DETAILED ACTION
Notice of 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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Withdrawal of Objections and Rejections
Applicant's response, filed 03/12/2026, has been fully considered.
In view of the amendment and remarks from 03/12/2026, the rejection of the following claims are withdrawn:
claims 9, 18, 22, and 26 under 35 USC § 112(b);
The following rejections and/or objections are either maintained or newly applied for claims 1-2, 4, 6-11, 13, 15-19, 22, 24-26. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Final Rejection of 12/12/2025.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/12/2026 has been entered.
Status of the Claims
Claims 3, 5, 12, 14, 20-21, 23 and 27 are canceled.
Claims 1-2, 4, 6-11, 13, 15-19, 22, 24-26 and 28 are pending.
Claims 1 and 10 are objected to.
Claims 1-2, 4, 6-11, 13, 15-19, 22, 24-26 and 28 are rejected.
Priority
This application is a 371 of PCT/IB2020/055985 (06/24/2020), which claims priority from US Application No. 62/865,875 (06/24/2019), as reflected in the filing receipt mailed on April. 05, 2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-2, 4, 6-11, 13, 15-19, 22, 24-26 and 28 is 06/24/2019.
Claim objections
Claims 1 and 10 are objected to because of the following informalities related to grammar/punctuation. Appropriate correction is required.
In claim 1, the recited "and" at the end of the 2nd claim element should be removed and an "and" should be added at the end of the 3rd claim element.
Claim 10 is missing a semi-colon at the end of the recited "one or more actuators connected with the anaerobic digestion reactor" (4th claim element).
Claim Rejections - 35 USC § 112(b)
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.
Claims 4, 9, 13, 18, 22 and 26 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. Any newly recited portions are necessitated by claim amendment. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
Claims 4, 13 and 22 recite "(dissociated)" - parenthetical limitation which is indefinite because it is unclear if the term is meant to limit the "acetate" to being dissociated or is just exemplary.
Claims 9, 18 and 26 recite "the four model state variables" which is indefinite because it lacks antecedent basis. A recitation of "four model state variables" has not been previously recited. The examiner suggests an amendment to “the model state variables of the four or fewer model state variables” to overcome the rejection.
Claims 9, 18 and 26 recite "four model state variables comprises an effluent concentration of total acetate from the anaerobic digestion reactor, a concentration of aceticlastic methanogens in the anaerobic digestion reactor, an effluent concentration of total inorganic carbon from the anaerobic digestion reactor, and a total alkalinity in the anaerobic digestion reactor effluent" which is indefinite because the independent claims from which claims 9, 18 and 26 depend from recite "four or fewer variables." Thus it is unclear if the nonlinear model predictive controller requires all four variables or could it be fewer than four (as recited in the independent claims). For compact prosecution, the latter interpretation is being applied. If the latter interpretation is the one intended by the Applicant, claims 9, 18 and 26 should be amended to recite “and/or” instead of just “and.”
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1, 6, 8, 10, 15, 17, 19, 24 and 28 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nguyen (“Automatic process control in anaerobic digestion technology: A critical review” Bioresource Technology 193:513–522 (2015)) in view of Lauwers (“Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations” Progress in Energy and Combustion Science 39:383-402 (2013)) as evidenced by Mendez (“A robust control scheme to improve the stability of anaerobic digestion processes” Journal of Process Control 20:375–383 (2010)) in view of Gaida (“Optimal Control of Biogas Plants using Nonlinear Model Predictive Control” Trinity College Dublin ISSC pp. 1-6 (2011)) as evidenced by Batstone (“The IWA Anaerobic Digestion Model No 1 (ADM1)” Water Sci Technol. 45(10):65-73 (2002)), as cited on the 06/17/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 1 recites:
one or more sensors connected with an anaerobic digestion reactor;
one or more actuators connected with the anaerobic digestion reactor, wherein the one or more actuators are configured to receive one or more output signals; and
a processor coupled to the one or more sensors and the one or more actuators;
a memory having instructions that when executed by the processor cause the processor to perform one or more operations comprising:
receiving, from the one or more sensors in real-time, one or more input signals associated with a condition in the anaerobic digestion reactors
• Nguyen teaches an overview of the available automatic control methods that can be implemented in anaerobic digestion (AD) systems at different scales (pg. 513 para. 1); wherein neural network control is used in anaerobic reactors (i.e. connected system) (pg. 519 col. 2 para. 1) to measure data from sensory devices and feed said data as inputs (i.e. input signals) to the nodes in input layer and interconnected with other nodes in intermediate layers to generate suitable output signals in nodes of output layer (pg. 519 col. 1 para. 4); wherein a sensor, a controller and an actuator make up the three basic elements in any loop-control system (pg. 516 col. 2 para. 2); wherein the AD monitoring as the initial step and crucial component of automatic control systems via enabling the on-line (real-time) monitoring of critical parameters in the system for early detection of process disturbances (pg. 514 col. 1 para. 4).
determining based at least in part on the one or more input signals, one or more values of one or more input variables of a nonlinear model predictive controller, the nonlinear model predictive controller having a nonlinear model of anaerobic digestion having four or fewer model state variables based at least in part on the one or more input variables that are available due to the one or more input signals;
updating the nonlinear model predictive controller based at least in part on the one or more values of the one or more input variables;
causing the one or more output signals to be generated based at least in part on one or more values of one or more output variables of the nonlinear model predictive controller
• Nguyen teaches controlled inputs or manipulated variables in automatic control of AD process are corrected actions triggered from the control algorithm (i.e. reading on updating and generating output signals) (pg. 516 col. 2 para. 5) but does not teach all the limitations described above. However, Lauwers teaches the mathematical modelling of anaerobic digestion (pg. 383 para. 1) and a robust non-linear controller model (pg. 395 Table 4); wherein models consist of several ordinary differential equations (ODE) that use the vector of state variables (pg. 384 col. 2 para. 6); wherein the number of input variables was reduced for an anaerobic digestion model 1 (ADM1) (pg. 395 col. 2 para. 4); wherein in the first model selection step, a trade-off should be made between accuracy and model complexity - determined by the number of state variables and parameters included (i.e. model state variables of any number of variables) (pg. 385 col. 1 para. 7). Furthermore, Gaida teaches the development of a nonlinear model predictive control (NMPC) algorithm to optimally control the substrate feed of the anaerobic digestion process on biogas plants (pg. 1 para. 1); wherein the total time needed by the NMPC algorithm to perform the amount of iterations is referred to as the ‘controller execution time (i.e. update the nonlinear model predictive controller based at least in part on the one or more values of the one or more input variables) (pg. 4 col. 2 para. 3); wherein NMPC processes are simulated using an anaerobic digestion model 1 (pg. 2 col. 2 para. 4) in which an output flow is controlled as evidenced by Batstone (i.e. cause the one or more output signals to be generated based at least in part on one or more values of one or more output variables of the nonlinear model predictive controller ) (pg. 69 para. 3 Batstone).
• Regarding the recited "four or fewer state variables", MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection. In In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977), the CCPA held that a particular parameter must first be recognized as a result-effective variable, i.e., a variable which achieves a recognized result, before the determination of the optimum or workable ranges of said variable might be characterized as routine experimentation, because "obvious to try" is not a valid rationale for an obviousness finding. However, in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007), the Supreme Court held that "obvious to try" was a valid rationale for an obviousness finding, for example, when there is a "design need" or "market demand" and there are a "finite number" of solutions. 550 U.S. at 421, 82 USPQ2d at 1397 ("The same constricted analysis led the Court of Appeals to conclude, in error, that a patent claim cannot be proved obvious by showing that the combination of elements was ‘obvious to try.’ ... When there is a design need or market pressure to solve a problem and there are a finite number of identified, predictable solutions, a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense. Thus, after KSR, the presence of a known result-effective variable would be one, but not the only, motivation for a person of ordinary skill in the art to experiment to reach another workable product or process. In this instance, it would be obvious to try a choice from a finite number of identified, predictable solutions, with a reasonable expectation of success.
storing historical data associated with the one or more output signals, the one or more input signals, and the four or fewer model state variables; and
automatically controlling the one or more actuators to control flow rate based at least in part on the one or more output signals and the condition, wherein the flow rate is associated with a water dilution flow rate into the anaerobic digestion reactor, an external alkali input into the anaerobic digestion reactor, or a combination thereof
• Nguyen teaches that the output data from the sensors can be transmitted to the display, stored in disk, and send to controller for subsequent calculation (pg. 516 col. 2 para. 2); wherein the controller (e.g., microprocessor controller or computer) receives data from sensors for tracking error/deviation by comparing with desired set point, and then the tracking error is plugged in the control algorithm to calculate the variable output (pg. 516 col. 2 para. 2); wherein the actuator or final control element receives variable output in the form of electrical signal and converts it into physical actions, like open/close valves, activate/deactivate pumps or regulate flow rate (pg. 516 col. 2 para. 2); wherein automatic anaerobic digestion process control system enables quick process stabilization with less operation and maintenance inconveniences (pg. 514 col. 1 para. 2)
• Nguyen does not teach “wherein the flow rate is associated with a water dilution flow rate into the anaerobic digestion reactor, an external alkali input into the anaerobic digestion reactor, or a combination thereof.” However, Lauwers teaches the mathematical modelling of anaerobic digestion (pg. 383 para. 1) with a dynamic nonlinear system as evidenced by Mendez (pg. 377 col. 1 para. 4 Mendez); including the vector of state variables such as dilution rate, i.e. the ratio between volumetric inflow and digester volume of the digester liquid (i.e. comprises water) (pg. 384 col. 2 para. 6).
Claim 10 recites:
recites one or more computer-readable media to perform steps described in claim 1
Claim 19 recites:
a method to perform steps described in claim 1
• Nguyen teaches an overview of the available automatic control methods (i.e. as in claim 19) that can be implemented in AD systems at different scales (pg. 513 para. 1); wherein there are three basic elements in any loop-control system, namely sensor, controller and actuator; with the output data of the AD process measured by sensors being transmitted to the display, stored in disk (i.e. computer-readable media as in claim 10), and send to controller for subsequent calculation (pg. 516 col. 2 para. 2 and Fig. 3).
• Lauwers teaches the self-implementation of the model in Simulink/Matlab (i.e. usage of memory - computer-readable media as in claim 10); wherein several methods (i.e. as in claim 19) are disclosed to apply the modelling of anaerobic digestion as described above (pg. 385 col. 1 para. 6).
Claims 6, 15 and 24 recites:
wherein the one or more output variables of the nonlinear model predictive controller comprise: a volumetric inflow rate of organic substrate to the anaerobic digestion reactor, a volumetric inflow rate of dilution water to the anaerobic digestion reactor, and a volumetric inflow rate of alkali addition to the anaerobic digestion reactor
• Nguyen does not teach the recited limitation. However, Lauwers teaches the ODEs used along with a dynamic nonlinear controller system as evidenced by Mendez (pg. 377 col. 1 para. 4 Mendez); including the vector of state variables such as dilution rate, i.e. the ratio between volumetric inflow and digester volume of the digester liquid (i.e. comprises water) (pg. 384 col. 2 para. 6); wherein during the digestion process, a fraction of the organic matter is converted into an energy-rich biogas (pg. 384 col. 1 para. 2); wherein the volumetric organic loading rate (i.e. volumetric inflow rate of organic substrate to the anaerobic digestion reactor), total chemical oxygen demand removal rate, influent alkalinity, and influent and effluent pH have been used as input variables (pg. 396 col. 1 para. 6); wherein teaches self-implementation of the model in done using Simulink/Matlab (i.e. usage of memory - computer-readable media as in claims 15 and 24).
Claims 8 and 17 recite:
wherein the anaerobic digestion reactor comprises a continuous anaerobic digestion reactor with solids retention
• Nguyen teaches an overview of the available automatic control technologies that can be implemented in anaerobic digestion processes at different scales (pg. 513 para. 1); wherein one approach uses a continuous incoming feedstock that needs to be digested (pg. 517 col. 1 para. 3); wherein solids retention time is considered as a parameter to optimize AD processes (pg. 515 Table 1).
Claim 28 recites:
wherein the one or more sensors are one or more hardware sensor devices and the one or more actuators are one or more hardware actuator devices
• Nguyen teaches an overview of the available automatic control methods that can be implemented in AD systems at different scales (pg. 513 para. 1); wherein there are three basic elements in any loop-control system, namely sensor, controller and actuator; with the output data of the AD process measured by sensors being transmitted to the display, stored in disk (i.e. computer-readable media), and send to controller for subsequent calculation (pg. 516 col. 2 para. 2 and Fig. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1, 6, 8, 10, 15, 17, 19, 24 and 28, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Nguyen in view of Lauwers and Gaida because all references disclose methods for controlling an anaerobic digestion reactor operation. The motivation would have been to:
• address certain caveats or specific digestion situations (pg. 399 col. 1 para. 1 Lauwers); and
• exploit the optimization potential of the NMPC algorithm by using this optimal control scheme (pg. 5 col.2 para. 2 Gaida).
Therefore it would have been obvious to one of ordinary skill in the art to substitute controlling an anaerobic digestion reactor operation method of Nguyen to the methods by Lauwers and Gaida because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for controlling an anaerobic digestion reactor operation.
B. Claims 2, 4, 9, 11, 13, 18, 22 and 26 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nguyen, Lauwers as evidenced by Mendez and Gaida as evidenced by Batstone as applied to claims 1, 10 and 19 above further in view of Zhao (“Dynamic Modeling the Anaerobic Reactor Startup Process” Ind. Eng. Chem. Res. 9(16):7193-7200 (2010) in view of Williams (“Monitoring methanogenic population dynamics in a full-scale anaerobic digester to facilitate operational management” Bioresource Technology 140:234–242 (2013)), as cited on the 06/17/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claims 2 and 11 recite:
wherein the nonlinear model of anaerobic digestion comprises a system of ordinary differential equations, wherein the system of ordinary differential equations is based at least in part on variables corresponding to: an effluent concentration of total acetate from the anaerobic digestion reactor, a concentration of aceticlastic methanogens in the anaerobic digestion reactor, a total alkalinity in the anaerobic digestion reactor, a methane production rate of the anaerobic digestion reactor, an effluent concentration of total inorganic carbon from the anaerobic digestion reactor, an effluent concentration of organic substrate from the anaerobic digestion reactor, and a partial pressure of carbon dioxide in an output of the anaerobic digestion reactor
• Lauwers teaches the mathematical modelling of anaerobic digestion (pg. 383 para. 1); wherein models consist of several ordinary differential equations (ODE) (pg. 384 col. 2 para. 6); wherein control approaches involved the control of total alkalinity by controlling the influent flow rate and the addition of alkaline solution (i.e. a total alkalinity in the anaerobic digestion reactor) (pg. 395 Table 4); wherein Eq. 12 discloses the methanogenesis reaction rate for the production of methane (i.e. methane production rate) (pg. 394 col. 2); wherein the inorganic carbon content is calculated at the ADM1/ASM1 interface (pg. 391 col. 2 para. 1) which is located downstream from the reactor (i.e. effluent concentration of total inorganic carbon from the anaerobic digestion reactor) (pg. 392 Fig. 4); wherein the concentration of organic substrate is analyzed (394 col. 2 para. 1) with substrate components fed to the digester (pg. 387 col. 1 para. 8) which is downstream from the reactor (i.e. an effluent concentration of organic substrate from the anaerobic digestion reactor) (pg. 392 Fig. 4) and the partial pressure of CO2 in the gas-phase was considered for calculations to account for the solubility of CO2 in water and its mass-transfer to the gas phase (i.e. a partial pressure of carbon dioxide in an output of the anaerobic digestion reactor) (pg. 386 col. para. 5).
• Neither Nguyen or Lauwers or Gaida teach “an effluent concentration of total acetate from the anaerobic digestion reactor”. However, Zhao teaches a model to describe the dynamic behavior of the startup of anaerobic reactors (pg. 7193 para. 1); wherein the concentrations of acetate, in the effluent were determined with another gas chromatograph (i.e. an effluent concentration of total acetate from the anaerobic digestion reactor) (pg. 7194 col. 1 para. 4).
• Neither Nguyen or Lauwers or Gaida teach “a concentration of aceticlastic methanogens in the anaerobic digestion reactor”. However, Williams teaches a method for monitoring methanogenic population dynamics in a full-scale anaerobic digester to facilitate operational management (pg. 234 Title); wherein the methanogenic community was dominated by aceticlastic methanogens from the family Methanosaetaceae (abundance of 4.6x108 gene copies per mL) (i.e. concentration of aceticlastic methanogens) (pg. 235 col. 2 para. 1).
Claims 4, 13 and 22 recite:
wherein the nonlinear model of anaerobic digestion includes methane production occurring through (i) an aceticlastic methanogenesis pathway, and (ii) a hydrogenotrophic methanogenesis pathway, and the nonlinear model of anaerobic digestion determines total alkalinity from acetate (dissociated), bicarbonate, and hydroxide ions alone
• Lauwers teaches the mathematical modelling of anaerobic digestion (pg. 383 para. 1) with a dynamic nonlinear system as evidenced by Mendez (pg. 377 col. 1 para. 4 Mendez); wherein both aceticlastic methanogenesis pathway and hydrogenotrophic methanogenesis pathway are disclosed for the production of methane and carbon dioxide (pg. 390 Fig. 3); wherein in anaerobic digestion, total alkalinity is formally defined as the sum of equivalents of all the bases (i.e. comprising hydroxide ions) and total alkalinity is most importantly due to bicarbonates and volatile fatty acids – where volatile fatty acids are mainly composed by dissolved acetate as evidenced by Mendez (pg. 376 col. 1 para. 3 Mendez).
Claims 9, 18 and 26 recite:
wherein the four model state variables comprises an effluent concentration of total acetate from the anaerobic digestion reactor, a concentration of aceticlastic methanogens in the anaerobic digestion reactor, an effluent concentration of total inorganic carbon from the anaerobic digestion reactor, and a total alkalinity in the anaerobic digestion reactor effluent, and
four or fewer model state variables are used by the nonlinear model predictive controller during the start-up phase of the anaerobic digestion reactor, and wherein the nonlinear model predictive controller optimizes a performance during the start-up phase by:
maintaining, during the start-up phase, the effluent acetate concentration below a first predetermined setpoint;
maintaining, during the start-up phase, the aceticlastic methanogen concentration above a second predetermined setpoint;
maintaining, during the start-up phase, the total alkalinity above a third predetermined setpoint; and
maintaining, during the start-up phase, the effluent concentration of total inorganic carbon from the anaerobic digestion reactor above a fourth predetermined setpoint
• Nguyen teaches an overview of the available automatic control technologies that can be implemented in anaerobic digestion (AD) processes at different scales (pg. 513 para. 1); wherein one approach uses a continuous incoming feedstock that needs to be digested (pg. 517 col. 1 para. 3); wherein solids retention time is considered as a parameter to optimize AD processes (pg. 515 Table 1).
• Lauwers teaches the mathematical modelling of anaerobic digestion (pg. 383 para. 1); wherein models consist of several ordinary differential equations (ODE) that use the vector of state variables (pg. 384 col. 2 para. 6); wherein the number of variables was subsequently reduced for an anaerobic digestion model (pg. 395 col. 2 para. 4); wherein the inorganic carbon content is calculated at the ADM1/ASM1 interface (pg. 391 col. 2 para. 1) which is located downstream from the reactor (i.e. effluent concentration of total inorganic carbon from the anaerobic digestion reactor) (pg. 392 Fig. 4); wherein control approaches involved the control of total alkalinity produced by controlling the influent flow rate and the addition of alkaline solution (i.e. a total alkalinity in the anaerobic digestion reactor) (pg. 395 Table 4); wherein the dilution and feed concentration consist of terms that ensure nominal system stability (pg. 394 Table 3); wherein system stability is described as using numerical simulations capable to maintain the desired set-points while satisfying practical stability criteria as evidenced by Mendez (i.e. maintaining set parameters predetermined set points) (pg. 380 col. 2 para. 2); wherein teaches self-implementation of the model in done using Simulink/Matlab (i.e. usage of memory - computer-readable media as in claims 18 and 26).
• The claims are interpreted as requiring only one of the "four" variables because the claim recites “four or fewer” – See 112(b) Claim Rejections above. Even though Nguyen and Lauwers teach at least one of these four variables, and therefore teaches the requirements of the claim, it is noted that the other variables are taught as follows.
• Neither Nguyen or Lauwers or Gaida teach “an effluent concentration of total acetate from the anaerobic digestion reactor”. However, Zhao teaches a model to describe the dynamic behavior of the startup of anaerobic reactors (pg. 7193 para. 1); wherein the concentrations of acetate, in the effluent were determined with another gas chromatograph (i.e. an effluent concentration of total acetate from the anaerobic digestion reactor) (pg. 7194 col. 1 para. 4).
• Neither Nguyen or Lauwers or Gaida teach “a concentration of aceticlastic methanogens in the anaerobic digestion reactor”. However, Williams teaches a method for monitoring methanogenic population dynamics in a full-scale anaerobic digester to facilitate operational management (pg. 234 Title); wherein the methanogenic community was dominated by aceticlastic methanogens from the family Methanosaetaceae (abundance of 4.6x108 gene copies per mL) (i.e. concentration of aceticlastic methanogens) (pg. 235 col. 2 para. 1).
• Regarding all steps related to maintaining predetermined setpoint for different parameters, one of ordinary skill would be motivated to maintain a desired set point as taught by Lauwers and Mendez for each of the recited parameters to achieve routine optimization towards determination of the optimum anaerobic digestion process. MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection.
Rationale for combining (MPEP §2142-2143)
Regarding claims 2, 4, 9, 11, 13, 18, 22 and 26, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Nguyen, Lauwers and Gaida in view of Zhao and Williams because all references disclose methods for controlling an anaerobic digestion reactor operation. The motivation would have been to:
• describe the startup process of anaerobic systems capturing the variation trends of components (pg. 7199 col. 1para. 4 Zhao) and
• predict AD stability and facilitate digester optimization (pg. 234 para. 1 Williams)
Therefore it would have been obvious to one of ordinary skill in the art to substitute controlling an anaerobic digestion reactor operation method of Nguyen, Lauwers and Gaida to the methods by Zhao and Williams because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for controlling an anaerobic digestion reactor operation.
C. Claims 7, 16 and 25 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nguyen, Lauwers as evidenced by Mendez and Gaida as evidenced by Batstone as applied to claims 1, 10 and 19 above further in view of Donoso (“Model selection, identification and validation in anaerobic digestion: A review” Water Research 45:5347-5364 (2011)) in view of Williams, as cited on the 06/17/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claims 7, 16 and 25 recite:
wherein an objective function of the nonlinear model predictive controller is based at least in part on: an effluent concentration of volatile fatty acids as acetate from the anaerobic digestion reactor, a concentration of aceticlastic methanogens in the anaerobic digestion reactor, a methane production rate of the anaerobic digestion reactor, and a cost term penalizing an amount of alkali added proportional to a volumetric inflow rate of alkali addition to the anaerobic digestion reactor
• Lauwers teaches a dynamic nonlinear controller system as evidenced by Mendez (pg. 377 col. 1 para. 4 Mendez); wherein in anaerobic digestion, total alkalinity is due to many species but the most important are bicarbonates and volatile fatty acids - mainly composed by dissolved acetate as evidenced by Mendez (pg. 376 col. 1 para. 3 Mendez); wherein volatile fatty acids content of the effluent is considered for the mathematical modelling of anaerobic digestion (i.e. effluent concentration of total acetate from the anaerobic digestion reactor) (pg.396 col. 1 para. 1); wherein Equation 12 discloses the methanogenesis reaction rate for the production of methane (i.e. methane production rate) (pg. 394 Eq. 12); wherein various cost functions or objective functions have been used for the parameter estimation (pg. 385 col. 2 para. 2); wherein said cost functions are used to maintain process stability and influence how optimization procedures adjusts parameters as evidenced by Donoso (pg. 5354 col. 2 para. 3; wherein one parameter (i.e. a term in the cost function) to be adjusted to obtain stability of AD processes is the inlet flow rate of an alkali solution as evidenced by Mendez (i.e. a cost term penalizing an amount of alkali added proportional to a volumetric inflow rate of alkali addition to the anaerobic digestion reactor) (pg. 377 col. 2 para. 2); wherein teaches self-implementation of the model in done using Simulink/Matlab (i.e. usage of memory - computer-readable media as in claims 16 and 25).
• Neither Nguyen or Lauwers or Gaida teach “a concentration of aceticlastic methanogens in the anaerobic digestion reactor”. However, Williams teaches a method for monitoring methanogenic population dynamics in a full-scale anaerobic digester to facilitate operational management (pg. 234 Title); wherein the methanogenic community was dominated by aceticlastic methanogens from the family Methanosaetaceae (abundance of 4.6x108 gene copies per mL) (i.e. concentration of aceticlastic methanogens) (pg. 235 col. 2 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 7, 16 and 25, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Nguyen, Lauwers and Gaida in view of Zhao and Williams because all references disclose methods for controlling an anaerobic digestion reactor operation. The motivation would have been to:
• describe the startup process of anaerobic systems capturing the variation trends of components (pg. 7199 col. 1para. 4 Zhao) and
• predict AD stability and facilitate digester optimization (pg. 234 para. 1 Williams).
Therefore it would have been obvious to one of ordinary skill in the art to substitute controlling an anaerobic digestion reactor operation method of Nguyen, Lauwers and Gaida to the methods by Zhao and Williams because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for controlling an anaerobic digestion reactor operation.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 03/12/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 13 para. 1:
Applicant submits that the amendments to independent claim 1 are not recited in any cited reference, alone or in combination. For example, the Office Action on pp. 6-8 seemingly relies on Nguyen to teach an overview of different control methods using neural networks (p. 513, para. 1 of Nguyen). However, p. 9 of the Office Action states that Nguyen fails to teach a non-linear model of anaerobic digestion having four model state variables. The Office Action relies on Lauwers to remedy the deficiencies of Nguyen. For example, on p. 10, the Office Action states Lauwers teaches, "the mathematical modelling of anaerobic digestion (pg. 383 para. I) and a robust nonlinear controller model (pg. 395 Table 4); wherein models consist of several ordinary differential equations (ODE) that use the vector of state variables (pg. 384 col. 2 para. 6); wherein the number of input variables was subsequently reduced for an anaerobic digestion model (pg. 395 col. 2 para. 4)". However, Lauwers uses 19-37 state variables (see ,Table 1 and section 3.2 of Lauwers, for example) … This results in at least five state variables being tracked during process simulation and control. Nowhere in Mendez is there a teaching, suggestion, or example of a four-state-variable model for anaerobic digestion start-up control. The control strategies, objective functions, and system architecture in Mendez are all built upon this five-variable structure, and do not anticipate or enable the reduction to the specific four or fewer state variables recited in the present application
It is respectfully submitted that this is not persuasive because the argued reduction of the "four or fewer state variables model" and "a non-linear model of anaerobic" were indeed taught by the prior art to Nguyen and Lauwers. Nguyen teaches controlled inputs or manipulated variables in automatic control of AD process are corrected actions triggered from the control algorithm (pg. 516 col. 2 para. 5) but does not teach all the limitations described above. Furthermore, Lauwers teaches the mathematical modelling of anaerobic digestion (pg. 383 para. 1) and a robust non-linear controller model (pg. 395 Table 4); wherein models consist of several ordinary differential equations (ODE) that use the vector of state variables (pg. 384 col. 2 para. 6); wherein the number of input variables was reduced for an anaerobic digestion model 1 (ADM1) (pg. 395 col. 2 para. 4); wherein in the first model selection step, a trade-off should be made between accuracy and model complexity - determined by the number of state variables and parameters included (i.e. model state variables of any number of variables) (pg. 385 col. 1 para. 7). Lauwers' teachings regarding determining the number of state variables and parameters would motivated one of ordinary skill in the art to utilize any number of variables to address certain caveats or specific digestion situations (pg. 399 col. 1 para. 1 Lauwers).
Furthermore, regarding the recited "four or fewer state variables", MPEP 2144.05 II states - The Supreme Court has clarified that an "obvious to try" line of reasoning may properly support an obviousness rejection. In In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977), the CCPA held that a particular parameter must first be recognized as a result-effective variable, i.e., a variable which achieves a recognized result, before the determination of the optimum or workable ranges of said variable might be characterized as routine experimentation, because "obvious to try" is not a valid rationale for an obviousness finding. However, in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007), the Supreme Court held that "obvious to try" was a valid rationale for an obviousness finding, for example, when there is a "design need" or "market demand" and there are a "finite number" of solutions. 550 U.S. at 421, 82 USPQ2d at 1397 ("The same constricted analysis led the Court of Appeals to conclude, in error, that a patent claim cannot be proved obvious by showing that the combination of elements was ‘obvious to try.’ ... When there is a design need or market pressure to solve a problem and there are a finite number of identified, predictable solutions, a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense. Thus, after KSR, the presence of a known result-effective variable would be one, but not the only, motivation for a person of ordinary skill in the art to experiment to reach another workable product or process. In this instance, it would be obvious to try a choice from a finite number of identified, predictable solutions, with a reasonable expectation of success.
Due to the described reasons above, it is interpreted that the claims do not patentably distinguish the claimed invention from the teachings found in the prior art. Furthermore, in this instant application, the amendments support existing claim rejections, in which the recited limitations are all addressed, see Claim Rejections above
Conclusion
No claims are allowed.
Referring to the 101 analysis as organized in MPEP 2106, claims 1-2, 4, 6-11, 13, 15-19, 22, 24-26 and 28 are free of 101 issues in view of the analysis Step 2A, 2nd prong, 3rd consideration regarding implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b). In view of the amendment and remarks from 09/17/2025, the rejection under 35 USC § 101 was withdrawn in view of Applicant's amendments, rendering the ground of rejection moot. The examiner agrees with the argument that claims implement the abstract idea into a practical application that is control of a particular machine similar to the claims in Diamond v. Diehr because the claims are effecting the function of the reactor by altering the flow rate
The instant improvement is to the operation of the recited distributed computer system applied to the field of analysis of molecular electronic structure, the improvement in this instance comprising a distributed computing system that uses classical and quantum/non-classical/hybrid computing (not equivalent to a generic computer) to generate and dispatch information that specifies what type of electronic structure solver and the parameters to use. Said limitation integrates the judicial exception into a practical application (by controlling how the distributed computing system operates) and could improve the operation of the distributed computing system at Step 2A, Prong 2.analogous to the reasoning in MPEP-cited case law including Enfish, BASCOM and McRO (MPEP 2106.04(d) and (d)(1)). In this regard, Applicant's 2/26/2025 remarks at pgs. 8-9 further support withdrawal of the rejection.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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