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 .
Response to Amendment
The amendment filed August 17, 2026 has been entered.
Claims 1-20 remain pending in the application.
Applicant’s amendments to the Claims have overcome each and every objection and 112(b) rejection previously set forth in the Non-Final Office Action mailed May 19, 2026.
Based on Applicant’s amendments and remarks, the previous prior art rejection has been modified to address the claim amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claims 2, 9, and 16 recite that the parameters comprise at least one of an injection water rate, a wash water rate, a dehydrator voltage, a dehydrator current, HPPT oil or gas temperature, dry crude rate, demulsifier rate, dehydrator outlet water rate, steam consumption, or a dehydrator inlet temperature, which is broader than what is recited in claim 1 (on which claim 2 depends), claim 8 (on which claim 7 depends), and claim 15 (on which claim 16 depends), which recite that the parameters include at least a dehydrator voltage or a dehydrator current of the electrostatic coalescer dehydrator. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 6-9, 13-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Andreu et al. (US Pub. No. 2022/0017828; hereinafter Lopez Andreu; already of record) in view of Gajbhiye et al. (US Pub. No. 2022/0081622; hereinafter Gajbhiye; already of record).
Regarding claim 1, Lopez Andreu discloses a computer-implemented method ([0007], [0011]). The method comprises:
monitoring, using at least one hardware processor, at a gas oil separation plant (GOSP) facility that includes a high-pressure production trap (HPPT) apparatus and an electrostatic coalescer dehydrator, parameters associated with the GOSP facility, wherein the parameters include at least a dehydrator voltage or a dehydrator current of the electrostatic coalescer dehydrator ([0007], [0011], [0030]-[0034], see Fig. 1 at SDC architecture 100 that uses dehydrator outlet water rate 101, injection water rate 102, wash water rate 103, HPPT water rate 104, and average gas temperature 113 of HPPT gas temperature 105 to determine a HPPT separation efficiency at 115, which is input into a HPPT separation PID at 116, and uses transformer voltages A, B, and C in voltage override PID controller 118).
Inputting, using the at least one hardware processor, the parameters to a system that outputs a minimum HPPT separation efficiency to avoid a process upset in the electrostatic coalescer dehydrator in real time ([0030]-[0034], see Fig. 1. See also [0025], [0036], [0041], [0061]).
Determining, using the at least one hardware processor, a target HPPT separation efficiency based on the minimum HPPT separation efficiency ([0030]-[0034], see Fig. 1 at HPPT separation efficiency PID controller 116).
Adjusting, using the at least one hardware processor, a demulsifier dosage being injected into the HPPT apparatus to separate dry oil at the target HPPT separation efficiency, wherein the demulsifier dosage is adjusted below a high output limit derived from the dehydrator voltage or the dehydrator current to prevent de-energization of the electrostatic coalescer dehydrator ([0030]-[0034], see Fig. 1 at HPPT separation efficiency PID controller 116 and voltage override PID controller 118, which are used to provide a high value calculation 119 for the setpoint of the demulsifier concentration PID controller 123, which drives demulsifier flow controller PID 124, where the demulsifier concentration can be increased if the separation efficiency is lower than the set point, and decreased if separation efficiency is higher than the set point. See also [0036], [0043]).
Lopez Andreu fails to explicitly disclose that the parameters are input to a trained machine learning model.
Gajbhiye is in the analogous field of optimizing gas-oil separation plants (Gajbhiye [0018]). Gajbhiye teaches parameters that are input to trained machine learning model (Gajbhiye; [0018], [0077], [0088]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of Lopez Andreu with the teachings of Gajbhiye so that the parameters are input to a trained machine learning model, as Gajbhiye teaches that machine learning models can be used to develop models for the complex relationships between components that were previous considered to be too complex to analyze due to complex behavior or a huge amount of data (Gajbhiye [0074]), thereby allowing for a system that will effectively output a minimum HPPT separation efficiency at a higher accuracy than was previously possible, optimizing the gas oil separation plant facility.
Regarding claim 2, modified Lopez Andreu discloses the computer-implemented method of claim 1. Lopez Andreu further discloses that the parameters comprise at least one of an injection water rate, a wash water rate, a dehydrator voltage, a dehydrator current, HPPT oil or gas temperature, dry crude rate, demulsifier rate, dehydrator outlet water rate, steam consumption, or a dehydrator inlet temperature (Lopez Andreu; [0030]-[0034], see Fig. 1).
Regarding claim 6, modified Lopez Andreu discloses the computer-implemented method of claim 1. Modified Lopez Andreu further discloses that the demulsifier dosage is adjusted below a high output limit derived from a dehydrator voltage or current using artificial intelligence algorithms (Lopez Andreu; [0030]-[0034], particularly at [0033]. Further, see Claim 1 above at Gajbhiye teaching artificial intelligence algorithms in [0018], [0077], [0088]).
Regarding claim 7, modified Lopez Andreu discloses the computer-implemented method of claim 1. Modified Lopez Andreu further discloses that adjusting the demulsifier dosage is based on a gas temperature indication at the HPPT apparatus using artificial intelligence algorithms (Lopez Andreu; [0030]-[0034], see Fig. 1 at SDC architecture 100 that uses average gas temperature 113 of HPPT gas temperature 105 to determine a HPPT separation efficiency at 115. Further, see Claim 1 above at Gajbhiye teaching artificial intelligence algorithms in [0018], [0077], [0088]).
Regarding claim 8, Lopez Andreu discloses a system ([0007], [0011]). The system comprises:
at least one processor ([0007], [0011]).
At least one non-transitory storage media storing instructions that, when executed by the at least one processor ([0007], [0011]), cause the at least one processor to:
monitor, at a gas oil separation plant (GOSP) facility that includes a high-pressure production trap (HPPT) apparatus and an electrostatic coalescer dehydrator, parameters associated with the GOSP facility wherein the parameters include at least a dehydrator voltage or a dehydrator current of the electrostatic coalescer dehydrator ([0007], [0011], [0030]-[0034], see Fig. 1 at SDC architecture 100 that uses dehydrator outlet water rate 101, injection water rate 102, wash water rate 103, HPPT water rate 104, and average gas temperature 113 of HPPT gas temperature 105 to determine a HPPT separation efficiency at 115, which is input into a HPPT separation PID at 116, and uses transformer voltages A, B, and C in voltage override PID controller 118).
Input the parameters to a system that outputs a minimum HPPT separation efficiency to avoid a process upset in the electrostatic coalescer dehydrator in real time ([0030]-[0034], see Fig. 1. See also [0025], [0036], [0041], [0061]).
Determine a target HPPT separation efficiency based on the minimum HPPT separation efficiency ([0030]-[0034], see Fig. 1 at HPPT separation efficiency PID controller 116).
Adjust a demulsifier dosage being injected into the HPPT apparatus to separate dry oil at the target HPPT separation efficiency, wherein the demulsifier dosage is adjusted below a high output limit derived from the dehydrator voltage or the dehydrator current to prevent de-energization of the electrostatic coalescer dehydrator ([0030]-[0034], see Fig. 1 at HPPT separation efficiency PID controller 116 and voltage override PID controller 118, which are used to provide a high value calculation 119 for the setpoint of the demulsifier concentration PID controller 123, which drives demulsifier flow controller PID 124, where the demulsifier concentration can be increased if the separation efficiency is lower than the set point, and decreased if separation efficiency is higher than the set point. See also [0036], [0043]).
Lopez Andreu fails to explicitly disclose that the parameters are input to a trained machine learning model.
Gajbhiye is in the analogous field of optimizing gas-oil separation plants (Gajbhiye [0018]). Gajbhiye teaches parameters that are input to trained machine learning model (Gajbhiye; [0018], [0077], [0088]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the system of Lopez Andreu with the teachings of Gajbhiye so that the parameters are input to a trained machine learning model, as Gajbhiye teaches that machine learning models can be used to develop models for the complex relationships between components that were previous considered to be too complex to analyze due to complex behavior or a huge amount of data (Gajbhiye [0074]), thereby allowing for a system that will effectively output a minimum HPPT separation efficiency at a higher accuracy than was previously possible, optimizing the gas oil separation plant facility.
Regarding claim 9, modified Lopez Andreu discloses the system of claim 8. Lopez Andreu further discloses that the parameters comprise at least one of an injection water rate, a wash water rate, a dehydrator voltage, a dehydrator current, HPPT oil or gas temperature, dry crude rate, demulsifier rate, dehydrator outlet water rate, steam consumption, or a dehydrator inlet temperature (Lopez Andreu; [0030]-[0034], see Fig. 1).
Regarding claim 13, modified Lopez Andreu discloses the system of claim 8. Modified Lopez Andreu further discloses that the demulsifier dosage is adjusted below a high output limit derived from a dehydrator voltage or current using artificial intelligence algorithms (Lopez Andreu; [0030]-[0034], particularly at [0033]. Further, see Claim 8 above at Gajbhiye teaching artificial intelligence algorithms in [0018], [0077], [0088]).
Regarding claim 14, modified Lopez Andreu discloses the computer-implemented method of claim 8. Modified Lopez Andreu further discloses that adjusting the demulsifier dosage is based on a gas temperature indication at the HPPT apparatus using artificial intelligence algorithms (Lopez Andreu; [0030]-[0034], see Fig. 1 at SDC architecture 100 that uses average gas temperature 113 of HPPT gas temperature 105 to determine a HPPT separation efficiency at 115. Further, see Claim 8 above at Gajbhiye teaching artificial intelligence algorithms in [0018], [0077], [0088]).
Regarding claim 15, Lopez Andreu discloses at least one non-transitory storage media storing instructions that, when executed by at least one processor ([0007], [0011]), cause the at least one processor to:
monitor, at a gas oil separation plant (GOSP) facility that includes a high-pressure production trap (HPPT) apparatus and an electrostatic coalescer dehydrator, parameters associated with the GOSP facility, wherein the parameters include at least a dehydrator voltage or a dehydrator current of the electrostatic coalescer dehydrator ([0007], [0011], [0030]-[0034], see Fig. 1 at SDC architecture 100 that uses dehydrator outlet water rate 101, injection water rate 102, wash water rate 103, HPPT water rate 104, and average gas temperature 113 of HPPT gas temperature 105 to determine a HPPT separation efficiency at 115, which is input into a HPPT separation PID at 116, and uses transformer voltages A, B, and C in voltage override PID controller 118).
Input the parameters to a system that outputs a minimum HPPT separation efficiency to avoid a process upset in the electrostatic coalescer dehydrator in real time ([0030]-[0034], see Fig. 1. See also [0025], [0036], [0041], [0061]).
Determine a target HPPT separation efficiency based on the minimum HPPT separation efficiency ([0030]-[0034], see Fig. 1 at HPPT separation efficiency PID controller 116).
Adjust a demulsifier dosage being injected into the HPPT apparatus to separate dry oil at the target HPPT separation efficiency, wherein the demulsifier dosage is adjusted below a high output limit derived from the dehydrator voltage or the dehydrator current to prevent de-energization of the electrostatic coalescer dehydrator ([0030]-[0034], see Fig. 1 at HPPT separation efficiency PID controller 116 and voltage override PID controller 118, which are used to provide a high value calculation 119 for the setpoint of the demulsifier concentration PID controller 123, which drives demulsifier flow controller PID 124, where the demulsifier concentration can be increased if the separation efficiency is lower than the set point, and decreased if separation efficiency is higher than the set point. See also [0036], [0043]).
Lopez Andreu fails to explicitly disclose that the parameters are input to a trained machine learning model.
Gajbhiye is in the analogous field of optimizing gas-oil separation plants (Gajbhiye [0018]). Gajbhiye teaches parameters that are input to trained machine learning model (Gajbhiye; [0018], [0077], [0088]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the at least one non-transitory storage media of Lopez Andreu with the teachings of Gajbhiye so that the parameters are input to a trained machine learning model, as Gajbhiye teaches that machine learning models can be used to develop models for the complex relationships between components that were previous considered to be too complex to analyze due to complex behavior or a huge amount of data (Gajbhiye [0074]), thereby allowing for a system that will effectively output a minimum HPPT separation efficiency at a higher accuracy than was previously possible, optimizing the gas oil separation plant facility.
Regarding claim 16, modified Lopez Andreu discloses the at least one non-transitory storage media of claim 15. Lopez Andreu further discloses that the parameters comprise at least one of an injection water rate, a wash water rate, a dehydrator voltage, a dehydrator current, HPPT oil or gas temperature, dry crude rate, demulsifier rate, dehydrator outlet water rate, steam consumption, or a dehydrator inlet temperature (Lopez Andreu; [0030]-[0034], see Fig. 1).
Regarding claim 20, modified Lopez Andreu discloses the at least one non-transitory storage media of claim 15. Modified Lopez Andreu further discloses that the demulsifier dosage is adjusted below a high output limit derived from a dehydrator voltage or current using artificial intelligence algorithms (Lopez Andreu; [0030]-[0034], particularly at [0033]. Further, see Claim 8 above at Gajbhiye teaching artificial intelligence algorithms in [0018], [0077], [0088]).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Andreu in view of Gajbhiye as applied to claims 1-2, 6-9, 13-16, and 20 above, and further in view of Raynel et al. (US Pub. No. 2021/0102127; hereinafter Raynel; already of record).
Regarding claim 3, modified Lopez Andreu discloses the computer-implemented method of claim 1, and all limitations recited therein.
Modified Lopez Andreu fails to explicitly disclose that the target HPPT separation efficiency is continuously updated in real time in response to real-time process data associated with the GOSP.
Raynel is in the analogous field of optimizing gas oil separation plants (Raynel [0003]). Raynel teaches continuously updating a process variable in real time in response to real-time process data associated with a GOSP (Raynel; [0009]-[0013], [0071]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lopez Andreu with the teachings of Raynel so that the target HPPT separation efficiency is continuously updated in real time in response to real-time process data associated with the GOSP, in order to ensure that the system maintains optimal efficiency in real-time and can quickly adjust based on real-time events.
Regarding claim 10, modified Lopez Andreu discloses the system of claim 8, and all limitations recited therein.
Modified Lopez Andreu fails to explicitly disclose that the target HPPT separation efficiency is continuously updated in real time in response to real-time process data associated with the GOSP.
Raynel is in the analogous field of optimizing gas oil separation plants (Raynel [0003]). Raynel teaches continuously updating a process variable in real time in response to real-time process data associated with a GOSP (Raynel; [0009]-[0013], [0071]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the system of modified Lopez Andreu with the teachings of Raynel so that the target HPPT separation efficiency is continuously updated in real time in response to real-time process data associated with the GOSP, in order to ensure that the system maintains optimal efficiency in real-time and can quickly adjust based on real-time events.
Regarding claim 17, modified Lopez Andreu discloses the at least one non-transitory storage media of claim 15, and all limitations recited therein.
Modified Lopez Andreu fails to explicitly disclose that the target HPPT separation efficiency is continuously updated in real time in response to real-time process data associated with the GOSP.
Raynel is in the analogous field of optimizing gas oil separation plants (Raynel [0003]). Raynel teaches continuously updating a process variable in real time in response to real-time process data associated with a GOSP (Raynel; [0009]-[0013], [0071]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the at least one non-transitory storage media of modified Lopez Andreu with the teachings of Raynel so that the target HPPT separation efficiency is continuously updated in real time in response to real-time process data associated with the GOSP, in order to ensure that the system maintains optimal efficiency in real-time and can quickly adjust based on real-time events.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Andreu in view of Gajbhiye as applied to claims 1-2, 6-9, 13-16, and 20 above, and further in view of Pruneda et al., “Optimum Temperature in the Electrostatic Desalting of Maya Crude Oil”, 2005, Journal of the Mexican Chemical Society, Vol. 49, No. 1, Pgs. 14-19 (hereinafter Pruneda; already of record).
Regarding claim 4, modified Lopez Andreu discloses the computer-implemented method of claim 1, and all limitations recited therein. Modified Lopez Andreu further teaches an artificial intelligence generated algorithm (see Claim 1 above at Gajbhiye teaching an artificial intelligence generated algorithm in [0018], [0077], [0088]).
Modified Lopez Andreu fails to explicitly disclose determining a set point of an inlet temperature of a dehydrator using an artificial intelligence generated algorithm to optimize an overall energy consumption.
Pruneda is in the analogous field of crude oil processing (Pruneda Pg. 14 Col. 1 at 1st Para.). Pruneda teaches determining a set point of an inlet temperature of a crude oil processing equipment using a model to optimize an overall energy consumption (Pruneda; Pg. 14 Col. 1 at 1st Para., Pg. 16 Col. 1 at 1st-3rd Paras.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lopez Andreu with the teachings of Pruneda to comprise determining a set point of an inlet temperature of a dehydrator using an artificial intelligence generated algorithm to optimize an overall energy consumption, as Pruneda teaches that optimizing the temperature will allow for improved separation performance while also minimizing the power requirement of the process (Pruneda; Pg. 14 Col. 1 at 1st Para., Pg. 16 Col. 1 at 1st-3rd Paras.).
Regarding claim 11, modified Lopez Andreu discloses the system of claim 8, and all limitations recited therein. Modified Lopez Andreu further teaches an artificial intelligence generated algorithm (see Claim 1 above at Gajbhiye teaching an artificial intelligence generated algorithm in [0018], [0077], [0088]).
Modified Lopez Andreu fails to explicitly disclose determining a set point of an inlet temperature of a dehydrator using an artificial intelligence generated algorithm to optimize an overall energy consumption.
Pruneda is in the analogous field of crude oil processing (Pruneda Pg. 14 Col. 1 at 1st Para.). Pruneda teaches determining a set point of an inlet temperature of a crude oil processing equipment using a model to optimize an overall energy consumption (Pruneda; Pg. 14 Col. 1 at 1st Para., Pg. 16 Col. 1 at 1st-3rd Paras.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the system of modified Lopez Andreu with the teachings of Pruneda to comprise determining a set point of an inlet temperature of a dehydrator using an artificial intelligence generated algorithm to optimize an overall energy consumption, as Pruneda teaches that optimizing the temperature will allow for improved separation performance while also minimizing the power requirement of the process (Pruneda; Pg. 14 Col. 1 at 1st Para., Pg. 16 Col. 1 at 1st-3rd Paras.).
Regarding claim 18, modified Lopez Andreu discloses the at least one non-transitory storage media of claim 15, and all limitations recited therein. Modified Lopez Andreu further teaches an artificial intelligence generated algorithm (see Claim 1 above at Gajbhiye teaching an artificial intelligence generated algorithm in [0018], [0077], [0088]).
Modified Lopez Andreu fails to explicitly disclose determining a set point of an inlet temperature of a dehydrator using an artificial intelligence generated algorithm to optimize an overall energy consumption.
Pruneda is in the analogous field of crude oil processing (Pruneda Pg. 14 Col. 1 at 1st Para.). Pruneda teaches determining a set point of an inlet temperature of a crude oil processing equipment using a model to optimize an overall energy consumption (Pruneda; Pg. 14 Col. 1 at 1st Para., Pg. 16 Col. 1 at 1st-3rd Paras.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the at least one non-transitory storage media of modified Lopez Andreu with the teachings of Pruneda to comprise determining a set point of an inlet temperature of a dehydrator using an artificial intelligence generated algorithm to optimize an overall energy consumption, as Pruneda teaches that optimizing the temperature will allow for improved separation performance while also minimizing the power requirement of the process (Pruneda; Pg. 14 Col. 1 at 1st Para., Pg. 16 Col. 1 at 1st-3rd Paras.).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Andreu in view of Gajbhiye as applied to claims 1-2, 6-9, 13-16, and 20 above, and further in view of Cui et al. (Translation of CN Pub. No. 102746876; hereinafter Cui; already of record).
Regarding claim 5, modified Lopez Andreu discloses the computer-implemented method of claim 1, and all limitations recited therein.
Modified Lopez Andreu fails to explicitly disclose that the electrostatic coalescer dehydrator comprises a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device.
Cui is in the analogous field of crude oil dehydrators (Cui Pg. 1 1st Para.). Cui teaches a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device (Cui; Pg. 1 1st Para., Pg. 2 Last Para.-Pg. 3 1st Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of Lopez Andreu with the teachings of Cui so that the electrostatic coalescer dehydrator comprises a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device, as Cui teaches that a dehydrator transformer can be used to reduce water content in crude oil that requires dehydration (Cui; Pg. 1 1st Para., Pg. 2 Last Para.-Pg. 3 1st Para.).
Regarding claim 12, modified Lopez Andreu discloses the system of claim 8, and all limitations recited therein.
Modified Lopez Andreu fails to explicitly disclose that the electrostatic coalescer dehydrator comprises a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device.
Cui is in the analogous field of crude oil dehydrators (Cui Pg. 1 1st Para.). Cui teaches a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device (Cui; Pg. 1 1st Para., Pg. 2 Last Para.-Pg. 3 1st Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the system of Lopez Andreu with the teachings of Cui so that the electrostatic coalescer dehydrator comprises a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device, as Cui teaches that a dehydrator transformer can be used to reduce water content in crude oil that requires dehydration (Cui; Pg. 1 1st Para., Pg. 2 Last Para.-Pg. 3 1st Para.).
Regarding claim 19, modified Lopez Andreu discloses the at least one non-transitory storage media of claim 15, and all limitations recited therein.
Modified Lopez Andreu fails to explicitly disclose that the electrostatic coalescer dehydrator comprises a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device.
Cui is in the analogous field of crude oil dehydrators (Cui Pg. 1 1st Para.). Cui teaches a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device (Cui; Pg. 1 1st Para., Pg. 2 Last Para.-Pg. 3 1st Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the at least one non-transitory storage media of Lopez Andreu with the teachings of Cui so that the electrostatic coalescer dehydrator comprises a dehydrator transformer that is a component of an electrostatic coalescer or a modulated AC/DC electrostatic coalescer device, as Cui teaches that a dehydrator transformer can be used to reduce water content in crude oil that requires dehydration (Cui; Pg. 1 1st Para., Pg. 2 Last Para.-Pg. 3 1st Para.).
Response to Arguments
Applicant's arguments filed August 17, 2026 have been fully considered and are persuasive in part.
Applicant’s arguments on Pgs. 7-10 of their Remarks regarding the claims being eligible subject matter under 35 U.S.C. 101 are persuasive. Specifically, the Examiner is persuaded that the judicial exception is integrated into a practical application, by specifying how the demulsifier dosage is adjusted in order to prevent de-energization of an electrostatic coalescer dehydrator.
Applicant’s argues on Pgs. 10-12 of their Remarks that Gajbhiye, relied upon to teach a trained machine learning model, does not teach or suggest demulsifier dosing control, dehydrator grid de-energization or process upsets, or a machine learning model that outputs an HPPT separation efficiency. The Applicant argues that Lopez Andreu only teaches determining an HPPT separation efficiency using static mathematical formulas, and that modifying Lopez Andreu with Gajbhiye would not yield the real time upset avoidance as recited by the amended claims. The Examiner respectfully disagrees. Lopez Andreu already teaches all of the above limitations, except for the use of a trained machine learning model to output the minimum HPPT separation efficiency, as Lopez Andreu discusses preventing grid de-energization and avoiding process upsets in at least ([0036], [0041]). Gajbhiye is only relied upon to teach inputting parameters into a trained machine learning model, as opposed to using the static mathematical formulas of Lopez Andreu, and a person having ordinary skill in the art before the effective filing date of the invention would have recognized the benefits of a trained machine learning model for increased accuracy, throughput, reduced calculation times, etc. Further, the limitations “to avoid a process upset in the electrostatic coalescer dehydrator in real time” and “to prevent de-energization of the electrostatic coalescer dehydrator” are equivalent to a "whereby" clause, and are therefore not given weight, as they simply express the intended results of the positively recited steps of outputting a minimum HPPT separation efficiency and adjusting a demulsifier dosage. See MPEP 2111.04. As Lopez Andreu teaches outputting a minimum HPPT separation efficiency and adjusting a demulsifier dosage, and Gajbhiye teaches a trained machine learning model, the teachings of modified Lopez Andreu meet the claim. For a more detailed explanation, please see the rejection of Claims 1, 8, or 15 in the Claims Rejections-35 USC 103 section of this instant Office Action.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JOHN MCGUIRK/Primary Examiner, Art Unit 1798
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