CTNF 18/093,033 CTNF 101458 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Drawings 06-22 AIA The drawings are objected to because figures 1 - 11 do not textually label the figures. The figures should be corrected to include both textual and numerical labeling for clarity and better understanding of the invention . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Claim Rejections - 35 USC § 112 07-34-01 Claim 5, 12, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. These claims are indefinite under 35 U.S.C 112(b) because it requires prediction “based on one or more statistical relationships.” while the parent claims require prediction by “the trained machine learning model”, two distinct approaches. The examiner interprets this to mean the machine learning model’s learned relationships. Correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 a judicial exception without significantly more. Claim 1. STEP 1: Yes. The claim recites a “method” which is a process. STEP 2A PRONG ONE: The claim recites multiple mathematical concepts. predicting … water washing parameters of an unseen well in real time, wherein inputs to the trained machine learning model are gas composition data associated with the unseen well and the trained machine learning model outputs the water washing parameters; This is a mathematical function used with entered data to find the probability of the desired parameters. creating … a water washing intensity index based on the predicted water washing parameters This is a mathematical calculation to define severity for the water washing parameters. STEP 2A PRONG TWO: The claim does not integrate the exception into a practical application. STEP 2B: The claim does not recite an inventive concept or significantly more than the judicial exception. 2106.05(a) No improvement to computer functionally or other technology – The claim does not purport to improve the function of a computer or to improve some other than exception. building … a training dataset comprising gas composition data and corresponding water washing parameters This limitation is pre solution data gathering activity. MPEP 2106.05(g). training … a machine learning model using the training dataset to output water washing parameters This limitation amounts to mere instruction to “apply it with machine learning.”. MPEP 2106.05(f). Integrating … the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations. This limitation merely links the field of use for the index. MPEP 2106.05(h). Conclusion: Claim 1 is directed to a mathematical concept, not integrated into a practical application and lacks an inventive concept. Therefore, it is ineligible under 35 U.S.C 101. Regarding Claims 2, 3, 4, and 7: The added limitations merely characterize the source / timing / type of data and does not integrate the judicial exception into a practical application. MPEP 2106.05(g) and MPEP 2106.05(h). These claims also do not resolve the issues from the claim they depend upon. Regarding Claims 5 and 6: The added limitations merely narrow the mathematical concept itself by specifying the parameters predicted [Claim 5] and by adding another mathematical calculation [Claim 6]. This does not integrate the judicial exception into a practical application. These claims also do not resolve the issues from the claim they depend upon. Claim 8. STEP 1: Yes. The claim recites an “apparatus” which is a machine. STEP 2A PRONG ONE: The claim recites multiple mathematical concepts. predicting water washing parameters of an unseen well in real time, wherein inputs to the trained machine learning model are gas composition data associated with the unseen well and the trained machine learning model outputs the water washing parameters; This is a mathematical function used with entered data to find the probability of the desired parameters. creating a water washing intensity index based on the predicted water washing parameters This is a mathematical calculation to define severity for the water washing parameters. STEP 2A PRONG TWO: The claim does not integrate the exception into a practical application. STEP 2B: The claim does not recite an inventive concept or significantly more than the judicial exception. 2106.05(a) No improvement to computer functionally or other technology – The claim does not purport to improve the function of a computer or to improve some other than exception. 2106.05(b) Particular Machine – “non-transitory, computer readable, storage medium” and “processor” are not a particular machine, they are generic computer components. building a training dataset comprising gas composition data and corresponding water washing parameters This limitation is pre solution data gathering activity. MPEP 2106.05(g). training a machine learning model using the training dataset to output water washing parameters This limitation amounts to mere instruction to “apply it with machine learning.”. MPEP 2106.05(f). Integrating the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations. This limitation merely links the field of use for the index. MPEP 2106.05(h). Conclusion: Claim 8 is directed to a mathematical concept, not integrated into a practical application and lacks an inventive concept. Therefore, it is ineligible under 35 U.S.C 101. Regarding Claims 9, 10, 11, and 14: The added limitations merely characterize the source / timing / type of data and does not integrate the judicial exception into a practical application. MPEP 2106.05(g) and MPEP 2106.05(h). These claims also do not resolve the issues from the claim they depend upon. Regarding Claims 12 and 13: The added limitations merely narrow the mathematical concept itself by specifying the parameters predicted [Claim 12] and by adding another mathematical calculation [Claim 13]. This does not integrate the judicial exception into a practical application. These claims also do not resolve the issues from the claim they depend upon. Claim 15. STEP 1: Yes. The claim recites an “system” which is a machine. STEP 2A PRONG ONE: The claim recites multiple mathematical concepts. predicting water washing parameters of an unseen well in real time, wherein inputs to the trained machine learning model are gas composition data associated with the unseen well and the trained machine learning model outputs the water washing parameters; This is a mathematical function used with entered data to find the probability of the desired parameters. creating a water washing intensity index based on the predicted water washing parameters This is a mathematical calculation to define severity for the water washing parameters. STEP 2A PRONG TWO: The claim does not integrate the exception into a practical application. STEP 2B: The claim does not recite an inventive concept or significantly more than the judicial exception. 2106.05(a) No improvement to computer functionally or other technology – The claim does not purport to improve the function of a computer or to improve some other than exception. 2106.05(b) Particular Machine – “one or more memory modules; one or more hardware processors” are not a particular machine, they are generic computer components. building a training dataset comprising gas composition data and corresponding water washing parameters This limitation is pre solution data gathering activity. MPEP 2106.05(g). training a machine learning model using the training dataset to output water washing parameters This limitation amounts to mere instruction to “apply it with machine learning.”. MPEP 2106.05(f). Integrating the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations. This limitation merely links the field of use for the index. MPEP 2106.05(h). Conclusion: Claim 15 is directed to a mathematical concept, not integrated into a practical application and lacks an inventive concept. Therefore, it is ineligible under 35 U.S.C 101. Regarding Claims 16, 17, and 18: The added limitations merely characterize the source / timing / type of data and does not integrate the judicial exception into a practical application. MPEP 2106.05(g) and MPEP 2106.05(h). These claims also do not resolve the issues from the claim they depend upon. Regarding Claims 19 and 20: The added limitations merely narrow the mathematical concept itself by specifying the parameters predicted [Claim 19] and by adding another mathematical calculation [Claim 20]. This does not integrate the judicial exception into a practical application. These claims also do not resolve the issues from the claim they depend upon. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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 non-obviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1, 2, 4, 8, 9, 11, 15, 16, and 18 are rejected under 35 U.S.C 103 as being unpatentable over US 20220163503 A1 by YANG et al [herein “YANG”] (2022) and US 20100132450 A1 by POMERANTZ et al [herein “POMERANTZ”] (2010) . Regarding Claim 1, YANG teaches A computer-implemented method for predicting water washing parameters, the method comprising: building, using at least one hardware processor, a training dataset comprising gas composition data and corresponding water washing parameters; “…generating an input data set comprising input data and target data, the input data comprising (measured or estimated) mud-gas data, or a derivative thereof, for the second plurality of sample locations, and the target data comprising the at least one property of the hydrocarbon reservoir for each of the second plurality of sample locations;”. (0017). “The model has been found to predict a gas-oil ratio of the reservoir fluid based on C.sub.1 to C.sub.5 mud-gas data with MAPE of about 36% for the screened test data.”. (0163). “The methods in accordance with the present invention may be implemented at least partially using software, e.g. computer programs. It will thus be seen that when viewed from further aspects the present invention provides computer software specifically adapted to carry out the methods described herein when installed on a data processor, a computer program element comprising computer software code portions for performing the methods described herein when the program element is run on a data processor, and a computer program comprising code adapted to perform all the steps of a method or of the methods described herein when the program is run on a data processing system.”. (0055). This shows building a training data set of gas data and water washing parameters like gas oil ratio (GOR). training, using the at least one hardware processor, a machine learning model using the training dataset to output water washing parameters; “The method is preferably a computer-implemented method, and generating the model may comprise instructing a machine learning algorithm to generate the model using the screened data such that the model can be used to predict the at least one property of the fluid at the sample location based on measured mud-gas data for the sample location.”. (0019). “The inventors identified that a Gaussian Process algorithm was the most accurate model, followed by Universal Kriging, Random Forest, KMean and Elastic Net. However, it will be appreciated that any suitable algorithm may be used. Those operating within this field will be familiar with the procedures for selecting and utilising a machine learning algorithm. Therefore, this will not be discussed in detail.”. (0156). “The methods in accordance with the present invention may be implemented at least partially using software, e.g. computer programs. It will thus be seen that when viewed from further aspects the present invention provides computer software specifically adapted to carry out the methods described herein when installed on a data processor, a computer program element comprising computer software code portions for performing the methods described herein when the program element is run on a data processor, and a computer program comprising code adapted to perform all the steps of a method or of the methods described herein when the program is run on a data processing system.”. (0055). “The model has been found to predict a gas-oil ratio of the reservoir fluid based on C.sub.1 to C.sub.5 mud-gas data with MAPE of about 36% for the screened test data.”. (0163). This shows training a machine learning model to output a target water washing parameter like GOR as latter specified in applicants claim 5. predicting, using the at least one hardware processor, water washing parameters of an unseen well in real time, wherein inputs to the trained machine learning model are gas composition data associated with the unseen well and the trained machine learning model outputs the water washing parameters; “Viewed from a second aspect, the present invention provides a computer-based model for predicting at least one property of a fluid at a sample location within a hydrocarbon reservoir based on measured mud-gas data for that sample location, the computer-based model having been generated by the method above.”. (0037). “The model has been found to predict a gas-oil ratio of the reservoir fluid based on C.sub.1 to C.sub.5 mud-gas data with MAPE of about 36% for the screened test data. This is acceptable for predictions made during the drilling phase, as such predictions were not previously possible.”. (0163). This shows predicting water washing parameters (GOR) from a model and the models input are gas composition data. YANG does not teach but POMERANTZ teaches creating, using the at least one hardware processor, a water washing intensity index based on the predicted water washing parameters; and “(b) correlating the fluid property in the base model to heavy oil recovery performance at the particular depth to produce a theoretical recovery performance model;”. (0041). “For example, if a reservoir has been water washed, then molecules with high water solubility will be preferentially underrepresented in the zones that have experienced water washing… Furthermore, using prior knowledge from petrophysical logs about the geological strata that likely would have undergone water washing and the extent of water washing as determined by these measurements, predictions of compositional grading can be refined…”. (0035). “The first step involves obtaining or creating a base model of the fluid property at a particular depth. Fluid property gradients of interest with respect to heavy oils include, but are not limited to, parabolic shaped profiles rates of biodegradation, filling or charging rates, and diffusive mixing. It is desirable to keep the reservoir model simple enough so that the CPU time usage for each simulation run is relatively short and within the realistic run time on the rig.”. (0044). This shows running on a hardware processor the creation of a performance index (i.e. water washing index) based on the predicted water washing parameters. integrating, using the at least one hardware processor, the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations. “(d) comparing the real-time data of the fluid property at a particular depth to the theoretical recovery performance model to predict heavy oil recovery performance at a particular depth in the underground reservoir. FIG. 2 shows a flow diagram for evaluating heavy oil recovery performance using the methods described herein. In general, the method helps evaluate the impact a fluid property or gradient has on production and recovery of heavy oil and other related underground fluids.”. (0043). “After a sufficient amount of real-time data has been obtained to predict the impact of production performance based upon one or more fluid properties, the post-job stage (3 in FIG. 2) involves building a more complex geological model 30 using the real-time fluid property data obtained above coupled with the best representative fluid property data obtained from Pre-job stage 1. For example, production performance can be mapped out at different depths and locations within the reservoir in view of one or more fluids. Ultimately, the model provides a useful tool in predicting recovery performance of the heavy oil at different depths and locations throughout the reservoir where it is suspected that one or more fluid properties are not in equilibrium. A variety of different sources of data are used to produce the geological model, which includes data acquired during the exploration stage (e.g., seismic surfaces, well tops, formation evaluation logs, and pressure measurements). Other considerations include wireline petrophysics, fluid data, pressure data, production data, mud gas isotope analysis, and geochemistry.”. (0050). This shows integrating on a hardware processor the water washing intensity index (performance index) with a gross deposition environment map (e.g., seismic surfaces, well tops, formation evaluation logs, and pressure measurements) to guide water washing parameters predictions. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of POMERANTZ’s water washing index with YANG’s machine learning model for water washing parameters. The motivation for doing so would have been to create a machine learning system that creates a water washing index “…sophisticated analytical techniques can be used to identify the principal processes responsible for the observed variations in fluid properties and to assist in generating the base model to describe the spatial variation of one or more fluid properties for a downhole fluid.”. (0033). Regarding Claim 2, POMERANTZ does not explicitly teach but YANG teaches The computer implemented method of claim 1, comprising obtaining gas compositions associated with the unseen well in real time, during drilling. “The application of this technique allows a continuous log of the selected property to be generated using mud-gas data collected during the well drilling process.”. (Abstract). This shows the data coming from the well during the drilling process. Regarding Claim 4, YANG does not explicitly teach but POMERANTZ teaches The computer implemented method of claim 1, wherein the gas composition data and corresponding water washing parameters of the training dataset are obtained from offset wells. “The pre-job stage generally involves creating a base model of a fluid property suspected to be in non-equilibrium. For example, the pre job stage can include anticipating reservoir fluid property heterogeneities from sample data from comparable offset wells or by petroleum geochemical or basin knowledge of the factors controlling fluid properties, which includes petroleum geochemical interpretations.”. (0020). “The following is an exemplary pre-job stage. Real-time fluid property measurements, such as downhole fluid analysis (DFA) station data and/or lab measurements from downhole fluid samples versus depth, and/or data from offset wells or similar regional sands, are gathered and incorporated into a reservoir model…”. (0022).“(d) comparing the real-time data of the fluid property at a particular depth to the theoretical recovery performance model to predict heavy oil recovery performance at a particular depth in the underground reservoir. FIG. 2 shows a flow diagram for evaluating heavy oil recovery performance using the methods described herein. In general, the method helps evaluate the impact a fluid property or gradient has on production and recovery of heavy oil and other related underground fluids.”. (0043). This shows gathering a dataset from offset wells and water washing parameters. (oil recovery performance). Claims 8 and 15 recite substantially the same limitations as claim 1 except these claims are directed to an “apparatus” or an “system”. Therefore these, claims are rejected under the same rationale as addressed above. Claims 9 and 16 recite substantially the same limitations as claim 2 except these claims are directed to an “apparatus” or an “system”. Therefore these, claims are rejected under the same rationale as addressed above. Claims 11 and 18 recite substantially the same limitations as claim 4 except these claims are directed to an “apparatus” or an “system”. Therefore these, claims are rejected under the same rationale as addressed above . 07-21-aia AIA Claim s 3, 10, and 17 are rejected under 35 U.S.C 103 as being unpatentable over US 20220163503 A1 by YANG et al [herein “YANG”] (2022), US 20100132450 A1 by POMERANTZ et al [herein “POMERANTZ”] (2010), and US 20220391998 A1 by SONG et al [herein “SONG”] (2022) . Regarding Claim 3, YANG and POMERANTZ do not explicitly teach but SONG teaches The computer implemented method of claim 1, comprising obtaining gas compositions of the training dataset after drilling by analyzing rock samples obtained during drilling. “Any method described herein, said samples selected from one or more of core samples; cutting samples; produced oil, water or gas samples; fractions of produced oil, water or gas samples; drilling mud samples; or mud gas samples.”. (Table 0001 h). “… “Drilling cuttings” or “cutting samples” are the small irregular rock samples generated during drilling and returned with the drilling mud.”. (0030). “However, geochemical fingerprinting is by necessity an extremely data intensive process, and significant work is needed to improve data collection and analysis, as well to develop new applications of this powerful technology.”. (0007). “This application obviates the need for data simplification and provides more powerful predictive methods for applications to reservoir optimization.”. (0009). This shows obtaining a dataset from analyzed samples during drilling. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of SONG’s analyzed rock samples data with YANG-POMERANTZ’s machine learning model for water washing parameters. The motivation for doing so would have been to create a machine learning system that takes into account “…predicting production characteristics of a hydrocarbon well using time lapse geochemistry fingerprinting and using machine learning to train a reservoir model to accurately predict production characteristics.”. (Abstract). Claims 10 and 17 recite substantially the same limitations as claim 3 except these claims are directed to an “apparatus” or an “system”. Therefore these, claims are rejected under the same rationale as addressed above . 07-21-aia AIA Claim s 5, 12, and 19 are rejected under 35 U.S.C 103 as being unpatentable over US 20220163503 A1 by YANG et al [herein “YANG”] (2022), US 20100132450 A1 by POMERANTZ et al [herein “POMERANTZ”] (2010), “Reservoir Connectivity, Water Washing and Oil to Oil Correlation: An Integrated Geochemical & Petroleum Engineering Approach” by GHASSAL et al [herein “GHASSAL”] (2019), and “Evaluation of artificial neural networks for the prediction of deep reservoir temperatures using the gas-phase composition of geothermal fluids” [herein “ZARATE”] (2019) . Regarding Claim 5, YANG does not explicitly teach but POMERANTZ teaches The computer implemented method of claim 1, wherein predicting water washing parameters of an unseen well in real time comprises predicting a gas oil ratio (GOR) in real time based on one or more statistical relationships. “Methods for optimizing petroleum reservoir analysis and sampling using a real-time component wherein heterogeneities in fluid properties exist.”. (Abstract). “(c) fitting the real-time data in the base model to produce an optimized model of the fluid property.”. (0032). This shows predicting GOR in real time based on fitting which is a relationship. YANG and POMERANTZ do not explicitly teach but GHASSAL teaches predicting water washing parameters comprises a C7 transformation ratio (Tr1) “Reservoir engineering commonly utilizes a multidisciplinary approach to unlock complex reservoir properties utilizing various geochemical, petrophysical, geological as well as engineering parameters and techniques. The current paper discusses how molecular geochemistry can be a very beneficial tool to clarify the oil reservoir continuity and to predict some critical reservoir engineering parameter such as gas to oil ratio (GOR) and perhaps formation water salinity… The first possess five correlation parameters of compounds that are very resistant to reservoir alteration processes and share the same very low water solubility (Table 1). The C7OTSD star diagram consists of 8 oil transformation ratios (Tr1 to Tr8) (Table 2).”. (Pg. 2). This shows predicting the parameter transformation ratio Tr1. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of GHASSALS’s water washing parameter prediction with YANG-POMERANTZ’s machine learning model for water washing parameters. The motivation for doing so would have been to create a machine learning system “…to clarify the oil reservoir continuity and to predict some critical reservoir engineering parameter such as gas to oil ratio (GOR) and perhaps formation water salinity.”. (Pg. 2). YANG, POMERANTZ, and GHASSAL do not explicitly teach but ZARATE teaches a present-day reservoir temperature (PDRT) “Three-layer artificial neural networks were used for the multivariate analysis of the gas-phase composition of fluids, and the prediction of geothermal reservoir temperatures.”. (Abstract).This shows predicting a present-day reservoir temperature. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of GHASSAL’s water washing parameter prediction with YANG-POMERANTZ-GHASSAL’s machine learning model for water washing parameters. The motivation for doing so would have been to create a machine learning system “…for the first time, the use of ANN as a gas geothermometry tool to predict geothermal reservoir temperatures.”. (Abstract). Claims 12 and 19 recite substantially the same limitations as claim 5 except these claims are directed to an “apparatus” or an “system”. Therefore these, claims are rejected under the same rationale as addressed above . 07-21-aia AIA Claim 6, 7, 13, 14, and 20 are rejected under 35 U.S.C 103 as being unpatentable over US 20220163503 A1 by YANG et al [herein “YANG”] (2022), US 20100132450 A1 by POMERANTZ et al [herein “POMERANTZ”] (2010), and US 20210062650 A1 by MOLLA et al [herein “MOLLA”] (2021) . Regarding Claim 6, YANG and POMERANTZ do not explicitly teach but MOLLA teaches The computer implemented method of claim 1, wherein the training dataset is built by estimating water washing parameters from gas composition data. “After the validity of the classification 505 , the C6+ prediction 510 , 511 , the GOR prediction, and/or the STO density prediction is established, the collected C1-C5 data and the corresponding prediction can be added to the training data 430 to enrich subsequent iterations of the models 445 , 465 , 466 by reiterating at least a portion of the workflow 400 of FIG. 11. However, this step is optional, e.g., when a client wants to train the model on their data (e.g., for a specific field) and the workflow may not generally include this step.”. (0070). This shows making a training dataset by estimating GOR a washing parameter. In this case it reuses the estimated parameters. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of MOLLA’s water washing parameter and composition data with YANG-POMERANTZ’s machine learning model for water washing parameters. The motivation for doing so would have been to create a machine learning system for “…predicting an unknown characteristic of an investigated fluid property of the hydrocarbon resource utilizing the obtained input data and one or more predetermined models.”. (0004). Regarding Claim 7, YANG and POMERANTZ do not explicitly teach but MOLLA teaches The computer implemented method of claim 1, wherein the training dataset is based on historical gas composition data and corresponding water washing parameters. “The classification model is used for estimating/predicting an output based on one or more inputs. The classification model is trained with historical reservoir fluid data.”. (0038). “A database containing fluid properties of reservoir fluids was used to build, train, and validate the statistical models. The database contained 1000+ samples distributed into the three type of fluids (oil, gas condensate and gas).”. (0039). “After the validity of the classification 505 , the C6+ prediction 510 , 511 , the GOR prediction, and/or the STO density prediction is established, the collected C1-C5 data and the corresponding prediction can be added to the training data 430 to enrich subsequent iterations of the models 445 , 465 , 466 by reiterating at least a portion of the workflow 400 of FIG. 11. However, this step is optional, e.g., when a client wants to train the model on their data (e.g., for a specific field) and the workflow may not generally include this step.”. (0070). This shows making a training dataset by estimating GOR a washing parameter and by using historical gas data. In this case it reuses the estimated parameters. Claims 13 and 20 recite substantially the same limitations as claim 6 except these claims are directed to an “apparatus” or an “system”. Therefore these, claims are rejected under the same rationale as addressed above. Claim 14 recites substantially the same limitations as claim 7 except this claim is directed to an “apparatus” or an “system”. Therefore this, claim is rejected under the same rationale as addressed above . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. “DIGITAL FLUID SAMPLING IN DEEP WATER RESERVOIRS USING RESERVOIR FLUID GEODYNAMICS. THE BEGINNING OF THE DIGITAL FLUID SAMPLING REVOLUTION” by GELVEZ et al and “Effect of biodegradation and water washing on oil properties” by BATA et al . Any inquiry concerning this communication or earlier communications from the examiner should be directed to NARCISO EDUARDO MONTES whose telephone number is (571)272-5773. The examiner can normally be reached Mon-Fri 8-5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, REHANA PERVEEN can be reached at (571) 272-3676 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /N.E.M./Examiner, Art Unit 2189 /REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189 Application/Control Number: 18/093,033 Page 2 Art Unit: 2189 Application/Control Number: 18/093,033 Page 3 Art Unit: 2189 Application/Control Number: 18/093,033 Page 4 Art Unit: 2189 Application/Control Number: 18/093,033 Page 5 Art Unit: 2189 Application/Control Number: 18/093,033 Page 6 Art Unit: 2189 Application/Control Number: 18/093,033 Page 7 Art Unit: 2189 Application/Control Number: 18/093,033 Page 8 Art Unit: 2189 Application/Control Number: 18/093,033 Page 9 Art Unit: 2189 Application/Control Number: 18/093,033 Page 10 Art Unit: 2189 Application/Control Number: 18/093,033 Page 11 Art Unit: 2189 Application/Control Number: 18/093,033 Page 12 Art Unit: 2189 Application/Control Number: 18/093,033 Page 13 Art Unit: 2189 Application/Control Number: 18/093,033 Page 14 Art Unit: 2189 Application/Control Number: 18/093,033 Page 15 Art Unit: 2189 Application/Control Number: 18/093,033 Page 16 Art Unit: 2189 Application/Control Number: 18/093,033 Page 17 Art Unit: 2189 Application/Control Number: 18/093,033 Page 18 Art Unit: 2189 Application/Control Number: 18/093,033 Page 19 Art Unit: 2189 Application/Control Number: 18/093,033 Page 20 Art Unit: 2189 Application/Control Number: 18/093,033 Page 21 Art Unit: 2189 Application/Control Number: 18/093,033 Page 22 Art Unit: 2189
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