DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/18/2026 has been entered.
Examiner notes that the instant claims have two sets of Applicant arguments, dated 06/18/2026 and 08/07/2026, both of which are addressed herein.
Claims 1 – 8 and 16 – 20 have been presented for examination. Claims 9 – 15 are cancelled. Claims 1 and 16 are currently amended.
Response to Rejection Under 35 U.S.C. § 112(a)
Applicant’s amendments overcome the 112(a) rejection. Therefore, it is withdrawn.
Response to Rejection Under 35 U.S.C. § 101
Applicant’s arguments with regard to Step 2A, Prong I have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues (dated 08/07/2026): “First, the MPEP does not require claiming the complexity, or size, of any claimed elements to support the conclusion that the claims do not recite mental processes as the Examiner contends. Second, a skilled person, upon reading the claims, would understand the high complexity, and large size of certain claimed elements, preclude the human mind from practically performing the claims. For example, a skilled person would know that the claimed observed seismic dataset is on the order of gigabytes or even terabytes in size. Processing this dataset through the claimed trained machine learning (ML) network is then outside of what the human mind can practically perform. In another example, a skilled person would know that the claimed forward modeling relies on physics-based equations (e.g., the elastic or acoustic wave equation) to determine the claimed simulated seismic dataset and that this modeling process is also outside of what the human mind can practically perform” (emphasis added)
Applicant refutes the alleged contention by Examiner that claims require being limited by complexity, or size to support the conclusion that they do not recite mental process. This is the opposite of what was contended by Examiner, since Examiner merely noted that the absence (i.e., non-claiming) of such limitations can support a conclusion that a limitation covers a mental process, and not that the claiming of such limitations is required to support a conclusion that a limitation does not cover a mental process.
Applicant argues that one or more elements are not practically performed in the mind, such as the size of the dataset and processing through the ML network (see emphasis above). Examiner notes that the claims do not limit in any way the size of the datasets utilized, and the mere existence of large datasets does not preclude the claimed invention step(s) from being performed mentally in combination with existing datasets. Further, the processing of the data through the ML network is not positively recited since there is merely claimed “inputting the observed seismic dataset into the a trained machine learning (ML) network” and “predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset”. The “predicting” is after the ML network has data inputted and processing, therefore it requires no more than judgements and evaluations based on observations of the outputs of a previously trained ML network.
Applicant argues that the recited “forward modeling method” is not practically performed in the mind. However, the recited “determining, using a forward modeling method” is analyzed at Step 2A, Prong II (see Claim Rejections - 35 USC § 101).
Applicant argues (dated 06/18/2026): “The amended independent claims do not recite any "mental processes" because none of limitations (b )- (i) can practically be performed in the human mind because of the large size and/or complexity of the observed seismic dataset, the trained ML network, the forward modeling method, the data penalty function, the model penalty function, the composite penalty function, the refined seismic model, and locating the hydrocarbon reservoir. Further, the human mind cannot practically drill a wellbore.”
Applicant’s arguments are not persuasive based on the preceding remarks.
Applicant’s arguments with regard to Step 2A, Prong II have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues (dated 06/18/2026): “Namely, the amended independent claims recite an improvement in how the trained ML network itself operates by "[c]ombining a physics-based inversion with a ML-based prediction [to] decrease the extent to which the problem of determining the seismic model is underdetermined and increase the similarity between the determined seismic model and the true characteristics of the subterranean region." Original specification, para. [0022]. This contrasts with how trained ML networks are conventionally used” (emphasis added)
Applicant acknowledges that the claimed invention uses the ML in a different manner, as contrasted with improved how it is trained (see emphasis in Applicant’s remarks).
Applicant argues (dated 06/18/2026): “Further, the amended independent claims are patent eligible under Step 2A, Prong 2 because they recite an improvement in the technical field of wellbore drilling … Accordingly, the drilling system can be more accurately steered towards the hydrocarbon reservoir during the drilling of the well bore.”
Examiner notes that limiting the claimed invention to a field of application does not amount to a practical application (see MPEP 2106.04(d)(I)). Further, the recited “drilling, using a drilling system” amounts to reciting the words “apply it”.
Applicant argues (dated 08/07/2026): “The claims recite an improvement in several technical fields or technologies, such as hydrocarbon exploration and, in turn, wellbore drilling as follows … How the observed seismic dataset is collected and processed
to locate the hydrocarbon reservoir, as claimed, improves the technical field of hydrocarbon exploration by combining the use of seismic inversion and machine learning, which thereby enables the geologists and geophysicists to more clearly locate the hydrocarbon reservoir.”
As previously remarked, limiting the claimed invention to a field of application does not amount to a practical application (see MPEP 2106.04(d)(I)).
Applicant’s arguments with regard to Step 2B have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues (dated 06/18/2026): “Conventionally, either limitations (a)-(b) or limitations (c )-(d) and (g), where the composite penalty function of (g) is replaced with the data penalty function (for physics-based inversion) are performed. In contrast, it is unconventional to perform both sets of limitations that are intertwined with one another as claimed.”
Applicant argues that one or more combinations of recited limitations, which largely include limitations of the abstract idea itself, are unconventional. Examiner notes that the novelty of the limitations is of no relevance (see MPEP 2106.05(I) “As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter”). Further, simply appending one or more additional elements does not amount to significantly more, especially when such activities are recited at high-level of generality (see MPEP 2106.05(I)(A) “ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception,”). Therefore, Applicant’s arguments are not persuasive since the combination(s) being “unconventional to perform” appear to proceed largely from the abstract idea limitations themselves, and which form the core of the inventive concept.
Applicant argues: “In the response filed June 18, 2026, Applicant argues that "the specific ordered combination of limitations is not well-understood, routine, or conventional" and does not argue "that one or more limitations, including those of the abstract idea, are unconventional" as the Examiner contends. Advisory Action, p. 3. Namely, it is unconventional to intertwine subsets of limitations with one another to form the specific ordered combination of limitations as claimed.”
Applicant’s arguments are not persuasive based on the preceding remarks.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “seismic acquisition system” and “drilling system” in claim 16; “wellbore planning system” in claim 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Looking to the specification, the “seismic acquisition system” is interpreted as covered by seismic receivers (see the instant application Paragraph 45 “When a seismic dataset is acquired by activating physical seismic sources and recording the actual resulting vibrations of the Earth using physical seismic receivers, the seismic dataset may be termed an "observed seismic dataset" and the component seismic traces "observed seismic traces”). The “drilling system” is interpreted as covering generic drilling equipment (see the instant application Paragraph 24 “The drilling system 110 comprises a drill string (105), drill bit (107), and a mud circulation system (109) for use in boring the wellbore 104 into the formation 106.”). The “wellbore planning system” is interpreted as covering generic computer hardware and software components (see the instant application Paragraph 89 “The wellbore planning system may comprise one or more computer processors in communication with computer memory containing the seismic and geomechnical models, information relating to drilling hazards, and the constraints imposed by the limitations of the drillstring and the drilling system. The wellbore planning system may further include dedicated software to determine the planned wellbore path and associated drilling parameters, such as the planned wellbore diameter, the location of planned changes of the wellbore diameter, the planned depths at which casing will be inserted to support the wellbore and to prevent formation fluids entering the wellbore, and the mud weights (densities) and types that may be used during drilling the wellbore”).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Allowable Subject Matter
The following is an examiner’s statement for the indication of allowable subject matter, subject to overcoming the 112(b) and 101 rejections:
None of the prior art of record taken individually or in combination discloses the claim 1 (and similarly claim 16, and by incorporation claims 2 – 8 and 17 – 20) method for determining a refined seismic model of a subterranean region, the method comprising: “inputting the observed seismic dataset into a trained machine learning (ML) network; predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset; determining, using a forward modeling method and a seismic wavelet, a simulated seismic dataset from the current seismic model; determining a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset and a data covariance matrix, wherein the data covariance matrix comprises a data-weighting matrix; determining a model penalty function based on a difference between the current seismic model and the predicted seismic model; determining a composite penalty function based on a weighted sum of the data penalty function and the model penalty function; determining the refined seismic model based on finding an extremum of the composite penalty function”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record.
Adler et al. “Deep Learning for Seismic Inverse Problems” teaches using CNN for seismic modeling using data penalty function and model penalty function and composite penalty function in an iterative full-waveform inversion for seismic inversion problems. However, does not appear to explicitly disclose: inputting the observed seismic dataset into a trained machine learning (ML) network; predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset; determining a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset and a data covariance matrix, wherein the data covariance matrix comprises a data-weighting matrix.
Ahmed, I. (US 2018/0156931) teaches performing a reverse time migration on seismic data to estimate a full waveform inversion. However, does not appear to explicitly disclose: inputting the observed seismic dataset into a trained machine learning (ML) network; predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset; determining, using a forward modeling method and a seismic wavelet, a simulated seismic dataset from the current seismic model; determining a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset and a data covariance matrix, wherein the data covariance matrix comprises a data-weighting matrix; determining a model penalty function based on a difference between the current seismic model and the predicted seismic model; determining a composite penalty function based on a weighted sum of the data penalty function and the model penalty function; determining the refined seismic model based on finding an extremum of the composite penalty function.
Ray et al. (US 2019/0011583) teaches performing a full-waveform inversion using a probabilistic approach. However, does not appear to explicitly disclose: inputting the observed seismic dataset into a trained machine learning (ML) network; predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset; determining, using a forward modeling method and a seismic wavelet, a simulated seismic dataset from the current seismic model; determining a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset and a data covariance matrix, wherein the data covariance matrix comprises a data-weighting matrix; determining a model penalty function based on a difference between the current seismic model and the predicted seismic model; determining a composite penalty function based on a weighted sum of the data penalty function and the model penalty function; determining the refined seismic model based on finding an extremum of the composite penalty function.
Cai et al. “Data weighted full-waveform inversion with adaptive moment estimation for near-surface seismic refraction data” teaches a data penalty function with a data covariance matrix. However, does not appear to explicitly disclose: inputting the observed seismic dataset into a trained machine learning (ML) network; predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset; determining, using a forward modeling method and a seismic wavelet, a simulated seismic dataset from the current seismic model; determining a model penalty function based on a difference between the current seismic model and the predicted seismic model; determining a composite penalty function based on a weighted sum of the data penalty function and the model penalty function; determining the refined seismic model based on finding an extremum of the composite penalty function.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 18 – 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.
With regard to claim 18, it recites “further comprising training the ML network comprising: forming a composite training penalty function comprising: a model training penalty function measuring a difference between a seismic training model and a predicted seismic training model, predicted by the ML network from a corresponding seismic training dataset, and a data training penalty function based on a ML Jacobian operator; and training the ML network based on finding an extremum of the composite training penalty function.” However, there is no recited structure for perform the recited function(s) since the limitations does not invoke 112(f). Specifically, the functional language does not recite "means" and is not coupled with a generic placeholder. Therefore, it amounts to pure functional claiming without boundaries. The limitation is interpreted for examination purposes as the structure of the computer system, similar to claim 20.
With regard to claim 19, it recites “further comprising training the ML network comprising: forming a composite training penalty function comprising: a model training penalty function measuring a difference between a seismic training model and a predicted seismic training model, predicted by the ML network from a corresponding seismic training dataset, and a data training penalty function based on a deterministic Jacobian operator; and training the ML network based on finding an extremum of the composite training penalty function”. However, there is no recited structure for perform the recited function(s) since the limitations does not invoke 112(f). Specifically, the functional language does not recite "means" and is not coupled with a generic placeholder. Therefore, it amounts to pure functional claiming without boundaries. The limitation is interpreted for examination purposes as the structure of the computer system, similar to claim 20.
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 – 8 and 16 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 1 recites at Step 1 a statutory category (i.e. a process) method for determining a refined seismic model of a subterranean region, comprising: predicting a predicted seismic model from the trained ML network in response to the observed seismic dataset; determining a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset and a data covariance matrix; determining a model penalty function based on a difference between the current seismic model and the predicted seismic model; determining a composite penalty function based on a weighted sum of the data penalty function and the model penalty function; and determining the refined seismic model based on finding an extremum of the composite penalty function; locating a hydrocarbon reservoir using the refined seismic model. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover performance in the mind in combination with a piece of paper (see MPEP 2106.04(a)(2)(III)). For example, the “predicting” and “locating” require no more than judgements and evaluations based on observations from a previously trained model. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, the “difference between” and “weighted sum” and “finding an extremum” explicitly recite mathematical operations in words. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: obtaining an observed seismic dataset from the subterranean region; obtaining a current seismic model of the subterranean region; inputting, the observed seismic dataset into a trained machine learning (ML) network; determining, using a forward modeling method and a seismic wavelet, a simulated seismic dataset from the current seismic model; drilling, using a drilling system steered towards the hydrocarbon reservoir, a wellbore that intersects the hydrocarbon reservoir. The “obtaining” and “inputting” amounts to insignificant data gathering since it recites at a high-level of generality gathering a model and/or inputs to a model, and since it is recited at a high-level of generality how the data is obtained or inputted (see MPEP 2106.04(d)). The “determining” recites the idea of an outcome since it covers determining a dataset using any type of forward modelling from specific inputs (i.e., seismic model, seismic wavelet). The “drilling, using a drilling system” amounts to reciting the words “apply it” since it recites the idea of an outcome. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The recited “obtaining” and “inputting” covers well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The “determining” covers well-known seismic forward modelling to generate seismic data waves (see Ahmed, I. (US 2018/0156931) Paragraph 46 “Returning to FIG. 1 and FIG. 2A, the process 100 begins by forward modeling (at 110) on the velocity model 216 of the subterranean region to generate a set of low frequency seismic data 218 … The general concept of two-way forward modeling on a velocity model is, in itself, well known to the art.”). The “drilling, using a drilling system” requires no more than ordinary equipment used in its ordinary capacity to achieve a desired effect. Considering the additional elements in combination does not add anything more than when considering them individually since the “obtaining” and “inputting” and “determining” and “drilling, using a drilling system” require no more than generic equipment. For at least these reasons, the claim is not patent eligible.
Dependent claim 2 – 8 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): Claim 2 determining an updated data penalty function based on a difference between the observed seismic dataset and the updated simulated seismic dataset and an updated data covariance matrix; determining an updated model penalty function based on a difference between the refined seismic model and the predicted seismic model; determining an updated composite penalty function based on a weighted sum of the updated data penalty function and the updated model penalty function; and determining an updated seismic model based on finding an updated extremum of the updated composite penalty function; Claim 3 planning a wellbore path to intersect the hydrocarbon reservoir; Claim 4 forming a composite training penalty function comprising: a model training penalty function measuring a difference between a seismic training model and a predicted seismic training model, predicted by the ML network from a corresponding seismic training dataset, and a data training penalty function based on a ML Jacobian operator; and training the ML network based on finding an extremum of the composite training penalty function; Claim 6 wherein training the ML network further comprises: forming a composite training penalty function comprising: a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and a data penalty function based, at least in part, on a deterministic Jacobian operator; and training the ML network based, at least in part, on finding an extremum of the composite training penalty function; Claim 7 wherein the extremum of the composite penalty function comprises a minimum. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, the “difference between” and “weighted sum” and “finding an updated extremum” and “extremum of the composite penalty function comprises a minimum” explicitly recite mathematical operations in words. The “Jacobian operator” recites a specific type of mathematical operator. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover performance in the mind in combination with a piece of paper (see MPEP 2106.04(a)(2)(III)). The “planning” require(s) no more than judgements and evaluations based on modeling results. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: Claim 2 determining using the forward modeling method and a refined seismic wavelet, an updated simulated seismic dataset from the refined seismic model and a refined seismic wavelet using the forward modeling method; Claim 3 that the “planning” is using a wellbore planning system; Claim 4 training the ML network comprising; Claim 5 training the ML network comprising; augmenting the plurality of seismic training models and the corresponding seismic training datasets with the current seismic model and the simulated seismic dataset for the current seismic model; and retraining the trained ML network based, at least in part, on the augmented plurality of seismic training models and the corresponding seismic training datasets; Claim 6 training the ML network comprising; Claim 8 wherein the forward modelling method comprises a physics-based forward modelling method. The “determining” and “forward modelling method” recites the idea of an outcome since it covers determining a dataset using any type of forward modelling from specific inputs (i.e., seismic model, seismic wavelet), including being based on physics which does not recite how the modeling is performed. The “using a wellbore planning system” recites the idea of an outcome since it requires no more than ordinary equipment used in its ordinary capacity. The “training” amounts to reciting the idea of an outcome since it recites at a high-level of generality producing another model, which is then used as an input to the “predicting” of the abstract idea. Specifically, the “training” does not recite details of the training beyond what the training is based on, and does not explicitly recite what the model outputs beyond being a “trained ML network”. Looking to the disclosure, the algorithm used to train the ML network is not explicitly disclosed, beyond being based on backpropagation and gradient descent algorithms (see the instant application Paragraph 38 “Training may be defined as the process of determining the values of the weights within each array of weights and the values of each bias term such that the neural network (200) makes accurate predictions. Training may be performed iteratively, where each iteration is referred to as an "epoch", using training data and backpropagation.”). The “augmenting” amounts to insignificant data-gathering since it merely increases training data for a later recited training step. The “retraining” amounts to reciting the idea of an outcome since it recites at a high-level of generality producing another model, which is then used as an input to the “predicting” of the abstract idea, as discussed previously. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The “determining” and “forward modeling comprises” covers well-known seismic forward modelling to generate seismic data waves (see Ahmed, I. (US 2018/0156931) Paragraph 46 “Returning to FIG. 1 and FIG. 2A, the process 100 begins by forward modeling (at 110) on the velocity model 216 of the subterranean region to generate a set of low frequency seismic data 218 … The general concept of two-way forward modeling on a velocity model is, in itself, well known to the art.”). The “using a wellbore planning system” amounts to reciting the words “apply it” since it requires no more than ordinary equipment used in its ordinary capacity. The “training” covers well-understood, routine, and conventional activity since it covers using backpropagation and any desired gradient descent algorithm to train the model which are well-known (see Amari Abstract “The backpropagation learning method has opened a way to wide applications of neural network research. It is a type of the stochastic descent method known in the sixties. The present paper reviews the wide applicability of the stochastic gradient descent method to various types of models and loss functions.”). The “augmenting” covers well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since the “determining” and “augmenting” and “retraining” requires no more than generic computer functions. For at least these reasons, the claim(s) are not patent eligible.
Independent claim 16 recites at Step 1 a statutory category (i.e. a machine) system, configured to perform substantially the identical steps as claim 1. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover performance in the mind in combination with a piece of paper (see MPEP 2106.04(a)(2)(III)). The recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). Accordingly, the claim recites an abstract idea for the same reasons as in claim 1 and above.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims substantially the identical additional elements as claim 1, and further claims: a seismic data acquisition system; a computer system; receive the observed seismic dataset, receive a current seismic model of the subterranean region. The various “system” require no more than generic equipment and therefore amount to reciting the words “apply it”. The “require” covers insignificant data gathering since it is recited at a high-level of generality how the data is gathering. The claim is directed to an abstract idea for the same reasons as in claim 1, and above.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above, the various “system” amount to reciting the words “apply it”. The “receive” covers well-understood, routine, and conventional activity (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). For at least these reasons, the claim is not patent eligible for the same reasons as in claim 1, and above.
Dependent claim 17 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recites: plan a wellbore path to intersect the hydrocarbon reservoir. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that in part, under its broadest reasonable interpretation, cover performance in the mind in combination with a piece of paper (see MPEP 2106.04(a)(2)(III)). The “plan” requires no more than judgement and evaluations. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: Claim 17 a wellbore planning system configured to; The “wellbore planning system” recites the idea of an outcome since it requires no more than ordinary equipment used in its ordinary capacity. The claim is directed to an abstract idea.
At Step 2B the clam(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The “drilling system to drill” amounts to reciting the words “apply it” since it requires no more than ordinary equipment used in its ordinary capacity. For at least these reasons, the claim(s) are not patent eligible.
Dependent claim 18 – 20 recite(s) at Step 1 the same statutory category as the parent claim(s). Claims 18 – 20 may be compared to claims 4, 6 and 5, respectively. Accordingly, the claim(s) recite(s) an abstract idea for the same reasons.
At Step 2A, Prong II this judicial exception is not integrated into a practical application. Claims 18 – 20 may be compared to claims 4, 6 and 5, respectively. The claim is directed to an abstract idea for the same reasons.
At Step 2B the claims do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. Claims 18 – 20 may be compared to claims 4, 6 and 5, respectively. For at least the same reasons, the claims are not patent eligible.
Conclusion
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/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
/ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187
