DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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 Claims
This communication is in response to communications received on 8/21/24. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification as there are not any amendments and/or new claims. Therefore, Claims 1-20 is/are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/11/25 was/were considered by the examiner.
Response to Arguments
There are no arguments.
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(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 8, and 16 that, under its broadest reasonable interpretation, is directed to geothermal data foundation.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 1-7 recites a series of steps) and system (claim(s) 8-20 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 16: obtaining data from at least one source, the data being in a plurality of formats and related to one of an energy exploration stage, an energy development stage, and an operations stage;
specifying at least one data item from the at least one source for visualization;
processing the data, wherein the processing includes parsing, extracting, and ingesting the data, the data including the specified at least one data item;
adding a macro to the integrated platform by copying the macro from a second source;
leveraging machine learning to obtain an optimum forecasting model, the leveraging including using at least one of autoregressive integrated moving average modelling and temporal fusion transformers;
visualizing the specified at least one data item, the visualizing providing an analytics dashboard having sub-dashboards for each of a plurality of stages of a lifecycle of a geothermal system;
determining whether the data includes an anomaly;
providing a real-time alert when the data is determined to include the anomaly; and
providing a forecasting summary based on the optimum forecasting model, wherein:
the visualizing produces a display screen that is substantially similar to a display screen produced by a different product.
Claim(s) 1 and 8: same analysis as claim(s) 1.
Dependent claims 2-7, 9-15, and 17-20 recite the same or similar abstract idea(s) as independent claim(s) 1, 8, and 16 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with geothermal data foundation.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include a platform, machine learning (claim(s) 1, 8, 16), a system for providing an integrated platform, the system comprising: a processor; a memory; and a bus connecting the processor with the memory (claim(s) 8), non-transitory computer-readable medium, a processor of an integrated platform (claim(s) 16), dashboard, sub-dashboard (claim(s) 3, 10), machine learning, autoregressive integrated moving average modelling, temporal fusion transformers (claim(s) 15).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0090]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0090]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Skoff et al. (US 2023/0017966 A1).
Regarding claim 1, 8, and 16, Skoff teaches a method for providing an integrated platform, the method comprising:
{ a system for providing an integrated platform, the system comprising:
a processor; a memory; and a bus connecting the processor with the memory, wherein the memory includes instructions for the processor to perform operations comprising: - claim 8}
{a non-transitory computer-readable medium having instructions stored thereon for a processor of an integrated platform, such that when the processor executes the instructions, a plurality of operations are performed, the plurality of operations comprising: - claim 16}
obtaining data from at least one source, the data being in a plurality of formats and related to one of an energy exploration stage, an energy development stage, and an operations stage [see at least [0080] “As shown in FIG. 2 , the example system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260 and instructions 270 (e.g., organized as one or more sets of instructions). As to the one or more computers 254, each computer may include one or more processors (e.g., or processing cores) 256 and memory 258 for storing the instructions 270 (e.g., one or more sets of instructions), for example, executable by at least one of the one or more processors.”;
[0060] “Various features can be included for processing various types of data such as, for example, one or more of: land, marine, and transition zone data; time and depth data; 2D, 3D, and 4D surveys; isotropic and anisotropic (TTI and VTI) velocity fields; and multicomponent data.”;
[0170] “As explained, a drill bit may be rotated via one or more mechanisms (e.g., rotary drive, top drive, mud motor, etc.). Such modes of operation can be associated with particular types of energy utilization.”];
specifying at least one data item from the at least one source for visualization [see at least [0063] “In the example of FIG. 1 , the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.”;
[0065] “As an example, visualization features can provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features can provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores.”;];
processing the data, wherein the processing includes parsing, extracting, and ingesting the data, the data including the specified at least one data item [see at least [0259] “As explained, a workflow can include data engineering to import and prepare the data; exploratory analysis to extract meaningful insights and understand the features that feed the system; training a ML model or models to create a trained model or model (as learned from data); validating results, for example, on individual jobs.”];
{adding a macro to the integrated platform by copying the macro from a second source - claim 16} [see at least [0070] “A modeling and simulation workflow for multiphase flow in porous media (e.g., reservoir rock, etc.) can include generalizing real micro-scale data from macro scale observations (e.g., seismic data and well data) and upscaling to a manageable scale and problem size. Uncertainties can exist in input data and solution procedure such that simulation results too are to some extent uncertain. A process known as history matching can involve comparing simulation results to actual field data acquired during production of fluid from a field. Information gleaned from history matching, can provide for adjustments to a model, data, etc., which can help to increase accuracy of simulation.”];
leveraging machine learning to obtain an optimum forecasting model, the leveraging including using at least one of autoregressive integrated moving average modelling and temporal fusion transformers;
visualizing the specified at least one data item {visualizing the specified at least one data item such that a display screen is produced that is substantially similar to a display screen produced by a different product – claim 8} {visualizing the specified at least one data item, the visualizing providing an analytics dashboard having sub-dashboards for each of a plurality of stages of a lifecycle of a geothermal system; – claim 16} [for the limitations above, see at least [0070] “A simulator can be utilized to simulate the exploitation of a real reservoir, for example, to examine different productions scenarios to find an optimal one before production or further production occurs.”;
[0230] “As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naïve Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.”;
[0175] “For example, consider a system that generates rate of penetration values, which may be, for example, rate of penetration set points. Such a system may be an automation assisted system and/or a control system. For example, a system may render a GUI that displays one or more generated rate of penetration values and/or a system may issue one or more commands to one or more pieces of equipment to cause operation thereof at a generated rate of penetration (e.g., per a WOB, a RPM, etc.).”;
[0223] “For example, a GUI may render the predicted features 1450 to a display where a user can visualize and understand the features that may be suitable for a drilling run with or without progressing to the search engine 1462. Where the user desires additional information (e.g., output), the user may progress to the search engine 1462.”;
[0260] “Run and well summaries can provide information such as distance and duration, tool choices, location and average performance output as ROP. As explained, data cleansing can be performed, which may facilitate unsupervised machine learning (e.g., consider outlier detection, range feasibility and outlier, standardization important for distance based algorithms).”]; and
{determining whether the data includes an anomaly; – claim 16} [see at least [0260] “Run and well summaries can provide information such as distance and duration, tool choices, location and average performance output as ROP. As explained, data cleansing can be performed, which may facilitate unsupervised machine learning (e.g., consider outlier detection, range feasibility and outlier, standardization important for distance based algorithms).”]
providing a forecasting summary based on the optimum forecasting model [see at least [0073] “The MANGROVE simulator (Schlumberger Limited, Houston, Tex.) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well).”]
{providing a real-time alert when the data is determined to include the anomaly; and providing a forecasting summary based on the optimum forecasting model, wherein: the visualizing produces a display screen that is substantially similar to a display screen produced by a different product - claim 16} [see at least [0175] “For example, consider a system that generates rate of penetration values, which may be, for example, rate of penetration set points. Such a system may be an automation assisted system and/or a control system. For example, a system may render a GUI that displays one or more generated rate of penetration values and/or a system may issue one or more commands to one or more pieces of equipment to cause operation thereof at a generated rate of penetration (e.g., per a WOB, a RPM, etc.).”;
[0223] “For example, a GUI may render the predicted features 1450 to a display where a user can visualize and understand the features that may be suitable for a drilling run with or without progressing to the search engine 1462. Where the user desires additional information (e.g., output), the user may progress to the search engine 1462.”].
Regarding claim 2 and 9, Skoff teaches the method of Claim 1, wherein the one of the energy exploration stage, the energy development, and the operations stage are associated with geothermal energy [see at least [0073] “The VISAGE simulator includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO2 disposal, etc. The MANGROVE simulator (Schlumberger Limited, Houston, Tex.) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment.”].
Regarding claim(s) 3 and 10, the claim(s) recite(s) analogous limitations to claim(s) 16 above and is/are therefore rejected on the same premise.
Regarding claim 4 and 11, Skoff teaches the method of Claim 2, wherein the visualizing initially provides an analytics dashboard having sub-dashboards for each of a plurality of stages of a lifecycle of a geothermal system [see at least [0231] “The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data.”].
Regarding claim(s) 5 and 12, the claim(s) recite(s) analogous limitations to claim(s) 16 above and is/are therefore rejected on the same premise.
Regarding claim(s) 6 and 13-14, the claim(s) recite(s) analogous limitations to claim(s) 16 above and is/are therefore rejected on the same premise.
Regarding claim(s) 7, the claim(s) recite(s) analogous limitations to claim(s) 8 above and is/are therefore rejected on the same premise.
Regarding claim(s) 15, the claim(s) recite(s) analogous limitations to claim(s) 1, 8, and 16 above and is/are therefore rejected on the same premise.
Regarding claim 17, Skoff teaches the non-transitory computer-readable medium of claim 16, wherein the operations further comprise performing a wellsite action in response to the real-time alert or the forecasting summary [see at least [0296] “As an example, a computational controller operatively coupled to equipment at a rigsite (e.g., a wellsite, etc.) can utilize one or more APIs to interact with a computational framework that includes an agent or agents. In such an example, one or more calls may be made where, in response, one or more actions are provided (e.g., control actions for drilling).”;
[0338] “As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.”].
Regarding claim 18, Skoff teaches the non-transitory computer-readable medium of claim 17, wherein the wellsite action comprises a physical action at a wellsite [see at least [0296] “As an example, a computational controller operatively coupled to equipment at a rigsite (e.g., a wellsite, etc.) can utilize one or more APIs to interact with a computational framework that includes an agent or agents. In such an example, one or more calls may be made where, in response, one or more actions are provided (e.g., control actions for drilling).”;
[0338] “As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.”].
Regarding claim 19, Skoff teaches the non-transitory computer-readable medium of claim 17, wherein the wellsite action comprises generating and transmitting a signal that causes a physical action to occur at a wellsite [see at least [0120] “As an example, the system 300 can include one or more sensors 366 that can sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit).”].
Regarding claim 20, Skoff teaches the non-transitory computer-readable medium of claim 17, wherein the wellsite action comprises, in a geothermal system, drilling a well, varying a weight and/or torque on a drill bit that is drilling the well, varying a drilling trajectory of the well, or varying a concentration and/or flow rate of a fluid pumped into the well [see at least [0296] “As an example, a computational controller operatively coupled to equipment at a rigsite (e.g., a wellsite, etc.) can utilize one or more APIs to interact with a computational framework that includes an agent or agents. In such an example, one or more calls may be made where, in response, one or more actions are provided (e.g., control actions for drilling).”;
[0338] “As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.”].
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Skoff et al. – WO 2023/287784 A1 (relevant because it teaches same as task assignment US 2023/0017966 A1)
Makhotin et al. – Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation (relevant because it teaches “Well known oil recovery factor estimation techniques such as analogy, volumetric calculations, material balance, decline curve analysis, hydrodynamic simulations have certain limitations. Those techniques are time-consuming, require specific data and expert knowledge. Besides, though uncertainty estimation is highly desirable for this problem, the methods above do not include this by default. In this work, we present a data-driven technique for oil recovery factor estimation using reservoir parameters and representative statistics. We apply advanced machine learning methods to historical worldwide oilfields datasets (more than 2000 oil reservoirs).”)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES WEBB/Examiner, Art Unit 3624
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