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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claims 1-6 recite the generic placeholders, “data collection module”, “simulation module”, “performance monitoring module” followed by functional limitation without reciting structure to perform the functional limitations. To find support for structure for the generic placeholder, Examiner looked into page 6 and page 8 of the specification where it is recited the modules can be implement as software and hardware. Page 8 recite a computer which is viewed as the hardware performing all the functional limitations of the claimed invention.
Claim 7 recites the generic placeholder, “performance monitoring module” followed by functional limitation without reciting structure to perform the functional limitations. Examiner looked into page 6 and page 8 of the specification where it is recited the modules can be implemented as software and hardware. Page 8 recite a computer which is viewed as the hardware performing all the functional limitations of the claimed invention.
Claim 8 recites the generic placeholder, “feedback module” followed by functional limitation without reciting structure to perform the functional limitations. Examiner looked into page 6 and page 8 of the specification where it is recited the modules can be implemented as software and hardware. Page 8 recite a computer which is viewed as the hardware performing all the functional limitations of the claimed invention.
Claim 11 recite the generic placeholders, “simulation module” and “feedback loop” followed by functional limitation without reciting structure to perform the functional limitations. Examiner looked into page 6 and page 8 of the specification where it is recited the modules can be implemented as software and hardware. Page 8 recite a computer which is viewed as the hardware performing all the functional limitations of the claimed invention.
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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-8 and 10-11 are rejected under 35 U.S.C.103 as being unpatentable over Feng et al. (US 20240177076 A1) in view of Husain et al. (US 20240367136 A1).
Regarding claim 1, Feng et al. teaches, a system for optimizing a gas flow of ammonia in an industrial plant (system referred to as digital twin model aiming to optimize the operation of ammonia production plant, [0036] and [0042]), the system comprising:
a physical plant (low carbon ammonia production facility, [0036] and [0042]);
a digital twin model for the physical plant (“…In examples, the digital twin as described herein may be an accurate representation of the topology of a low carbon ammonia production facility with respect to the type and size of all equipment of the facility, the steady state and dynamic response of all processes of facility, and/or the actual operational constraints of the facility…”, [0036] and [0042]);
a data collection module (digital twin model in a computer has all the necessary modules residing in the computer, [0055]) configured to acquire real-time data from the physical plant (the digital twin model receiving information at intervals (real-time) related to plant operating parameters and other factors affecting the operation of the facility /plant, [0056] and [0063]-[0065]);
a simulation module (digital twin model in a computer has all the necessary modules residing in the computer, [0055]) configured to simulate at least one scenario using the digital twin model and identify optimal operating conditions for the gas flow of ammonia based on simulation results (after receiving all the data, the digital twin model runs series of iterative simulations to generate operating condition for each subprocess to achieve the best/optimized condition for operating the ammonia production plant while being subject to constraints, [0056] and [0068], see also [0042]).
Feng et al. does not teach the details of a feedback loop configured to implement a feedback loop between the digital twin model and the physical plant to adjust the
physical plant based on the simulation results; and a performance monitoring module configured to monitor performance of the physical plant to track effectiveness of the
optimization process. However Feng et al. explicitly teaches in [0098], that based on the recommendation generated by the digital twin model, the operator can change operating setpoints to improve ammonia production performance. Another obvious variation is the recommendations are automatically generated by the computer without operator intervention.
On the other hand Husain et al. teaches, a feedback loop configured to implement a feedback loop (the digital twin model has feedback loop, [0052] and [0227]) between the digital twin model and the physical plant to adjust the physical plant based on the simulation results (using the digital twin model of the reactor – physical plant, optimized performance plant operational parameters are generated and automatically implemented to the physical plant, [0007], [0009] and [0052]); and
a performance monitoring module (feedback loop, [0292]) configured to monitor performance of the physical plant to track effectiveness of the optimization process (the feedback loop monitors the real-time performance (tracking effectiveness of the optimization process) of the plant after implementing a digital twin model recommended actions, [0277], [0292] and [0294]).
Feng et al. and Husain et al. are analogous art because they are from the same field of endeavor that is implementing digital twin model for a production plant to obtain optimized operation settings.
Therefore it would have been obvious before the effective date of the claimed invention to a person of ordinary skill in the art to modify the system for optimizing a gas flow of ammonia in an industrial plant implementing digital twin model to identify optimal operating conditions as taught by Feng et al. by applying the known technique of implementing the identified optimal operating condition setpoints in automatically and track the performance of the plant after the implementation as taught by Husain et al. to yield predictable results of optimizing plant operating parameters and due to automatic implementation, establish a best performance for the process/plant immediately as taught by Husain et al. in [0052].
Feng et al. teach:
[0042] Disclosed herein is a system referred to as that includes a detailed and representative model that accurately reflects the structure and operation of a facility. In examples, a digital twin of the entire facility as described herein may be configured to analyze the differences in dynamics and response times between the front end and the back end and provide decision support capabilities, aiming to optimize the design and operation of the entire low carbon production.
[0063] In examples, the digital twin 200 may receive information 260 over a period of time and/or at time intervals. In examples, the duration of a period of time over which information 260 is received may vary. In examples, it may be dynamically modified, it may be preset, and/or it may be overridden by a user. In examples, the duration of a period of time over which information 260 is received may vary depending on the type of information 260 and/or amount of information 260 received. The frequency and/or duration of the time intervals may be uniform or varying. In examples, the time intervals may reflect updates made to information 260. In examples, the time intervals may be preset, dynamically adjusted1, or a combination thereof. In examples, the time intervals may be overridden by a user.
[0068] The digital twin may receive information 260 consisting of facility specific information 262 such as current operating conditions of the facility, non-facility specific information 264, such as low carbon energy forecast for a period of time, and other information 266, such as market price of ammonia at pre-defined time intervals. Once the digital twin receives information 260 at each time interval, the may be configured to run a series of iterative simulations. In examples, the iterative simulations may be run by varying the process conditions received by the as facility specific information or non-facility specific information. As previously described, the facility specific information and/or non-facility specific information may be defined by the user (e.g. step size, total number of calculations, constraints, etc.). In examples, through the iterative simulations, the digital twin may be configured to predict one or more operating conditions for each sub-process area of a facility from which the digital twin may select to achieve the best or desired conditions. For example, the may select conditions that result in the most energy efficient way to produce ammonia subject to constraints.
Husain et al. teach:
[0052] In the present invention a digital twin application of the commercial EO reactor is built to mimic the real plant. This application will run in real time on a digital processing unit (e.g. a desktop computer) and collect plant data historic data (also named “historian” in this disclosure) e.g. at a regular interval of e.g. one hour. The software will collect the data in real time and carry out all the analysis in order to collect information for efficiently controlling the plant. With the help of its in-built AI based model of any other suitable algorithm, it will assess the current status of the plant, carry out root cause analysis for performance deterioration of catalyst (if any), predict the future performance and optimize the reactor operating parameters to increase catalyst performance. Moreover, this application will generate and communicate the decision to change chloride and other process parameters to respective control devices (valves, dosage systems, heating or pumping devices etc.) in order to establish a best performance for the process immediately2 or in order to communicate suggestions and/or target values for the parameters (e.g. on a dashboard and/or infographics) to an engineer.
[0292] Feedback loop will monitor the real of (selectivity and temperature) after implementing a recommendation generated out of feedforward action3.
Regarding claim 2, combination of Feng et al. and Husain et al. teach system according to claim 1. In addition Feng et al. teaches, wherein the physical plant comprises pipes (pipeline for flowing gas, [0019]), valves (flow control devices including valves, [0055]), pumps (plant equipment such as pumps, [0055]), and sensors (performance of equipment are collected and gathered, must be using sensors for data collection, [0055]).
Regarding claim 3, combination of Feng et al. and Husain et al. teach system according to claim 1. In addition Feng et al. teaches, wherein the data collection module collects the real-time data including at least one of temperature, pressure, flow rate, pump speed (the digital twin model receives real time data related to pressure, temperature, flow rates and others in real time, [0055] and [0056]), pipe characteristics (non-facility information can include pipeline information, [0055], [0053]), and valve position from the sensors4.
Regarding claim 4, combination of Feng et al. and Husain et al. teach system according to claim 3. In addition Feng et al. teaches, wherein the simulation
module minimizes an energy consumption of the system by adjusting
operating conditions including at least one of the valve position, the pump speed, and the pipe characteristics (based on received inputs, the digital twin model simulates best operating conditions for pumps, compressors, flow rates, and any other operational setpoints related to the ammonia plant while considering energy consumption of the ammonia plant, [0055], [0068]-[0069]), and maximizes a throughput processed in the system (the digital twin model minimize investment cost and maximizes ammonia production-throughput, [0092]).
Regarding claim 5, combination of Feng et al. and Husain et al. teach system according to claim 1. In addition Husain et al. teaches, wherein the feedback loop
adjusts at least one of pipes5, valves, pumps, and sensors included in the physical plant in real time based on the simulation results (based on simulation result of the digital twin of the plant, several process parameters related to pump control, valves, and other devices are generated and implemented immediately by feedforward control, [0013], [0052] and [0286]).
Regarding claim 6, combination of Feng et al. and Husain et al. teach system according to claim 1. In addition Husain et al. teaches, wherein the performance
monitoring module (feedback loop) continuously monitors the performance of the physical plant and the digital twin model to determine whether the performances of the physical plant and the digital twin model satisfy preset targets (the feedback loop monitors the real-time performance (tracking effectiveness of the optimization process) of the plant after implementing a digital twin model recommended actions that satisfied certain threshold value (preset targets) which will result in best operating conditions, [0275], [0277], [0292] and [0294]).
Regarding claim 7, combination of Feng et al. and Husain et al. teach system according to claim 1. In addition Husain et al. teaches, further comprising an
update module configured to continuously update parameter settings
of the physical plant based on the real-time data of the physical plant (based on real time data of the plant, the plant performance is tracked by the feedback module. During monitoring, if the plant performance deteriorates, new recommendation is generated and implemented that is plant parameters are updated in real time based on monitored real time plant parameters. The feedback recommendation and feedforward recommendation together work as the update module for correcting and updating plant parameters at all times, [0293] and [0294]).
Regarding claim 8, combination of Feng et al. and Husain et al. teach system according to claim 1. In addition Husain et al. teaches, further comprising a
feedback module configured to adjust the physical plant in response
to at least one optimized setting identified by the digital twin model (the feedback module recommends optimized operational setpoints which can be implemented immediately using combination of feedback and feedforward action, [0073], [0293] and [0294], see also [0052]).
Regarding claim 10, combination of Feng et al. and Husain et al. teach the claimed system. Therefore together they teach the method performing the functional limitations of the system as discussed above in claim 1. Therefore claim 10 is rejected for the reasons discussed above in claim 1.
Regarding claim 11, combination of Feng et al. and Husain et al. teach the claimed system. Therefore together they teach the system performing the functional limitations of the system as discussed above in claim 1. Therefore claim 10 is rejected for the reasons discussed above in claim 1.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Feng et al. (US 20240177076 A1) in view of Husain et al. (US 20240367136 A1) and Jo et al. (US 20220389864 A1).
Regarding claim 9 combination of Feng et al. and Husain et al. teach the system according to claim 1. In addition Feng et al. teaches, collect an input indicating an energy amount required by the system and an output of the physical plant from the digital twin mode (the digital twin model receives inputs such as operating parameters, energy usage (energy required), energy availability and other related data and generate outputs such as optimized operating parameters for pumps and others, [0063]-[0065], see also [0087]).
Neither in combination nor individually Feng et al. and Husain et al. teach the details of collect a feed flow rate for a reforming reaction, a feed flow rate for a combustion reaction, and information on specific activity of catalysts in each reaction unit where the reforming reaction and combustion reaction occur from the digital twin model; and adjust, as an optimized scenario, an opening ratio of a flow rate control valve for each feed based on the collected information. However Feng et al. explicitly teaches to create a digital twin model for ammonia production plant and simulate various process parameters to achieve optimum conditions as taught in [0036], [0042] and [0065]-[0068].
Jo et al. teaches, collect a feed flow rate for a reforming reaction (reforming flow rates varied by the controller, [0633]), a feed flow rate for a combustion reaction (flow rates of the combustor are varied by the controller, [0633] and [0685]), and information on specific activity of catalysts in each reaction unit where the reforming reaction and combustion reaction occur from the digital twin model (based on received sensor readings related to temperature, flow rates, catalyst information and others of the first and second portions of the ammonia reactor having reforming and combustion portions, flow rates for both reforming portion and combustion portions of the ammonia reactor/power pack system are simulated using various algorithms (digital twin model), [0487], [0551], [0651], [0693], see also [0540]); and
adjust, as an optimized scenario, an opening ratio of a flow rate control valve for each feed based on the collected information (based on simulations, the flow of various fluids between the components of the ammonia power pack system are controlled by opening and closing corresponding valves, [0571],[0540], [0551] and [0693] see also [0487]).
Feng et al., Husain et al. and Jo et al. are analogous art because they are from the same field of endeavor that is controlling operational setpoints of a chemical plant based on simulation6 and received data related to the chemical plant.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the system for optimizing a gas flow of ammonia implementing digital twin model of an ammonia plant to determine and implement various optimized operational setpoints as taught by combination of Feng et al. and Husain et al. by applying the known technique of adjusting or controlling flow rates for reforming reaction and combustion reaction in a ammonia reactor based on received data and simulations of the ammonia reactor as taught by Jo et al. to yield predictable results of controlling flow of ammonia thus improving spatial uniformity of temperature and flow distribution within the ammonia reactor as taught by Jo et al. in [0710].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO25 (WO 2023069025 A2) teaches an energy dispatch system where a digital twin model of the energy dispatch system is generated to determine operational setpoints for the system. Once setpoints are automatically implemented, based feedback of the plant, the digital twin model is further updated and the operation setpoints are also updated and optimized.
Wang et al. (US 20190173109 A1) teaches a system and method of a fuel cell implementing digital twin model of the fuel cell to generate optimized operational setpoints.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tue 8:00am-12:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMINI SHAH can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANZUMAN SHARMIN/ Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 Real time data collection by the digital twin model.
2 Automatically implementing digital twin model generated control decisions to the control devices of the plant.
3 Tracking control decision action’s effectiveness after being implemented.
4 Husain et al. teaches valve positions alterations based on gathered data from sensors as taught in [0011] and [0007].
5 Pipeline as non-facility information related to external factors affection ammonia production as predicted by digital twin model in view of [0019] and [0065] of Feng et al.
6 To perform simulation of a chemical plant, there must be a plant model represented by digital twin or set of equations representing various portions of the plant.
Prosecution Insights