DETAILED ACTION
Claims 1-20 are pending for examination.
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 .
Priority
Acknowledgment is made of applicant's claim under US PRO 63/284601 filed on 11/30/2021.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1, 2, 4, 6, 7, 9, 12, 13, 15, 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fox et al, US 2013/0124176 A1 (Fox).
Regarding Claim 1, Fox discloses a method for completing a first well, comprising:
obtaining, for the first well, a first plurality of features (Fox [0066] – the 3D geometric model 215 can determine the forces involved in moving a portion of a tool in the tool string at a specified location in the well, accounting for the interaction of features on the end and lateral facing surfaces of the tool string and well);
determining, by applying a trained classification machine learning model to the first plurality of features, a completion requirement for completing the first well (Fox [0072] – The illustrated tool passage modeling system 200 also includes the adaptive machine learning model 210, which receives inputs 250 and provides outputs 270 based on the inputs 250 and data retrieved from a history store 235 (e.g., database or repository; [0073] – The adaptive machine learning model 210 may retrieve inputs 250, data from the tools/well/fluids specifications store 230, and data from a history store 235 to perform clustering and classification in characterizing borehole trajectory, geometry and feasibility of deployment. The adaptive machine learning model 210 may also, based on the inputs 250 and/or data from the stores 230 and 235, interpret deployment simulation results and generate graphical outputs depicting such results); and
recommending, by applying the trained classification machine learning model to the completion requirement, a tool type (Fox [0080] – The adaptive machine learning model 210 may search the data stored the history store 235 for matches (or “next-best” matches) to the specified and/or retrieved data regarding the tool string, wellbore, and/or fluids. For example, the adaptive machine learning model 210 may search and find instances of geometric and solid model data for well tool strings in the history store 235 that most closely resemble (or match) tool string data provided by inputs 250 and/or retrieved from the data store 230 based on the inputs 250. The adaptive machine learning model 210 may also search and find instances of geometric data for wellbore designs in the history store 235 that most closely resemble (or match) wellbore (or other tubular) data provided by inputs 250 and/or retrieved from the data store 230 based on the inputs 250).
Regarding Claim 2, Fox discloses method of claim 1, wherein the trained classification machine learning model is based on a deep learning model or a neural network (Fox [0125] – the adaptive machine learning model may be a neural network executed on a computing system, such as the computing system 150 shown in FIG. 1).
Regarding Claim 4, Fox discloses the method of claim 1 any of the preceding claims, further comprising: pre-processing training data, training a classification machine learning model to obtain the trained classification machine learning model (Fox [0073] – the adaptive machine learning model 210 may retrieve inputs 250 (e.g., measurement values and recorded tension, accelerometer and other forces from logging data, tool and cable movement characteristic information), data from the tools/well/fluids specifications store 230, and data from a history store 235 to perform clustering and classification in characterizing borehole trajectory, geometry and feasibility of deployment (e.g., passage of the well tool string through a portion of the wellbore or other tubular). The adaptive machine learning model 210 may also, based on the inputs 250 and/or data from the stores 230 and 235, interpret deployment simulation results and generate graphical outputs depicting such results).
Regarding Claim 6, Fox discloses the method of claim 1, further comprising: testing the recommendation using a classification model (Fox [0075] – the adaptive machine learning model 210 comprises a support vector machine (SVM) that analyzes data and recognize patterns, and may be used for classification and regression analysis).
Regarding Claim 7, Fox discloses the method of claim 6, wherein the classification model is selected from a group consisting of: Stochastic Gradient Descent (SGD), Naive Bayes, K-nearest neighbor, Random Forest, Support Vector Machine (SVM), and gradient boosting (Fox [0075] – the adaptive machine learning model 210 comprises a support vector machine (SVM) that analyzes data and recognize patterns, and may be used for classification and regression analysis).
Regarding Claim 9, Fox discloses the method of claim 1, further comprising: obtaining, for a second well, a second plurality of features; obtaining, for a plurality of reference wells, a plurality of reference features; grouping, by applying a cluster machine learning model to the plurality of reference features, the plurality of reference wells into a plurality of well clusters; determining, for the second plurality of well features, a well cluster of the plurality of well clusters that is similar to the second well; and recommending, using tool recommendations for the well cluster, a tool for completing the second well (Fox Fig.6b, [0106] – In step 662, the user may then input geometric characteristics of a configuration of a second well. In some embodiments, the second well may have different characteristics, such as different geometric characteristics, as compared to the well-defined in 656; [0107] – In step 664, the user initiates a determination of a prediction of the force to pass the first configuration of the well string through the second well. Step 664 also includes an initiation, by the user, of a determination of a prediction of the force to pass the second configuration of the well string through the second well. The determination may be made by the 3D modeling system based on, for example, the inputs provided in step 652, 654, and 662).
With regard to claim 12, the claim limitations are essentially the same as claim 1 but in a different embodiment. Therefore, the rational used to reject claim 1 is applied to claim 12.
With regard to claim 13, the claim limitations are essentially the same as claim 2 but in a different embodiment. Therefore, the rational used to reject claim 2 is applied to claim 13.
With regard to claim 15 the claim limitations are essentially the same as claim 6 but in a different embodiment. Therefore, the rational used to reject claim 6 is applied to claim 15.
With regard to claim 17 the claim limitations are essentially the same as claim 9 but in a different embodiment. Therefore, the rational used to reject claim 9 is applied to claim 17.
With regard to claim 20, the claim limitations are essentially the same as claim 1 but in a different embodiment. Therefore, the rational used to reject claim 1 is applied to claim 20.
Claim Rejections - 35 USC § 103
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.
Claim(s) 3, 8, 14, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Fox, in view of Maniar et al, US 2020/0040719 A1 (Maniar).
Regarding Claim 3, Fox discloses the method of claim 1, as outlined above
However, Fox does not explicitly disclose the trained classification machine learning model is based on convolutional neural networks (CNNs), random forests, stochastic gradient descent (SGD), a lasso classifier, gradient boosting, bagging, adaptive boosting (AdaBoost), ridges, elastic nets, or Nu Support Vector Regression (NuSVR), or a combination thereof
Maniar the trained classification machine learning model is based on convolutional neural networks (CNNs), random forests, stochastic gradient descent (SGD), a lasso classifier, gradient boosting, bagging, adaptive boosting (AdaBoost), ridges, elastic nets, or Nu Support Vector Regression (NuSVR), or a combination thereof (Maniar [0046] – In Block 204, a drilling model that predicts the ROP profile of the target well is generated using a machine learning algorithm based on the training data set…an ensemble method using tree-based weak-learners (e.g., Random-Forest, Least-Squares Boosting, etc.) is used as the machine-learning algorithm to generate the drilling model).
Therefore, it 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 to modify Fox to have a classification ML model based on random forests, as taught by Maniar. One would be motivated as the random-forest uses data from decision trees to assist in the classification.
Regarding Claim 8, Fox discloses the method of claim 1, as outlined above
However, Fox does not explicitly disclose adding one or more optimization functions to the trained classification machine learning model to match different well completion objectives.
Maniar teaches adding one or more optimization functions to the trained classification machine learning model to match different well completion objectives (Maniar [0032] – improve the accuracy of the drilling model (224), and thereby improve the field operations performed. In other words, because embodiments perform drilling operations based on a more accurate drilling model, one or more embodiments improve the efficiency and productivity of the drilling operations).
Therefore, it 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 to modify Fox to add optimization function(s), as taught by Maniar. One would be motivated as the optimization function(s) can be used to assist the system to create accurate models based on certain preferences.
With regard to claim 14, the claim limitations are essentially the same as claim 3 but in a different embodiment. Therefore, the rational used to reject claim 3 is applied to claim 14.
With regard to claim 16, the claim limitations are essentially the same as claim 8 but in a different embodiment. Therefore, the rational used to reject claim 8 is applied to claim 16.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Fox, in view of Guevara Diaz et al, US 2020/0150305 A1 (GD)
Regarding Claim 5, Fox discloses the method of claim 4, as outlined above.
However, Fox does not explicitly disclose the pre-processing includes data cleaning, feature engineering, oversampling, or a combination thereof.
GD teaches wherein the pre-processing includes data cleaning, feature engineering, oversampling, or a combination thereof (GD [0006] – The machine learning platform is utilized to process geological data involving unconventional reservoirs (i.e., shale), including production data and completion parameters with the aim to perform sweet spot identification. The platform provides an end-to-end data-driven solution that preprocesses and performs feature engineering of geological data and integrates those features with production data and completions).
Therefore, it 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 to modify Fox to have feature engineering as a part of the pre-processing, as taught by Guevara Diaz. One would be motivated as the feature engineering is used to analyze the image of geological data.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claim falls outside the scope of patent-eligible subject matter at least because the claimed computer-readable recording medium in light of the supporting disclosure is broad enough to encompass transitory embodiments. “One or more computer-readable storage media” is detailed in the specification as signal which is not eligible subject matter under 35 U.S.C. 101. See MPEP 2106(I).
Non-limiting examples of claims that are not directed to one of the statutory categories: i. transitory forms of signal transmission (for example, a propagating electrical or electromagnetic signal per se), In re Nuijten, 500 F.3d 1346, 1357, 84 USPQ2d 1495, (Fed.Cir. 2007).
A broad but reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and also transitory propagating signals in view of the ordinary and customary meaning of computer readable media, particularly when the specification does not limit the computer readable medium to one that is non-transitory. See MPEP 2111.01
Allowable Subject Matter
Claims 10, 11, 18 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIR SHAHNAMI whose telephone number is (571)270-0707. The examiner can normally be reached Monday - Friday 8:00 am to 4:00 pm.
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/AMIR SHAHNAMI/ Primary Examiner, Art Unit 2483
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