DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-8 are presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
3.1 Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A- Prong One
The claim(s) recite(s) a state quantity prediction device (claim 1) and method (claim 8) for predicting, …, a state quantity of the equipment, comprising: The step of: “dealing with a nonlinear component of a function whose variables are dynamic characteristics of the state quantity with respect to the input parameter and a difference value between a past predictive value of the state quantity and a predictive value of the physical model, input the input parameter and the past predictive value of the state quantity, and include a learned neutral network for outputting a first differential predictive value”; “dealing with a linear component of the function, input the input parameter and the past predictive value, and output a second differential predictive value”; and “calculating a predictive value of the state quantity by integrating a differential predictive value calculated based on the first differential predictive value and the second differential predictive value”, under the broadest reasonable interpretation fall under a mathematical concept / mathematical relationship. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a first differential predictive value calculation unit”, “a second differential predictive value calculation unit”, “a state quantity predictive value calculation unit”, and “a learned neutral network”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0021]-[0022], and fig.1) which can be of any type, including general-purpose computer previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a first differential predictive value calculation unit”, “a second differential predictive value calculation unit”, “a state quantity predictive value calculation unit”, and “a learned neutral network”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0021]-[0022], and fig.1) which can be of any type, including general-purpose computer previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 2-7 merely include limitations pertaining to further mathematical computations (claim 2), “wherein the neural network is learned together with a linear coefficient of the linear component and a physical parameter regarding the equipment included in the physical model” (mathematical process). (claim 3); “wherein the physical parameter is regularized if the physical parameter deviates from a preset allowable range” (mathematical process); (claim 4); “integrate the differential predictive value by using, as an initial value, the state quantity satisfying a condition where the differential predictive value becomes zero” (mathematical concept); (claim 5); “wherein the equipment is a flue gas desulfurization plant for desulfurizing a flue gas by bringing an absorption liquid into contact with the flue gas in an absorption tower, and wherein the state quantity is an absorbent concentration of the absorption liquid in the absorption tower” (mental process or otherwise a mathematical concept); (claim 6) “wherein the input parameter includes at least one of a desulfurization outlet SO2 concentration of the absorption tower, a desulfurization inlet SO2 concentration of the absorption tower, a flow rate or a concentration of limestone slurry produced in the absorption tower, a power generation command signal with respect to a generator for generating electricity with steam produced in a boiler for discharging the flue gas, an air flow rate in the boiler for discharging the flue gas, an oxidizing air flow rate supplied to the absorption tower, pH of the absorption liquid in the absorption tower, or a level of the absorption liquid in the absorption tower” (data gathering or otherwise a mental process); (claim 7); “wherein the physical model includes, as a physical parameter regarding the equipment, at least one of a limestone activity, a water content in inlet gas of the absorption tower, or a humidifying rate in the absorption tower” (data gathering or otherwise a mental process); all of which further amount to further mathematical concept and/or mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Interpretation
4. 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.
4.1 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.
4.2 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a first differential predictive value calculation unit”, “a second differential predictive value calculation unit”, “a state quantity predictive value calculation unit”, and “a learned neutral network” in claim 1.
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.
Claim Rejections - 35 USC § 112
5. 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.
5.1 Claims 1-8 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 provide for dealing with a linear component (claim 1, 8); the claim’s recitation of dealing is vague and indefinite, as neither the claims nor the specification provides the manner by which said linear component is dealt with and what is meant by said dealing and thus lead to lack of clarity in the claims. Further clarification is respectfully requested in response to this office correspondence. Further in claim 4 “the state quantity prediction value calculation unit”, however, previously “a state quantity predictive value calculation unit”; for recitation consistency, it is requested that the claim be amended consistent with the previous claim language.
Claim Rejections - 35 USC § 103
6. 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.
6.0 Claim(s) 1-4, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Izawa et al. (USPG_PUB No. 2021/0201034), in view of Roehrl et al. (Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics, 2020 (6 pages)).
6.1 In considering claims 1 and 8, Izawa et al. teaches a state quantity prediction device for predicting, by using a physical model for supporting an equipment in a static state, a state quantity of the equipment corresponding to an input parameter regarding the equipment (see title, abstract), comprising:
a first differential predictive value calculation unit configured to deal with a nonlinear component of a function whose variables are dynamic characteristics of the state quantity with respect to the input parameter and a difference value between a past predictive value of the state quantity and a predictive value of the physical model (see fig.1-2, para [0030] The following Equation 13 is a prediction function of the Kalman filter. In Equation 13, “t” represents the number of computations, xa.sub.t−1 represents a state quantity which acts on the vehicle and which has been estimated in a previous computation, and xb.sub.t represents a predictive quantity of a state (predictive state quantity) which acts on the vehicle and which has been obtained in a current computation. The state quantity which acts on the vehicle is a physical quantity of the vehicle. Examples of the state quantity include the weight of the vehicle (vehicle weight) and a slope component of a road. [0031]-[0032], [0038] FIG. 1 is a block diagram illustrating an example of a functional configuration of a state quantity estimating device in accordance with Embodiment 1. As illustrated in FIG. 1, a state quantity estimating device 100 includes a data storing section 101, a predictive quantity computing section 102, a Kalman gain computing section 103, an estimated quantity computing section 104, a weight component extracting section 105, a process noise covariance correcting section 106, an obtaining section 107, and sensors 108. [0040] The predictive quantity computing section 102 computes a predictive state quantity (xb) from the state quantity xa. The predictive quantity computing section 102 further computes predictive covariance (P.sup.−) from the state covariance P and the process noise covariance Q.), input the input parameter and the past predictive value of the state quantity (see fig.1-2, para [0032] Further, covariance (predictive covariance) P.sup.−.sub.t of the predictive state quantity xb.sub.t is expressed by the following Equation 16. In Equation 16, P.sub.t−1 represents covariance which has been estimated in the previous computation (state covariance), and Q.sub.t−1 represents process noise covariance which has been estimated in the previous computation. The state covariance is, for example, covariance which takes, as two variables, (i) the state quantity which acts on the vehicle and (ii) the process noise covariance. [0056] Meanwhile, the obtaining section 107 obtains, from the sensors 108, various sensor values (also referred to as “observables z”), in accordance with which a state of a vehicle is detected. For example, the obtaining section 107 consecutively obtains the sensor values at predetermined intervals. Each of the observables z may be a sensor value itself of a sensor or may be alternatively a value of a physical quantity of the vehicle in a moving state, the physical quantity depending on the vehicle and having been calculated from the sensor value. For example, in a step S205, the obtaining section 107 obtains (i) G.sub.senst which is a sensor value of a longitudinal G sensor, (ii) vehicle speed V.sub.t,) and outputting a first differential predictive value (para [0089], the state quantity estimating device may further include an output section which outputs, to an external member, the state quantity including a weight component. [0090] The output section can be selected as appropriate, provided that the output section can output, to the external member, the state quantity including the vehicle weight. In a case where the output section outputs the vehicle weight, it is preferable that the output section be a weight component extracting section which extracts a weight component m. In this manner, in a case where the state quantity estimating device is applied to a control device for controlling a vehicle on the basis of a vehicle weight, the weight component extracting section 105 may output an extracted weight component m.sub.t as a current weight of the vehicle. [0056] and (iii) a value of an engine-derived output F.sub.xt of the vehicle. Note, here, that G.sub.senst is H.sub.t in Equation 20. [0058] Equation 19 with use of the observables z.sub.t and the predictive covariance P.sup.−.sub.t. For example, the Kalman gain computing section 103 consecutively computes the Kalman gain K at predetermined intervals.); a second differential predictive value calculation unit configured to deal with a linear component of the function, input the input parameter and the past predictive value (see fig.1-2, para [0059] In a step S207, the estimated quantity computing section 104 computes an estimated state quantity xa.sub.t in accordance with Equation 21 from the Kalman gain Kt, the predictive state quantity xb.sub.t, and the observables z.sub.t (H.sub.t) with use of the Kalman filter. [0053] The predictive quantity computing section 102 then computes a predictive state quantity from the state quantity which has an initial value or which has been obtained in a previous computation. The predictive quantity computing section 102 further computes predictive covariance, which is covariance of the predictive state quantity, from (i) the state covariance which has an initial value or which has been obtained in the previous computation and (ii) the process noise covariance which has an initial value or which has been obtained in the previous computation. For example, the predictive quantity computing section 102 consecutively computes the predictive state quantity and the predictive covariance at predetermined intervals. [0054] That is, in a step S203, the predictive quantity computing section 102 computes a predictive state quantity xb in accordance with Equation 13. The predictive quantity computing section 102 sets, as a predictive state quantity xb.sub.t to be determined, the state quantity xa.sub.t−1 which has been obtained in the previous computation.), and output a second differential predictive value (see 0059] In a step S207, the estimated quantity computing section 104 computes an estimated state quantity xa.sub.t in accordance with Equation 21 from the Kalman gain Kt, the predictive state quantity xb.sub.t, and the observables z.sub.t (H.sub.t) with use of the Kalman filter.); and a state quantity predictive value calculation unit configured to calculate a predictive value of the state quantity (see fig.1-2, para [0056] Meanwhile, the obtaining section 107 obtains, from the sensors 108, various sensor values (also referred to as “observables z”), in accordance with which a state of a vehicle is detected. For example, the obtaining section 107 consecutively obtains the sensor values at predetermined intervals. Each of the observables z may be a sensor value itself of a sensor or may be alternatively a value of a physical quantity of the vehicle in a moving state, the physical quantity depending on the vehicle and having been calculated from the sensor value. For example, in a step S205, the obtaining section 107 obtains (i) G.sub.senst which is a sensor value of a longitudinal G sensor, (ii) vehicle speed V.sub.t, and (iii) a value of an engine-derived output F.sub.xt of the vehicle. Note, here, that G.sub.senst is H.sub.t in Equation 20. [0057] In a step S206, the Kalman gain computing section 103 computes Kalman gain K.sub.t in accordance with [0058] Equation 19 with use of the observables z.sub.t and the predictive covariance P.sup.−.sub.t. For example, the Kalman gain computing section 103 consecutively computes the Kalman gain K at predetermined intervals. 0059] In a step S207, the estimated quantity computing section 104 computes an estimated state quantity xa.sub.t in accordance with Equation 21 from the Kalman gain Kt, the predictive state quantity xb.sub.t, and the observables z.sub.t (H.sub.t) with use of the Kalman filter.). But does not specifically show the use of a learned neutral network and the integration of the differential predictive value.
Roehrl et al. provide for the use of a neutral network (see title, abstract “modeling system dynamics with physics-informed neural networks based on Lagrangian mechanics” physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems. This new approach directly incorporates the equations of motion originating from the Lagrange mechanics into a deep neural network structure.), and further teaches an integrator usable for performing the integration of the differential predictive value (see intro, Our approach uses the equations of motions to structure the neural network. The model is then integrated to obtain the final model output. Page 2 left column, To obtain a favorable coupling between input and states, we suggest first deriving the equations of motion with the Lagrange formalism and then integrating them into a neural network structure. And further fig.1, We then apply the integration method, described in Equation (6), to obtain the position qn+1 and velocity ˙ qn+1 for the next state.).
Izawa et al. and Roehrl et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Roehrl et al. is similar to that of Izawa et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Roehrl et al. with that of Izawa et al. because Roehrl et al. teaches a model that is accurate and data-efficient while ensuring physical plausibility (see abstract).
6.2 As per claim 2, the combined teachings of Izawa et al. and Roehrl et al. teach that wherein the neural network is learned together with a linear coefficient of the linear component and a physical parameter regarding the equipment included in the physical model (see Roehrl et al. page 1, To reduce model bias and bridge the gap between both model types, various approaches have been employed, including learning of correction terms and semi-physical models based on different subsystems or multi-fidelity modeling.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Roehrl et al. with that of Izawa et al. because Roehrl et al. teaches a model that is accurate and data-efficient while ensuring physical plausibility (see abstract).
6.3 Regarding claim 3, the combined teachings of Izawa et al. and Roehrl et al. teach that wherein the physical parameter is regularized if the physical parameter deviates from a preset allowable range (see Izawa et al. para [0074] It is possible to determine, in accordance with a plurality of thresholds, whether the standard deviation m.sub.σ is large or small, and possible to correct, in accordance with the plurality of thresholds, Q.sub.1 or Q.sub.2 stepwise as described above. For example, by reading, from a map, Q.sub.1 or Q2 which corresponds to m.sub.σ, it is possible to determine a suitable value of Q.sub.1 or Q.sub.2 which suitable value varies depending on m.sub.σ, and possible to correct Q.sub.1 or Q.sub.2 as described above on the basis of the suitable value. 0078] In a step S602, the weight determining section determines whether or not the standard deviation m.sub.σt is lower than a predetermined threshold of 10.sup.−6. [0079] In a case where it is determined, in the step S602, that the standard deviation m.sub.σt is lower than the threshold, the weight determining section determines, in a step S603, the average m.sub.avet of the weight component as an extracted weight component m.sub.t. The weight determining section then sets the stability flag to ON.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Roehrl et al. with that of Izawa et al. because Roehrl et al. teaches a model that is accurate and data-efficient while ensuring physical plausibility (see abstract).
6.4 With regards to claim 4, the combined teachings of Izawa et al. and Roehrl et al. teach that wherein the state quantity prediction value calculation unit is configured to integrate the differential predictive value by using, as an initial value, the state quantity satisfying a condition where the differential predictive value becomes zero (see Izawa et al. para [0051] The data storing section 101 stores therein a state quantity xa.sub.t, state covariance P.sub.t, and process noise covariance Q.sub.t (step S201). Note that “t” represents the number of computations and an initial value of “t” is 0 (zero).-[0053], The predictive quantity computing section 102 then computes a predictive state quantity from the state quantity which has an initial value or which has been obtained in a previous computation. The predictive quantity computing section 102 further computes predictive covariance, which is covariance of the predictive state quantity, from (i) the state covariance which has an initial value or which has been obtained in the previous computation and (ii) the process noise covariance which has an initial value or which has been obtained in the previous computation. For example, the predictive quantity computing section 102 consecutively computes the predictive state quantity and the predictive covariance at predetermined intervals.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Roehrl et al. with that of Izawa et al. because Roehrl et al. teaches a model that is accurate and data-efficient while ensuring physical plausibility (see abstract).
7. Claim(s) 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Izawa et al. (USPG_PUB No. 2021/0201034), in view of Roehrl et al. (Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics, 2020 (6 pages), further in view of Boyden et al. (USPG_PUB No. 2006/0047347).
7.1 As per claim 5, Izawa et al., as modified by Roehrl et al., teaches most of the instant invention; however, he does not expressly teach that wherein the equipment is a flue gas desulfurization plant for desulfurizing a flue gas by bringing an absorption liquid into contact with the flue gas in an absorption tower. Boyden et al. teaches that wherein the equipment is a flue gas desulfurization plant for desulfurizing a flue gas by bringing an absorption liquid into contact with the flue gas in an absorption tower (see Boyden et al. para [0008] The SO.sub.2 laden flue gas 114 exhausted from the other APC subsystems 122 is directed to the WFGD subsystem 130. SO.sub.2 laden flue gas 114 is processed by the absorber tower 132. As will be understood by those skilled in the art, the SO.sub.2 in the flue gas 114 has a high acid concentration. Accordingly, the absorber tower 132 operates to place the SO.sub.2 laden flue gas 114 in contact with liquid slurry 148 having a higher pH level than that of the flue gas 114. [0009], WFGD processing units having other absorption/oxidation equipment configurations could, if desired, be utilized in lieu of that shown in FIG. 1 and still provide similar flue gas desulfurization functionality and achieve similar benefits from the advanced process control improvements presented in this application.), and wherein the state quantity is an absorbent concentration of the absorption liquid in the absorption tower (see Boyden et al. para [0008], As will be understood by those skilled in the art, the SO.sub.2 in the flue gas 114 has a high acid concentration. Accordingly, the absorber tower 132 operates to place the SO.sub.2 laden flue gas 114 in contact with liquid slurry 148 having a higher pH level than that of the flue gas 114. [0014] As shown, the slurry 148 is fed to an upper portion of the absorber tower 132. The tower 132 typically incorporates multiple levels of spray nozzles to feed the slurry 148 into the tower 132. The absorber 132, is operated in a countercurrent configuration: the slurry spray flows downward in the absorber and comes into contact with the upward flowing SO.sub.2 laden flue gas 114 which has been fed to a lower portion of the absorber tower.).
Izawa et al., Roehrl et al., Boyden et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Boyden et al. is similar to that Izawa et al. and Roehrl et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Boyden et al. with that of Izawa et al. and Roehrl et al. because Boyden et al. teaches the improvement of efficiency (see para [0176]).
7.2 Regarding claim 6, the combined teachings of Izawa et al., Roehrl et al., Boyden et al. teaches that wherein the input parameter includes at least one of a desulfurization outlet SO2 concentration of the absorption tower, a desulfurization inlet SO2 concentration of the absorption tower, a flow rate or a concentration of limestone slurry produced in the absorption tower, a power generation command signal with respect to a generator for generating electricity with steam produced in a boiler for discharging the flue gas, an air flow rate in the boiler for discharging the flue gas, an oxidizing air flow rate supplied to the absorption tower, pH of the absorption liquid in the absorption tower, or a level of the absorption liquid in the absorption tower (see Boyden et al. para [0008] The SO.sub.2 laden flue gas 114 exhausted from the other APC subsystems 122 is directed to the WFGD subsystem 130. SO.sub.2 laden flue gas 114 is processed by the absorber tower 132. As will be understood by those skilled in the art, the SO.sub.2 in the flue gas 114 has a high acid concentration. Accordingly, the absorber tower 132 operates to place the SO.sub.2 laden flue gas 114 in contact with liquid slurry 148 having a higher pH level than that of the flue gas 114. [0009], WFGD processing units having other absorption/oxidation equipment configurations could, if desired, be utilized in lieu of that shown in FIG. 1 and still provide similar flue gas desulfurization functionality and achieve similar benefits from the advanced process control improvements presented in this application.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Boyden et al. with that of Izawa et al. and Roehrl et al. because Boyden et al. teaches the improvement of efficiency (see para [0176]).
7.3 As per claim 7, the combined teachings of Izawa et al., Roehrl et al., Boyden et al. teaches that wherein the physical model includes, as a physical parameter regarding the equipment, at least one of a limestone activity, a water content in inlet gas of the absorption tower, or a humidifying rate in the absorption tower (Boyden et al. para [0010] During processing in the countercurrent absorber tower 132, the SO.sub.2 in the flue gas 114 will react with the calcium carbonate-rich slurry (limestone and water) 148 to form calcium sulfite, which is basically a salt and thereby removing the SO.sub.2 from the flue gas 114. The SO.sub.2 cleaned flue gas 116 is exhausted from the absorber tower 132, either to an exhaust stack 117 or to down-steam processing equipment (not shown). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of applicant’s invention to combine the method of Boyden et al. with that of Izawa et al. and Roehrl et al. because Boyden et al. teaches the improvement of efficiency (see para [0176]).
Conclusion
8. Claims 1-8 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON C PUENTE can be reached at 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 March 31, 2026
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