DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications filed on 4/5/2022
Claim 1 pending in the application
Claim 1 is rejected
Claim 1 is objected to
Priority
Application Data Sheet filed on 02/23/2022 claims foreign priority to application CN 2021107333068 for 06/30/2021 filing date. Priority document received on 04/05/2022
Information Disclosure Statement
No IDS received
Drawings
The drawings are objected to because of the following reasons:
Figure 4 is not labeled nor is figure 4 properly explained in the specifications. The figure should have labels to explain the parts of the drawing
Page 10 line 9: “Fig. 8 is the error diagram of the nitrate nitrogen concentration control result in this invention.” However, fig 8 does not depict an error diagram, and instead seems to just be a copy of figure 7 which is a result diagram.
Page 16 line 8: “Fig. 4 shows the dissolved oxygen concentration of the system, X-axis: time, unit: day, Y-axis: dissolved oxygen concentration, unit: mg/L, the solid line is the expected dissolved oxygen concentration, the dotted line is the actual dissolved oxygen concentration; “ This describes figure 5 rather than figure 4.
Page 16 line 12: “the error between the actual output dissolved oxygen concentration and the expected dissolved oxygen concentration is shown in Fig. 5, X-axis: time, unit: day, Y-axis: dissolved oxygen concentration error, unit: mg/L” This describes figure 6 rather than figure 5.
Page 16 line 13: “Fig. 6 shows the nitrate concentration value of the system, X- axis: time, unit: day, Y-axis: nitrate concentration value, unit: mg/L, solid line is expected nitrate concentration value, dotted line is actual nitrate concentration value” This describes figure 7 not figure 6.
Page 16 line 16: “the error between actual output nitrate concentration and expected nitrate concentration is shown in Fig. 7, X- axis: time, unit: day, Y-axis: nitrate concentration error value, unit: mg/L. Figure 7 does not depict this drawing. Figure 7 depicts the nitrite concentration.
Figure 8 is a copy of figure 7. Figure 8 was likely meant to show the error of nitrite concentration.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because the length is greater than 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 1 objected to because of the following informalities:
In Page 1 line 6, “dissolved oxygen detector” should be written as “a dissolved oxygen detector” to provide clarity that his is introducing a new limitation to the claim
In page 1 line 6, “nitrate nitrogen detector” should be written as “a nitrate nitrogen detector” to provide clarity that this is introducing a new limitation to the claim.
In page 1 line 10 “working power frequency of motor” should be “working power frequency of a motor”
In page 1 line 14 “concentration in wastewater treatment process” should be “concentration in the wastewater treatment process.”
In page 1 line 15, “comprising two fuzzy neural network” should be “comprising two fuzzy neural networks”
In page 1 line 27 “prediction instant of slow sampling” should be “prediction instant of a slow sampling”
In page 1 line 28 “prediction instant of slow sampling” should be “prediction instant of the slow sampling”
In page 1 line 30 “to predict dissolved oxygen concentration with time scale Tf” should be “to predict the dissolved oxygen concentration with the time scale Tf” (examiner note: Tf is defined as the sampling interval of dissolved oxygen concentration, not as timescale. It would be more appropriate to say “to predict the dissolved oxygen concentration with the sampling interval of dissolved oxygen concentration Tf”
In page 2 line 12 “σfij(tf)” may have been meant to be written as “σ2fij(tf)” See also page 3 line 1.
The same symbol is used to represent “the maximum common divisor” in page 1 line 27 as well as “time scale” in page 2 line 16.
In page 3 line 20 “at time tq; using the error between nitrate nitrogen concentration value in dataset 9 and predicted value Es'(t,)=1/2[ysq(t,)-fsq(t)]2 at time t., correct parameters of slow sampling fuzzy neural network:” should be “at time tq; using the error between the nitrate nitrogen concentration value in dataset 9 and the predicted value Es'(t,)=1/2[ysq(t,)-fsq(t)]2 at time t., the correct parameters of slow sampling fuzzy neural network are:”
In page 4 line 5. “converted by programmable logic controller, predict nitrate nitrogen concentration at time t. using slow sampling fuzzy neural network;” should be “converted by the programmable logic controller, predict the nitrate nitrogen concentration at time t. using the slow sampling fuzzy neural network;”
In page 6 lines 5-10 “is control vector converting into analog signal through programmable logic controller and transmitting to variable frequency driver at time tf, u(trl)=[u1(tfrl), u2(tfi)]Tis control vector converting into analog signal through programmable logic controller and transmitting to variable frequency driver at time tfri,” might have intended to be written as “is a control vector converted into analog signal through the programmable logic controller and transmitted to the variable frequency driver at time tf, u(trl)=[u1(tfrl), u2(tfi)]Tis the control vector converted into an analog signal through the programmable logic controller and transmitted to the variable frequency driver at time tfri,
In page 6 line 21 “is transferred to programmable logic controller” should be “is transferred to the programmable logic controller”
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.
Claim 1 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 claims are generally narrative and indefinite, failing to conform with current U.S. practice. In general, the claim pertains to a method without actual discussion of actionable steps being performed. Step 1 describes the predictive control system; this is not a step being performed. Step 2 describes the time scales of dissolved oxygen concentration and nitrate nitrogen concentration; this is not a step being performed. Step 3 describes the fast sampling fuzzy neural network; this is not a step. Step 4 describes the slow sampling fuzzy neural network; this is not a step. Step 6 attempts to relate the mathematic recitation from steps 2-5 with the structure in step 1, this is not actionable steps being performed. Only step 5 outlines actions being performed in a method. For example, in step 5 page 4 lines 3-10: set s=1 .. predict nitrate nitrogen concentration … if tn=tf set ys(tf)=ys(tn). These are steps being performed in a method. In contrast, step 4 page 2 line 15 “a slow sampling fuzzy neural network is designed to predict nitrate nitrogen concentration” is not a step. The examiner recommends re-writing the claims to describe steps in a method being performed. For example, step 4 could become, “calculating the predicted nitrate nitrogen concentration with a slow sampling fuzzy neural network.”
Claim 1 recites the limitation "The multi-time scale model predictive control system" in page 1 line 3. Line 1 seems to define a multi-time scale model predictive control method, therefore there is insufficient antecedent basis for this limitation in the claim for the system.
Claim 1 recites the limitation “measuring devices” in page 1 line 5, this should be “the set of measuring devices” to reference the preamble.
Claim 1 recites the limitation “related to a parameter of wastewater treatment process” This limitation is ambiguous, as it does not properly relate the parameter to either a general wastewater treatment process or to the wastewater treatment process mentioned in the preamble. For instance, “related to a parameter of the wastewater treatment process” would relate the term back to the preamble.
Claim 1 recites the limitation "the air-blower and electronic valve" in page 1 line 9. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the working power frequency” in page 1 line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the required oxygen" in page 1 line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “the wastewater treatment process” in page 1 line 22, there is insufficient antecedent basis for this limitation. Line 4 references a “system for wastewater treatment process control” but does not explicitly introduce “a process for wastewater treatment” into the claim.
Claim 1 recites the limitation "the control law" in page 1 line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the dissolved oxygen concentration and nitrate nitrogen concentration" in page 1 line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the multi-scale model predictive control module" in page 1 line 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the system outputs" in page 1 line 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the prediction time scales" in page 1 line 16. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the time scales of dissolved oxygen concentration and nitrate nitrogen concentration" in page 1 line 18. While the claim mentions the prediction time scales, There is insufficient antecedent basis for this limitation in the claim when applied to dissolved oxygen and nitrate nitrogen concentration.
Claim 1 recites the limitation "the sampling interval of dissolved oxygen concentration" in page 1 line 20. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the sampling instant of dissolved oxygen concentration" in page 1 line 21. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the number of sampling steps of dissolved oxygen concentration" in page 1 line 21. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the sampling interval of nitrate nitrogen concentration" in page 1 line 24. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the sampling instant of nitrate nitrogen concentration" in page 1 line 26. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the maximum common divisor” there is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the number of sampling steps of nitrate nitrogen concentration" in page 1 line 26. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the prediction instant " in page 1 line 28. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the number of prediction steps” in page 1 line 28, there is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the input” in page 2 line 2. There is insufficient antecedent basis for this limitation
Claim 1 recites the limitation “the transposition of the matrix” in page 2 line 3. There is insufficient antecedent basis for both the terms transposition as well as matrix in this limitation.
Claim 1 recites the limitation “the output of the fast sampling” in page 2 line 3. This should be either “the system output” or “a output” to clarify the antecedent basis for the term.
Claim 1 recites the limitation “the predicted value of dissolved oxygen concentration” in page 2 line 4. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “the ith input of the fast sampling fuzzy neural network” in page 2 line 7. There is insufficient antecedent basis for ith input in this limitation.
Claim 1 recites the limitation “the weight between the jth regular layer neuron and the output layer neuron of the fast sampling” page 2 line 8. There is insufficient antecedent basis for the terms weight, jth regular neuron, and output layer neuron for a fast sampling fuzzy neural network.
Claim 1 recites the limitation "wfj(t0)" in page 2 line 9. There is insufficient antecedent basis for this limitation in the claim since wfj(t0) was not yet defined. The examiner understands that wfj(ti) was already defined and implies wfj(t0), however, it could be more appropriate to say, an “initial weight wfj(t0) is randomly assigned…”
Claim 1 recites “the initial instant” in page 2 line 10, this should be written as either “the initial, sampling instant”, “the initial predication instant”, or “an initial instant” to provide clarity on what the appropriate antecedent basis of the term is.
Claim 1 recites the limitation “the center” in page 2 line 10, there is insufficient antecedent basis for the limitation
Claim 1 recites the limitation “the ith input neuron” in page 2 line 10, there is insufficient antecedent basis for the limitation.
Claim 1 recites the limitation “ jth radial basis function neuron” in page 2 line 11. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “the width of the ith input neuron” in page 2 line 12. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “The input of the slow sampling fuzzy neural network” in page 2 line 17. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “the output of the slow sampling fuzzy neural network” in page 2 line 18. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “the predicted value of nitrate nitrogen concentration” in page 2 line 19. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “the ith input of the slow sampling fuzzy neural network at time” in page 2 line 21. There is insufficient antecedent basis for this term.
Claim 1 recites the limitation “the weight between the jth regular layer neuron and the output layer neuron of the slow sampling” page 2 line 23. There is insufficient antecedent basis for the terms weight, jth regular neuron, and output layer neuron for a slow sampling fuzzy neural network.
Claim 1 recites the limitation “cs,(t,) is the center of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time t” in page 2 line 25. There is insufficient antecedent basis for the terms center, ith input neuron, and jth radial basis, in relation to a slow sampling neural network.
Claim 1 recites the limitation “the width of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time tq,” in page 3 line 2. There is insufficient antecedent basis for this term in relation to a fast sampling neural network.
Claim 1 recites the limitation "the virtual value of aeration " in page 3 line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the actual value of aeration " in page 3 line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the virtual value of internal reflux " in page 3 line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the actual value of internal reflux " in page 3 line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the virtual estimated value of nitrate " in page 3 line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the actual value of nitrate " in page 3 line 11. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation " ys(ts+1) is the actual value of the nitrate nitrogen concentration converted by the programmable logic controller at time ts+1;" in page 3 line 11. There is insufficient antecedent basis for this limitation in the claim in relation to time ts+1.
Claim 1 recites “the training input” in page 3 line 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “ the nitrate nitrogen concentration at time t.1i in 9, us1'(t_1) is the aeration rate at time t.1i in 9, us2(t.1) is the internal reflux at time t._1” There is insufficient antecedent basis for these terms in the claim in relation to a time tn-1.
Claim 1 recites “the training output” in page 3 line 17. There is insufficient antecedent basis for this term in the claim.
Claim 1 recites “the error” in page 3 line 19. There is insufficient antecedent basis for this term in the claim.
Claim 1 in page 3 lines 25-30 recites “where ws(t+1) is the weight between the jth regular layer neuron and the output layer neuron of the slow sampling fuzzy neural network at time t,+1, csy(t,+1) is the center of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time tq+1,sy(t+1) is the width of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time t,+1;” There is insufficient antecedent basis for the terms weight, jth regular neuron, output layer neuron, center of the ith input neuron, jth radial basis function, and width of irth input neuron in relation to time tn+1.
Claim 1 recites “the multi-time scale model predictive control method” in page 4 line 1 there is insufficient antecedent basis for this term in relation to a method.
Claim 1 recites “the sampling information” in page 4 line 3, there is insufficient antecedent basis for this term.
Claim 1 recites “the set-points” in page 5 line 11. There is insufficient antecedent basis for this term.
Claim 1 page 5 lines 15-20 recites “rf(tpi)=2mg/l represents the set-point of dissolved oxygen concentration at time tf+l,rf(tp2)=2mg/l represents the set-point of dissolved oxygen concentration at time tf+2,rf(tf+3)=2mg/l represents the set-point of dissolved oxygen concentration at time tf+3;” There is insufficient antecedent basis for these terms in relation to different times tf+1, tf+2, and tf+3.
Claim 1 page 5 lines 20-23 “f/tfrl) is the prediction value of dissolved oxygen concentration at time tfrl,yf(t+2) is the prediction value of dissolved oxygen concentration at time tf+2, jf(tf+3) is the prediction value of dissolved oxygen concentration at time tf+3; rs(t)=[rs(tfr), rs(tf+2),” There is insufficient antecedent basis for these terms in relation to different times tf+1, tf+2, and tf+3.
Claim 1 page 5 lines 23-26 “rs(tfrl)=1mg/l represents the set-point of nitrate nitrogen concentration at time tf+l, rs(tf+2)=lmg/l represents the set-point of nitrate nitrogen concentration at time tf+2, rs(tf+3)=1mg/l represents the set-point of nitrate nitrogen concentration at time tf+3;” There is insufficient antecedent basis for these terms in relation to different times tf+1, tf+2, and tf+3.
Claim 1 page 5 lines 26-30 “ps(tfrl) is the prediction value of nitrate nitrogen concentration at time tfi,fs(t+2) is the prediction value of nitrate nitrogen concentration at time tf+2,fs(tf+3) is 5 the prediction value of nitrate nitrogen concentration at time tf+3;” There is insufficient antecedent basis for these terms in relation to different times tf+1, tf+2, and tf+3.
Claim 1 page 6 line 2 “the incremental control moves” there is insufficient antecedent basis for this term
Claim 1 page 6 line 2 “the aeration rate adjustment amount” there is insufficient antecedent basis for this term.
Claim 1 page 6 line 3 “the internal reflux adjustment amount” There is insufficient antecedent basis for this term.
Claim 1 page 6 line 10 “the maximum adjustment vector” there is insufficient antecedent basis for this term.
Claim 1 page 6 line 11 “the maximum aeration adjustment amount” there is insufficient antecedent basis for this term.
Claim 1 page 6 line 12 “the maximum internal reflux adjustment amount” there is insufficient antecedent basis for this term.
Claim 1 page 6 line 13 “the control system equipment” There is insufficient antecedent basis for this term.
The term “slow sampling” in page 1 line 28 of claim 1 is a relative term which renders the claim indefinite. The term “slow sampling” 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 term “fast sampling” in page 1 line 30 of claim 1 is a relative term which renders the claim indefinite. The term “fast sampling” 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.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claim 1:
Step 1: Is the claimed invention one of the four statutory categories? YES. The claim recites “A multi-time scale model predictive control method of wastewater treatment process” which is a process.
Step 2:
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?": YES.
Claim 1 step 2 recites: "(2) the time scales of dissolved oxygen concentration and nitrate nitrogen concentration in wastewater treatment process are different, specifically: Tis the sampling interval of dissolved oxygen concentration, TfE [6, 10] is a positive integer in minutes, t=fT represents the sampling instant of dissolved oxygen concentration, f is the number of sampling steps of dissolved oxygen concentration, andfE [1, 1000] is a positive integer; Ts is the sampling interval of nitrate nitrogen concentration, TsE[12, 20] is a positive integer in minutes, ts=sTs represents the sampling instant of nitrate nitrogen concentration, s is the number of sampling steps of nitrate nitrogen concentration, and se[1, 400] is a positive integer;C is the maximum common divisor of T and Ts, tq=tI is the prediction instant of slow sampling fuzzy neural network, q is the number of prediction steps of slow sampling fuzzy neural network, qE [1, 2000] is a positive integer; “
The MPEP states “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A),” MPEP 2106.04(a)(2)(A). This limitation defines the terms to be used in a mathematical relationship. Ie: “C is the maximum common divisor of T and Ts” Therefore, this limitation pertains to a mathematical relationship between variables and numbers which is an abstract idea.
Claim 1 step 3 further recites “(3) a fast sampling fuzzy neural network is designed to predict dissolved oxygen concentration with time scale T, which is as follows: the input of the fast sampling fuzzy neural network is xf(tr)=[xfi(ti), xp2(tfi), xf3(tyi)]TTis the transposition of the matrix, and the output of the fast sampling fuzzy neural network is the predicted value of dissolved oxygen concentration %f(t) at time tf, the output is defined as follows
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where xp(t1) is the ith input of the fast sampling fuzzy neural network at time tf,1=1, 2, 3, wfi(tf) is the weight between the jth regular layer neuron and the output layer neuron of the fast sampling fuzzy neural network at time tf,wfi(to) is randomly assigned within [0, 1], j= 1, 2, 3, 4, 5, 6, to is the initial instant, cp1(t) is the center of the ith input neuron corresponding to the jth radial basis function neuron of the fast sampling fuzzy neural network at time t,cp1(to) is randomly assigned within [0,1], a-(t) is the width of the ith input neuron corresponding to the jth radial basis function neuron of the fast sampling fuzzy neural network at time t, and afj(to) is randomly assigned within [0,1];”
This MPEP states “A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. MPEP 2106.04(a)(2)(B). This limitation explicitly recites the equation for the output of the fast sampling fuzzy neural network in terms of variables and numbers. Therefore, this claim recites a numerical equation and recites an abstract idea.
Claim 1 step 4 further recites “(4) a slow sampling fuzzy neural network is designed to predict nitrate nitrogen concentration with time scale 4, which is as follows: The input of the slow sampling fuzzy neural network is xs(tq)=[xsi(t.i), xs2(tq.1), xs3(tq.i)]T and the output of the slow sampling fuzzy neural network is the predicted value of nitrate nitrogen concentration fs(tq) at time t., the output is defined as follows
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where xs,(t,.u) is the ith input of the slow sampling fuzzy neural network at time tq, wsj(tq) is the weight between the jth regular layer neuron and the output layer neuron of the slow sampling fuzzy neural network at time t,, ws1(to) is randomly assigned within [0, 1], cs,(t,) is the center of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time t,, csy(to) is randomly assigned within [0,1], os-(t,) is the width of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time tq, and 6s(to) is randomly assigned within [0,1]; a dataset 9 whose time scale is Cis constructed as follows, when ts<t,<ts+1:
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where usiO(t.) is the virtual value of aeration rate at time t., us1(ts) is the actual value of aeration rate at time ts, us2(t.) is the virtual value of internal reflux at time t., us2(ts) is the actual value of internal reflux at time ts, ys(t) is the virtual estimated value of nitrate nitrogen concentration at time t., ys(ts) is the actual value of the nitrate nitrogen concentration converted by the programmable logic controller at time ts, ys(ts+1) is the actual value of the nitrate nitrogen concentration converted by the programmable logic controller at time ts+1; the dataset 9 is composed of usi(t ), us2(t ), and ys'(t);The dataset 9 is used to pre-train the slow sampling fuzzy neural network offline, and the training input is xs'(tq)=[ys(tq.1), us1(t,.1), us2l(tq.1)]T, ys(tqi-1) is the nitrate nitrogen concentration at time t.1i in 9, us1'(t_1) is the aeration rate at time t.1i in 9, us2(t.1) is the internal reflux at time t._1 in 9, the training output is the prediction value of nitrate nitrogen concentration ps(tq) at time tq; using the error between nitrate nitrogen concentration value in dataset 9 and predicted value Es'(t,)=1/2[ysq(t,)-fsq(t)]2 at time t., correct parameters of slow sampling fuzzy neural network:
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where ws(t+1) is the weight between the jth regular layer neuron and the output layer neuron of the slow sampling fuzzy neural network at time t,+1, csy(t,+1) is the center of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time tq+1,sy(t+1) is the width of the ith input neuron corresponding to the jth radial basis function neuron of the slow sampling fuzzy neural network at time t,+1;”
As stated above in relation to step 3, “A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. MPEP 2106.04(a)(2)(B). This limitation explicitly defines multiple numerical formulas and equations and therefore recites an abstract idea.
Claim 1 further recites in step 5 “(5) The multi-time scale model predictive control method is designed to control the dissolved oxygen concentration and nitrate nitrogen concentration in time scale Tf, specifically: ( set s=1, f-1,1q=1; O according to the sampling information converted by programmable logic controller, predict nitrate nitrogen concentration at time t. using slow sampling fuzzy neural network; the inputs of the slow sampling fuzzy neural network are as follows: xsi(tq-i) is the actual value of nitrate nitrogen concentration ys(ty.i) at time t,.1,xs2(t,.1) is the aeration rate ui(t,.1) at time t.i,xs3(t.1) is the internal reflux u2(t,,4) at time t,.l; the output of the slow sampling fuzzy neural network is the prediction value of nitrate nitrogen concentration fs(tq) at time tq;@ if t,=t, set ps(tf)=fs(t ), where fs(tf) is the prediction value of nitrate nitrogen concentration at time t, go to step © after performing step @; if tqytf, go to step © after performing step (;@ if tq=ts, increase the value of s by 1, update the parameters of the slow sampling fuzzy neural network by the error between the predicted value and the actual value of nitrate nitrogen concentration Es(t)=1/2[ys(ts)-fs(t)]2:
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if tqts, the parameters of slow sampling fuzzy neural network are not updated; set ys(tq)=fs(t), ui(tj)=ui(tf), u2(tq)=u2(tf), increase the value of 1 by 1, go to step 0, where ys(tq) is the actual nitrate nitrogen concentration converted by the programmable logic controller at time t., ui(t) is the aeration rate at time t.,u2(tq) is the internal reflux at time tq, ui(tf) is the aeration rate at time t, u2(tf) is the internal reflux at time t;© predict dissolved oxygen concentration at time tf by the fast sampling fuzzy neural network; the inputs of the fast sampling fuzzy neural network are as follows: xfl(tr1) is the actual value of dissolved oxygen concentration yf(tri) converted by the programmable logic controller at time t>1, xf2(tfi) is the aeration rate ui(tf-) at time t>1, xf3(ti) is the internal reflux u2(tf1) at time tfi; the output of the fast sampling fuzzy neural network is the prediction value of dissolved oxygen concentration fy(tf) at time tf; update the parameters of the fast sampling fuzzy neural network by the error between the predicted value and the actual value of dissolved oxygen concentration E(t)=1/2t[y)-fy(t)]2:
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where wfi(trl) is the weight between the jth regular layer neuron and the output layer neuron of the slow sampling fuzzy neural network at time tfri, cfiJ(tfri) is the center of the ith input neuron corresponding to the jth radial basis function neuron of the fast sampling fuzzy neural network at time tfri,afi(tfl) is the width of the ith input neuron corresponding to the jth radial basis function neuron of the fast sampling fuzzy neural network at time tfrl;0 design an objective function of multi-time scale model predictive control to track the set- points of nitrate nitrogen concentration and dissolved oxygen concentration, and calculate the control law at time tf
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where rf(tf)=[rf(tf+), rf(tf+2), rf(tf+3)]T is the set-point of dissolved oxygen concentration, rf(tpi)=2mg/l represents the set-point of dissolved oxygen concentration at time tf+l,rf(tp2)=2mg/l represents the set-point of dissolved oxygen concentration at time tf+2,rf(tf+3)=2mg/l represents the set-point of dissolved oxygen concentration at time tf+3;y5ft)=[Vftfrl),Jftf+2), ftf+3)]T is the prediction output of the fast sampling fuzzy neural network, f/tfrl) is the prediction value of dissolved oxygen concentration at time tfrl,yf(t+2) is the prediction value of dissolved oxygen concentration at time tf+2, jf(tf+3) is the prediction value of dissolved oxygen concentration at time tf+3; rs(t)=[rs(tfr), rs(tf+2), rs(tf+3)]T is the set-point of nitrate nitrogen concentration; rs(tfrl)=1mg/l represents the set-point of nitrate nitrogen concentration at time tf+l, rs(tf+2)=lmg/l represents the set-point of nitrate nitrogen concentration at time tf+2, rs(tf+3)=1mg/l represents the set-point of nitrate nitrogen concentration at time tf+3; ys(ts)-[s(ts.1), ps(ts+2), fs(ts+3)]T is the prediction output of slow sampling fuzzy neural network, ps(tfrl) is the prediction value of nitrate nitrogen concentration at time tfi,fs(t+2) is the prediction value of nitrate nitrogen concentration at time tf+2,fs(tf+3) is 5 the prediction value of nitrate nitrogen concentration at time tf+3; Au(t)=[Aui(t), Au2(t)]T is the incremental control moves at time tf,Aui(t) is the aeration rate adjustment amount at time tf,Au2(tf) is the internal reflux adjustment amount at time tf, where
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u(ty)=[ui(tf), u2(t)]Tiscontrol vectorconverting into analog signal through programmable logic controller and transmitting to variable frequency driver at time tf, u(trl)=[u1(tfrl), u2(tfi)]Tis control vector converting into analog signal through programmable logic controller and transmitting to variable frequency driver at time tfri, ui(t-i) is the aeration rate at time tfi,u2(tri) is the internal reflux at time tfri;Aumax[AKLamax,AQamax]T is the maximum adjustment vector allowed by the controller, AKLamax = 100L/min is the maximum aeration adjustment amount, AQamax = 50000L/min is the maximum internal reflux adjustment amount, Aumax is set through the blower and internal reflux valve in the control system equipment; an aeration rate and internal reflux adjustment vector are calculated by minimizing Eq.(15):
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adjust the aeration rate and internal reflux at time t;
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®iff< 1000, increase the value off by 1, increase the value of 1 by 1, go to step 2; iff> 1000, end the cycle;”
This limitation in sub-step 1 firsts sets the value of variables to be used in the previous equations. The MPEP 2106.04(a)(2)(A) states “A mathematical relationship is a relationship between variables or numbers.” This limitation is the relationship between variables (s, f, n)with their initial values (1), which is a relationship between variables and numbers.
In sub-step 2 the limitation predicts the values using the slow sampling fuzzy neural network equation. The MPEP 2106.04(a)(2)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Where the word “predicting” implies the mathematical calculation performed in the limitation. This limitation pertains to a mathematical calculation.
In sub step 3, the limitation describes a flow chart. If tn = tf (some condition) then go to step 6 after 4, else go to step 6 after 5. This flowchart is meant to be followed by the user of the method, and is simple instructions that someone reasonably skilled in the art is expected to perform by the writers of this claim. The MPEP 2106.04(A)(2)(III) defines the "mental processes" abstract idea grouping as “concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Harvard Business Review in their article “Use If-Then Thinking to Change Your Behavior” (Harvard_2018) describes how to use if-then thinking to plan out behaviors in your day. Humans have been known to think in terms of if..then terminology. For instance, Harvard_2018 line 6: “If I start to feel uncomfortable about not completing the work myself, then I’ll ask for updates on it in our next team meeting.” This describes making an observation (I start to feel uncomfortable) and then making a judgement based on that observation (I will ask for updates). If condition do … else do …. Is the process of decision making, which is a mental process done by humans. "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". MPEP 2106.04(A)(2)(III)(C) Therefore this limitation pertains to a mental process.
In sub-step 4, the limitation adds a mathematic recitation to a flowchart. Stating If tn = tf increase s by 1 and update the slow sampling fuzzy neural network parameters. Else, do not update the parameters. As stated previously, following a flow chart is the human process of decision making. Secondarily, the process of updating the parameters is given through mathematic formulas and changing variables. states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Increasing s by 1 is the mathematic calculation of addition. “update the parameters” is the word in the limitation that implies performing a mathematical operation.
In sub-step 5, we perform a very similar step where we set parameters, and then increase the value of n. As stated previously in sub-step 4, this is following a flow chart using if-then decision making (which is a mental process) and performing a mathematic calculation. "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". MPEP 2106.04(A)(2)(III)(C) Therefore this limitation pertains to a mental process.
In sub-step 6, we predict the value of dissolved oxygen concentration by using the fast sampling fuzzy neural network, and then update the weights of the neural network according to different mathematic calculations. “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” MPEP 2106.04(a)(2)(C). This claim states “predict the value” where the word predict implies the use of the formulas in the claim and therefore recites a mathematic calculation.
In sub-step 7, an objective function is defined using an equation and also adjusts the aeration rate and internal reflux. This step is another mathematic calculation as stated previously since it explicitly mentions “calculate” as well as the equations and values used in that calculation. Therefore this limitation recites a mathematic calculation.
Sub-step 8 teaches that if f<= 1000 we increase f and n by 1 and go to step 2, if f>1000 then we end the cycle. As mentioned under sub-step 5, this is following a flowchart with “if then” decision making (which is a mental process) and performing rudimentary mathematic calculation based on the result of our process (increasing f and n by 1). This limitation recites a mathematic calculation and a mental process.
The limitations in this claim all pertain to mental processes, mathematical relationships, and mathematic calculations. As for the mental processes, "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". MPEP 2106.04(A)(2)(III)(C). Therefore as a whole this limitation recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
Claim 1 additionally recites in step 1 “ (1) the multi-time scale model predictive control system for wastewater treatment process control comprising a set of measuring devices arranged to obtain a dataset, measuring devices include dissolved oxygen detector, nitrate nitrogen detector, the dataset comprises a plurality of process variables related to a parameter of wastewater treatment process; a programmable logic controller arranged to perform digital/analog conversion and analog/digital conversion; a variable-frequency drive arranged to control the air-blower and electronic valve by changing the working power frequency of motor; an air-blower arranged to provide the required oxygen to the microorganisms in the wastewater treatment process; an electronic valve arranged to adjust internal return flow; a multi-time scale model predictive control module arranged to calculate the control law to track the dissolved oxygen concentration and nitrate nitrogen concentration in wastewater treatment process with different time scales; the multi-time scale model predictive control module comprising two fuzzy neural network to predict the system outputs, a time scale conversion mechanism to unify the prediction time scales to fast time scale, and an optimization control module to calculate the control law;”
The limitations recited in claim 1, do not pertain to a method being performed, rather, it describes a wastewater process control system. Firstly, it states that the measuring devices used are a dissolved oxygen detector and a nitrate nitrogen detector. The MPEP states the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” MPEP 2106.05(f)(2). The judicial exception uses the oxygen and nitrate concentrations in the mathematic equations. These values are received from the oxygen and nitrogen detectors. The purpose of an oxygen detector is to detect oxygen. The use of a nitrate nitrogen detector is the detect nitrogen. These machines from the name of the device itself is defined by its use. These are completely generic definitions and thus do not provide a meaningful limitation, since as stated previously, the use of machinery for its ordinary use does not integrate the judicial exception into a practical application.
The claim also recites a dataset which contains a plurality of process variables related to parameters of wastewater treatment. This as stated by MPEP 2106.05(g) is insignificant extra-solution activity, as the courts find Mere Data Gathering to be insignificant extra-solution activity. For example, the courts found “Performing clinical tests on individuals to obtain input for an equation” to be mere data gathering. This is the same as gathering data from some device and using it in an equation. Gathering data from sensors to use in the later defined equations as defined in this claim is therefore insignificant extra-solution activity.
Furthermore, this limitation recites various parts of a treatment plant. As stated previously, the claimed invention preamble defines it as a model predictive control method. The inventive concept of this method as outlined is the mathematic fuzzy neural network calculations performed which are a judicial exception. The above recitation of the components of the treatment plant are interpreted by the examiner as simply “Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., … a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)). ” This claim is very similar, in that this claim limits the use of the mathematic formula to the wastewater treatment field. To justify this assertion, evidence is provided in prong 2B showing that the structures listed are well understood and routine objects present in wastewater field for someone ordinarily skilled in the art before the effective filing date.
Furthermore, As stated previously, The MPEP states the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” MPEP 2106.05(f)(2). The elements considered below are recited absent of structure, and are defined strictly through their function in implementing the abstract exception. “predictive modules” do not impose a significant limitation on the claim, sin