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 .
Status of Claims
Claims 1-20 are pending and under examination.
Information Disclosure Statement
The information disclosure statement (IDS) document(s) submitted on 03/22/2024 and 01/30/2025 is/are compliant with the provisions of 37 CFR 1.97. Accordingly, the IDS document(s) has/have been fully considered by the examiner.
Claim Objections
Claims 9, 13-14, 16, and 20 are objected to because of the following informalities:
Claims 9 and 16 recite the acronym “SLIC” without previously defining the acronym. The examiner requests applicant amend the claim to recite “Simplified Logic Injection Control (SLIC)” as in claim 2.
Claims 13, and 20 recite the acronym “BS&W” but do not recite a definition for the acronym. The examiner requests applicant amend the claim to recite “basic sediment and water (BS&W)”.
Claim 14 recites the acronym “GOSP” but do not clearly define the acronym in the claims. The examiner requests applicant amend claim 8 to recite “obtaining sensor data in a gas-oil separator plant (GOSP)”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 2 recites “wherein the predicted process variables are used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters”. However, the claim is not directed to a method step because the claims do not positively recite an active method/process step in the claim body. Instead, the claims appear to recite an intended use (“are used to…”) of the information. It is unclear whether any of the obtained information in claim 1 would be capable of being used as recited in the dependent claims. In other words, it would be unclear to a potential infringer whether any of the information obtained in claim 1 would automatically satisfy claims 2-4, or whether applicants are attempting to recite an actual method/process step in claims 2-4. Therefore, because claims 2-4 do not provide an active/positive recitation of the method step in the claim body, then it is unclear what method step Applicant is attempting to recite. A similar rejection is also made over claim 3. Claim 4 is also rejected by its dependency from claim 3.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is directed toward a method. Claim 8 is directed towards a non-transitory, computer readable, storage medium. Claim 15 is directed toward a system.
Step 2A, Prong One: Identify the law of nature/natural phenomenon/abstract ideas.
Claims 1, 8, and 15 recite the abstract ideas “calculating … demulsifier input parameters”, “predicting … process variables comprising at least water content”, “determining … an injection rate in real time based on the predicted process variables, which are mental processes and could be performed by a human person or by pen and paper or by a generic computer. The one or more hardware processors configured to calculate, predict, and determine are merely a general-purpose computer for which to apply the abstract ideas, and/or a model using a mathematical relationship between variables or numbers, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (III). In other words, MPEP 2106.04(a)(2)III is clear that using a computer/controller to perform the abstract idea does not preclude the steps from being considered an abstract idea. The examiner also believes that the “obtaining…sensor data” is also something that can be done by a human mind as an observation.
Step 2A, Prong Two: Has the abstract idea been integrated into a particular practical application?
No. Upon calculating, predicting, and determining, no further action is performed, and therefore is not a particular practical application.
Claims 1, 8, and 15 also recites obtaining sensor data in a gas-oil separation plant and/or one or more memory modules. If this is deemed not to be an abstract idea under step 2A prong one, then data gathering and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application because data gathering is merely insignificant extra-solution activity. See MPEP § 2106.05(g), Insignificant Extra-Solution Activity and § 2106.05(f), Mere Instructions To Apply an Exception. Further, receiving or transmitting data over a network has been recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity to apply an exception. See MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity. Additionally, the claims appear to just be using a computer in it’s normal fashion, and have not transformed the computer into a particular machine because a general-purpose computer is not a particular machine, and performing the abstract idea on a general purpose computer is not enough to integrate the exception into a practical application (MPEP 2106.05(b)I.).
Step 2B: Does the claim recite any elements which are significantly more than the abstract idea?
Claims 1, 8, and 15 recite the additional elements of “obtaining sensor data in a gas-oil separation plant and/or one or more memory modules”. Although the examiner believes this is an abstract idea, if this is deemed not to be an abstract idea under step 2A prong one, then these additional elements do not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more than well-understood, routine, and conventional activities previously known to the industry (See MPEP § 2106.05(c), Particular Transformation and MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity) as evidenced by Salu (US 2018/0195010 – hereinafter “Salu”) and Ahmed (US 2023/0152296 – hereinafter “Ahmed”). Salu and Ahmed disclose obtaining sensor data in a gas-oil separation plant (Salu; figs. 1A & 1B, #70, #72, #105, [0020-0023, 0076, 0078] & Ahmed; [0181]) and/or one or more memory modules (Salu; fig. 1B, #150, #162, [0022-0023] & Ahmed; fig. 15, #1506, [0222]). Additionally, the claims appear to just be using a computer in it’s normal fashion, and have not transformed the computer into a particular machine, where a computer is WURC in the art of controlling injection (see references above).
Claim 2, 9, and 16 recite the abstract idea “the predicted process variables are used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters”. However, generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application. See MPEP § 2106.05(f), Mere Instructions To Apply an Exception.
Claims 3, 10, and 17 recites the abstract idea of “the predicted process variables are to update a trained AI model that predict the process variables” (step 2A prong 1), but does not integrate the exception under 2A prong 2 because receiving or transmitting data over a network has been recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity to apply an exception (MPEP 2106.04(d), Well-Understood, Routine, Conventional Activity, and 2106.05(f), Mere Instructions To Apply an Exception). Further, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I).
Claims 4, 11, and 18 further limits the abstract idea in claims 3, 10, and 17 as being based on an error between the sensor data and predicted data. However, generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application. See MPEP § 2106.05(f), Mere Instructions To Apply an Exception. Further, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I).
Claims 5, 12, and 19 further limit the calculated demulsifier input parameters as being based on an empirical approach. However, generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application. See MPEP § 2106.05(f), Mere Instructions To Apply an Exception. Further, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I).
Claims 6, 13, and 20 recite the predicted variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies. However, generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application. See MPEP § 2106.05(f), Mere Instructions To Apply an Exception. Further, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I).
Claims 7 and 14 recite the injection rate is used to determine input parameters applied throughout the GOSP to enable a demulsifier process. However, generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application. See MPEP § 2106.05(f), Mere Instructions To Apply an Exception. Further, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I).
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 8 and 15 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Ahmed et al. (US 2023/0152296 – hereinafter “Ahmed”).
Regarding claim 1, Ahmed disclose a computer-implemented method for real-time adaptive control of demulsifier injection using self-learning Al models (Ahmed; [0006, 0031]), the method comprising:
obtaining, with one or more hardware processors, sensor data in a gas-oil separator plant (GOSP) (Ahmed; [0181]);
calculating, with the one or more hardware processors, demulsifier input parameters (Ahmed; Table 6, demulsifier rate to hydrator);
predicting, with the one or more hardware processors, process variables comprising at least water content (Ahmed; [0031, 0046, 0174]); and
determining, with the one or more hardware processors, an injection rate in real time based on the predicted process variables (Ahmed; [0155]).
Regarding claim 8, Ahmed disclose an apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations (Ahmed; [0006, 0031]) comprising:
obtaining sensor data in a gas-oil separator plant (Ahmed; [0181]);
calculating demulsifier input parameters (Ahmed; Table 6, demulsifier rate to hydrator);
predicting process variables comprising at least water content (Ahmed; [0031, 0046, 0174]); and
determining an injection rate in real time based on the predicted process variables (Ahmed; [0155]).
Regarding claim 15, Ahmed disclose a system, comprising:
one or more memory modules (Ahmed; fig. 15, #1506, [0222]);
one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations (Ahmed; fig. 15, [0006, 0031]) comprising:
obtaining sensor data in a gas-oil separator plant (Ahmed; [0181]);
calculating demulsifier input parameters (Ahmed; Table 6, demulsifier rate to hydrator);
predicting process variables comprising at least water content (Ahmed; [0031, 0046, 0174]); and
determining an injection rate in real time based on the predicted process variables (Ahmed; [0155]).
Claim 8, 12-15 and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Salu et al. (US 2018/0195010 – hereinafter “Salu”).
Regarding claim 8, Salu disclose an apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations (Salu; figs. 1A, 1B, 2, 5 & 6, #130, #150, #161, #162, [0003, 0022-0023, 0036, 0046, 0053, 0076-0079]) comprising:
obtaining sensor data in a gas-oil separator plant (Salu; figs. 1A & 1B, #70, #72, #105, [0020-0023, 0076, 0078]);
calculating demulsifier input parameters (Salu disclose calculating performance factors for different demulsifiers; [0029, 0076, 0078]);
predicting process variables comprising at least water content (Salu disclose predicting the crude production rate which comprises water content, and a method for predicting the production rate based on changes in crude type or properties of crude or demulsifier type; fig. 2, 5 & 6, [Table 1, 0030-0036, 0076-0079]); and
determining an injection rate in real time based on the predicted process variables (Salu disclose optimizing demulsifer injection rate in a GOSP according to method 200 which includes adjustment if there is any significant change in crude type or properties of crude or demulsifier type; figs. 2, 5 & 6, [0036, 0046, 0076-0079]).
Regarding claim 12, Salu disclose the apparatus of claim 8 above, wherein an empirical based approach is used to calculate demulsifier input parameters in real time (Salu; [0029]).
Regarding claim 13, Salu disclose the apparatus of claim 8 above, wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, BS&W, and separator efficiencies (Salu disclose predicting crude production rate in view of the claimed process variables [0030-0048]).
Regarding claim 14, Salu disclose the apparatus of claim 8 above, wherein the injection rate is used to determine input parameters applied throughout the GOSP to enable a demulsifier process (Salu teach “the first step in the control strategy for automation of primary demulsifier injection is to individually control all necessary process variables to their optimum value and adjust demulsifier injection rate whenever there is a deviation in the crude specification after optimization of all other parameters”; [0027]).
Regarding claim 15, Salu disclose a system (Salu; figs. 1A, 1B, 2, 5 & 6, [0003, 0036, 0046, 0053, 0076-0079]), comprising:
one or more memory modules (Salu; fig. 1B, #150, #162, [0022-0023]);
one or more hardware processors communicably coupled to the one or more memory modules (Salu; fig. 1B, #130, #161, [0022-0023]), the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations (Salu; figs. 1A, 1B, 2, 5 & 6, [0003, 0036, 0046, 0053, 0076-0079]) comprising:
obtaining sensor data in a gas-oil separator plant (Salu; figs. 1A & 1B, #70, #72, #105, [0020-0023, 0076, 0078]);
calculating demulsifier input parameters (Salu disclose calculating performance factors for different demulsifiers; [0029, 0076, 0078]);
predicting process variables comprising at least water content (Salu disclose predicting the crude production rate which comprises water content, and a method for predicting the production rate based on changes in crude type or properties of crude or demulsifier type; fig. 2, 5 & 6, [Table 1, 0030-0036, 0076-0079]); and
determining an injection rate in real time based on the predicted process variables (Salu disclose optimizing demulsifer injection rate in a GOSP according to method 200 which includes adjustment if there is any significant change in crude type or properties of crude or demulsifier type; figs. 2, 5 & 6, [0036, 0046, 0076-0079]).
Regarding claim 19, Salu disclose the system of claim 15 above, wherein an empirical based approach is used to calculate demulsifier input parameters in real time (Salu; [0029]).
Regarding claim 20, Salu disclose the system any of claim 15 above, wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, BS&W, and separator efficiencies (Salu disclose predicting crude production rate in view of the claimed process variables [0030-0048]).
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-7, 10-11, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Salu and further in view of Elyas et al. (US 2020/0285216 – hereinafter “Elyas”).
Regarding claim 1, Salu disclose a computer-implemented method for real-time adaptive control of demulsifier injection using models (Salu; figs. 1A, 1B, 2, 5 & 6, [0003, 0036, 0046, 0053, 0076-0079]), the method comprising:
obtaining, with one or more hardware processors (Salu; fig. 1B, #130, #161, [0022-0023]), sensor data in a gas-oil separator plant (GOSP) (Salu; figs. 1A & 1B, #70, #72, #105, [0020-0023, 0076, 0078]);
calculating, with the one or more hardware processors, demulsifier input parameters (Salu disclose calculating performance factors for different demulsifiers; [0029, 0076, 0078]);
predicting, with the one or more hardware processors, process variables comprising at least water content (Salu disclose predicting the crude production rate which comprises water content, and a method for predicting the production rate based on changes in crude type or properties of crude or demulsifier type; fig. 2, 5 & 6, [Table 1, 0030-0036, 0076-0079]); and
determining, with the one or more hardware processors, an injection rate in real time based on the predicted process variables (Salu disclose optimizing demulsifer injection rate in a GOSP according to method 200 which includes adjustment if there is any significant change in crude type or properties of crude or demulsifier type; figs. 2, 5 & 6, [0036, 0046, 0076-0079]).
Salu does not teach the model is a self-learning AI model.
However, Elyas teach the analogous art of a system and method for real-time adaptive control using a model (Elyas; figs. 1 & 2, [0029, 0040]), the model comprising obtaining sensor data in a gas-oil environment (Elyas; figs. 1 & 2, #104-1, #104-2, #202, [0029, 0040-0041]), and calculating input parameters (Elyas; fig. 2, #206, [0042]), wherein the model is a self-learning AI model (Elyas; fig. 1, #128, [0032-0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, because Elyas teach the self-learning AI model are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data (Elyas; [0033]). One of ordinary skill in the art would have expected this modification could have been made with a reasonable expectation of success since Salu and Elyas both teach models for gas-oil environments that rely on sensor data to determine model parameters.
Regarding claim 3, modified Salu disclose the computer implemented method of claim 1 above, wherein the predicted process variables are to update a trained AI model that predict the process variables (The modification of the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, has previously been discussed in claim 1 above. Elyas additionally teach the self-learning AI models are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data; [0033]).
Regarding claim 4, modified Salu disclose the computer implemented method of claim 3 above, wherein the update of the AI models is based on an error between the sensor data and predicted data (The modification of the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, has previously been discussed in claim 1 above. Elyas additionally teach the self-learning AI models are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data; [0033]).
Regarding claim 5, modified Salu teach the computer implemented method of claim 1 above, wherein an empirical based approach is used to calculate demulsifier input parameters in real time (Salu; [0029]).
Regarding claim 6, modified Salu teach the computer implemented method of claim 1 above, wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies (Salu disclose predicting crude production rate in view of the claimed process variables [0030-0048]).
Regarding claim 7, modified Salu teach the computer implemented method of claim 1 above, wherein the injection rate is used to determine input parameters applied throughout the GOSP to enable a demulsifier process (Salu teach “the first step in the control strategy for automation of primary demulsifier injection is to individually control all necessary process variables to their optimum value and adjust demulsifier injection rate whenever there is a deviation in the crude specification after optimization of all other parameters”; [0027]).
Regarding claim 10, Salu teach the apparatus of claim 8 above.
Salu does not teach wherein the predicted process variables are to update a trained AI model that predict the process variables.
However, Elyas teach the analogous art of a system and method for real-time adaptive control using a model (Elyas; figs. 1 & 2, [0029, 0040]), the model comprising obtaining sensor data in a gas-oil environment (Elyas; figs. 1 & 2, #104-1, #104-2, #202, [0029, 0040-0041]), and calculating input parameters (Elyas; fig. 2, #206, [0042]), wherein the model is a self-learning AI model (Elyas; fig. 1, #128, [0032-0033]), and the predicted process variables are to update the AI model that predict the process variables (Elyas; [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, because Elyas teach the self-learning AI model are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data (Elyas; [0033]). One of ordinary skill in the art would have expected this modification could have been made with a reasonable expectation of success since Salu and Elyas both teach models for gas-oil environments that rely on sensor data to determine model parameters.
Regarding claim 11, modified Salu teach the apparatus of claim 10 above, wherein the update of the AI models is based on an error between the sensor data and the predicted data (The modification of the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, has previously been discussed in claim 10 above. Elyas additionally teach the self-learning AI models are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data; [0033]).
Regarding claim 17, Salu disclose the system of claim 15 above.
Salu does not teach wherein the predicted process variables are to update a trained AI model that predict the process variables.
However, Elyas teach the analogous art of a system and method for real-time adaptive control using a model (Elyas; figs. 1 & 2, [0029, 0040]), the model comprising obtaining sensor data in a gas-oil environment (Elyas; figs. 1 & 2, #104-1, #104-2, #202, [0029, 0040-0041]), and calculating input parameters (Elyas; fig. 2, #206, [0042]), wherein the model is a self-learning AI model (Elyas; fig. 1, #128, [0032-0033]), and the predicted process variables are to update the AI model that predict the process variables (Elyas; [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, because Elyas teach the self-learning AI model are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data (Elyas; [0033]). One of ordinary skill in the art would have expected this modification could have been made with a reasonable expectation of success since Salu and Elyas both teach models for gas-oil environments that rely on sensor data to determine model parameters.
Regarding claim 18, modified Salu teach the system of claim 17 above, wherein the update of the AI models is based on an error between the sensor data and the predicted data (The modification of the method and system of real-time adaptive control of demulsifier injection using models of Salu with the self-learning AI model, as in Elyas, has previously been discussed in claim 16 above. Elyas additionally teach the self-learning AI models are trained using training data to predict process variables based on sensor data and operates in real time to provide analysis results in real time with respect to the generation of the sensor data; [0033]).
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Salu, in view of Elyas, and further in view of Syed Khuzzan et al. (US 2020/0040263 – hereinafter “Syed”).
Regarding claim 2, modified Salu disclose the computer implemented method of claim 1 above.
Modified Salu does not teach wherein the predicted process variables are used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters.
However, Syed teach the analogous art of a system and method for adaptive control of demulsifier injection using a model (Syed; figs. 1 & 15, [0002-0003, 0065]) comprising determining an injection rate based on calculation input parameters (Syed; figs. 4 & 15, [0003, 0034-0036, 0065-0071]), wherein the predicted process variables are used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters (Syed; figs. 2, 4 & 15, [Title, 0003, 0028-0029, 0034-0036, 0065-0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the predicted process variables in the system and method of modified Salu with the predicted process variables used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters, as in Syed, because Syed teach the SLIC for demulsifier chemical automation improves injection automation to be more reliable and cost effective, reduce input parameters, improves up-time of controller operations, improves operations without degradation of the dehydrator and desalter grid, and allows comparison of results with other time periods (Syed; [0005]). One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since modified Salu and Syed both teach automated control for demulsifer injection using determined injection rates from input parameters.
Claims 6, are additionally rejected under 35 U.S.C. 103 as being unpatentable over Salu, in view of Elyas, and further in view of Alshehri et al. (Designing and Testing a Chemical Demulsifier Dosage Controller in a Crude Oil Desalting Plant: An Artificial Intelligence-Based Network Approach, February 26, 2010, Chemical Engineering Technology, 33, No. 6, pp. 973-982).
Regarding claim 6, modified Salu teach the computer implemented method of claim 1 above, wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies (Salu disclose predicting crude production rate in view of the claimed process variables [0030-0048]).
If it is deemed that modified Salu does not teach wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies, Alshehri teach the analogous art of a system and method for real-time adaptive control of demulsifier injections using self-learning AI models (Alshehri; [Abstract]), wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies (Alshehri; pp. 975-978 Section 3 “Data Processing and Neural Network”, Table 1, Section 4 “Results and Discussion”, Figure 5). It would have been obvious to one of ordinary skill in the art to modify the self-learning AI models of modified Salu with the predicted process variables including water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies, as taught by Alshehri, because Alshehri teach the parameters set forth in the AI-model provide an accurate agreement with the predicted parameters and the plant readings when the demulsifier consumption rate is between 110 and 350 gallons per day (Alshehri; pp. 977-978, Section 4 “Results and Discussion”). One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since modified Salu and Alshehri both teach controlled demulsifier dosage in a gas-oil separation plant.
Claims 9 & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Salu, in view of Syed.
Regarding claim 9, Salu teach the apparatus of claim 8 above.
Salu does not teach wherein the predicted process variables are used in reinforcement learning that adjusts SLIC parameters.
However, Syed teach the analogous art of a system and method for adaptive control of demulsifier injection using a model (Syed; figs. 1 & 15, [0002-0003, 0065]) comprising determining an injection rate based on calculation input parameters (Syed; figs. 4 & 15, [0003, 0034-0036, 0065-0071]), wherein the predicted process variables are used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters (Syed; figs. 2, 4 & 15, [Title, 0003, 0028-0029, 0034-0036, 0065-0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the predicted process variables in the system and method of Salu with the predicted process variables used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters, as in Syed, because Syed teach the SLIC for demulsifier chemical automation improves injection automation to be more reliable and cost effective, reduce input parameters, improves up-time of controller operations, improves operations without degradation of the dehydrator and desalter grid, and allows comparison of results with other time periods (Syed; [0005]). One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Salu and Syed both teach automated control for demulsifer injection using determined injection rates from input parameters.
Regarding claim 16, Salu disclose the system of claim 15 above.
Salu does not teach wherein the predicted process variables are used in reinforcement learning that adjusts SLIC parameters.
However, Syed teach the analogous art of a system and method for adaptive control of demulsifier injection using a model (Syed; figs. 1 & 15, [0002-0003, 0065]) comprising determining an injection rate based on calculation input parameters (Syed; figs. 4 & 15, [0003, 0034-0036, 0065-0071]), wherein the predicted process variables are used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters (Syed; figs. 2, 4 & 15, [Title, 0003, 0028-0029, 0034-0036, 0065-0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the predicted process variables in the system and method of Salu with the predicted process variables used in reinforcement learning that adjusts Simplified Logic Injection Control (SLIC) parameters, as in Syed, because Syed teach the SLIC for demulsifier chemical automation improves injection automation to be more reliable and cost effective, reduce input parameters, improves up-time of controller operations, improves operations without degradation of the dehydrator and desalter grid, and allows comparison of results with other time periods (Syed; [0005]). One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Salu and Syed both teach automated control for demulsifer injection using determined injection rates from input parameters.
Claim 13 and 20 are additionally rejected under 35 U.S.C. 103 as being unpatentable over Salu, in view of Alshehri.
Regarding claim 13, Salu disclose the apparatus of claim 8 above, wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, BS&W, and separator efficiencies (Salu disclose predicting crude production rate in view of the claimed process variables [0030-0048]).
If it is deemed that Salu does not teach wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies, Alshehri teach the analogous art of a system and method for real-time adaptive control of demulsifier injections using self-learning AI models (Alshehri; [Abstract]), wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies (Alshehri; pp. 975-978 Section 3 “Data Processing and Neural Network”, Table 1, Section 4 “Results and Discussion”, Figure 5). It would have been obvious to one of ordinary skill in the art to modify the apparatus and instructions of Salu with the predicted process variables including water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies, as taught by Alshehri, because Alshehri teach the parameters set forth in the AI-model provide an accurate agreement with the predicted parameters and the plant readings when the demulsifier consumption rate is between 110 and 350 gallons per day (Alshehri; pp. 977-978, Section 4 “Results and Discussion”). One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Salu and Alshehri both teach controlled demulsifier dosage in a gas-oil separation plant.
Regarding claim 20, Salu disclose the system any of claim 15 above, wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, BS&W, and separator efficiencies (Salu disclose predicting crude production rate in view of the claimed process variables [0030-0048]).
If it is deemed that Salu does not teach wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies, Alshehri teach the analogous art of a system and method for real-time adaptive control of demulsifier injections using self-learning AI models (Alshehri; [Abstract]), wherein the predicted process variables include water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies (Alshehri; pp. 975-978 Section 3 “Data Processing and Neural Network”, Table 1, Section 4 “Results and Discussion”, Figure 5). It would have been obvious to one of ordinary skill in the art to modify the apparatus and instructions of Salu with the predicted process variables including water content, dehydrator voltage, desalter voltage, basic sediment and water (BS&W), and separator efficiencies, as taught by Alshehri, because Alshehri teach the parameters set forth in the AI-model provide an accurate agreement with the predicted parameters and the plant readings when the demulsifier consumption rate is between 110 and 350 gallons per day (Alshehri; pp. 977-978, Section 4 “Results and Discussion”). One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Salu and Alshehri both teach controlled demulsifier dosage in a gas-oil separation plant.
Other References Cited
The prior art of made of record and not relied upon is considered pertinent to Applicant’s disclosure include:
Robson et al. (US 2019/0185763) disclose a method for treating an emulsion.
Tomlinson et al. (US 2007/0175799) disclose a process for desalting crude oil.
Al-Shafei et al. (US 2013/0026082) disclose a dynamic demulsificaiton system for use in a gas-oil separation plant.
Citations to art
In the above citations to documents in the art, an effort has been made to specifically cite representative passages, however rejections are in reference to the entirety of each document relied upon. Other passages, not specifically cited, may apply as well.
Conclusion
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/C.A.T./Examiner, Art Unit 1798
/BENJAMIN R WHATLEY/Primary Examiner, Art Unit 1798