DETAILED ACTION
It is noted that the examiner and art unit of record have changed since the previous Office 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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-8, in the reply filed on 4/13/2026 is acknowledged. However, it is noted that after further consideration there is not a search and/or burden between the different groups of inventions. Therefore, the restriction requirement mailed 3/5/2026 is hereby withdrawn. In view of the withdrawal of the restriction requirement as to the rejoined inventions, applicant(s) are advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Once the restriction requirement is withdrawn, the provisions of 35 U.S.C. 121 are no longer applicable. See In re Ziegler, 443 F.2d 1211, 1215, 170 USPQ 129, 131-32 (CCPA 1971). See also MPEP § 804.01.
Claim Status
Claims 1-20 are currently pending and examined on the merits.
Priority
The instant application does not claim priority to any earlier filed applications. Therefore, the effective filing date of claims 1-20 is 6/20/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/3/2024 is in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Drawings
The drawings filed on 6/20/2023 are accepted.
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. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-8 are directed to a method (process). Claims 9-16 are directed to a system (machine). Claims 17-20 are directed to a non-transitory computer storage medium (machine). Therefore, these claims are encompassed by the categories of statutory subject matter and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1-4, 7-12, and 15-20 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claims 1, 9, and 17 further recite:
generating, using a first machine learning model and based on the real-time data, a corrosion rate for the component (i.e., mental processes, mathematical concepts);
determining that the corrosion rate is greater than a predetermined threshold (i.e., mental processes);
responsively using a second machine learning model to calculate for the component one or more mitigated corrosion rates when using one or more available corrosion inhibitors (i.e., mental processes, mathematical concepts);
selecting, based on the one or more mitigated corrosion rates, one of the available corrosion inhibitors to use to mitigate the corrosion rate of the component (i.e., mental processes).
Dependent claims 2, 10, and 18 further recite:
wherein the first and second machine learning models are trained using at least one of: historical field data, simulation data, or lab data (i.e., mental processes, mathematical concepts; this is further information limiting the judicial exceptions).
Dependent claims 3, 11, and 19 further recite:
wherein the first and second machine learning models are trained using at least one of gradient boosting classifiers or random forest classifiers (i.e., mental processes, mathematical concepts; this is further information limiting the judicial exceptions).
Dependent claims 4, 12, and 20 further recite:
wherein the at least one of the gradient boosting classifiers or the random forest classifiers are evaluated using at least one of coefficient of determination, root mean squared error (RMSE), mean absolute error (MAE), or mean absolute percentage error (MAPE) (i.e., mental processes, mathematical concepts; this is further information limiting the judicial exceptions).
Dependent claims 7 and 15 further recite:
wherein selecting, based on the one or more mitigated corrosion rates, one of the available corrosion inhibitors to use to mitigate the corrosion rate of the component further comprises: selecting a concentration of the selected available corrosion inhibitor (i.e., mental processes).
Dependent claims 8 and 16 further recite:
wherein the selected available corrosion inhibitor has the lowest one of the one or more mitigated corrosion rates (i.e., mental processes; this is further information limiting the judicial exceptions).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As the claims are currently recited, the method can be performed by writing down calculated corrosion rates and picking corrosion inhibitors using those corrosion rates. Writing out the corrosion rates and making decisions based on them can be practically performed in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. The claims recite using machine learning models, which can be represented as a series of mathematical equations. Therefore, solving these equations to generate corrosion rates is considered a mathematical concept.
Therefore, claims 1-4, 7-12, and 15-20 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 1, 5-6, 9, 11, 14, and 17 recite the additional non-abstract elements of data gathering:
receiving real-time data associated with a component of a hydrocarbon production system (claims 1, 9, and 17);
responsively performing one or more mitigating actions to mitigate the corrosion rate of the component (claims 1, 9, and 17);
wherein the real-time data includes at least one of: a chlorides concentration in parts per million (ppm), pH, hydrogen sulfide (
H
2
S
) mole percent (mole%), carbon dioxide (
C
O
2
) mole%, temperature (
℉
), pressure in pounds per square gauge (psig), test duration in hours (h), or flow speed in revolutions per minute (RPM) (claims 5 and 11; this is further information limiting the data gathered);
wherein responsively performing one or more mitigating actions to mitigate the corrosion rate of the component comprises at least one of displaying on a display device an indication of the selected corrosion inhibitor (claims 6 and 14);
sounding an audible alarm indicating that the corrosion rate is greater than the predetermined threshold (claims 6 and 14).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 6, 9, 14, and 17 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a display device (claims 6 and 14);
a system comprising one or more processors (claim 9);
a non-transitory computer storage medium (claim 17).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 5-6, 9, 11, 14, and 17 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 1, 5-6, 9, 11, 14, and 17: The courts have recognized that computer functions such as receiving or transmitting data over a network and storing and retrieving information in memory are well-understood, routine, and conventional. This is evidenced by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP 2106.05(d)(II). The courts have also recognized limitations that amount to data gathering and outputting are insignificant extra-solution activity and therefore well-understood, routine, and conventional. This is evidenced by Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
With respect to claims 6, 9, 14, and 17: The computer components are found to be well-understood, routine, and conventional per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984, which demonstrate applying a general-purpose computer to a method; OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 demonstrates storing and retrieving information in memory. Therefore, the computer components listed above help carry out the claimed method and are conventional. The non-transitory computer-readable storage medium storing instructions reflect storing information in memory, which is conventional as well. See MPEP 2106.05(a).
[Step 2B: NO]
Therefore, claims 1-20 are patent ineligible under 35 U.S.C. § 101.
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 (i.e., changing from AIA to pre-AIA ) 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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Seghier et al. (Journal of Natural Gas Science and Engineering, 2022, 99, 1-13), in view of Hernandez et al. [US8447529B2], Aghaaminiha et al. (Corrosion Science, 2021, 193, 1-12), Hegazy et al. (Journal of Molecular Liquids, 2015, 214, 347-356), and Lopez et al. (International Journal of Electrochemical Science, 2015, 10(9), 7166-7179).
With respect to claims 1, 9, and 17:
Claim 9 recites a system comprising one or more processors. Claim 17 recites a non-transitory computer storage medium.
Seghier et al. discloses that further improvements of the proposed ensemble learning models could be made, including improving the computational accuracy and efficiency through active learning or hybrid EL-models with meta-heuristic algorithms (pg. 9-10, col. 2, para. 3, lines 3-5). Also, further discloses that Seghier et al. used software to implement the models (pg. 10, col. 2, para. 2, lines 1-2). This indicates that the ensemble learning models require an operating system of a general-purpose computer such as Windows, Mac, or LINUX. These general-purpose computers require the recited system comprising processors and non-transitory computer storage medium.
Regarding the recited receiving real-time data associated with a component of a hydrocarbon production system, Seghier et al. discloses using an inspection report of a comprehensive database composed of 60 samples for a subsea oil and gas pipeline, where eight measured factors that influence the internal corrosion rate were considered as input variables to train the proposed ensemble learning techniques (pg. 4, col. 2, para. 2). This includes temperature, holdup of liquid, pressure,
C
O
2
partial pressure, rate of liquid flow, stress of wall shear (liquid phase), stress of wall shear (gas phase), and pH value. This teaches real-time data associated with oil and gas pipelines.
Regarding the recited generating, using a first machine learning model and based on the real-time data, a corrosion rate for the component, Seghier et al. discloses using four ensemble learning models: random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) to predict internal corrosion rates (pg. 3, col. 1, para. 3, lines 1-6; pg. 7, col. 2, para. 2, lines 6-7; pg. 8, col. 2, para. 2, lines 1-3). Also, further discloses training these models using an inspection report of a comprehensive database composed of 60 samples for a subsea oil and gas pipeline and eight measured factors that influence the internal corrosion rate (pg. 4, col. 2, para. 2). This teaches generating a corrosion rate using a machine learning model and real-time data.
Seghier et al. does not disclose determining that the corrosion rate is greater than a predetermined threshold.
However, Hernandez et al. discloses a threshold determination can be made to determine whether the final untreated corrosion rate from the process is above a threshold value (pg. 24, col. 23, lines 16-20). This teaches determining that a corrosion rate is greater than a threshold.
Seghier et al. and Hernandez et al. do not disclose responsively using a second machine learning model to calculate for the component one or more mitigated corrosion rates when using one or more available corrosion inhibitors.
However, Aghaaminiha et al. discloses using a random forest (RF) model to predict corrosion rates, which are plotted on a logarithmic scale to show on the same graph, both high corrosion rates at the beginning of experiments before inhibitor injection and low corrosion rates after inhibition (pg. 6, col. 1, para. 3, lines 5-11). This teaches using a machine learning model to calculate corrosion rates mitigated by corrosion inhibitor injection.
Seghier et al. and Hernandez et al. do not disclose selecting, based on the one or more mitigated corrosion rates, one of the available corrosion inhibitors to use to mitigate the corrosion rate of the component.
However, Hegazy et al. discloses evaluating prepared inhibitors through multiple weight loss measurements, such as the effect of concentration and temperature, to determine inhibition efficiency (pg. 351, col. 1, para. 2; pg. 355, col. 1, Section “4. Conclusions”, third point). It was found that inhibition efficiency increases with the increase in inhibitor concentration, which leads to a decrease in corrosion rate values. Hegazy et al. also discloses that the prepared cationic surfactant C has the greatest inhibition efficiency of the three observed inhibitors for the corrosion of carbon steel in acidic solutions. Therefore, it would be obvious to select the corrosion inhibitor with the highest inhibition efficiency to mitigate corrosion rates because the greater the inhibition efficiency, the lower the corrosion rates.
Hegazy et al. does not disclose selecting corrosion inhibitors based on the mitigated corrosion rates predicted by a machine learning model.
However, Aghaaminiha et al. discloses that it is important to determine optimal dosage of corrosion inhibitors needed to minimize corrosion of pipelines, but experimentation measuring corrosion rates from using corrosion inhibitors is time-consuming and costly (pg. 1, col. 1, para. 1, lines 7-15). Trained ML models were proposed to accurately predict time-dependent and steady-state corrosion rates of laboratory experiments (pg. 2, col. 1, para. 2). Therefore, it would be obvious to select for the most effective corrosion inhibitor based on mitigated corrosion rates predicted by a machine learning model because machine learning models predict corrosion rates more accurately and efficiently than experimenting with corrosion inhibitors.
Seghier et al., Hernandez et al., Aghaaminiha et al., and Hegazy et al. do not disclose responsively performing one or more mitigating actions to mitigate the corrosion rate of the component.
However, Lopez et al. discloses injecting a palm oil-based amide inhibitor into a pipeline carrying hydrocarbons, which reduced the corrosion rate significantly to values close to 2 mpy after the first two months of injection (pg. 7177-7178, para. 2, lines 1-4; pg. 7178, Figure 14). This teaches performing a mitigating action of corrosion inhibitor injection to reduce corrosion rates of oil and gas pipelines.
With respect to claims 2, 10, and 18:
Hernandez et al., Hegazy et al., and Lopez et al. do not disclose wherein the first and second machine learning models are trained using at least one of: historical field data, simulation data, or lab data.
However, Seghier et al. discloses using an inspection report of a comprehensive database composed of 60 samples for a subsea oil and gas pipeline, where eight measured factors that influence the internal corrosion rate were considered as input variables to train the proposed ensemble learning techniques (pg. 4, col. 2, para. 2). This teaches using historical field data from a case study of a subsea pipeline constructed in 2009 to train a first machine learning model.
Seghier et al. does not disclose training a second machine learning model using historical field data, simulation data, or lab data.
However, Aghaaminiha et al. discloses experimental data obtained from a series of experiments on corrosion inhibition of mild steel in
C
O
2
aqueous solutions, which were conducted independently at four different laboratories using mild steel coupons with identical chemical compositions and two different organic corrosion inhibitors (CI-1 and CI-2) (pg. 2, col. 2, para. 1, lines 1-7). This data was used to train the machine learning models (pg. 4, col. 2, para. 2). This teaches using lab data to train a second machine learning model.
With respect to claims 3, 11, and 19:
Hernandez et al., Hegazy et al., and Lopez et al. do not disclose wherein the first and second machine learning models are trained using at least one of gradient boosting classifiers or random forest classifiers.
However, Seghier et al. discloses using four ensemble learning models: random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) to predict internal corrosion rates (pg. 3, col. 1, para. 3, lines 1-6; pg. 7, col. 2, para. 2, lines 6-7; pg. 8, col. 2, para. 2, lines 1-3). This teaches a first machine learning model trained using gradient boosting or random forest classifiers.
Seghier et al. does not disclose training a second machine learning model using gradient boosting or random forest classifiers.
However, Aghaaminiha et al. discloses developing different predictive models of corrosion rates of carbon steel as a function of time, when corrosion inhibitors are added to the system in different dosages and dosing schedules, using different ML algorithms (ANN, RF, SVR, KNN) (pg. 3, col. 2, para. 5). This teaches a second machine learning model trained using random forest classifiers.
With respect to claims 4, 12, and 20:
Hernandez et al., Aghaaminiha et al., Hegazy et al., and Lopez et al. do not disclose wherein the at least one of the gradient boosting classifiers or the random forest classifiers are evaluated using at least one of coefficient of determination, root mean squared error (RMSE), mean absolute error (MAE), or mean absolute percentage error (MAPE).
However, Seghier et al. discloses performance evaluation metrics computed for each ensemble learning model, which includes root mean square error (RMSE) and mean absolute error (MAE) (pg. 7, col. 1, para. 2, lines 1-11). Also, further discloses using four ensemble learning models: random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) to predict internal corrosion rates (pg. 3, col. 1, para. 3, lines 1-6; pg. 7, col. 2, para. 2, lines 6-7; pg. 8, col. 2, para. 2, lines 1-3). This teaches evaluating gradient boosting and random forest classifiers using root mean squared error (RMSE) and mean absolute error (MAE).
With respect to claims 5 and 13:
Hernandez et al., Aghaaminiha et al., Hegazy et al., and Lopez et al. do not disclose wherein the real-time data includes at least one of: a chlorides concentration in parts per million (ppm), pH, hydrogen sulfide (
H
2
S
) mole percent (mole%), carbon dioxide (
C
O
2
) mole%, temperature (
℉
), pressure in pounds per square gauge (psig), test duration in hours (h), or flow speed in revolutions per minute (RPM).
However, Seghier et al. discloses using an inspection report of a comprehensive database composed of 60 samples for a subsea oil and gas pipeline, where eight measured factors that influence the internal corrosion rate were considered as input variables to train the proposed ensemble learning techniques (pg. 4, col. 2, para. 2). This includes temperature, holdup of liquid, pressure,
C
O
2
partial pressure, rate of liquid flow, stress of wall shear (liquid phase), stress of wall shear (gas phase), and pH value. This teaches real-time data including pH.
With respect to claims 6 and 14:
Seghier et al., Hernandez et al., Aghaaminiha et al., and Hegazy et al. do not disclose wherein responsively performing one or more mitigating actions to mitigate the corrosion rate of the component comprises at least one of displaying on a display device an indication of the selected corrosion inhibitor, sounding an audible alarm indicating that the corrosion rate is greater than the predetermined threshold, or causing the selected corrosion inhibitor to be injected into an appropriate hydrocarbon stream that comes into contact with the component.
However, Lopez et al. discloses injecting a palm oil-based amide inhibitor into a pipeline carrying hydrocarbons, which reduced the corrosion rate significantly to values close to 2 mpy after the first two months of injection (pg. 7177-7178, para. 2, lines 1-4; pg. 7178, Figure 14). This teaches performing a mitigating action of corrosion inhibitor injection into a stream of hydrocarbons in a pipeline to reduce corrosion rates.
With respect to claims 7 and 15:
Seghier et al., Hernandez et al., and Lopez et al. do not disclose wherein selecting, based on the one or more mitigated corrosion rates, one of the available corrosion inhibitors to use to mitigate the corrosion rate of the component further comprises: selecting a concentration of the selected available corrosion inhibitor.
However, Hegazy et al. discloses evaluating prepared inhibitors through multiple weight loss measurements, such as the effect of concentration and temperature, to determine inhibition efficiency (pg. 351, col. 1, para. 2; pg. 355, col. 1, Section “4. Conclusions”, third point). It was found that inhibition efficiency increases with the increase in inhibitor concentration, which leads to a decrease in corrosion rate values. Hegazy et al. also discloses that the prepared cationic surfactant C has the greatest inhibition efficiency of the three observed inhibitors for the corrosion of carbon steel in acidic solutions. Therefore, it would be obvious to select a concentration of the corrosion inhibitor with the highest inhibition efficiency to mitigate corrosion rates because the greater the inhibition efficiency, the lower the corrosion rates.
Hegazy et al. does not disclose selecting corrosion inhibitors based on the mitigated corrosion rates predicted by a machine learning model.
However, Aghaaminiha et al. discloses that it is important to determine optimal dosage of corrosion inhibitors needed to minimize corrosion of pipelines, but experimentation measuring corrosion rates from using corrosion inhibitors is time-consuming and costly (pg. 1, col. 1, para. 1, lines 7-15). Trained ML models were proposed to accurately predict time-dependent and steady-state corrosion rates of laboratory experiments (pg. 2, col. 1, para. 2). Therefore, it would be obvious to select for concentrations of the most effective corrosion inhibitor based on mitigated corrosion rates predicted by a machine learning model because machine learning models predict corrosion rates more accurately and efficiently than experimenting with corrosion inhibitors.
With respect to claims 8 and 16:
Seghier et al., Hernandez et al., and Lopez et al. do not disclose wherein the selected available corrosion inhibitor has the lowest one of the one or more mitigated corrosion rates.
However, Hegazy et al. discloses evaluating prepared inhibitors through multiple weight loss measurements, such as the effect of concentration and temperature, to determine inhibition efficiency (pg. 351, col. 1, para. 2; pg. 355, col. 1, Section “4. Conclusions”, third point). It was found that inhibition efficiency increases with the increase in inhibitor concentration, which leads to a decrease in corrosion rate values. Hegazy et al. also discloses that the prepared cationic surfactant C has the greatest inhibition efficiency of the three observed inhibitors for the corrosion of carbon steel in acidic solutions. Therefore, it would be obvious to select a corrosion inhibitor with the highest inhibition efficiency to mitigate corrosion rates because the greater the inhibition efficiency, the lower the corrosion rates. Thus, the selected corrosion inhibitor would have the lowest mitigated corrosion rate.
Hegazy et al. does not disclose selecting corrosion inhibitors based on the mitigated corrosion rates predicted by a machine learning model.
However, Aghaaminiha et al. discloses that it is important to determine optimal dosage of corrosion inhibitors needed to minimize corrosion of pipelines, but experimentation measuring corrosion rates from using corrosion inhibitors is time-consuming and costly (pg. 1, col. 1, para. 1, lines 7-15). Trained ML models were proposed to accurately predict time-dependent and steady-state corrosion rates of laboratory experiments (pg. 2, col. 1, para. 2). Therefore, it would be obvious to select for the most effective corrosion inhibitor based on mitigated corrosion rates predicted by a machine learning model because machine learning models predict corrosion rates more accurately and efficiently than experimenting with corrosion inhibitors.
It would have been prima facie obvious to one of ordinary skill in the art to modify the corrosion rate prediction method disclosed by Seghier et al. to incorporate the corrosion rate threshold disclosed by Hernandez et al., the machine learning model for mitigated corrosion rates disclosed by Aghaaminiha et al., corrosion inhibitor selection disclosed by Hegazy et al., and corrosion inhibitor injection disclosed by Lopez et al. One would be motivated to incorporate the corrosion rate threshold, machine learning model, selecting inhibitors, and mitigation actions because Hernandez et al. discloses defining a mechanistic model in order to derive expected corrosion rates, which is evaluated in an automated manner by way of modern computer systems and functionality, enabling accurate and efficient evaluation of existing pipelines as well as proposed designs (pg. 15, col. 6, lines 49-62). Implementing a corrosion rate threshold in the corrosion rate prediction method would provide accurate and efficient evaluation of corrosion in pipelines. Aghaaminiha et al. discloses that the random forest (RF) model was very accurate in predicting experimental results of corrosion rate measurements (pg. 6, col. 1, para. 2). Therefore, incorporating this second machine learning model for mitigated corrosion rates would improve the accuracy and efficiency of selecting corrosion inhibitors for mitigation. Hegazy et al. discloses that using inhibitors is one of the most practical methods for protecting metals against corrosion (pg. 347, col. 1, para. 1, lines 16-17). This means that inhibitor selection could be used to mitigate corrosion rates predicted from the method and protect pipelines from corrosion. Lopez et al. discloses that corrosion rates decreased up to four orders of magnitude when the palm oil-based amide was added to the pipelines (pg. 7178, para. 3, lines 6-7). Therefore, corrosion inhibitor injection is a very effective mitigation action that could be used to reduce corrosion rates predicted from the method. There is a likelihood of success, since corrosion prediction models and experiments, corrosion monitoring devices, and corrosion inhibitor selection are all well-known techniques in the field of materials science.
Conclusion
No claims are allowed.
It is noted that the particular embodiment of claims 6 and 14, which recite causing the selected corrosion inhibitor to be injected into an appropriate hydrocarbon stream that comes into contact with the component, is patent eligible under 35 U.S.C. 101. In Step 2A, Prong One, this integrates the corrosion inhibitor selected in independent claim 1 by injecting the inhibitor concentration into the hydrocarbon stream. Therefore, the claimed additional element integrates the abstract ideas into a practical application. However, the other embodiments of claims 6 and 14, which recite displaying on a display device an indication of the selected corrosion inhibitor or sounding an audible alarm indicating that the corrosion rate is greater than the predetermined threshold, are not patent eligible under 35 U.S.C. 101. It would appear that the rejection of claims 6 and 14 under 35 U.S.C. 101 could be overcome if the claims were amended such that they recite only injecting the selected corrosion inhibitor into an appropriate hydrocarbon stream.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.N.L./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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