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 .
Response to Arguments
Applicant’s arguments received 17 March, 2026, have been fully considered. Claims 1-5, 7-8, and 10-14 are pending. Claims 6 and 9 have been canceled. Claims 1, 3-5, 7, 10-12, and 14 have been amended.
The examiner acknowledges Applicant’s information disclosure statement filed 17 March, 2026, which includes an English translation of each reference listed in the information disclosure statement filed 22 February, 2024, that is not in the English language.
Applicant’s efforts to amend the abstract to address objections to the specification are satisfactory, therefore all objections to the specification are withdrawn.
Applicant’s efforts to amend the claims to address claim objections are satisfactory; however, the amendments introduce additional informalities. See claim objections below.
Applicant’s efforts to amend the claims to address rejections under 35 U.S.C. 112(b) are satisfactory, therefore all 112(b) rejections are withdrawn.
Applicant’s arguments regarding the provisional statutory type rejection of claims 1-3 and 5-14 under 35 U.S.C. 101 as claiming the same invention as that of claims 1-3, 5-7, 9-11, and 13-16 of copending Application No. 18/051,791, have been considered. While amendments to the independent claims of the present application, as well as to the independent claims of the copending application, are sufficient to overcome the statutory type rejection under 35 U.S.C. 101, they are not sufficient to overcome a nonstatutory double patenting rejection. See double patenting rejections below.
Applicant’s arguments regarding the provisional nonstatutory double patenting rejection of claim 4 over claim 4 of copending Application No. 18/051,791, have been considered but are not persuasive. While it is true that the independent claims of the present application and of the copending application have been amended such that both claims are not identical, it does not follow that claim 4 of the present application is patentably distinct from claim 4 of the copending application. Rather, claim 4 of the present application is obvious over claim 4 of the copending application in view of Al-Shafei and Young and Wikipedia and James. See double patenting rejections below.
Applicant’s arguments regarding the prior art rejection of the claims under 35 U.S.C. 103 have been considered.
Applicant warns that the examiner cannot use common sense in place of “reasoned analysis and evidentiary support”. The examiner agrees, but does not consider the previous rejection to be a wholesale substitution of common sense for reasoned analysis. As the many references to prior art suggest, the examiner did not rely exclusively on common sense. However, a number of limitations can be addressed by an appeal to simple reasoning when “given their broadest reasonable interpretation consistent with the specification” (MPEP 2111). For example, Applicant’s previous set of claims outlined what may reasonably be interpreted as a guess-and-check method for determining how to adjust parameters to optimize an output. The examiner considers that guessing at changes and checking for responses, as a concept, falls well within the scope of common sense, and so argued in the previous Office Action. This concept alone, however, only addresses some of the limitations of claim 1; for example, Al-Shafei must be relied upon to disclose many other limitations.
Applicant further argues that the prior art of record neither discloses nor suggests all of the amended claim limitations. The examiner agrees; however, the amendments necessitate new grounds of rejection. See 103 rejection below.
Claim Objections
Claim 12 is objected to because, as amended, it recites “…total parameters further include the the salt-parameters.” One instance of “the” should be removed.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-5, 7-8, and 10-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-5, 7, 9, 11, and 13-16 of copending Application No. 18/051,791 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the present application not found in copending Application No. 18/051,791 would have been obvious over Al-Shafei in view of Young and Wikipedia and James, as argued in the 103 rejections below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
The factual inquiries 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.
Claims 1-5, 7-8, and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Shafei (US 20130026082 A1) in view of Young ("University Physics with Modern Physics 15th Edition") and “Gradient descent” on Wikipedia.org (Accessed 16 April, 2026, with Wayback Machine Publication date of 17 September, 2020, having the link https://web.archive.org/web/20200917025906/https://en.wikipedia.org/wiki/Gradient_descent; hereinafter “Wikipedia”) and James (“An Introduction to Statistical Learning with Applications in R”).
Regarding claim 1, Al-Shafei discloses a method for increasing the quality of crude oil exiting a gas-oil separation plant (GOSP) (Abstract: the invention involves a “dynamic water/oil demulsification system for a gas-oil separation plant (GOSP)”; ¶3: “the invention relates to improvements in whole crude oil processing, and in particular to an improved method for the demulsification of whole crude oil”), wherein the GOSP comprises sensors that determine process parameters of the crude oil (Abstract: the system includes “sensors that monitor and transmit data corresponding to properties of the water-oil emulsion in or downstream of the respective vessel(s)”; see also Fig. 2, sensors 111, 112, 113, 114, 115, 116, and 117 placed between many elements of the GOSP, and ¶92: “Sensors can be included at various locations throughout the portion of the GOSP shown in Fig. 2”); and
determining, from the process parameters, a set of WiO-parameters (¶116: “sensors enable reliable detection of hydrocarbons and also provide valid indications of the thickness of the hydrocarbon layer and the percent of water in oily emulsions”; the WiO parameters are determined from some of the sensors that monitor process parameters).
Al-Shafei further discloses performing a feedback loop, wherein changes in process parameters are made (¶101: “data is collected by automated programs, such as distributed control system, and feedback and/or feedforward action is undertaken to adjust the characteristics of the electromagnetic energy emitted from in-line microwave subsystem 346. In addition, feedback and/or feedforward action can be undertaken regarding one or more of the operating conditions of the wet oil in water/oil separator vessel 371 (e.g., temperature, pressure, residence time)”; ¶102: “The dynamic demulsification system described herein can be implemented separately or in cooperation with a real-time optimization [RTO] system.” In ¶107, RTO is described as “a process of measuring or calculating control cycles at a given frequency to maintain the system’s optimal operating conditions.” This involves model updating and model-based optimization, as ¶104-¶105 attest, and may be a closed-loop (i.e. autonomous) system as ¶108 teaches).
It is clear from context that the feedback loop is repeated to improve the water-oil-separation, which would improve the quality of the crude oil exiting the GOSP (see ¶3 as quoted above).
Al-Shafei does not explicitly disclose that the feedback loop is repeated as long as the quality of the crude oil exiting the GOSP increases, but it would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei to do so to reach optimal oil quality.
Al-Shafei does not explicitly disclose determining at least one virtual parameter using the process parameters; determining total parameters as a set of the at least one virtual parameter and the set of WiO parameters; and causing the feedback loop to be applied to the total parameters.
One reasonable interpretation of virtual parameters would be derived parameters i.e. values which are not measured directly but which can be calculated. Examples of virtual parameters would include the rate of flow of a fluid through a pipe with no flow rate sensor, or the pressure of a gas assumed to be ideal if its volume and temperature are known.
Young teaches physical relationships that apply to fluids, such as the continuity equations for compressible and incompressible fluids (p. 377, see Eq. 12.12 and Eq. 12.10, respectively) or the ideal gas law (p. 581, Eq. 18.3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Young with the invention of Al-Shafei by applying physical relationships to determine at least one virtual parameter using the process parameters; determining total parameters as a set of the at least one virtual parameter and the set of WiO parameters; and causing the feedback loop to be applied to the total parameters. Doing so would enable one to use physical relationships between measured values to more fully capture the physical processes in the GOSP their relationship to crude oil quality.
Al-Shafei in view of Young does not explicitly teach that the feedback loop comprises the steps:
determining, with a machine learning (ML) model applied to the total parameters, a predicted quality of the crude oil exiting the GOSP,
determining a relative importance of each of the total parameters to the predicted quality of the crude oil based on the ML model,
determining at least one parameter of the total parameters to change based on the relative importance of each of the total parameters,
determining, with the ML model applied to the total parameters having changed the at least one parameter, another predicted quality of the crude oil exiting the GOSP, and
determining a change in quality based on the predicted quality and the another predicted quality, wherein
when the quality, based on the change in quality, is improved, the change in the at least one parameter is maintained, and
when the quality, based on the change in quality, is worsened, the change in the at least one parameter is reversed.
Wikipedia teaches a method of optimization known as gradient descent for finding an extremum of a model (Introduction; the model is described as a function). Let a model be described by the function
F
:
X
→
R
, which accepts a multi-parameter input
x
and outputs a real number (Pg. 1, “Description”, first paragraph). An initial estimate
x
0
of a set of parameters is chosen. Then, a relative importance of each of the parameters to the output is determined by calculating the gradient
∇
F
x
0
of the model at the point of the initial estimate (Pg. 1, “Description”, see first equation where
a
i
represent model inputs). Next, the method determines at least one parameter to change based on the relative importance of each of the parameters (i.e. the gradient) by determining a next input
x
1
which “steps” away from the previous estimate in the direction of negative gradient (again, Pg. 1, first equation under “Description”). Then, another output
F
(
x
1
)
is predicted using the changed set of parameters
x
1
. The process repeats while the output continues decreasing (see Pg. 1 under “Description” and the figure to the left), and terminates when any step will result in a higher output (i.e. when the minimum is reached). Note that a step size can either be variable (Pg. 1,
γ
n
, the index indicating that step size may change), which may improve convergence but require extra computation, or the step size may be fixed (Pg. 1,
γ
without an index, indicating a fixed step size), which saves computational effort but may result in slower convergence (Pg. 5, fourth paragraph under “Comments”). Finally, note that gradient descent is a model optimization method which has been known for many decades (See Introduction; it was first suggested in 1847 by Cauchy, and its convergence properties for non-linear optimization problems were studied as early as 1944).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Wikipedia with the invention of Al-Shafei in view of Young by causing the feedback loop to comprise the steps:
determining, with a model applied to the total parameters, a predicted quality of the crude oil exiting the GOSP,
determining a relative importance of each of the total parameters to the predicted quality of the crude oil based on the model,
determining at least one parameter of the total parameters to change based on the relative importance of each of the total parameters,
determining, with the model applied to the total parameters having changed the at least one parameter, another predicted quality of the crude oil exiting the GOSP, and
determining a change in quality based on the predicted quality and the another predicted quality, wherein
when the quality, based on the change in quality, is improved, the change in the at least one parameter is maintained.
Doing so would implement a known method for performing the model optimization described in Al-Shafei.
Furthermore, note that when the method is sufficiently close to a local best quality state, small changes in the current parameter values (mathematically, a step of size
γ
in the neighborhood of the local extremum) would result in worse quality. In that case, it would have been obvious to one of ordinary skill in the art to configure the feedback loop to comprise the step of reversing the change in the at least one parameter when the quality, based on the change in quality, is worsened. Doing so would prevent the feedback loop from moving parameters away when they are sufficiently close to maintaining an optimal quality.
Al-Shafei in view of Young and Wikipedia does not explicitly teach that the model is a machine learning (ML) model.
James teaches tree-based ML methods for regression including random forests, which may result in improved prediction accuracy (pg. 303, Introduction to Chapter 8 “Tree-Based Methods”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of James with the invention of Al-Shafei in view of Young and Wikipedia by training a ML model using random forests on the total parameters to determine a predicted quality of the crude oil exiting the GOSP and using it as the model. Doing so would be useful for making accurate predictions with input data.
Regarding claim 14, many of the limitations of claim 14 are found in claim 1 and are rejected for the same reasons. Of the remaining limitations, Al-Shafei discloses a non-transitory computer readable medium storing instructions executable by a computer processor (¶112: “Memory 414 includes program storage memory 416 and data storage memory 418.”). It would have been obvious to cause the instructions to comprise functionality for implementing the method of claim 1 in order to perform the functions autonomously.
Regarding claim 2, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1. Al-Shafei further discloses a GOSP comprising a high pressure production trap (HPPT), a low pressure production trap (LPPT), a dehydrator, a desalter, and a water oil separator (WOSEP) (¶28 in the summary states that the invention includes "in-line microwave treatment subsystems…incorporated in a GOSP". A typical example of a GOSP is given in Fig. 1. ¶17 states that the GOSP includes a HPPT unit 31 (note that ¶17 mistakenly refers to unit 31 as LPPT, but when reading ¶18 it's clear that unit 31 is a HPPT), a LPPT unit 41, a dehydrator unit 51, a desalter unit 61, and a water/oil separator (WOSEP) 71.).
Regarding claim 3, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 2, but does not explicitly disclose the limitations of claim 3. However, Al-Shafei discloses that fluid flows from the HPPT to the LPPT, the LPPT to the dehydrator, the dehydrator to the desalter, and the desalter to the WOSEP (Fig. 1; adding the in-line microwave treatment subsystems does not change this ordering of flow between units). Furthermore, Al-Shafei teaches that the microwave system’s power is dynamically adjusted based on features including flow rate (¶88), which implies that flow rate is measured or derived. Finally, Al-Shafei teaches that increased flow rate is an important objective of a GOSP (¶15: “The main objective of a GOSP is to increase the flowability and to produce dry crude oil as an end product”; ¶27: “The long-standing problem addressed by the present invention is how to improve whole crude oil processing to increase the crude oil flowability in a GOSP, and in particular, to improve the demulsification of whole crude oil in a GOSP”).
It would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to cause the process parameters to comprise a flow rate of crude oil from the HPPT to the LPPT, a flow rate of crude oil from the LPPT to the dehydrator, a flow rate of crude oil from the dehydrator to the desalter, and a flow rate of crude oil from the desalter to the WOSEP. Doing would be useful because flow rate is an important factor in efficiency and cost effectiveness.
Regarding claim 4, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 2. Furthermore, it would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to cause the process parameters to further comprise a salt concentration exiting the desalter, and a concentration of water exiting the LPPT. Doing so would be useful for ascertaining how well the desalter and LPPT performed their functions (¶18 describes the LPPT as a three-phase separator).
Regarding claim 5, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 2. Al-Shafei also teaches that emulsions of oil in water and water in oil occur naturally (¶24), therefore it would be reasonable to conclude that the crude oil comprises an emulsion that comprises oil in water (OiW) and water in oil (WiO).
Al-Shafei also discloses an instrument which measures the size and other properties of droplets in the fluids at the GOSP (¶115: "On-line, real-time analysis sensors are currently used to characterize the fluids at the GOSP facility. An example of such a system is the Video Imaging Particle Analyzer (ViPA)…[which provides] information on particle and/or droplet type, size and concentration."). It is implied that the ViPA measures oil droplets (¶115: “The ViPA can distinguish between solid particles and oil droplets”), but does not explicitly mention that the ViPA measures water droplets in oil. Nevertheless, Al-Shafei teaches that “according to Stokes’ law, decreased droplet size results in lower rising velocities” (¶13) or, for the case of water in oil, sinking velocities. Since slower droplet velocities slow the demulsification process, one would be motivated to measure the properties of both the oil droplets and water droplets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Al-Shafei with the invention of Al-Shafei in view of Young and Wikipedia and James by causing the process parameters to comprise the size of the droplets of the OiW and the WiO in the HPPT, LPPT, dehydrator, desalter, and WOSEP. Doing so would be useful for determining how tight the WiO and OiW emulsions are in each of the units, which is relevant to how efficiently demulsification can be performed.
Regarding claim 7, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1, and further teaches that the ML model uses a random forest regressor (see the rejection of claim 1).
Regarding claim 8, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1. Al-Shafei further teaches that the goal of a GOSP is to remove contaminants including bottom sediment and water (BS&W) (¶23) and water (¶6). Al-Shafei further describes a unit that aims to remove salt from the fluid (the “desalter”; see e.g. Abstract).
It would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to cause the quality of the crude oil exiting the GOSP to comprise concentrations of WiO, basic sediment and water (BS&W), salt, and water in the crude oil exiting the GOSP. This would provide a sensible set of metrics to define quality by measuring the impurities present in the extruded crude oil.
Regarding claim 10, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1. Furthermore, it would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to cause determining the relative importance of each of the total parameters to comprise aggregating redundant process parameters in order to remove unnecessary data.
Regarding claim 11, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1. Al-Shafei further teaches that BS&W is an impurity that decreases the quality of crude oil (¶23: "The ultimate goal of a GOSP is to reduce the content of contaminant to a suitable level, e.g., less than 0.2% bottoms, sediment and water (BS&W)”).
It would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to determine, from the process parameters, a set of basic sediment and water (BS&W)-parameters, and to include the BS&W-parameters in the total parameters. Doing so would enable the model to address parameters related to BS&W, which affects the crude oil’s quality (see rejection of claim 8).
Regarding claim 12, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1. Al-Shafei further describes a unit that aims to remove salt from the fluid (the “desalter”; see e.g. Abstract), implying that salt is a contaminant.
It would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to determine, from the process parameters, salt-parameters, and to include the salt-parameters in the total parameters.
Doing so would enable the model to address parameters related to salt concentration, which affects the crude oil’s quality (see rejection of claim 8).
Regarding claim 13, Al-Shafei in view of Young and Wikipedia and James teaches the limitations of claim 1. Furthermore, it would have been obvious to one of ordinary skill in the art practicing the invention of Al-Shafei in view of Young and Wikipedia and James to preprocess the process parameters after they are determined by the sensors. Doing so would enable one to clean and prepare data for further manipulation (e.g. by removing redundant values or outliers, changing data type, attaching or removing metadata, etc.), which is a basic task in data science.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yerashenia (“Semantic Data Pre-Processing for Machine Learning Based Bankruptcy Prediction Computational Model”) teaches that data preprocessing and cleaning are essential tasks to be completed before a dataset can be used to train a ML model (Pg. 68, column 1, under Section III(A)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN WESLEY EDWARDS whose telephone number is (571)272-0266. The examiner can normally be reached Monday - Friday, 7:30am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ETHAN WESLEY EDWARDS
Examiner
Art Unit 2857
/E.W.E./ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857
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