DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 17/982,839, filed 11/08/2022, claims priority from U.S. Provisional Application 63/276,884, filed 11/08/2021.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
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 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.
Status of the Application
This Non-Final Office Action is in response to Applicant’s communication of 10/28/2025.
Claims 1-20 are pending, of which claims 1, 14, 20 are independent.
All pending claims have been examined on the merits.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 2/28/2024 and 10/28/2025 has been considered.
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 non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
Based on the flowchart in MPEP § 2106, Step 1 of the Alice/Mayo analysis is: “Is the claim to a process, machine, manufacture or composition of matter?”
In regards to Step 1 of the Alice/Mayo analysis, independent claim 1 is a method claim, independent claim 14 is an article of manufacture claim or product by process claim (“non-transitory computer readable medium”), and independent claim 20 is an apparatus claim.
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
Step 2A, prong 1 of the Alice/Mayo analysis is: “Does the claim recite a law of nature, a natural phenomenon (product of nature), or an abstract idea?”
In regards to Step 2A, prongs 1 and 2 of the Alice/Mayo analysis, the abstract idea elements recited in independent claims 1 and 20 are shown in italic font. (The “additional elements” and “extra solution steps” are shown in italic and underlined font):
In regards to claim 1,
1. A method for modeling geological facies of a subsurface reservoir, the method comprising:
generating a predictive analytical model of the subsurface reservoir by:
creating one or more decision tree-based models trained with an input data set including well log data associated with the subsurface reservoir; and
assigning geological facies class as a target variable;
receiving target well data corresponding to a target well associated with the subsurface reservoir; and
generating, using the target well data and the predictive analytical model, a geological facies model for the target well.
In regards to claim 20,
20. A system for modeling geological facies of a subsurface reservoir, the system comprising:
a wellbore modeling platform configured to generate a predictive analytical model of the subsurface reservoir, the predictive model generated by
creating one or more decision tree-based models trained with an input data set associated with the subsurface reservoir and
assigning geological facies class as a target variable, the wellbore modeling platform receiving target well data corresponding to a target well and
generating a geological facies model for the target well using the target well data and the predictive analytical model.
Claims 1-20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include:
“generating a predictive analytical model of the subsurface reservoir”.
“creating one or more decision tree-based models trained with an input data set including well log data associated with the subsurface reservoir”.
“assigning geological facies class as a target variable”.
“generating, using the target well data and the predictive analytical model, a geological facies model for the target well”.
The “additional elements” include: “a wellbore modeling platform configured to generate a predictive analytical model of the subsurface reservoir”.
Moreover, “additional extra-solution elements” include: “receiving target well data corresponding to a target well associated with the subsurface reservoir”. Also, in independent claim 14: “One or more tangible non-transitory computer-readable storage media storing computer-executable instructions”.
Step 2A, prong 2 of the Alice/Mayo analysis is “Does the claim recite additional elements that integrate elements that integrate the judicial exception into a practical application?”
In regards to Step 2A, prong 2 of the Alice/Mayo analysis, this abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“a wellbore modeling platform”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“receiving target well data corresponding to a target well associated with the subsurface reservoir”, “storing computer-executable instructions”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo analysis is: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
In regards to Step 2B of the Alice/Mayo analysis, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“a wellbore modeling platform”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities (“receiving target well data corresponding to a target well associated with the subsurface reservoir”, “storing computer-executable instructions”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “storing” step, see the court cases Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (storing and retrieving information in memory).
More specifically, in regards to the “receiving” and “communicating” steps, see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Moreover, in regards to “apply it”, according to MPEP § 2106.05(f)(2):
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claim 14 is rejected on the same grounds as independent claim 1. Independent claim 14 is also rejected on the grounds that it recites a computer-readable medium, which is merely another generic computer component.
All dependent claims are also rejected, because they merely further define the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 and 9-20 are rejected under 35 U.S.C. §§102(a)(1) and (a)(2) as being anticipated by WO-2013/184208-A1 to Thorne. (“Thorne”. Eff. Filed on March 14, 2013. Published on Dec. 12, 2013).
In regards to claim 1,
1. A method for modeling geological facies of a subsurface reservoir, the method comprising:
generating a predictive analytical model of the subsurface reservoir by:
(See Thorne, para. [0001]: “The present invention relates generally to facies classification and more particularly to facies classification based on pattern recognition.”)
(See Thorne, para. [0004]: “An aspect of an embodiment of the present invention includes a method of automatically interpreting well log data indicative of physical attributes of a portion of a subterranean formation including obtaining training data comprising well logs including facies classification information for the well logs, dividing the training data into two subsets, a calibration set and a cross-validation set, using an automated supervised learning facies identification method to determine a preliminary identification of facies in the subterranean formation based on the calibration set, calculating a confusion matrix for the supervised learning facies identification method by comparing predicted and observed facies for the cross-validation set, calculating a facies transition matrix characterizing changes between contiguous facies, and using the preliminary identification, the facies transition matrix, and the confusion matrix, iteratively calculating updated facies identifications.”)
creating one or more decision tree-based models trained with an input data set including well log data associated with the subsurface reservoir; and
(See Thorne, para. [0003]: “Systems have been proposed for automated interpretation of log data including supervised machine learning processes based on pre-classified training sets. These systems generally employ back-propagation neural nets and decision tree methods. An example of a rule-based machine learning classification approach is described in U.S. Pat. No. 7,620,498 to Kowalik.”)
(See Thorne, para. [00013]: “The method proceeds with implementation of any conventional computer implemented supervised pattern recognition or machine learning method for identifying facies and trained using the training set. As will be appreciated, there are a variety of such methods including back-propagation, neural net, decision tree, and any number of additional supervised learning algorithms that can be applied to well log data.”)
assigning geological facies class as a target variable;
(See Thorne, para. [00011]: “In accordance with an embodiment of the present invention, a method for classifying facies in a borehole log makes use of information relating to patterns in successive facies samples. In this approach, a facies transition matrix (a quantitative representation of facies pattern) is used to modify predicted facies classifications in any supervised learning facies classification method. A confusion matrix is defined to characterize uncertainty in predicted classifications, and is used to predict a probability for each facies at each sample depth.”)
receiving target well data corresponding to a target well associated with the subsurface reservoir; and
(See Thorne, para. [00012]: “A set of well log data is obtained, and classifications are assigned on a plurality of well log samples, for example by use of core descriptions. As will be appreciated, the classifications may have been pre-assigned or may be assigned by expert analysis as part of the implementation of the present method. These assignments are considered to be known facies. A portion of the well log data with known facies is selected and removed and set aside prior to further processing. That is, the data with known facies is divided into training and testing sub-sets, where the testing sub-set may be referred to as "left-out" or "cross-validation" data. The left-out data may be selected randomly and a percentage of the data to be left out may be set as a parameter by a user or may be a constant percentage. When there are many data samples, the percentage of data to be left out can approach 50%.”)
generating, using the target well data and the predictive analytical model, a geological facies model for the target well.
(See Thorne, para. [00014]: “Once the machine learning method has been trained, it used to predict facies on all the samples which includes the left out data, and a confusion matrix Cy is generated by comparing the output of the trained machine learning algorithm against the previously assigned classifications for those portions of the data.”)
(See Thorne, para. [00015]: “A facies transition matrix is generated, which characterizes the changes between previously assigned facies in the well log data. A preliminary predicted facies transition matrix is generated, which characterizes the changes between facies in the preliminary predicted classification.”)
In regards to claim 2,
2. The method of claim 1, wherein the well log data of the input data set includes core data associated with a plurality of wells at the subsurface reservoir.
(See Thorne, para. [00013]: “The method proceeds with implementation of any conventional computer implemented supervised pattern recognition or machine learning method for identifying facies and trained using the training set. As will be appreciated, there are a variety of such methods including back-propagation, neural net, decision tree, and any number of additional supervised learning algorithms that can be applied to well log data.”)
In regards to claim 3,
3. The method of claim 1, further comprising labeling, using a subject matter expert (SME), the input data set with a plurality of geological facie class labels.
(See Thorne, para. [00012]: “A set of well log data is obtained, and classifications are assigned on a plurality of well log samples, for example by use of core descriptions. As will be appreciated, the classifications may have been pre-assigned or may be assigned by expert analysis as part of the implementation of the present method. These assignments are considered to be known facies. A portion of the well log data with known facies is selected and removed and set aside prior to further processing. That is, the data with known facies is divided into training and testing sub-sets, where the testing sub-set may be referred to as "left-out" or "cross-validation" data. The left-out data may be selected randomly and a percentage of the data to be left out may be set as a parameter by a user or may be a constant percentage. When there are many data samples, the percentage of data to be left out can approach 50%.”)
In regards to claim 4,
4. The method of claim 1, wherein the target well data lacks a core data set associated with the target well.
(See Thorne, para. [00024]: “Figure 3 illustrates results of the application of a method in accordance with an embodiment of the present invention. In the figure, the frequency (probability distribution function) of the original supervised facies prediction on 495 wells is shown to be very different then the frequencies of the five facies as interpreted in ten cores. The predicted facies are modified using the observed frequencies (as well as transition frequencies between different facies not shown here). By application of Equations (1) and (2), the observed frequencies act as a soft constraint (i.e., they influence, without forcing a specific outcome) such that the final modified frequencies are a compromise between the original predictions and the observed data. In the illustrated example, the core data is only available in ten wells. As will be appreciated, the number of wells available for use in compiling observed frequency data may influence the selection of an appropriate a for use in Equation (2), above, which has limited the influence of the observed facies frequencies on the modified resulting frequencies.”)
The Examiner interprets that Thorne’s teaching in para. [00024] implies the claimed feature (“wherein the target well data lacks a core data set associated with the target well”).
In regards to claim 5,
5. The method of claim 1, wherein generating the predictive analytical model includes artificially balancing a plurality of geological facies class labels associated with the input data set to create a balanced input data set.
(See Thorne, para. [00012]: “A set of well log data is obtained, and classifications are assigned on a plurality of well log samples, for example by use of core descriptions. As will be appreciated, the classifications may have been pre-assigned or may be assigned by expert analysis as part of the implementation of the present method. These assignments are considered to be known facies. A portion of the well log data with known facies is selected and removed and set aside prior to further processing. That is, the data with known facies is divided into training and testing sub-sets, where the testing sub-set may be referred to as "left-out" or "cross-validation" data. The left-out data may be selected randomly and a percentage of the data to be left out may be set as a parameter by a user or may be a constant percentage. When there are many data samples, the percentage of data to be left out can approach 50%.”)
In regards to claim 6,
6. The method of claim 5, wherein the plurality of geological facies class labels includes at least two geological facies class labels.
(See Thorne, para. [00011]: “In accordance with an embodiment of the present invention, a method for classifying facies in a borehole log makes use of information relating to patterns in successive facies samples. In this approach, a facies transition matrix (a quantitative representation of facies pattern) is used to modify predicted facies classifications in any supervised learning facies classification method. A confusion matrix is defined to characterize uncertainty in predicted classifications, and is used to predict a probability for each facies at each sample depth.”)
The Examiner interprets that “classifications” inherently requires at least two class labels.
In regards to claim 7,
7. The method of claim 1, wherein generating the predictive analytical model further includes providing vertical context data to the one or more decision tree-based models.
(See Thorne, para. [00011]: “In accordance with an embodiment of the present invention, a method for classifying facies in a borehole log makes use of information relating to patterns in successive facies samples. In this approach, a facies transition matrix (a quantitative representation of facies pattern) is used to modify predicted facies classifications in any supervised learning facies classification method. A confusion matrix is defined to characterize uncertainty in predicted classifications, and is used to predict a probability for each facies at each sample depth.”)
The Examiner interprets that Thorne’s “depth intervals” read upon the claimed “vertical context data”.
(See also Thorne, para. [00017]: “Once the observed and preliminary predicted transition matrices are calculated, a target probability matrix may be developed. In this regard, it is possible to calculate target probabilities based on a prediction, or it is possible to set the transition probability matrix strictly based on the observed transitions. Stated more generally, a predicted matrix Py and an observed matrix Oy describing a particular depth interval may be combined to give the target transition matrix TV …”)
In regards to claim 9,
9. The method of claim 1, wherein the well log data of the input data set represents at least five wells at the subsurface reservoir.
(See Thorne, para. [00024]: “Figure 3 illustrates results of the application of a method in accordance with an embodiment of the present invention. In the figure, the frequency (probability distribution function) of the original supervised facies prediction on 495 wells is shown to be very different then the frequencies of the five facies as interpreted in ten cores. The predicted facies are modified using the observed frequencies (as well as transition frequencies between different facies not shown here). By application of Equations (1) and (2), the observed frequencies act as a soft constraint (i.e., they influence, without forcing a specific outcome) such that the final modified frequencies are a compromise between the original predictions and the observed data. In the illustrated example, the core data is only available in ten wells. As will be appreciated, the number of wells available for use in compiling observed frequency data may influence the selection of an appropriate a for use in Equation (2), above, which has limited the influence of the observed facies frequencies on the modified resulting frequencies.”)
In regards to claim 10,
10. The method of claim 1, wherein the input data set includes at least one of resistivity data, gamma ray data, neutron porosity data, bulk density data, sonic log data, dielectric log data, or nuclear magnetic resonance (NMR) logs.
(See Thorne, para. [0002]: “Borehole log data is collected via a number of techniques including resistivity/conductivity measurements, ultrasound, NMR, and radiation scattering, for example. Conventionally, borehole data is analyzed by human interpreters in order to characterize a subsurface geological formation to allow decisions to be made regarding the potential of a well or to determine information about the nature of the surrounding geologic area.”)
In regards to claim 11,
11. The method of claim 1, wherein generating the geological facies model for the target well includes numerically mapping the target well data to specific geographic facies represented by the input data set.
(See Thorne, para. [00023]: “As is well known in statistics the uncertainty of estimating the mean value from a set of realizations decreases with the square root of the number of realizations. The resulting probability logs may be used to generate reservoir and/or facies probability volumes and maps for a reservoir earth model, which may in turn be used as a basis for exploration and/or production decisions for the formation.”)
In regards to claim 12,
12. The method of claim 1, further comprising selecting, based at least partly on the geological facies model, a section of the subsurface reservoir for resource characterization.
(See Thorne, para. [00023]: “As is well known in statistics the uncertainty of estimating the mean value from a set of realizations decreases with the square root of the number of realizations. The resulting probability logs may be used to generate reservoir and/or facies probability volumes and maps for a reservoir earth model, which may in turn be used as a basis for exploration and/or production decisions for the formation.”)
In regards to claim 13,
13. The method of claim 1, wherein the target well is a candidate well for drilling, the method further comprises:
determining, based at least partly on the geological facies model, an optimal drilling location for the candidate well; and
drilling the candidate well at the optimal drilling location.
(See Thorne, para. [00025]: “As will be appreciated, the method as described herein may be performed using a computing system having machine executable instructions stored on a tangible medium and a processor configured and arranged to execute the machine executable instructions. The instructions are executable to perform each portion of the method, either autonomously, or with the assistance of input from an operator. In an embodiment, the system includes structures configured and arranged to allow input and output of data, and a display that is configured and arranged to display the intermediate and/or final products of the process steps. A method in accordance with an embodiment may include an automated selection of a location for exploitation and/or exploratory drilling for hydrocarbon resources. Where the term processor is used, it should be understood to be applicable to multi-processor systems and/or distributed computing systems.”)
The Examiner interprets that Thorne’s “an automated selection of a location for exploitation and/or exploratory drilling for hydrocarbon resources” inherently is the optimal drilling location.
In regards to claim 14, it is rejected on the same grounds as claim 1.
In regards to claim 15, it is rejected on the same grounds as claim 4.
In regards to claim 16, it is rejected on the same grounds as claim 5.
In regards to claim 17, it is rejected on the same grounds as claim 7.
In regards to claim 18, it is rejected on the same grounds as claim 11.
In regards to claim 19, it is rejected on the same grounds as claim 13.
In regards to claim 20, it is rejected on the same grounds as claim 1.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over WO-2013/184208-A1 to Thorne. (“Thorne”. Eff. Filed on March 14, 2013. Published on Dec. 12, 2013) as applied to claim 1 above, and further in view of US 2024/0192646 A1 to Doraiswamy et al. (“Doraiswamy”. Eff. Filed on June 8, 2021).
In regards to claim 8, under a conservative interpretation of Thorne, it could be argued that Thorne does not explicitly teach the italicized portions below, which are taught by Doraiswamy:
8. The method of claim 1, wherein the one or more decision tree-based models include a gradient boosted decision tree.
(See Doraiswamy, para. [0068]: “FIG. 5 is a block diagram 500 for generating and using the subsurface models. Inputs X.sub.G, X.sub.C and X.sub.P (510) are input to physics simulator 520 in order to generate Outputs (530), which may be in the form of a time series. As discussed above, various outputs are contemplated. As one example, the outputs may comprise production data and include Y.sub.oil, Y.sub.gas, Y.sub.water. As another example, the outputs may include diagnostic data and comprise pressure information, fracture hits, etc. In this way, physics simulator 520 may generate a first set of subsurface forward models, pairing respective Inputs X.sub.G, X.sub.C and X.sub.P (510) with respective Outputs (530). The first set of subsurface forward models (acting as a training set of subsurface forward models) may then be used along with physics-based rules by Machine Learning Tools 540 for training in order to generate Forward Proxy Model 560. Various machine learning tools are contemplated, including histogram gradient, adaptive boosting on decision trees, random forest, or deep learning models (e.g., deep neural networks, deep belief networks, graph neural networks, recurrent neural networks, and convolutional neural networks).”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the “System and method for facies classification”, as taught by Thorne, with “Methods for accelerated development planning optimization using machine learning for unconventional oil and gas resources”, as further taught by Doraiswamy, because according to Doraiswamy, “adaptive boosting on decision trees” is one of “[v]arious machine learning tools [that] are contemplated”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The Effective filing date of these reference are too recent to qualify as prior art.
US 2025/0308645 A1 to Aselmann-Lemon et al. (“Aselmann-Lemon”. Filed on May 8, 2023. Published on Oct. 2, 2025). See para. [0079] for “model building method”, “well log data”, and “facies variations”. See para. [0165] for “gradient boosted regression tree”.
US 2025/0102700 A1 to Rocha (“Rocha”. Eff. Filed on Sept. 21, 2023. Published on Mar. 27, 2025).
US 11,636,240 B2 to Skripkin (“Skripkin”. Eff. Filed on Apr. 14, 2022. Published on Apr. 25. 2023). See col. 25, lines 4-15 for “gradient boosted regression tree”.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax 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. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
December 27, 2025