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 .
Information Disclosure Statement
IDS filed 4/29/2024 is being considered by the examiner
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
"adjusting, with a controller" in claim 1;
"determining, with an optimizer" in claim 6
"controller configured to: obtain" in claim 9
"determining, with the optimizer" in claim 14
"adjusting, with a controller" in claim 18; and
"determining, with an optimizer" in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 are directed towards the statutory category of a process. Claims 9-17 are directed towards the statutory category of a machine. Claims 18-20 are directed towards the statutory category of an article of manufacture.
With regard to claim 1:
Step 2A Prong 1: This claim is direct to a judicial exception.
determining … a set of corrosion metrics each indicative of corrosion at a location of the well based on well data (mental process - a person can manually look at the data determine which metrics are related to corrosion)
forming an aggregate corrosion prediction from the set of corrosion metrics (mental process - a person can manually look at the data and determine an aggregate prediction)
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. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
Additional elements:
obtaining well data from a well (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g))
obtaining a set of operation parameters that control operation of the well (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g))
determining, with a set of machine learning model comprising a first machine learning model (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – high level use of a machine learning model without technical details)
adjusting, with a controller (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – generic computer component), the set of operation parameters based on, at least, the aggregate corrosion prediction (generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h) – mere application of the determined result to the field of gas processing plants without further incorporation into the previous steps)
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining well data from a well (MPEP 2106.05(d)(II) indicates that merely "storing and retrieving information in memory" and/or "receiving or transmitting data over a network" are well-understood, routine, conventional functions when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer)
obtaining a set of operation parameters that control operation of the well (MPEP 2106.05(d)(II) indicates that merely "storing and retrieving information in memory" and/or "receiving or transmitting data over a network" are well-understood, routine, conventional functions when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer)
determining, with a set of machine learning model comprising a first machine learning model (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – high level use of a machine learning model without technical details)
adjusting, with a controller (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – generic computer component), the set of operation parameters based on, at least, the aggregate corrosion prediction (generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h) – mere application of the determined result to the field of gas processing plants without further incorporation into the previous steps)
Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With regard to claims 2-8: the claims merely elaborate on what data is collected, how the determination is made, what generic computer component is utilized, or where the results are applied; thus, the additional limitations do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
With regard to claim 9: claim 9 recites elements similar to those presented in claim 1; therefore, claim 9 is rejected along the same grounds under 35 U.S.C. 101 as claim 1.
With regard to claims 10-17: the claims merely elaborate on what data is collected, how the determination is made, what generic computer component is utilized, or where the results are applied; thus, the additional limitations do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
With regard to claim 18: claim 18 recites elements similar to those presented in claim 1; therefore, claim 18 is rejected along the same grounds under 35 U.S.C. 101 as claim 1.
With regard to claims 19 and 20: the claims merely elaborate on what data is collected, how the determination is made, what generic computer component is utilized, or where the results are applied; thus, the additional limitations do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
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, 3, 6, 8, 9, 11, 14, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pang et al. [US Pub. 2022/0112799] ("Pang") in view of Cernatescu et al. [US Pub. 2025/0334046] ("Cernatescu").
With regard to claim 1, Pang teaches a method, comprising:
obtaining well data from a well ("the temperature and pressure (T & P) of the fluid at points along the wellbore is entered into an empirical model at step 201 [par. 0044]");
obtaining a set of operation parameters that control operation of the well ("At step 203, production parameters are input into the model [par. 0044]" and "The production inputs may include barrels of water per day ('BWPD'), barrels of oil per day ('BOPD'), flow rate (which may be measured in, for example, millions of standard cubic feet (MMscf)), API gravity, the inner diameter of the production tubing, and the like. The concentration inputs may include concentrations (in Moles) of calcite barium strontium, iron, magnesium, weak organic acids, total alkalinity, sulfate, chloride, and the like [par. 0045]");
determining, with well data ("At step 210, the model calculates the scale tendency SI and the basic corrosion rate Vcor [par. 0047]" and "At step 212, a 'real' corrosion rate Vbase is calculated at points along the wellbore [par. 0049]"and "the empirical model described with respect to steps 201-212 may be replaced by a machine learning model that determines the real corrosion rate Vbase [par. 0050]");
adjusting, with a controller, the set of operation parameters based on, at least, the aggregate corrosion prediction ("determine the optimal solution for setting well parameters, such as production flow rate, GOR and alkalinity in the production fluids [par. 0054]" and "This approach allows computing equipment 20 to adjust (or recommend adjusting) production flow rates, alkalinity of the produced fluid, and other scaling and/or corrosion related parameters in a way that minimizes scaling and corrosion in conjunction with one another [par. 0014]").
Although Pang teaches determining, with a machine learning mode, a corrosion metric (as presented above), Bansal does not explicitly teach determining, with a set of machine learning models, a set of corrosion metrics, and forming an aggregate corrosion prediction from the set of corrosion metrics.
In an analogous art (well systems), Cernatescu teaches determining, with a set of machine learning models, a set of metrics, and forming an aggregate prediction from the set of metrics ("the ensemble model combines the outputs of a plurality of predictive models (e.g., decision trees or other types of predictive models such as neural networks, gradient boosting machines, support vector machines, and the like) to mitigate the trade-off in bias and variance by aggregating the predictions of multiple predictive models thereby reducing both bias and variance in the resulting ensemble model. Additionally, ensemble models minimize the impact of individually weak predictive models by combining their output with other predictive models such that the resulting ensemble model is substantially more robust than the predictive models from which it is comprised [par. 0026]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have modified Pang's teachings of using a model to predict corrosion, with Cernatescu's teachings of aggregating results into a single prediction, to provide a more robust model for predicting corrosion while reducing bias and variance in individual models.
With regard to claim 3, the combination above teaches the method of claim 1. Bansal in the combination teaches the method further comprising:
determining a remedial action for the well in response to the aggregate corrosion prediction exceeding a threshold ("determine the optimal solution for setting well parameters, such as production flow rate, GOR and alkalinity in the production fluids [par. 0054]" and Cernatescu: "the
voting classifier ensemble model may take a 'vote' of the predictive models 145 contained therein, such that the voting classifier ensemble model may offer a particular prediction (e.g., in the form of prediction output 180) should that prediction be shared by a predefined voting threshold (e.g., a predefined percentage) of the predictive models 145 contained therein. [par. 0058]"); and
executing the remedial action on the well ("determine the optimal solution for setting well parameters, such as production flow rate, GOR and alkalinity in the production fluids [par. 0054]" and "This approach allows computing equipment 20 to adjust (or recommend adjusting) production flow rates, alkalinity of the produced fluid, and other scaling and/or corrosion related parameters in a way that minimizes scaling and corrosion in conjunction with one another [par. 0014]").
With regard to claim 6, the combination above teaches the method of claim 1. Pang in the combination teaches the method further comprising:
determining, with an optimizer, a set of optimal operation parameters based on the aggregate corrosion prediction ("determine the optimal solution for setting well parameters, such as production flow rate, GOR and alkalinity in the production fluids [par. 0054]" and "This approach allows computing equipment 20 to adjust (or recommend adjusting) production flow rates, alkalinity of the produced fluid, and other scaling and/or corrosion related parameters in a way that minimizes scaling and corrosion in conjunction with one another [par. 0014]"),
wherein the set of optimal operation parameters maximize a production of hydrocarbons from the well ("empirical models alone do not allow for closed-loop control of a system for optimizing the flow of hydrocarbon fluids through a wellbore or pipeline under conditions that minimize the formation of scale or corrosion [par. 0003]" and "This approach allows computing equipment 20 to adjust (or recommend adjusting) production flow rates, alkalinity of the produced fluid, and other scaling and/or corrosion related parameters in a way that minimizes scaling and corrosion in conjunction with one another [par. 0014]").
With regard to claim 8, the combination above teaches the method of claim 1. Pang in the combination teaches the method further comprising:
detecting, based on the aggregate corrosion prediction, an area of corrosion in the well ("At step 212, a 'real' corrosion rate Vbase is calculated at points along the wellbore [par. 0049]").
With regard to claims 9, 11, 14, and 16, the combination above teaches claims 1, 3, 6, and 8. Claims 9, 11, 14, and 16 recite limitations having the same scope as those pertaining to claims 1, 3, 6, and 8, respectively; therefore, claims 9, 11, 14, and 16 are rejected along the same grounds as claims 1, 3, 6, and 8.
Claim 9 differs from claim 1 where claim 9 recites the additional limitations (which Pang in the combination teaches): a network model comprising a simulator and an optimizer ("Multi-objective optimization will yield a range of values for Is and Vbase and for corresponding well parameters such as BWPD, BOPD, alkalinity and GOR. Thus, at step 217, a specific solution may be chosen from these optimized solutions and actions may be taken to modify production conditions [par. 0056]"); and a controller that can configure one or more configurable parameters of the well ("This approach allows computing equipment 20 to adjust (or recommend adjusting) production flow rates, alkalinity of the produced fluid, and other scaling and/or corrosion related parameters in a way that minimizes scaling and corrosion in conjunction with one another [par. 0014]").
With regard to claim 17, the combination above teaches the system of claim 9. Pang in the combination further teaches wherein the controller is further configured to:
determine, using the simulator and based on the aggregate corrosion prediction, a region of corrosion in a pipe comprised by a well network that comprises the well ("At step 212, a 'real' corrosion rate Vbase is calculated at points along the wellbore [par. 0049]").
With regard to claims 18-20, the combination above teaches claims 1, 6, and 8. Claims 18-20 recite limitations having the same scope as those pertaining to claims 1, 6, and 8, respectively; therefore, claims 18-20 are rejected along the same grounds as claims 1, 6, and 8.
Claim 18 differs from claims 1 where claim 18 recites the additional limitations (which Pang in the combination teaches): a non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform steps ("computing equipment 20 may include known components such as computer processors 22, memory 24, one or more software 26 stored thereon, and input/output (I/O) interfaces 28. Software 26 may include production control software as well as a multi-objective corrosion and scaling optimization application 30 as described herein [par. 0015]").
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Pang in view of Cernatescu further in view of Hernandez de la Bastida et al. [US Pub. 2023/0399938] ("Hernandez").
With regard to claim 2, the combination of Pang and Cernatescu teaches the method of claim 1. Pang in the combination further teaches wherein the set of operation parameters comprises a ("determine the optimal solution for setting well parameters, such as production flow rate, GOR and alkalinity in the production fluids [par. 0054]" and "This approach allows computing equipment 20 to adjust (or recommend adjusting) production flow rates, alkalinity of the produced fluid, and other scaling and/or corrosion related parameters in a way that minimizes scaling and corrosion in conjunction with one another [par. 0014]").
The combination does not explicitly teach a choke setting for a choke of the well.
In an analogous art (well systems), Hernandez teaches a choke setting for a well ("the processor(s) 46 may be connected to a network interface 50 of the scale prediction and control system 42 to allow the scale prediction and control system 42 to communicate with the various surface sensors 28 and/or downhole sensors 32 described herein, as well as communicate with the actuators 52 and/or PLCs 54 of surface equipment 56 (e.g., the chemical injection system 12, the choke 24, the wellhead 38, and so forth) and/or of downhole equipment 58 (e.g., the electric submersible pump 14, other downhole tools, and so forth) for the purpose of controlling operation of the production system 10 [par. 0063]").
Hernandez further teaches, "With the foregoing in mind, chemical intervention is a common way of proactively preventing the deposition of scale … the chemicals 16 may be injected into the well 18 from the chemical injection system 12 and produced fluid 20 (e.g., produced oil, gas, and water) returning from the well 18 may flow back through a surface flowline 22, as controlled by a choke 24 [par. 0027],"
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Hernandez' teachings of preventing deposition of scale via a choke, with the teachings of Pang, for the benefit of adjusting flow rate dependent on scale and corrosion.
With regard to claim 10, the combination above teaches claim 2. Claim 10 recites limitations having the same scope as those pertaining to claim 2; therefore, claim 10 is rejected along the same grounds as claim 2.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Pang in view of Cernatescu further in view of Saumya et al. [US Pub. 2025/0116185] ("Saumya").
With regard to claim 4, the combination of Pang and Cernatescu teaches the method of claim 1. Pang in the combination further teaches wherein the well data comprises:
a downhole temperature of the well ("the model input parameters include temperature and pressure [par. 0045]").
The combination does not explicitly teach a concentration of hydrogen sulfide in a fluid produced by the well; and an age of the well.
In an analogous art (well systems), Saumya teaches a concentration of hydrogen sulfide in a fluid produced by the well ("well data 113, such as temperature, lithology, salinity, and the like, one or more flow rate and/or concentration reports 114, such as production rates, injection rates, CO2 concentration, H2S concentration, and the like [par. 0023]"); and
an age of the well ("the existing data 110 can include two or more of: an age of the well [par. 0023]").
Saumya further teaches, "The factors that drive corrosion can be vital inputs to the machine learning model along with time lapse corrosion metal loss data acquired through mechanical, ultrasonic, electromagnetic logging tools, or the like, and/or any combination thereof [par. 0017]."
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Saumya's teachings, with the teachings of Pang, for the benefit of providing more data to the learning model to produce a better informed decision.
With regard to claim 12, the combination above teaches claim 4. Claim 12 recites limitations having the same scope as those pertaining to claim 4; therefore, claim 12 is rejected along the same grounds as claim 4.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Pang in view of Cernatescu further in view of Gong et al. [US Pub. 2022/0316313] ("Gong").
With regard to claim 5, the combination of Pang and Cernatescu teaches the method of claim 1. Cernatescu in the combination further teaches wherein:
the set of machine learning models further comprises a second machine learning model and a third machine learning model ("predictive models 301A-301C [par. 0073]" and "the sensor dataset N is fed to a predictive model N of a plurality of predictive models … The plurality of predictive models may be same class or different [par. 0081]" and "each of the plurality of predictive models comprises similar or different models [par. 0004]"), the first machine learned model is a neural network ("neural network [par. 0026]"), ("support vector machines [par. 0026]").
Although Cernatescu teaches decision trees [par. 0022], the combination does not explicitly teach where the second machine learning model is a random forest.
In the same field of endeavor (predicting corrosion), Gong teaches a random forest ("The machine learning-based model 108 may include algorithms that include polynomials, generalized linear model, elastic net, least absolute shrinkage and selection operator (Lasso), Ridge, boosting, extreme gradient boosting, support vector machine, neural networks, decision trees/random forest methods, kernal methods, Multivariate adaptive regression (MARS) methods, polyMARS methods, reinforcement learning methods, Gaussian process models, and the like, and any ensemble thereof [par. 0035]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have substituted a random forest method as taught by Gong, for any of the models as taught by Cernatescu, since the combination would predictably use the models according to their established function to yield the predictable result of being able to output a result based on input data.
With regard to claim 13, the combination above teaches claim 5. Claim 13 recites limitations having the same scope as those pertaining to claim 5; therefore, claim 13 is rejected along the same grounds as claim 5.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Pang in view of Cernatescu further in view of Butvinik [US Pub. 2025/0245576].
With regard to claim 7, the combination of Pang and Cernatescu teaches the method of claim 1. Although Cernatescu in the combination teaches developing the models based on historical data [par. 0021], the combination does not explicitly teach obtaining historical data for the well comprising well data for the well; determining a statistical descriptor from the historical data; and generating synthetic data based on the statistical descriptor.
In the same field of endeavor (learning models), Butvinik teaches obtaining historical data; determining a statistical descriptor from the historical data; and generating synthetic data based on the statistical descriptor (" Consequently, model B.2.2 314 may be trained or pre-trained using appropriate data volumes from the past, or using historical data or dataset (1.3) 306, and may accordingly perform synthetic data generation according to its trained parameters, and according to learned statistical properties or distributions of historical data [par. 0076]").
Butvinik further teaches, "Synthetic data as used herein may refer to data points that may be artificially generated (e.g., by a machine learning model such as for example a GenAI model trained using historical of past data items or datasets, such as for example described herein) and that may not correspond to real-world observations or measurements (which may be referred to as 'real data')—but that can be used to mimic the characteristics of real data, for example by having statistical properties or characteristics similar to ones of real data. Various uses of synthetic data exist in different data analysis contexts, including, for example, generating synthetic data points for creating large enough datasets in cases only few real data points exist [par. 0030]."
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Butvinik's teachings of generating synthetic data, with the teaching s of Pang and Cernatescu, for the benefit of creating a large enough dataset to train the various models.
With regard to claim 15, the combination above teaches claim 7. Claim 15 recites limitations having the same scope as those pertaining to claim 7; therefore, claim 15 is rejected along the same grounds as claim 7.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sayed et al. [US Pub. 2024/0133796] teaches using machine learning to analyze and estimate corrosion in pipes in a well bore.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT W CHANG whose telephone number is (571)270-1214. The examiner can normally be reached (M-F) 10:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VINCENT WEN-LIANG CHANG
Examiner
Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
Prosecution Insights