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 .
Applicant's response filed 10/16/2025 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1-4, 6-11, 13-18, and 20 pending and examined on the merits.
Claim 5, 12, and 19 canceled.
Priority
The instant application claims no benefit of priority. Thus, the effective filing date of the claims is December 13, 2021.
Specification
The objections to the Abstract withdrawn in view of Applicant's claim amendments filed on 10/16/2025.
Drawings
The objections to the Drawings withdrawn in view of Applicant's claim amendments filed on 10/16/2025.
Claim Objections
The objections to claim 6 withdrawn in view of Applicant's claim amendments filed on 10/16/2025.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 5, 12, and 19 under 35 U.S.C. 112(b) withdrawn in view of Applicant's claim amendments (claims have been canceled) filed on 10/16/2025.
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 1-4, 6-11, 13-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 8, and 15: “predicting, using the processor, a success or failure of a predefined minimum inhibitor loading value” provides an evaluation (predicting an outcome) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“determining, using the processor, a minimum inhibitor loading value that is a successful corrosion inhibitor loading based on the inputs responsive to a failure of the predefined minimum inhibitor loading value” provides an evaluation (optimization of a value) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 3, 10, and 17: “predicting a success or failure of the predefined minimum corrosion inhibitor loading value” provides an evaluation (predicting an outcome) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“iteratively increasing predefined minimum corrosion inhibitor loading value and predicting success or failure of the increased predefined minimum corrosion inhibitor loading value until a success prediction is provided” provides an evaluation (iteratively increasing an input variable is simply rote optimization) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 4, 11, and 18: “applying best fit models to each respective dataset” provides a mathematical calculation (applying best fit models involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
“predicting success or failure of inhibitor loading values using the cumulative distribution function” provides an evaluation (predicting an outcome) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 6, 13, and 20: “the inputs are selected according to a corresponding scenario” provides an evaluation (selecting inputs) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“predicting a success or failure uses a machine learning model is trained based on the corresponding scenario” provides an evaluation (predicting an outcome) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 8-20 recite performing some aspects of the analysis on “A system, comprising: at least one processor, and at least one non-transitory storage media storing instructions” and “At least one non-transitory storage media storing instructions”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-4, 6-11, 13-18, and 20 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “obtaining, using a processor, inputs comprising at least one of an acid concentration, exposure time, temperature, or any combinations” provides insignificant extra-solution activities (obtaining inputs are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“stimulating a well to increase production rates using the minimum inhibitor loading value" provides insignificant extra-solution activities (well stimulation is a post-solution activity involving a well-understood, routine, and conventional application) that do not serve to integrate the judicial exceptions into a practical application.
Claim 3: “inputting a predefined minimum corrosion inhibitor loading value to a trained machine learning model” provides insignificant extra-solution activities (inputting data are pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“providing the predefined minimum corrosion inhibitor loading value as a final value to a user in response to predicting a success” provides insignificant extra-solution activities (outputting data are post-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4: “generating dataset associated with exposure time, temperature, and inhibitor loading” provides insignificant extra-solution activities (generating a dataset are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating a cumulative distribution function using the best-fit models” provides insignificant extra-solution activities (generating a distribution are post-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 7: “the inputs are entered to the virtual laboratory by a user inputting the required well input data using a graphical user interface (GUI)” provides insignificant extra-solution activities (inputting data are pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 8: “A system, comprising: at least one processor, and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to []” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application
Claim 15: “At least one non-transitory storage media storing instructions that, when executed by at least one processor, cause the at least one processor to []” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
The steps for inputting, generating, and outputting data, and running instructions on generic computer components are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-4, 6-11, 13-18, and 20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “A system, comprising: at least one processor, and at least one non-transitory storage media storing instructions” nor “At least one non-transitory storage media storing instructions” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for inputting, generating, and outputting data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are demonstrated to be well-understood, routine, and conventional: Singh et al., page 1 Introduction "Acid treatments have been applied to wells in oil and gas bearing rock formations for many years. Acidizing is probably the most widely used work-over and stimulation practice in the oil industry" (Singh et al. "Acidizing corrosion inhibitors: a review." J. Mater. Environ. Sci 6.1 (2015): 224-235).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-4, 6-11, 13-18, and 20 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 10/16/2025 are fully considered but they are not persuasive.
Applicant does not concede that independent claims 1, 8, and 15 are directed to an abstract idea, however has amended the claims to include "stimulating a well to increase production rates using the minimum inhibitor loading value" (Remarks 10/16/2025 Page 2). Examiner notes that this is merely an additional element that is a post-solution activity which provides insignificant extra-solution activities and does not serve to integrate the judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are demonstrated to be well-understood, routine, and conventional: Singh et al., page 1 Introduction "Acid treatments have been applied to wells in oil and gas bearing rock formations for many years. Acidizing is probably the most widely used work-over and stimulation practice in the oil industry" (Singh et al. "Acidizing corrosion inhibitors: a review." J. Mater. Environ. Sci 6.1 (2015): 224-235).
The Examiner also notes that MPEP 2106(I) states that if the claims are directed to a judicial exception, the second part of the Mayo test is to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. Id. citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). In the “search for an ‘inventive concept’” (the second part of the Alice/Mayo test), the additional elements identified do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception because adjusting parameters and storing data (data gathering and manipulation steps) are all well-understood, routine, and conventional techniques that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Therefore, combining insignificant extra-solution activities with any of the identified judicial exceptions would not result in patent eligible subject matter because integrating well-understood, routine, and conventional techniques does not yield “significantly more” to a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon.
Therefore, the rejection of claims 1, 8, and 15 under 35 USC 101 is maintained. All other claims are either canceled or depend from these independent claims; therefore, their rejection is likewise maintained.
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.
Claims 1, 2, 6-9, 13-16, 20 rejected under 35 U.S.C. 103 as being unpatentable over Hournbuckle et al. (US-20190087071).
Regarding claims 1, 8, and 15, Hournbuckle teaches obtaining inputs comprising at least one of an acid concentration, exposure time, temperature, or any combinations thereof (Para.0008 "Data characterizing user interaction can further include prediction inputs indicative of one or more of temperature, pressure, flow rates, sulphuric acid concentration, and hydrochloric acid concentration associated with a first location of the plurality of locations.").
Hournbuckle also teaches predicting a success or failure of a predefined minimum inhibitor loading value (Para.0003 "In some implementations, the interface can utilize a predictive model to predict the status of an asset into the future, for example, based on user input or actual conditions within the facility, thereby predicting a future status of an asset including time to failure (e.g., within a margin of error).").
Hournbuckle also teaches determining a minimum inhibitor loading value that is a successful corrosion inhibitor loading based on the inputs responsive to a failure of the predefined minimum inhibitor loading value (Para.0081 "Insights gathered from the analysis section can allow a user to make informed decisions, such as deciding whether the user needs a corrosion inhibitor, and if so, to help the user select the right corrosion inhibitor type and amount to protect the assets against the specific product mix.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to use the methods of Hournbuckle in order to forecast scenarios associated with industrial assets (para.0008 "The predictive model can be configured to receive prediction inputs and detected wall thickness over the first time period" and para.0054 "Given the significant amount of data collected and provided by these sensors, it can be difficult for a user to review this data in a meaningful way. Additionally, industrial equipment monitoring systems that can include a predictive model that can generate simulation data with predictions for the status of an asset into the future"), including those associated with oil and gas production and processing (para.0002 "The current subject matter can include an interface that can provide improved visualization of data related to industrial assets. The visualizations can allow a user to quickly ascertain the status of an asset, such as the profile of corrosion within a pipe, without having to review detailed sensor data"). Additionally, the analyses performed help a user determine if a corrosion inhibitor is needed, and what type and amount to use (para.0081 "Insights gathered from the analysis section can allow a user to make informed decisions, such as deciding whether the user needs a corrosion inhibitor, and if so, to help the user select the right corrosion inhibitor type and amount to protect the assets against the specific product mix"). Furthermore, while Hournbuckle does not explicitly cite a threshold level for "corrosion inhibition" effectiveness in its models, it does cover a user applying a corrosion inhibitor to one pipe and utilizing the knowledge of the outcome for other assets (Para.0083 "If the user decides to apply some type of corrosion inhibitor to the pipe they have been investigating, they can evaluate if the same treatment should be applied to these other similar assets that have been identified"). Hournbuckle goes on to describe a predictive model based on past and future conditions, and alerting if a predetermined threshold is crossed (Para.0092 "At 1020, a second value of the property of the industrial asset over a second period of time is determined. The second value can be determined using a predictive model that predicts, based on the past conditions, proposed future conditions, physics models, probabilistic machine learning algorithms, and/or sensor measurements, how the property will change over time." and para.0125 "Original data plus various derived data (e.g., thickness, signal to noise ratio, temperatures, alerts at time of calculation, equipment health) can be stored for visualization by user and to immediately notify user if predetermined alert thresholds are crossed"). These concepts together render the idea of modeling a success threshold for a corrosion inhibitor obvious. One skilled in the art would have a reasonable expectation of success because both methods aim to predict the effects of using corrosion inhibitors on oil and gas production equipment.
Regarding claims 2, 9, and 16, Hournbuckle teaches the methods of Claim 1 on which these claims depend, respectively. Hournbuckle also teaches the inputs further comprises a steel type, inhibitor type, a predefined minimum inhibitor loading value, or any combinations thereof (Para.0081 "Insights gathered from the analysis section can allow a user to make informed decisions, such as deciding whether the user needs a corrosion inhibitor, and if so, to help the user select the right corrosion inhibitor type and amount to protect the assets against the specific product mix.").
Regarding claims 6, 13, and 20, Hournbuckle teaches the methods of Claim 1 on which these claims depend, respectively. Hournbuckle also teaches the inputs are selected according to a corresponding scenario, wherein predicting a success or failure uses a machine learning model that is trained based on the corresponding scenario (Para.0007 "In one implementation, data characterizing the user interaction with the interactive graphical object can include forecast scenarios associated with the asset. The predictive model can be configured to receive the forecast scenarios as an input and generate data characterizing the first property of the asset over the second time period.").
Regarding claims 7 and 14, Hournbuckle teaches the methods of Claim 1 on which these claims depend, respectively. Hournbuckle also teaches the inputs are entered to the virtual laboratory by a user inputting the required well input data using a graphical user interface (GUI) (Para.0004 "The method can also include receiving data characterizing user interaction with an interactive graphical object." and para.0079 "Interactive element 510 can include elements for specifying the inputs (e.g., conditions) for predicting the future values. The inputs can be utilized by a predictive model including a digital model of the asset, to predict future values. In FIG. 5, the inputs can be specified either directly, through an editable text field, or using a slider object. Other implementations are possible, such as the automatic or manual ingestion of the values from an application programming interface (API), a file upload, or other automated data loading or access technique. When inputs are specified (e.g., changed), the predictive model can update and new future values can be displayed.").
Claims 3, 10, and 17 rejected under 35 U.S.C. 103 as being unpatentable over Hournbuckle et al. (US-20190087071) as applied to claims 1, 2, 6-9, 13-16, and 20 above, and further in view of Aghaaminiha et al. (Aghaaminiha et al., Machine learning modeling of time-dependent corrosion rates of carbon steel in presence of corrosion inhibitors, Corrosion Science, Volume 193, 2021, 109904, ISSN 0010-938X, https://doi.org/10.1016/j.corsci.2021.109904).
Hournbuckle et al. are applied to claims 1, 2, 6-9, 13-16, and 20.
Regarding claims 3, 10, and 17, Hournbuckle teaches the method of Claims 1, 8, and 15 on which these claims depend, respectively. Hournbuckle also teaches inputting a predefined minimum corrosion inhibitor loading value to a trained machine learning model (Para.0092 "At 1020, a second value of the property of the industrial asset over a second period of time is determined. The second value can be determined using a predictive model that predicts, based on the past conditions, proposed future conditions, physics models, probabilistic machine learning algorithms, and/or sensor measurements, how the property will change over time.").
Hournbuckle also teaches predicting a success or failure of the predefined minimum corrosion inhibitor loading value (Para.0074 "The red color can indicate that asset wall thickness is below an acceptable level and failure is likely to occur or has already occurred."), as well as providing the predefined minimum corrosion inhibitor loading value as a final value to a user in response to predicting a success because this value would be known beforehand given that it is an input into the predictive model that predicts a success or failure of the corrosion inhibitor value.
Hournbuckle does not explicitly teach a model optimization process involving iteratively increasing predefined minimum corrosion inhibitor loading value and predicting success or failure of the increased predefined minimum corrosion inhibitor loading value until a success prediction is provided.
However, Aghaaminiha teaches a model optimization process (Page 3 col 2 paragraph 4 "The performance of a ML algorithm on a dataset varies as one changes the hyperparameters. Therefore, values of the hyperparameters need to be tuned/adjusted to optimize performance. In order to tune the hyperparameters, first, the data should be split into a training set and a testing set. The training set is often taken as 70–80% of the entire dataset. The optimum values of the hyperparameters are found by evaluating the performance of all ML models on the training set. Once the hyperparameters are fixed, the best ML algorithm is trained on the training set, and then its performance is evaluated on the testing set. ").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Hournbuckle as taught by Aghaaminiha in order to develop a robust predictive model (page 3 col 1 paragraph 2 "Machine learning (ML) methods are suitable for developing predictive models in the cases where a large dataset is available, the outcome to be predicted depends on several variables, and when a mechanistic model of the relationship between the input variables and the outcome is not well established"). One skilled in the art would have a reasonable expectation of success because both approaches are utilizing machine learning models for prediction of corrosion inhibitor performance.
Regarding claims 1, 8, and 15, Hournbuckle teaches the methods of Claims 1, 8, and 15. However, Examiner would also like to point out that Aghaaminiha also teaches using a model to determine the effect of various parameters, including "inhibitor type" (page 11 Figure 7 legend "Fig. 7. A parametric study, using a trained RF model, to determine how the time-dependent corrosion rates are expected to vary with the A) inhibitor type, B) inhibitor concentration, C) CO2 partial pressure, D) temperature, E) wall shear stress, and F) brine type. Common condition in all figures, unless otherwise specified is as follow: inhibitor type = CI-1, inhibitor concentration = 100 ppm, exposure duration ~22 hrs., pCO2 = 12 bar, T = 130 oC, pH = uncontrolled, wall shear stress = 20 Pa, brine ionic strength = 0.615 M, brine type = A"). Given this model is able to predict corrosive effects into the future, it would be trivial to apply some time, wall thickness, etc. threshold that would be considered a success or failure if crossed.
Claims 4, 11, and 18 rejected under 35 U.S.C. 103 as being unpatentable over Hournbuckle et al. (US-20190087071) as applied to claims 1, 2, 6-9, 13-16, and 20 above, and further in view of Ituen et al. (Ituen et al., Evaluation of Performance of Corrosion Inhibitors Using Adsorption Isotherm Models: An Overview, Chemical Science International Journal, 18(1): 1-34, 2017; Article no.CSIJ.28976, DOI: 10.9734/CSIJ/2017/28976) and Tan et al. (Tan et al., Statistical methods for the analysis of corrosion data for integrity assessments, Brunel University London, 2017, http://bura.brunel.ac.uk/handle/2438/15275).
Hournbuckle et al. are applied to claims 1, 2, 6-9, 13-16, and 20.
Regarding claims 4, 11, and 18, Hournbuckle teaches the method of Claims 1, 8, and 15 on which these claims depend, respectively. Hournbuckle also teaches generating dataset associated with exposure time, temperature, and inhibitor loading (Para.0007 "The predictive model can be configured to receive the forecast scenarios as an input and generate data characterizing the first property of the asset over the second time period.").
Hournbuckle also teaches predicting success or failure of inhibitor loading values using the cumulative distribution function (Para.0071 "In the example of FIG. 4, wall thickness is predicted. As illustrated the thickness of the line illustrating predicted future values 417 indicates a probability/probability distribution (e.g., confidence or standard deviation) associated with the prediction.").
Hournbuckle does not explicitly teach applying best fit models to each respective dataset nor generating a cumulative distribution function using the best-fit models.
However, Ituen teaches applying best fit models to each respective dataset (Page 10 col 2 section 2.4 "The routine involves fitting the surface coverage data into different adsorption models and the isotherm that best fits the data is used to describe the adsorption behaviour. The best fit is usually the one that gives the highest regression coefficient (R2) value from the linear plots").
However, Tan teaches generating a cumulative distribution function using the best-fit models (Page 116 paragraph 1 "We can obtain the probability of failure for the deadleg component with current year in service through the cumulative function of 𝑋(𝑑𝑒𝑎𝑑𝑙𝑒𝑔 nom) ~ 𝐿𝑁(4.3535, 0.545).").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Hournbuckle as taught by Ituen in order to describe how well corrosion inhibitors may protect metal surfaces (Page 2 abstract "Adsorption isotherm models are an important tool for describing interaction of corrosion inhibitors with metal surfaces which they are aimed to protect"). One skilled in the art would have a reasonable expectation of success because both methods are aimed at characterizing corrosion inhibitors.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Hournbuckle and Ituen as taught by Tan in order to obtain a target failure date of an asset (Page 116 paragraph 1 "By using FORM, we obtained the target failure probability of 0.23 for which the thickness of the component will be thinned below its acceptable thickness at approximately year 52."). One skilled in the art would have a reasonable expectation of success because both are applying statistical frameworks for modeling and predicting failure of assets.
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 10/16/2025 are fully considered but they are not persuasive.
Applicant asserts that "the Examiner has failed to identify any chemical efficacy or threshold success of corrosion inhibitors in Hournbuckle" for claims 1, 2, 6-9, 13-16, and 20 (Remarks 10/16/2025 Page 2). Applicant also asserts that Hournbuckle does not teach or suggest "a determination of the effectiveness of a particular inhibitor", nor "predict whether a treatment will work" (Remarks 10/16/2025 Page 3). Examiner notes that while Hournbuckle does not explicitly cite a threshold level for "corrosion inhibition" effectiveness in its models, it does cover a user applying a corrosion inhibitor to one pipe and utilizing the knowledge of the outcome for other assets (Para.0083 "If the user decides to apply some type of corrosion inhibitor to the pipe they have been investigating, they can evaluate if the same treatment should be applied to these other similar assets that have been identified"). Hournbuckle goes on to describe a predictive model based on past and future conditions, and alerting if a predetermined threshold is crossed (Para.0092 "At 1020, a second value of the property of the industrial asset over a second period of time is determined. The second value can be determined using a predictive model that predicts, based on the past conditions, proposed future conditions, physics models, probabilistic machine learning algorithms, and/or sensor measurements, how the property will change over time." and para.0125 "Original data plus various derived data (e.g., thickness, signal to noise ratio, temperatures, alerts at time of calculation, equipment health) can be stored for visualization by user and to immediately notify user if predetermined alert thresholds are crossed"). These concepts together render the idea of modeling a success threshold for a corrosion inhibitor obvious. Finally, regarding Hournbuckle not predicting whether a treatment will work or not, this limitation is implied in the methods and models of Hournbuckle or Aghaaminiha, as the "predefined" minimum value is arbitrary, rendering a success or failure at literally any set level.
Applicant asserts that neither Aghaaminiha, Ituen, nor Tan remedy the deficiencies of Hournbuckle (Remarks 10/16/2025 Pages 3-4). Examiner notes that while Hournbuckle is not deficient for the reasons stated above, Aghaaminiha does in fact also remedy these perceived deficiencies by using a model to determine the effect of various parameters, including "inhibitor type" (page 11 Figure 7 legend "Fig. 7. A parametric study, using a trained RF model, to determine how the time-dependent corrosion rates are expected to vary with the A) inhibitor type, B) inhibitor concentration, C) CO2 partial pressure, D) temperature, E) wall shear stress, and F) brine type. Common condition in all figures, unless otherwise specified is as follow: inhibitor type = CI-1, inhibitor concentration = 100 ppm, exposure duration ~22 hrs., pCO2 = 12 bar, T = 130 oC, pH = uncontrolled, wall shear stress = 20 Pa, brine ionic strength = 0.615 M, brine type = A"). Given this model is able to predict corrosive effects into the future, it would be trivial to apply some time, wall thickness, etc. threshold that would be considered a success or failure if crossed.
Therefore, the rejection of claims 1, 8, and 15 under 35 USC 103 is maintained. All other claims are either canceled or depend from these independent claims; therefore, their rejection is likewise maintained.
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.
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Claims 1-4, 6-11, 13-18, and 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US-20240426741 in view of Ituen et al. (Ituen et al., Evaluation of Performance of Corrosion Inhibitors Using Adsorption Isotherm Models: An Overview, Chemical Science International Journal, 18(1): 1-34, 2017; Article no.CSIJ.28976, DOI: 10.9734/CSIJ/2017/28976), Tan et al. (Tan et al., Statistical methods for the analysis of corrosion data for integrity assessments, Brunel University London, 2017, http://bura.brunel.ac.uk/handle/2438/15275), and Hournbuckle et al. (US-20190087071). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve generating a prediction or risk assessment of corrosion and the effect of a corrosion inhibitor using machine learning models. Both use data including concentration of acids, temperature, time, and type and concentration of corrosion inhibitor. They also both uses gradient boosting (i.e. iterative testing and optimization), and allow user input/output through a graphical user interface. Finally, both implement the method on a system or non-transitory computer-readable medium.
While US-20240426741 does not explicitly teach applying best fit models to each respective dataset, it would have been obvious to one of ordinary skill in the art to modify the above methods, with those taught by Ituen in order to describe how well corrosion inhibitors may protect metal surfaces (Page 2 abstract "Adsorption isotherm models are an important tool for describing interaction of corrosion inhibitors with metal surfaces which they are aimed to protect"). One skilled in the art would have a reasonable expectation of success because both methods are aimed at characterizing corrosion inhibitors.
Finally, US-20240426741 does not explicitly teach generating a cumulative distribution function using the best-fit models, it would have been obvious to one of ordinary skill in the art to modify the above methods, with those taught by Tan in order to obtain a target failure date of an asset (Page 116 paragraph 1 "By using FORM, we obtained the target failure probability of 0.23 for which the thickness of the component will be thinned below its acceptable thickness at approximately year 52."). One skilled in the art would have a reasonable expectation of success because both are applying statistical frameworks for modeling and predicting failure of assets.
Response to Arguments under Nonstatutory Double Patenting
Applicant’s arguments filed 10/16/2025 are fully considered but they are not persuasive.
Applicant asserts that the non-statutory obviousness-type double patenting rejection is improper "because Examiner fails to identify any common assignee, applicant, or joint inventor between Hournbuckle and the present application", and that Ituen and Tan are "non-patent literature, devoid of any claims, and thus cannot be used in a double patenting rejection". The Examiner notes that the primary reference upon which the nonstatutory double patenting rejection is based (US-20240426741) does indeed share an assignee with the instant application (Saudi Arabian Oil Company). Additionally, regarding non-patent literature, MPEP 804 Definition of Double Patenting states: "Nonstatutory double patenting includes rejections based on anticipation, a one-way determination of "obviousness," or a two-way determination of "obviousness." It is important to note that the "obviousness" analysis for "obviousness-type" double-patenting is "similar to, but not necessarily the same as, that undertaken under 35 U.S.C. 103." In re Braat, 937 F.2d 589, 592-93, 19 USPQ2d 1289, 1292 (Fed. Cir. 1991) (citing In re Longi, 759 F.2d 887, 892 n.4, 225 USPQ 645, 648 n.4 (Fed. Cir. 1985)); Geneva Pharmaceuticals, 349 F.3d 1373, 1378 n.1, 68 USPQ2d 1865, 1869 n.1 (Fed. Cir. 2003). In addition, nonstatutory double patenting also includes rejections based on the equitable principle against permitting an unjustified timewise extension of patent rights. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968); see also subsection II.B.6, below."
Therefore, the rejection of claims 1-4, 6-11, 13-18, and 20 under nonstatutory double patenting is maintained.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686