the control parameter adjuster adjusts each of the control parameters of the adjustment target servomotor with use of the optimization algorithm in which both the search range and the search initial value of each of the control parameters of the adjustment target servomotor are initially set ([0035] The initial control parameter setting unit 114 sets an initial value of the parameter determination unit 132. Here, the initial control parameter setting unit 114 sets all parameters to be set to the motor 900, [0045] when the evaluation value is equal to or greater than the allowable value, the parameter determination unit 132 changes the control parameter using a known optimization algorithm and outputs the changed control parameter to the operation data calculation unit 133. The parameter determination unit 132 outputs the adopted control parameter to the driver 90 through the IF unit 14, [0056] As illustrated in FIG. 7, the control device (information processing device) sets a parameter region (S201). The parameter region specifies a range of settable values of the control parameter in a control parameter optimization algorithm. In addition, the control device (information processing device) changes a control parameter within an available control parameter range set by the parameter region (S206) when an evaluation value is equal to or greater than an allowable value (NO in S105), [0057] parameter regions Re1, Re2 and Re3 are set as shown in FIG. 8A and FIG. 8B. The parameter region Re1 corresponds to a case of using a control parameter in which the waveform of operation data gradually rises and thus initial followability to normative data is low, and the operation data coincides with the normative data over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamaguchi teaching servo optimization based on machine conditions with Murai’s teaching of using initial parameters and search ranges. The combined teaching provides an expected result of servo optimization based on machine conditions using initial parameters and search ranges. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by restricting optimization to suitable parameter regions as shown by Murai [0052].
Regarding claim 5, Yamaguchi teaches The control parameter adjustment device according to claim 1, further comprising: a calculator that calculates …based on the adjustment target mechanical characteristic, the plurality of mechanical characteristics, and the plurality of adjustment histories, wherein the initial setter initially sets, into the optimization algorithm ([0007], perform machine learning so that a priority factor is optimized based on information (position deflection, cycle time, a motor load, consumption power, a speed fluctuation rate, or the like) on each axis and information (characteristics of a motor, rigidity of the machine, a type of a workpiece, viscosity of a resin, a used tool, a jig, weight and a shape of a mold, a type and viscosity of a resin, or the like) on the operating conditions of each machine collected from the controller of the machine. In the control system of the present invention, an estimated initial value of optimum gain is set in a controller according to the operating conditions of each machine and the latest data of a value function, and a reward based on information on each axis obtained from the controller is calculated and used in the leaning of a machine learning device. In the control system of the present invention, a plurality of value functions used in machine learning may be stored in advance according to at least a priority factor, and an optimum one of the value functions may be selectively used from a data base according to a situation, [0054] performs dispersed machine learning in a state of being connected to each other via a network or the like, learning of the adjustment of a machining path of a turning cycle command and machining conditions in the numerical control controllers of the lathe machining machines may be efficiently performed, [0096] to send/receive and share a value function stored in each of the value function storage sections 40. Thus, the machine learning may be more efficiently performed. For example, parallel learning is advanced between a plurality of the high-order apparatuses 2 in such a way that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range)
Yamaguchi does not teach at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor,… the at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor, calculated by the calculator
Murai teaches at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor,… the at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor, calculated by the calculator ([0035] The initial control parameter setting unit 114 sets an initial value of the parameter determination unit 132. Here, the initial control parameter setting unit 114 sets all parameters to be set to the motor 900, [0040] The operation data calculation unit 133 outputs operation data by using a control parameter determined by the parameter determination unit 132, the physical model of the driver, the physical model of the device, and the command data. The operation data is a numerical value representing an operation of the motor 900 and indicates a speed, a torque, a position and the like. For example, the operation data may be represented by the solid line of FIG. 3. The operation data calculation unit 133 outputs the operation data to the data evaluation unit 131, [0056] As illustrated in FIG. 7, the control device (information processing device) sets a parameter region (S201). The parameter region specifies a range of settable values of the control parameter in a control parameter optimization algorithm. In addition, the control device (information processing device) changes a control parameter within an available control parameter range set by the parameter region (S206) when an evaluation value is equal to or greater than an allowable value (NO in S105).) .
Claim(s) 3-4, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Yamuguchi (US20180164756A1), and in view of Murai et al. (US20180224824A1, herein Murai), and in further view of Iijima et al. (US20180224829A1, herein Iijima).
Regarding claim 3, Yamaguchi teaches The control parameter adjustment device according to claim 1,
Yamaguchi does not teach wherein the adjustment target mechanical characteristic includes a resonance frequency of the adjustment target apparatus, the plurality of mechanical characteristics each include a resonance frequency of a corresponding one of the plurality of adjusted apparatuses, the control parameter adjustment device further comprises: an operation command outputter that outputs, to a driver that controls the adjustment target servomotor, an operation command that causes the adjustment target servomotor to operate; a third obtainer that obtains position information indicating a position of a driving target driven by the adjustment target servomotor that operates based on the operation command, by being controlled by the driver; and a resonance frequency calculator that calculates a resonance frequency of the adjustment target apparatus based on the position information, and outputs the adjustment target mechanical characteristic that includes the resonance frequency calculated, and the first obtainer obtains the adjustment target mechanical characteristic calculated by the resonance frequency calculator.
Murai teaches an operation command outputter that outputs, to a driver that controls the adjustment target servomotor, an operation command that causes the adjustment target servomotor to operate ([0029] The control device 10 is connected to the driver 90 through the IF unit 14. The driver 90 is a device to which a control parameter set by the control device 10 is applied. The driver 90 is connected to the motor 900 which is a control target and the motor 900 is connected to the target device 910, [0030] The driver 90 controls the motor 900 according to a control parameter applied by the control device 10. For example, if the motor 900 is a servo motor, the driver 90 is a servo driver. A predetermined tact in the target device 910 is executed according to the operation of the motor 900, [0036] The command data setting unit 115 sets command data and outputs the command data to the operation data calculation unit 133. The command data is a basis of operation data, and subsequent operation data of the next time is calculated from the difference between the operation data up to a certain moment in time and the command data); a third obtainer that obtains position information indicating a position of a driving target driven by the adjustment target servomotor that operates based on the operation command, by being controlled by the driver … position ([0038] FIG. 2 shows some control parameters, various control parameters related to control of position, speed and torque, for example, a position loop gain, a speed loop gain, a speed loop integral time constant, a speed feedback filter time constant, a torque feed forward gain, and the like may be included, [0040] The operation data calculation unit 133 outputs operation data by using a control parameter determined by the parameter determination unit 132, the physical model of the driver, the physical model of the device, and the command data. The operation data is a numerical value representing an operation of the motor 900 and indicates a speed, a torque, a position and the like. For example, the operation data may be represented by the solid line of FIG. 3. The operation data calculation unit 133 outputs the operation data to the data evaluation unit 131.) ;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamaguchi teaching servo adjustment system with Murai’s teaching of outputting position information. The combined teaching provides an expected result of servo adjustment system outputting position information. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by including position data supported by Murai [0007] adjusting control parameters without the need to continuously operate a driver of a control target.
Murai does not teach wherein the adjustment target mechanical characteristic includes a resonance frequency of the adjustment target apparatus, the plurality of mechanical characteristics each include a resonance frequency of a corresponding one of the plurality of adjusted apparatuses, the control parameter adjustment device further comprises:…and a resonance frequency calculator that calculates a resonance frequency of the adjustment target apparatus based on the position information, and outputs the adjustment target mechanical characteristic that includes the resonance frequency calculated, and the first obtainer obtains the adjustment target mechanical characteristic calculated by the resonance frequency calculator.
Iijima teaches wherein the adjustment target mechanical characteristic includes a resonance frequency of the adjustment target apparatus, the plurality of mechanical characteristics each include a resonance frequency of a corresponding one of the plurality of adjusted apparatuses, the control parameter adjustment device further comprises:…and a resonance frequency calculator that calculates a resonance frequency of the adjustment target apparatus based on the …information, and outputs the adjustment target mechanical characteristic that includes the resonance frequency calculated, and the first obtainer obtains the adjustment target mechanical characteristic calculated by the resonance frequency calculator ([0030] Sinusoidal disturbance is input from the sinusoidal disturbance input unit 15 to the frequency characteristics calculation unit 22. The frequency characteristics calculation unit 22 estimates the gain and the phase of an input/output signal of the speed control loop 30 from the output of the speed control loop 30 when the sinusoidal disturbance was input to the speed control loop 30 of the servo controller 10. Furthermore, the frequency characteristics calculation unit 22 expresses the output of the speed control loop 30 using a Fourier series which has an arbitrary number of terms and which uses the frequency of disturbance input from the sinusoidal disturbance input unit 15 as a fundamental frequency and calculates the amplitude and the phase of a fundamental wave component of the Fourier series to thereby calculate the frequency characteristics online. The frequency characteristics calculated by the frequency characteristics calculation unit 22 are transmitted to the parameter adjustment unit 23, [0031] The parameter adjustment unit 23 outputs a signal for adjusting the parameters stored in the parameter storage unit 24 on the basis of the frequency characteristics input from the frequency characteristics calculation unit 22, [0028] In the adder 20, the speed detection value detected by the speed detection unit 13 is subtracted from the speed command value created by the speed command creation unit 11, and sinusoidal disturbance created by the sinusoidal disturbance input unit 15 that performs sinusoidal sweeping on the speed control loop 30 is added. The frequency of the sinusoidal disturbance input unit 15 is changed sequentially).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamaguchi teaching servo adjustment system with Iijima’s teaching of calculating frequency characteristics. The combined teaching provides an expected result of servo adjustment system calculating frequency characteristics. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by calculating frequency data supported by Iijima [0048] parameters correspond to the actual frequency characteristics determined according to a machine structure, a machining object, or the like, the actual state of the machine tool can be applied to the restoring process with high accuracy .
Regarding claim 4, the combination of Yamaguchi, Murai, and Iijima teach The control parameter adjustment device according to claim 3,
Iijima further teaches further comprising: an outputter that outputs, to a database, the adjustment target mechanical characteristic output from the resonance frequency calculator and an adjustment history of adjusting each of the control parameters of the adjustment target servomotor by the control parameter adjuster, wherein the second obtainer obtains the plurality of mechanical characteristics and the plurality of adjustment histories from the database ([0033] In the parameter storage unit 24, the frequency characteristics calculated by the frequency characteristics calculation unit 22 are stored in correlation with the corresponding parameters and the related information. The related information is a frequency characteristics graph such as a Bode diagram, a gain margin, a phase margin, machining accuracy information, and the like and these pieces of related information are stored in combination. Text information such as a memo indicating that a predetermined accuracy condition is exceeded, for example, can be used as the machining accuracy information, [0036] When a machining object which is a machining object of the machine tool 1 is changed, automatic adjustment for fine adjustment is performed by the servo controller 10. When the parameter adjustment unit 23 starts automatic adjustment for the speed control gain 14 and the filter 21 (step S101), the parameter storage unit 24 stores the frequency characteristics and the parameters before execution of automatic adjustment in the history (step S102). For example, the frequency characteristics illustrated in FIG. 3 and the parameters corresponding to the frequency characteristics are stored in the parameter storage unit 24, [0040] The flow ends when it is determined in step S109 that the parameters are not to be restored. When it is determined in step S109 that the parameters are to be restored, a predetermined frequency characteristic is designated from the frequency characteristics stored in the history and parameters corresponding to the predetermined frequency characteristic are restored by the parameter storage unit 24 (step S110), [0041] FIG. 5 is an explanatory diagram illustrating the frequency characteristics after the parameters are restored. In steps S109 and S110, another frequency characteristic that satisfies the accuracy condition in the past is selected from the history, and the parameters corresponding to the frequency characteristic are restored by the parameter storage unit 24. According to the restored parameters, the characteristics of the speed control gain 14 and the filter 21 are changed. As a result, the frequency characteristics are changed from the state illustrated in FIG. 4 to the state illustrated in FIG. 5, and it is possible to avoid downtime resulting from adjustment time ).
Regarding claim 7, Yamaguchi teaches The control parameter adjustment device according to claim 1, further comprising: …at least part of the adjustment target mechanical characteristic, the plurality of mechanical characteristics, …of each of the control parameters of the adjustment target servomotor ([0100], adjustment of the servo gain of the servo motor provided in the one machine 3, [0007] plurality of controllers to perform machine learning so that a priority factor is optimized based on information (position deflection, cycle time, a motor load, consumption power, a speed fluctuation rate, or the like) on each axis and information (characteristics of a motor, rigidity of the machine, a type of a workpiece, viscosity of a resin, a used tool, a jig, weight and a shape of a mold, a type and viscosity of a resin, or the like) on the operating conditions of each machine collected from the controller of the machine. In the control system of the present invention, an estimated initial value of optimum gain is set in a controller according to the operating conditions of each machine and the latest data of a value function, and a reward based on information on each axis obtained from the controller is calculated and used in the leaning of a machine learning device, [0053] learning to the adjustment of servo gain used to control a machine by each controller, additional learning under new machining preconditions is performed based on the learning of the adjustment of past servo gain. Thus, it becomes possible to perform the learning of the adjustment of servo gain in a short period of time, [0096] send/receive and share a value function stored in each of the value function storage sections 40. Thus, the machine learning may be more efficiently performed. For example, parallel learning is advanced between a plurality of the high-order apparatuses 2 in such a way that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range ),
Yamaguchi does not teach a presenter that presents, to a user of the control parameter adjustment device,… and an input receiver that, after the presenting by the presenter, receives, from the user, input of at least one of the search range or the search initial value… wherein the initial setter initially sets, into the optimization algorithm, the at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor, received by the input receiver
Murai teaches a presenter that presents, to a user of the control parameter adjustment device,… and an input receiver that, after the presenting by the presenter, receives, from the user, input of at least one of the search range or the search initial value… wherein the initial setter initially sets, into the optimization algorithm, the at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor, received by the input receiver ([0031] The manipulation receiving unit 100 is configured as a keyboard, a mouse, a touch panel or the like and receives manipulation from an operator. For example, the manipulation receiving unit 100 receives a control parameter setting manipulation from the operator. The manipulation receiving unit 100 instructs the target setting unit 11, the model identification unit 12 and the control parameter setting unit 13 to execute a control parameter optimization process on the basis of reception of the manipulation, [0035] The initial control parameter setting unit 114 sets an initial value of the parameter determination unit 132. Here, the initial control parameter setting unit 114 sets all parameters to be set to the motor 900, [0056] As illustrated in FIG. 7, the control device (information processing device) sets a parameter region (S201). The parameter region specifies a range of settable values of the control parameter in a control parameter optimization algorithm. In addition, the control device (information processing device) changes a control parameter within an available control parameter range set by the parameter region (S206) when an evaluation value is equal to or greater than an allowable value (NO in S105), [0062] The display unit 15 displays data related to control parameter setting performed in the control device 10A. For example, the display unit 15 displays waveforms of operation data and normative data according to a control parameter set by the aforementioned optimization algorithm, as illustrated in FIG. 3. Accordingly, the operator can easily check how the motor 900 is operating according to the control parameter set on the basis of the normative data. In addition, the display unit 15 displays the above-described parameter regions and thus a position at which a control parameter is set according to execution of the optimization algorithm for each region can be easily recognized).
The combination of Yamaguchi and Murai do not teach and the plurality of adjustment histories
Iijima teaches and the plurality of adjustment histories ([0043] The parameter storage unit 24 stores the history of the past frequency characteristics obtained by the frequency characteristics calculation unit 22 in correlation with the past parameter history, [0042] The parameters may be restored according to the result of a user's selection and designation (a user's request) via an interface (not illustrated) such as a display screen or a manipulation unit555) ;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamaguchi teaching servo adjustment system using past data with Iijima’s teaching of having a plurality of history data of past frequency characteristics and parameter history. The combined teaching provides an expected result of servo adjustment using a plurality of past parameter and frequency data. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by calculating frequency data supported by Iijima [0048] parameters correspond to the actual frequency characteristics determined according to a machine structure, a machining object, or the like, the actual state of the machine tool can be applied to the restoring process with high accuracy .
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yamuguchi (US20180164756A1), and in view of Murai et al. (US20180224824A1, herein Murai), and in further view of Stoll et al. (US20220051138A1, herein Stoll).
Regarding claim 6, the combination of Yamaguchi, and Murai teach The control parameter adjustment device according to claim 5, wherein when test control has been performed a plurality of times on the adjusted servomotor in a process of adjusting each of the control parameters of the adjusted servomotor, …and an information calculator that, with regard to a first adjustment history, among the plurality of adjustment histories, of a first servomotor in a first adjusted apparatus having the first mechanical characteristic, calculates, for each of control parameters of the first servomotor ([0007] machine learning so that a priority factor is optimized based on information (position deflection, cycle time, a motor load, consumption power, a speed fluctuation rate, or the like) on each axis and information (characteristics of a motor, rigidity of the machine, a type of a workpiece, viscosity of a resin, a used tool, a jig, weight and a shape of a mold, a type and viscosity of a resin, or the like) on the operating conditions of each machine collected from the controller of the machine, [0009] a value function is updated by the operating conditions of a separate machine and an estimated result of optimum gain and the shared value function may be used in the learning of another machine, the optimum gain may be automatically and efficiently estimated and set, [0053] adjustment of servo gain used to control a machine by each controller, additional learning under new machining preconditions is performed based on the learning of the adjustment of past servo gain. Thus, it becomes possible to perform the learning of the adjustment of servo gain in a short period of time, [0096] that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range),
Murai further teaches each of the plurality of adjustment histories includes, for each instance of the test control, (1) setting values of each of the control parameters of the adjusted servomotor and (2) evaluation values obtained by evaluating the test control, the control parameter adjustment device further comprises: (1) the setting values, .. and (3) evaluation value information pertaining to the evaluation values corresponding to the setting values, and the ([0042] The data evaluation unit 131 calculates an evaluation value by using the operation data and the normative data. Specifically, as shown in FIG. 3, the data evaluation unit 131 performs discrete sampling on the operation data and the normative data on the time axis, and acquires operation data Xr(tn) and normative data Xc(tn) of each time tm (m=1 to n). The data evaluation unit 131 calculates a difference value (based on the normative data Xc(tn)) between the operation data Xr(tn) and the normative data Xc(tn) at each time tm. The data evaluation unit 131 calculates a norm from this difference value and regards the norm as an evaluation value. The data evaluation unit 131 outputs the evaluation value to the parameter determination unit 132, [0045] the parameter determination unit 132 changes the control parameter using a known optimization algorithm and outputs the changed control parameter to the operation data calculation unit 133, [0053] calculating an evaluation value for one control parameter has been illustrated in the above description, evaluation values for a plurality of control parameters can be calculated through the following methods. (1) A plurality of evaluation values are weighted and added. (2) One evaluation value is restricted and other evaluation values are minimized (within an allowable value). More appropriate control parameters can be set in the case of(1), and a settling time can be reduced in the case of (2), [0063] FIG. 10A is a diagram illustrating a relationship between a control parameter value and a settable range, in which the horizontal axis represents the number of updates (the number of times the setting of the control parameter is changed by using the optimization algorithm) and the vertical axis represents the control parameter value)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamaguchi teaching servo optimization based on machine conditions with Murai’s teaching of using setting values. The combined teaching provides an expected result of servo optimization based on machine conditions using setting values. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by restricting optimization to suitable parameter regions as shown by Murai [0052].
The combination of Yamaguchi and Murai do not teach (2) setting frequency information pertaining to setting frequencies with which the setting values are set, ..an extractor that extracts, from the plurality of mechanical characteristics, a first mechanical characteristic similar to the adjustment target mechanical characteristic …calculator calculates at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor, based on the setting values, the setting frequency information, and the evaluation value information calculated by the information calculator
Stoll teaches (2) setting frequency information pertaining to setting frequencies with which the setting values are set, ..an extractor that extracts, from the plurality of mechanical characteristics, a first mechanical characteristic similar to the adjustment target mechanical characteristic …calculator calculates at least one of the search range or the search initial value of each of the control parameters of the adjustment target servomotor, based on the setting values, the setting frequency information, and the evaluation value information calculated by the information calculator ([0011] The hyperparameter configurations in each case are a set of values for the hyperparameters. The search spaces are defined in each instance on the basis of predetermined value ranges of the hyperparameters. If there is a multiplicity of hyperparameters, then the search space is multidimensional and bounded by the value ranges. The value range is a discreet/continuous quantity of values between a predetermined start value and target value, e.g., for the learning rate from 0.001 to 0.99, [0020] TPE utilizes kernel density estimators to model the densities l(x) and g(x), where l(x) calculates a probability that hyperparameter configuration x is worse or better (g(x)) than the best hyperparameter configuration x* found till now, particularly as a function of the previous candidate configurations evaluated via the loss function, [0022] provided that in creating the reduced search space, the best hyperparameter configurations be selected from the ranking and all values of the hyperparameters of the best hyperparameter configurations be determined, and that a reduced search space be created based on the current search space, the value ranges of the hyperparameters being defined as a function of the minimum and maximum values of the individual hyperparameters of the best hyperparameter configurations, [0045] This step may be referred to as collecting of candidates, because candidate configurations are then drawn repeatedly at random from reduced search space Xi ˜ and search space X) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamaguchi teaching servo optimization based on machine conditions with Stoll’s teaching of extracting similar characteristics. The combined teaching provides an expected result of servo optimization based on machine conditions extracting similar characteristics. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy by reusing knowledge form comparable task to improve optimization efficiency as shown by Stoll [0009].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yamuguchi (US20180164756A1), and in view of Murai et al. (US20180224824A1, herein Murai), in further view of Iijima et al. (US20180224829A1, herein Iijima), and in further view of Stoll et al. (US20220051138A1, herein Stoll).
Regarding claim 8, the combination of Yamaguchi, Murai, and Iijima teach The control parameter adjustment device according to claim 7, wherein when test control has been performed a plurality of times on the adjusted servomotor in a process of adjusting each of the control parameters of the adjusted servomotor, each of the plurality of adjustment histories… from the plurality of mechanical characteristics ([0007] machine learning so that a priority factor is optimized based on information (position deflection, cycle time, a motor load, consumption power, a speed fluctuation rate, or the like) on each axis and information (characteristics of a motor, rigidity of the machine, a type of a workpiece, viscosity of a resin, a used tool, a jig, weight and a shape of a mold, a type and viscosity of a resin, or the like) on the operating conditions of each machine collected from the controller of the machine, [0009] a value function is updated by the operating conditions of a separate machine and an estimated result of optimum gain and the shared value function may be used in the learning of another machine, the optimum gain may be automatically and efficiently estimated and set, [0053] adjustment of servo gain used to control a machine by each controller, additional learning under new machining preconditions is performed based on the learning of the adjustment of past servo gain. Thus, it becomes possible to perform the learning of the adjustment of servo gain in a short period of time, [0096] that state data, determination data, and value functions acting as learning results are exchanged between the high-order apparatuses 2 while adjustment targets and adjustment amounts different between the plurality of high-order apparatuses 2 are fluctuated within a prescribed range),w
Murai further teaches includes, for each instance of the test control, (1) setting values of each of the control parameters of the adjusted servomotor and (2) evaluation values obtained by evaluating the test control, the control parameter adjustment device further comprises:… with regard to a first adjustment history, among the plurality of adjustment histories, of a first servomotor in a first adjusted apparatus having the first mechanical characteristic, calculates, for each of control parameters of the first servomotor, …and the presenter presents the at least part to the user ([0042] The data evaluation unit 131 calculates an evaluation value by using the operation data and the normative data. Specifically, as shown in FIG. 3, the data evaluation unit 131 performs discrete sampling on the operation data and the normative data on the time axis, and acquires operation data Xr(tn) and normative data Xc(tn) of each time tm (m=1 to n). The data evaluation unit 131 calculates a difference value (based on the normative data Xc(tn)) between the operation data Xr(tn) and the normative data Xc(tn) at each time tm. The data evaluation unit 131 calculates a norm from this difference value and regards the norm as an evaluation value. The data evaluation unit 131 outputs the evaluation value to the parameter determination unit 132, [0045] the parameter determination unit 132 changes the control parameter using a known optimization algorithm and outputs the changed control parameter to the operation data calculation unit 133, [0053] calculating an evaluation value for one control parameter has been illustrated in the above description, evaluation values for a plurality of control parameters can be calculated through the following methods. (1) A plurality of evaluation values are weighted and added. (2) One evaluation value is restricted and other evaluation values are minimized (within an allowable value). More appropriate control parameters can be set in the case of(1), and a settling time can be reduced in the case of (2), [0063] FIG. 10A is a diagram illustrating a relationship between a control parameter value and a settable range, in which the horizontal axis represents the number of updates (the number of times the setting of the control parameter is changed by using the optimization algorithm) and the vertical axis represents the control parameter value)
The combination of Yamaguchi, Murai, and Iijima do not teach an extractor that extracts…, a first mechanical characteristic similar to the adjustment target mechanical characteristic; and an information calculator that, … (1) the setting values, (2) setting frequency information pertaining to setting frequencies with which the setting values are set, and (3) evaluation value information pertaining to the evaluation values corresponding to the setting values, …based on the setting values, the setting frequency information, and the evaluation value information calculated by the information calculator
Stoll teaches an extractor that extracts…, a first mechanical characteristic similar to the adjustment target mechanical characteristic; and an information calculator that, … (1) the setting values, (2) setting frequency information pertaining to setting frequencies with which the setting values are set, and (3) evaluation value information pertaining to the evaluation values corresponding to the setting values, …based on the setting values, the setting frequency information, and the evaluation value information calculated by the information calculator ([0011] The hyperparameter configurations in each case are a set of values for the hyperparameters. The search spaces are defined in each instance on the basis of predetermined value ranges of the hyperparameters. If there is a multiplicity of hyperparameters, then the search space is multidimensional and bounded by the value ranges. The value range is a discreet/continuous quantity of values between a predetermined start value and target value, e.g., for the learning rate from 0.001 to 0.99, [0020] TPE utilizes kernel density estimators to model the densities l(x) and g(x), where l(x) calculates a probability that hyperparameter configuration x is worse or better (g(x)) than the best hyperparameter configuration x* found till now, particularly as a function of the previous candidate configurations evaluated via the loss function, [0022] provided that in creating the reduced search space, the best hyperparameter configurations be selected from the ranking and all values of the hyperparameters of the best hyperparameter configurations be determined, and that a reduced search space be created based on the current search space, the value ranges of the hyperparameters being defined as a function of the minimum and maximum values of the individual hyperparameters of the best hyperparameter configurations, [0045] This step may be referred to as collecting of candidates, because candidate configurations are then drawn repeatedly at random from reduced search space Xi ˜ and search space X)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Nakajima (US20220077800) teaches an adjustment support device and control parameter adjustment for servo motors.
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/YVONNE T FOLLANSBEE/
Examiner, Art Unit 2117
/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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