DETAILED ACTION
Claims 1-11 (filed 09/04/2024) have been considered in this action. Claims 1-11 are newly filed.
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: “controller”, “predictor” and “corrector” in claims 1-10, and “abnormality detector” in claim 7.
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. According to the provided specification, the recited structure for performing the functional limitations includes “[page 32] using circuitry or processing circuitry which includes general purpose processors, special purpose processors, integrated circuits, ASICs (“Application Specific Integrated Circuits”), FPGAs (“Field Programmable Gate Arrays”), conventional circuitry and/or combinations thereof which are programmed, using one or more programs stored in one or more memories, or otherwise configured to perform the disclosed functionality”. In other words, some form of processor and memory.
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.
Claim Objections
Claim 1 is objected to because of the following informalities: the term "a predictor configured to predict the output value contains a typographical error in that the first use of the word “on” is superfluous and does not appear to have any intended meaning. For the sake of compact prosecution, the examiner shall interpret the claim such that the above underlined “on” does not exist.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “output value is close” and “output value is far” in claim 2 is a relative term which renders the claim indefinite. The terms “close” and “far” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The terms “close” and “far” are utilized to describe a state of being, rather than an action taken, thus making their meaning indefinite because they do not conform to standard English grammar. For the sake of compact prosecution and based upon the provided specification, the use of “close and far” are intended as being used as actions performed by the corrector which push the value of the control value so that it is closer to the command value (i.e. close) or which instead push the value of the control value so that it is further from the command value (i.e. far).
Claims 3-6 are dependent upon claim 2, and thus inherit the rejection under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea without significantly more. The claims are directed to the statutory category of invention of a machine or product in the form of a control device.
Step 2A Prong One:
Claim(s) 1-10 are apparatus directed to abstract ideas in the form of Mental processes capable of being performed in the human mind. The acts of generate a control value based on a command value and an output value output from a control target; predict the output value on based on an input value input to the control target and a predictive model of the control target and generate a prediction value indicating a result of predicting the output value; and correct the control value based on the command value, the output value, and the prediction value, wherein the input value input to the control target is the control value corrected by the corrector are directed to abstract ideas that are processes capable of being performed in the human mind. For example, based on the BRI of the claim, the generation of the control value, prediction of a prediction value and correction of the control value are broad in scope and could entail a person in their mind, determining values with the aid of pen and paper so that these values are “based” on the corresponding other values. The claim is nothing more than a series of calculations/determinations performed by a computer in that they only direct towards what the various values are “based on” rather than being specific and definitive relationships incapable of being performed in the human mind. The scope of the claimed “predictive model” has no details, and thus even a simple predictive relationship such as the linear relationship between output y and input x as y(t) = A* x(t) would be covered by such scope. A person could mentally with the aid of pen and paper predict a value from the basis of such a simple linear predictive model, and the BRI of what is claimed covers such scope. Thus, all of the above claimed steps are considered mental processing steps under the BRI of what is claimed.
Step 2A Prong Two:
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination because the additional elements, which are recited at a high level of generality, provide conventional functions that do not add meaningful limits to practicing the abstract idea.
Claim 1 recites, in part, the additional elements of a controller, a predictor and a corrector. These limitations invoke interpretation under 35 U.S.C. 112(f), and thus their scope is defined as software executed on a processor using memory, which amounts to is mere instructions to apply an exception without significantly more, which amounts to little more than a recitation of the words “apply it”. The identified limitations only recite the idea of a solution or outcome without claiming details of how a solution is accomplished (see MPEP 2106.05(f): “…The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)” or are elements that amount to little more than generally linking the use of the judicial exception to a particular technological environment or field of use that fail to place meaningful limits on the claim (see MPEP 2106.05(h): “…The courts often cite to Parker v. Flook as providing a classic example of a field of use limitation. See, e.g., Bilski v. Kappos, 561 U.S. 593, 612, 95 USPQ2d 1001, 1010 (2010) ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable") (citing Parker v. Flook, 437 U.S. 584, 198 USPQ 193 (1978)).”).
The abstract idea described in claim 1 is not meaningfully different than those abstract ideas found by the courts, therefor the claim is considered to be directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claim recites the additional elements of a controller, a predictor and a corrector. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. As noted above, the additional element reflect the fact that the abstract ideas are being performed on a computer, but the mere inclusion of a computer fails to afford a practical application nor afford anything more than a recitation of “apply it” as the abstract ideas being performed on a computer. There is no indication that the combination of elements improves the functioning of a computer or improves another technology because the claims are not directed towards any improvement in control technology as they do not positively recite any control action being performed. Their collective functions merely provide conventional computer implementations and functions. Claim 1 fails to afford any practical application because the claim is not directed towards any controlled process, such as the control device being controlled on the basis of the control value corrected by the corrected, and instead merely claims a series of processes capable of broadly being performed in human mind without significantly more.
Dependent claims 2-7 and 9 are drawn to further processes performed by the computer that can broadly be considered processes capable of being performed in the human mind, such as making determinations of values being further/closer, performing addition/subtraction, adjusting values, performing anomaly detection, etc. These limitations are considered to be drawn to the abstract idea without adding significantly more. Claims 8 and 10 provide generic limitations that links the claimed invention to a particular field of use, such as PID/machine learning controls and the target being a motor, but fails to perform anything significant beyond the insignificant recitation of the motor or forms of feedback control loops.
In regards to Claim 11, the claim is directed to substantially similar subject matter as claim 1, albeit in a different statutory category of invention of a method. Accordingly, a similar analysis as applied to claim 1 can be applied to claim 11 in terms of eligible subject matter under consideration of 35 U.S.C. 101. Claim 11 is therefore rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more as applied to claim 1.
Claims 1-11 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. Accordingly, claim 1-11 are rejected under 35 U.S.C. 101.
It is recommended to overcome the rejection of claims 1-11 under 35 U.S.C. 101 to integrate a practical application into the claims such as by claiming that the control target is controlled using the control value corrected by the corrector, as this would positively recite a control action that cannot be reasonably interpreted as steps capable of being performed in the human mind.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Fujii (US 20240385577, hereinafter Fujii) in view of Tsuchikawa et al (US 20240281674, hereinafter Tsuchikawa).
In regards to Claim 1, Fujii teaches “A control device comprising: a controller configured to generate a control value based on a command value and an output value output from a control target” ([0034] FIG. 1 is a block diagram illustrating a functional configuration of a control device 100 according to a first embodiment. As illustrated in FIG. 1, the control device 100 includes a feedback control unit 110, a feedforward compensation unit 120, a learning unit 130, a storage unit 140, a subtracter 150, and an adder 160. [0036] The subtracter 150 outputs an error eq (=qr-q) between the target value qr and the control amount q to the feedback control unit 110. The feedback control unit 110 determines the feedback operation amount rb based on the error eq and outputs the same to the adder 160. [0080] FIG. 15 is a block diagram illustrating an example hardware configuration of the control device 300 of FIG. 14. As illustrated in FIG. 15, the control device 300 includes a processor 302, a main memory 304, a storage 360, a memory card interface 312, a host network controller 306, a field network controller 308, a local bus controller 316, and a USB (Universal Serial Bus) controller 370 that provides a USB interface. These components are connected to each other via a processor bus 318.
[0081] The processor 302 corresponds to an arithmetic processing unit that executes a control operation, and includes a CPU (Central Processing Unit) and/or a GPU (Graphics Processing Unit). Specifically, the processor 302 reads out a program stored in the storage 360, develops the program in the main memory 304, and executes the program, thereby implementing a control operation on a control object; wherein the feedback control unit is a controller and the processor and memory corresponds with the control device, the target value is the command value, and the control amount is an output value) “a predictor configured to predict … based on an input value input to the control target and a predictive model of the control target and generate a prediction value indicating a result of predicting the output value” ([0038] The learning unit 130 performs machine learning on the prediction model Mp using the supervised data Ds. The learning unit 130 obtains at least one combination Cm1 (first combination) which includes the target value qr, the operation amount r corresponding to the target value qr, the disturbance d, and the control amount q corresponding to both the disturbance d and the operation amount r. The learning unit 130 adds, to the supervised data Ds, at least one combination Cm2 (second combination) which is a part of the at least one combination Cm1 and includes the disturbance d and the operation amount r when the absolute value of the error eq is smaller than a reference value α (first reference value) (i.e., when a high-precision control is performed on the control object 200). The reference value α may be appropriately determined based on, for example, actual experiments, simulations, standard values of products, or management values of a manufacturing step. [0039] The learning unit 130 calculates an average value rb0 of the operation amount r when the absolute value of the disturbance d is smaller than a reference value β (second reference value) in the supervised data Ds (i.e., when the control system is in a steady state). The reference value β may be appropriately determined, for example, based on actual experiments, simulations, standard values of products, or management values of a manufacturing step. The learning unit 130 approximates the relationship between the disturbance d and the feedforward compensation value rf which is a difference (=r−rb0) between the operation amount r and the average value rb0 as a function (regression curve) which uses the feedforward compensation value rf as an objective variable and uses the disturbance d as an explanatory variable; wherein the learning unit is a predictor, as it generates a prediction value indicative of an output value (operation amount) and uses a predictive model) “and a corrector configured to correct the control value based on the command value, the output value, and the prediction value, wherein the input value input to the control target is the control value corrected by the corrector” ([0009] The feedforward compensation unit may determines a value obtained by subtracting the average value from the operation amount predicted from the disturbance by the prediction model as the feedforward compensation value. [0035] The control device 100 outputs the sum of a feedback operation amount rb for a control object 200 and a feedforward compensation value rf to the control object 200 as an operation amount r so as to approximate a control amount q, which is the output value of the control object 200 subjected to a disturbance d, to a target value qr. [0036] The subtracter 150 outputs an error eq (=qr-q) between the target value qr and the control amount q to the feedback control unit 110. The feedback control unit 110 determines the feedback operation amount rb based on the error eq and outputs the same to the adder 160. The feedforward compensation unit 120 predicts the feedforward compensation value rf from the disturbance d using the prediction model Mp and outputs the predicted feedforward compensation value rf to the adder 160. The adder 160 outputs the sum of the feedback operation amount rb and the feedforward compensation value rf to the control object 200 and the learning unit 130 as the operation amount r; [0057] The control device 100A determines the feedforward compensation value rf of the feedback operation amount rb for the control object 200 subjected to the disturbance d so as to approximate the control amount q of the control object 200 to the target value qr. According to the control device 100A, while maintaining the conventional feedback control system, it is possible to easily extend a conventional feedback control system to a control system that includes a feedforward control system and a learning function, by adding a control device to the conventional feedback control system. wherein the feedforward compensation unit is a corrector that corrects the control value as an input value to a control target).
Fujii fails to teach “a predictor configured to predict the output value”.
Tsuchikawa teaches “a predictor configured to predict the output value” ([0030] FIG. 1 is a diagram schematically illustrating an example of a control system that executes predictive control according to the present embodiment. With reference to FIG. 1, a control system 1 that executes the predictive control calculates a manipulated variable u to be given to a controlled object 4. A PID controller 2 that is a component of a feedback loop of control system 1 calculates an output u.sub.0 from a deviation e in accordance with PID operation. A subtractor 6 outputs a difference between a desired value r and an output value y of controlled object 4 as deviation e. [0031] Control system 1 includes a prediction compensation unit 10. Prediction compensation unit 10 predicts future changes in controlled variable y and calculates a compensation variable u.sub.1. An adder 8 outputs, as manipulated variable u, the sum of output u.sub.0 from PID controller 2 and compensation variable u.sub.1 from prediction compensation unit 10).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the invention to have improved the control device with controller, predictor and compensator that controls a control object as taught by Fujii, with the use of a predictor that also predicts an output value of a control target as taught by Tsuchikawa because it would gain the obvious benefit stated by Tsuchikawa, namely “[0034] Prediction compensation unit 10 can predict future changes in controlled variable y and correct manipulated variable u before deviation e becomes a non-zero value, so that it is possible to reduce the predicted changes. That is, the performance as the control system can be improved”. More particularly, both Fujii and Tsuchikawa relate to control systems using predictors, compensators and feedback controllers that have improved disturbance response on the basis of a prediction model, thus furthering the obviousness to take this feature from Tsuchikawa and implement it with Fujii. By combining these elements, it can be considered taking the known use of a predictor that predicts an output value for a control target from an input value and implementing those features into Fujii in a known way that achieves predictable results.
In regards to Claim 8, the combination of Fujii and Tsuchikawa teaches the control device as incorporated by claim 1 above. Fujii further teaches “The control device according to claim 1, wherein the controller generates the control value by performing proportional-integral-derivative (PID) control based on the command value and the output value, or the controller generates the control value based on the command value, the output value, and a control model obtained through machine learning” ([0050] As illustrated in FIG. 8, in S111, the subtracter 150 calculates an error eq between the target value qr and the control amount q, and outputs the error eq to the feedback control unit 110. In S112, the feedback control unit 110 determines a feedback operation amount rb based on the error eq, outputs the feedback operation amount rb to the adder 160, and ends the procedure). Tsuchikawa further teaches “wherein the controller generates the control value by performing proportional-integral-derivative (PID) control” ([0033] The feedback control system including PID controller 2 calculates output u.sub.0 (manipulated variable u) after deviation e becomes a non-zero value, so that it is not possible to control a disturbance or the like that is more rapid than a response time of PID controller 2).
In regards to Claim 9, the combination of Fujii and Tsuchikawa teaches the control device as incorporated by claim 1 above. Fujii further teaches “The control device according to claim 1, wherein the corrector corrects a disturbance parameter included in the predictive model based on the command value, the output value, and the prediction value” ([0006] The feedforward compensation unit determines the feedforward compensation value from the disturbance using a prediction model. The learning unit performs machine learning on the prediction model using supervised data. The learning unit obtains at least one first combination which includes the target value, the operation amount corresponding to the target value, the disturbance, and the control amount corresponding to both the disturbance and the operation amount. The learning unit adds, to the supervised data, at least one second combination which is a part of the at least one first combination and includes the disturbance and the operation amount when the absolute value of the error is smaller than a first reference value. [0009] The learning unit may approximate a relationship represented by the prediction model between the disturbance and the operation amount as a function which uses the operation amount as an objective variable and uses the disturbance as an explanatory variable. The learning unit may finish the machine learning when a ratio of the number of fourth combinations to the number of third combinations is greater than the third reference value, the third combinations being a part of the at least one first combination and in the third combinations the absolute value of the disturbance being greater than the second reference value, the fourth combinations being a part of the at least one second combination and in the fourth combinations the absolute value of the disturbance being greater than the second reference value. The feedforward compensation unit may determines a value obtained by subtracting the average value from the operation amount predicted from the disturbance by the prediction model as the feedforward compensation value).
In regards to Claim 10, the combination of Fujii and Tsuchikawa teaches the control device as incorporated by claim 1 above. Fujii further teaches “The control device according to claim 1, wherein the control target includes a motor” ([0072] In the control system 3, the field device 200C includes a plurality of servo drivers 220_1 and 220_2, and a plurality of servo motors 222_1 and 222_2 connected to the plurality of servo drivers 220_1 and 220_2, respectively. The field device 200C is an example of a “control object”).
In regards to Claim 11, Fujii teaches “A control method comprising:
generating a control value based on a command value and an output value output from a control target;” ([0034] FIG. 1 is a block diagram illustrating a functional configuration of a control device 100 according to a first embodiment. As illustrated in FIG. 1, the control device 100 includes a feedback control unit 110, a feedforward compensation unit 120, a learning unit 130, a storage unit 140, a subtracter 150, and an adder 160. [0036] The subtracter 150 outputs an error eq (=qr-q) between the target value qr and the control amount q to the feedback control unit 110. The feedback control unit 110 determines the feedback operation amount rb based on the error eq and outputs the same to the adder 160. [0080] FIG. 15 is a block diagram illustrating an example hardware configuration of the control device 300 of FIG. 14. As illustrated in FIG. 15, the control device 300 includes a processor 302, a main memory 304, a storage 360, a memory card interface 312, a host network controller 306, a field network controller 308, a local bus controller 316, and a USB (Universal Serial Bus) controller 370 that provides a USB interface. These components are connected to each other via a processor bus 318. [0081] The processor 302 corresponds to an arithmetic processing unit that executes a control operation, and includes a CPU (Central Processing Unit) and/or a GPU (Graphics Processing Unit). Specifically, the processor 302 reads out a program stored in the storage 360, develops the program in the main memory 304, and executes the program, thereby implementing a control operation on a control object; wherein the feedback control unit is a controller and the processor and memory corresponds with the control device, the target value is the command value, and the control amount is an output value) “predicting…based on an input value input to the control target and a predictive model of the control target and generating a prediction value indicating a result of predicting the output value;” ([0038] The learning unit 130 performs machine learning on the prediction model Mp using the supervised data Ds. The learning unit 130 obtains at least one combination Cm1 (first combination) which includes the target value qr, the operation amount r corresponding to the target value qr, the disturbance d, and the control amount q corresponding to both the disturbance d and the operation amount r. The learning unit 130 adds, to the supervised data Ds, at least one combination Cm2 (second combination) which is a part of the at least one combination Cm1 and includes the disturbance d and the operation amount r when the absolute value of the error eq is smaller than a reference value α (first reference value) (i.e., when a high-precision control is performed on the control object 200). The reference value α may be appropriately determined based on, for example, actual experiments, simulations, standard values of products, or management values of a manufacturing step. [0039] The learning unit 130 calculates an average value rb0 of the operation amount r when the absolute value of the disturbance d is smaller than a reference value β (second reference value) in the supervised data Ds (i.e., when the control system is in a steady state). The reference value β may be appropriately determined, for example, based on actual experiments, simulations, standard values of products, or management values of a manufacturing step. The learning unit 130 approximates the relationship between the disturbance d and the feedforward compensation value rf which is a difference (=r−rb0) between the operation amount r and the average value rb0 as a function (regression curve) which uses the feedforward compensation value rf as an objective variable and uses the disturbance d as an explanatory variable; wherein the learning unit is a predictor, as it generates a prediction value indicative of an output value (operation amount) and uses a predictive model) “correcting the control value based on the command value, the output value, and the prediction value, wherein the input value input to the control target is the control value corrected on the basis of the command value, the output value, and the prediction value.” ([0009] The feedforward compensation unit may determines a value obtained by subtracting the average value from the operation amount predicted from the disturbance by the prediction model as the feedforward compensation value. [0035] The control device 100 outputs the sum of a feedback operation amount rb for a control object 200 and a feedforward compensation value rf to the control object 200 as an operation amount r so as to approximate a control amount q, which is the output value of the control object 200 subjected to a disturbance d, to a target value qr. [0036] The subtracter 150 outputs an error eq (=qr-q) between the target value qr and the control amount q to the feedback control unit 110. The feedback control unit 110 determines the feedback operation amount rb based on the error eq and outputs the same to the adder 160. The feedforward compensation unit 120 predicts the feedforward compensation value rf from the disturbance d using the prediction model Mp and outputs the predicted feedforward compensation value rf to the adder 160. The adder 160 outputs the sum of the feedback operation amount rb and the feedforward compensation value rf to the control object 200 and the learning unit 130 as the operation amount r; [0057] The control device 100A determines the feedforward compensation value rf of the feedback operation amount rb for the control object 200 subjected to the disturbance d so as to approximate the control amount q of the control object 200 to the target value qr. According to the control device 100A, while maintaining the conventional feedback control system, it is possible to easily extend a conventional feedback control system to a control system that includes a feedforward control system and a learning function, by adding a control device to the conventional feedback control system. wherein the feedforward compensation unit is a corrector that corrects the control value as an input value to a control target).
Fujii fails to teach “predicting the output value”.
Tsuchikawa teaches “predicting the output value” ([0030] FIG. 1 is a diagram schematically illustrating an example of a control system that executes predictive control according to the present embodiment. With reference to FIG. 1, a control system 1 that executes the predictive control calculates a manipulated variable u to be given to a controlled object 4. A PID controller 2 that is a component of a feedback loop of control system 1 calculates an output u.sub.0 from a deviation e in accordance with PID operation. A subtractor 6 outputs a difference between a desired value r and an output value y of controlled object 4 as deviation e. [0031] Control system 1 includes a prediction compensation unit 10. Prediction compensation unit 10 predicts future changes in controlled variable y and calculates a compensation variable u.sub.1. An adder 8 outputs, as manipulated variable u, the sum of output u.sub.0 from PID controller 2 and compensation variable u.sub.1 from prediction compensation unit 10).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the invention to have improved the control device with controller, predictor and compensator that controls a control object as taught by Fujii, with the use of a predictor that also predicts an output value of a control target as taught by Tsuchikawa because it would gain the obvious benefit stated by Tsuchikawa, namely “[0034] Prediction compensation unit 10 can predict future changes in controlled variable y and correct manipulated variable u before deviation e becomes a non-zero value, so that it is possible to reduce the predicted changes. That is, the performance as the control system can be improved”. More particularly, both Fujii and Tsuchikawa relate to control systems using predictors, compensators and feedback controllers that have improved disturbance response on the basis of a prediction model, thus furthering the obviousness to take this feature from Tsuchikawa and implement it with Fujii. By combining these elements, it can be considered taking the known use of a predictor that predicts an output value for a control target from an input value and implementing those features into Fujii in a known way that achieves predictable results.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fujii and Tsuchikawa as applied to claim 1 above, and further in view of Suyama et al. (US 20130024172, hereinafter Suyama).
In regards to Claim 7, the combination of Fujii and Tsuchikawa teaches the control device as incorporated by claim 1 above.
The combination of Fujii and Tsuchikawa fails to teach “The control device according to claim 1, further comprising an abnormality detector configured to detect an abnormality on based on a deviation between the output value and the prediction value”.
Suyama teaches “The control device according to claim 1, further comprising an abnormality detector configured to detect an abnormality on based on a deviation between the output value and the prediction value” ([0003] One of methods for detecting an abnormality in a control device is model-based anomaly detection. Model-based anomaly detection refers to making a model of a process to be monitored in advance, taking a difference between data observed as an output of a process for a given input and an output predicted from the model, and outputting the degree of abnormality of a control device on the basis of a value of the difference. Model-based anomaly detection includes a model learning function of constructing an approximate model having an unknown parameter and estimating the unknown parameter from actual performance data without an abnormality).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the invention to have taken the abnormality detector of Suyama that determines an abnormality based on output values and predictive values predicted with a model, and using it to improve the control device of Fujii by incorporating those features into Fujii because it would gain the stated benefit of Suyama, namely “[0007] According to an aspect of the present invention, there is provided an anomaly detecting apparatus which allows high-accuracy detection of an abnormality in a control device having an individual difference and an unknown characteristic.”. By combining these elements, it can be considered taking the known methods of anomaly detection of Suyama and incorporating those features into the control device of Fujii in a known way that achieves predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tsuneki (US 20240058950) – teaches a feedback control loop with simulated speed and position compensation
Fujii (US 12311431) – teaches a feedback control loop with predictive feedback compensation through feedforward correction that operates a straightener
Samuels (US 20230282462) – teaches an adaptive predictive controller that estimates how a delay in output is realized and compensates for this delay
Flores-Moran (“Model predictive control and genetic algorithm PID for DC motor position”) – teaches how model predictive control is utilized to improve control parameters of a PID controller
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/JONATHAN MICHAEL SKRZYCKI/ Examiner, Art Unit 2116
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