DETAILED ACTION
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 .
Response to Amendment
The amendment filed August 10, 2026 has been entered. Claims 1-18 remain pending in the instant application. Applicant’s amendments to the Claims have overcome each and every 112(b) rejection previously set forth in the Non-Final Office Action mailed May 26, 2026.
Response to Arguments
Applicant’s arguments, filed August 10, 2026, in light of amendments regarding rejections under 35 U.S.C. 101 have been fully considered and are persuasive. The rejection of Claims 1-18 under 35 U.S.C. 101 is withdrawn.
Applicant’s arguments regarding rejections under 35 U.S.C. 103 have been fully considered, but they are not persuasive.
Applicant argues that Revelo, Mishev, and Le Calvez do not teach various limitations of instant Claim 1. Applicant generally argues that the instant claims are different from the cited references, however Applicant has not pointed out specifically how the language of the instant claims differs from the cited references. An updated rejection under 35 U.S.C. 103, necessitated by Applicant’s amendment, is provided below.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1–18 are rejected under 35 U.S.C. § 103 as being unpatentable over Eddy Dario Revelo Obando, Moment tensor estimation through inversion of borehole microseismic data with machine learning (2021) (hereinafter “Revelo”) in view of Mishev et al., U.S. Patent No. US 9,134,454 B2 (hereinafter “Mishev”), further in view of Le Calvez et al., U.S. Patent No. US 11,215,722 B2 (hereinafter “Le Calvez”), further in view of Li et al. (U.S. Pub. No. 2020/0158899 A1), hereinafter Li. The Revelo reference was provided by the Applicant in the IDS.
Regarding Claim 1:
Revelo teaches “a method for training a machine learning model to process microseismic data recorded during fracturing of a subterranean geological formation”. Specifically, Revelo discloses that “In this thesis the inversion of microseismic measurements for retrieving the moment tensor has been tackled by using a deep feedforward neural network” (Abstract), which teaches using a machine learning model to process microseismic measurements associated with subterranean fracturing operations. Revelo further teaches that “The energy produced during hydraulic fracturing is in general weak compared to the background noise” (Section 1.3), directly linking the microseismic measurements to fracturing operations in a subterranean geological formation.
Revelo further teaches “determining seismogram data for sources at the vertices of the volume and at the center of the volume by executing a forward modeling simulation of seismic wave propagation” by disclosing a structured source geometry used for seismic waveform simulation. Specifically, Revelo discloses “an array of four sources… located in the corners of a square… with one in the center” (Section 3.2 and Chapter 4). Revelo further explains that “The seismograms used to train the network were generated through the discrete-wavenumber method… to compute full-waveform seismograms” (Abstract), thereby teaching determining seismogram data from spatially distributed source locations including boundary locations and a center location. Revelo further discusses the discrete wavenumber method as part of the forward problem, interpreted as a forward modeling simulation (e.g., Section 2.1.2).
Revelo further teaches “generating training data for the sources at the vertices from the seismogram data, the training data relating values of seismogram data to values of moment tensor components” and “training a machine learning model using the training data generated for the sources at the vertices” Specifically, Revelo discloses that “for every set of angles, the true moment tensor is calculated,” and the resulting seismograms are “used as inputs for the neural network… to predict the moment-tensor components” (Section 3.2), thereby teaching generating labeled training data pairing seismogram inputs with corresponding moment tensor outputs. Revelo further teaches that “the training set is used to update the parameters of the network that minimizes the difference between the predicted and true values” (Section 3.1.1), and additionally explains that “the optimal result is obtained by iteratively updating the weights so that the difference between the input and the calculated output is reduced” (Section 2.2). These disclosures teach training a machine learning model using the generated training data through iterative adjustment of neural network parameters.
Revelo further teaches predicting, using the trained machine learning model, values of moment tensor components from the seismogram data determined for the source at the center of the volume (“After feeding the data at the input layer, the network propagates it up to the hidden layers to produce the estimated values y for the moment-tensor components.”) (e.g., page 33, paragraph 4).
Revelo further teaches generating, based on additional seismogram data from the sources positioned based on the second dimension, the trained machine learning model configured to generate the outputs that satisfy the threshold when processing said seismogram data (“This section aims to describe the training process of a neural network to compute the optimal weights w that reduce the error between the input and the predicted output […] To calculate the optimal set of parameters ϴ, the loss function defined in equation (2-44) needs to be minimized.” Minimizing an loss function is interpreted as generating a trained machine learning model such that the outputs satisfy a threshold (i.e., the threshold is an acceptable loss), wherein the model is trained on seismogram data.) (e.g., page 33, paragraphs 2 and 5).
Revelo does not explicitly disclose “selecting a volume in the subterranean geological formation, the volume comprising a set of vertices and a center, the set of vertices defining a first dimension representing a spatial distance between the vertices” because Revelo’s source arrangement is not explicitly tied to a vertex-defined computational volume having a defined dimension.
However, Mishev discloses “selecting a volume in the subterranean geological formation, the volume comprising a set of vertices and a center, the set of vertices defining a first dimension representing a spatial distance between the vertices” Specifically, Mishev discloses that “a primary mesh is generated by first selecting a point 1810 inside each coarse interaction region 1806,” where the selected point may be the “center of mass” of the interaction region (Col. 19, lines 34–42). Mishev further teaches that the computational regions are defined using coarse mesh points and vertices, explaining that “some of the fine mesh points… may be selected to be coarse mesh points” (Col. 19, lines 11–18). These disclosures teach selecting a discretized computational volume having boundary-defining vertices together with an associated center point within the volume. “The closest point will be identified as the center point 1812 of the interaction region 1806. If there are more than one point in the fine mesh (e.g., 1814) that are at the same distance to the selected point 1816, the first point can be chosen as the center point 1812.” (e.g., column 19, lines 43-46).
Neither Revelo nor Mishev appears to explicitly disclose “determining, based on the trained machine learning model, a second dimension defined for the set of vertices, the second dimension being a maximum value enabling an accuracy for outputs of the trained machine learning model that satisfies a threshold.”
However, Le Calvez discloses “determining, based on an estimation accuracy of the predicted values for the source at the center of the volume, a second dimension defined for the set of vertices, the second dimension being a maximum spatial distance between the vertices enabling an accuracy for outputs of the trained machine learning model that satisfy a threshold.” Specifically, Le Calvez discloses that “Iterations can continue until the objective function is converged to a maximum… when true sensor orientation and model calibration parameter is obtained” (Col. 22, lines 36–39), which teaches iterative optimization toward a maximum acceptable value. Le Calvez further teaches that the optimization is tied to output accuracy, explaining that the system provides outputs with “a desired amount of accuracy” (Col. 17, lines 13–25). These disclosures teach iteratively determining a maximum parameter value that satisfies a required output accuracy threshold. Further, Le Calvez discloses a maximum coalescence, interpreted as a maximum spatial distance that enables accuracy for outputs. “ In such an example, the mapping can output a continuous value representing a maximum coalescence from a spatial map, along with a location of the maximum coalescence, per block 740. As shown in FIG. 7, the time and location of maximum coalescence above a detection threshold can correspond to a measured time and location that an event is reported to have occurred as indicated per block 750.” (e.g., column 19, lines 36-43).
Neither Revelo, Mishev, nor Le Calvez teach the second dimension minimizing a number of forward modeling simulations to generate the training data while the accuracy of the outputs satisfies the threshold.
On the other hand, Li, which relates similarly as a method for real time seismic event localization, does teach the second dimension minimizing a number of forward modeling simulations to generate the training data while the accuracy of the outputs satisfies the threshold (“In some embodiments, this can be based on Pereyra and Kaelin (2008) which proposes a fast acoustic wavefield propagation simulation procedure by constructing an order-reduced modeling operator, which provides a possible solution to the issue of high computational cost mentioned above. In some instances, this may speed up simulation by projecting the spatially discretized wave equations from a higher dimensional system to a much lower dimensional system, still keeping sufficient accuracy […] Two characteristics of microseismic monitoring permit and encourage the creation of a reduced-order time-reversal extrapolation for real-time microseismic event localization, namely a limited number of receivers and long monitoring/recording time (usually from several hours to days). Borehole acquisitions typically use up to a dozen geophones, whereas surface acquisitions can be substantially larger ((Duncan and Eisner, 2010; van der Baan et al, 2013). Yet it is not required to simulate a source at every possible spatial position in depth, greatly reducing the number of simulations which eventually leads to a more interesting snapshot matrix A.”) (e.g., paragraphs [0054] and [0120]).
Revelo, Mishev, and Le Calvez are analogous art because they are all directed to computational modeling and interpretation of subsurface geological formations using seismic or microseismic measurement data. More specifically, each reference uses numerical modeling, discretized spatial representations, and data-driven analysis techniques to estimate physical properties or characteristics associated with subterranean formations.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Revelo, Mishev, and Le Calvez before them, to modify Revelo’s microseismic machine learning workflow to use Mishev’s structured vertex-and-center volumetric regions together with Le Calvez’s iterative accuracy optimization techniques, so that the seismic source regions used for training and analysis are systematically refined until the machine learning outputs satisfy a desired accuracy threshold.
The motivation for doing so would have been that Revelo already teaches using different spatial sampling arrangements to improve moment tensor prediction performance, but does not provide a structured framework for defining those spatial regions or a systematic way to optimize them. Mishev provides a known technique for organizing subsurface regions into vertex-defined computational volumes with associated center points, which would improve the consistency and physical organization of Revelo’s spatial sampling workflow. Le Calvez further teaches iteratively refining model-related parameters until a desired accuracy level is achieved, which would provide a predictable way to optimize the spatial region dimensions used in Revelo’s modeling process. Combining these teachings would have improved the accuracy, organization, and reliability of Revelo’s machine-learning-based subsurface modeling workflow by applying known spatial discretization and optimization techniques to the existing system (Le Calvez, Col. 17, lines 13–25).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the Applicant's claimed invention to combine the modified reference of Revelo in view of Mishev and Le Calvez with Li. The claimed invention is considered to be using a known technique to improve similar devices (methods, or products) in the same way, see MPEP § 2143(I)(C). Revelo teaches a method for generating moment tensors using a neural network, and Le Calvez teaches a method including optimization towards a maximum accepted value. However, Revelo-Le Calvez does not appear to specifically teach wherein the maximum acceptable value is a second dimension minimizing a number of simulations to generate the training data. On the other hand, Li, which relates similarly as a method for simulating wave propagation data, does teach wherein a second dimension minimizes a number of simulations. As both Revelo-Le Calvez and Li relate to microseismic monitoring (e.g., Revelo, page 7, paragraph 2; Le Calvez, abstract; Li, paragraph [0014]), one of ordinary skill in the art could have applied the known improvement of constructing an order-reduced modeling operator as in Li in order to reduce the number of required simulations required to generate training data as in Revelo-Le Calvez, and the results would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the Applicant's claimed invention to combine the modified reference of Revelo in view of Mishev and Le Calvez with Li in order to reduce the computational cost of performing forward simulations.
Regarding Claim 2:
Revelo teaches “executing the trained machine learning model on the microseismic data; and generating an estimate of the values of the moment tensor components for the given subterranean formation”. In particular, Revelo teaches that the trained network is used to predict the six independent moment-tensor components and the fault angles from seismic sources, and further explains that “once the network is trained, the moment-tensor components are predicted on the test set” (Section 3.2). Revelo further confirms that “the generated seismograms are then preprocessed and used as inputs for the neural network” (Section 3.2), which makes clear that the microseismic data is processed and fed into the trained model as input, with the model producing the corresponding moment tensor component predictions as output.
Mishev teaches computational modeling and analysis of subterranean geological regions using structured computational representations and numerical processing workflows for evaluating subsurface properties. Although Mishev does not explicitly disclose borehole-acquired microseismic data or machine-learning-based moment tensor prediction, Mishev further supports the use of computational modeling techniques for processing and analyzing subsurface geological data within subterranean formations.
Neither Revelo nor Mishev appears to explicitly disclose “acquiring microseismic data during a borehole acquisition survey associated with a given subterranean formation.”
However, Le Calvez discloses “acquiring microseismic data during a borehole acquisition survey associated with a given subterranean formation.” Specifically, Le Calvez teaches “receiving microseismic data of microseismic events as acquired by sensors during hydraulic fracturing of a geologic region” (Col. 23, lines 12–15). Le Calvez further teaches that sensing equipment may be positioned “in a field, in a bore, etc.” (Col. 11, lines 40–48), which directly maps to borehole-deployed acquisition systems operating within a subterranean formation context. Taken together, these disclosures teach acquisition of microseismic data during borehole-based hydraulic fracturing operations within a geologic formation, consistent with a borehole acquisition survey associated with a given subterranean formation.
Revelo, Mishev, and Le Calvez are analogous art because they are all directed to subsurface geophysical and reservoir-related modeling and interpretation using acquired subsurface measurement data. More specifically, they each operate in the shared technical space of using computational and data-driven techniques to process seismic or microseismic inputs and derive subsurface characteristics or model parameters for understanding underground formations.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Revelo, Mishev, and Le Calvez before them, to modify the microseismic model execution and moment tensor output workflow of Revelo and Mishev to include the borehole acquisition and geologic processing framework of Le Calvez, such that the trained neural network of Revelo is applied to microseismic data collected from downhole or borehole-deployed sensors in a monitored subterranean formation and used to generate moment tensor component estimates tied to that same formation.
The motivation for doing so would have been that Revelo itself recognizes the advantages of borehole-acquired microseismic measurements for improving moment tensor estimation accuracy and subsurface characterization. In particular, Revelo explains that “Surface and borehole seismic surveys are standard in microseismic monitoring, being the latter the preferred one for obtaining higher resolution results... The information obtained from borehole acquisition can be used to extract relevant properties of the target. Recorded data from borehole receivers can be inverted to estimate the seismic moment tensor...” (Chapter 4). A person of ordinary skill in the art would therefore have been motivated to incorporate the borehole acquisition framework of Le Calvez into Revelo’s machine learning workflow because Le Calvez provides a practical implementation of the same borehole-based monitoring approach identified by Revelo as preferable for higher resolution microseismic analysis. Combining these teachings would improve the quality and reliability of the seismic inputs provided to Revelo’s trained neural network, resulting in more accurate moment tensor estimates and better characterization of fracture behavior within the monitored subterranean region.
Regarding Claim 3:
Revelo teaches “generating a seismic based on the estimate of the values of the moment tensor components.” In particular, Revelo teaches that after the neural network produces the moment tensor components, “the results of the inversions can be depicted as scatter plots showing the difference between the predicted and true values, or by using the so-called ‘beach ball’ diagrams” (Section 3.2). This teaches that the estimated moment tensor components are not merely numerical outputs, but are used to construct a seismic representation of the event that reflects the underlying source mechanism. Revelo further explains that these representations encode physical wave motion behavior, stating that the shaded regions correspond to compressional motion at the event location and the unshaded regions correspond to dilatational motion (Section 3.2). This directly ties the moment tensor estimates to a seismic output that visualizes the seismic event characteristics derived from the model.
Regarding Claim 4:
Revelo discloses generating moment tensor component estimates using a trained machine learning model applied to microseismic data. In particular, Revelo teaches that the trained network is used to predict the six independent moment-tensor components and fault angles from seismic sources, and further explains that “once the network is trained, the moment-tensor components are predicted on the test set” (Revelo, Section 3.2). This teaches generating quantitative subsurface characterization outputs from microseismic data using a trained neural network. Mishev discloses evaluating subsurface model outputs using accuracy criteria to ensure the results are reliable for downstream use. In particular, Mishev teaches “determination whether a desired accuracy has been reached” (Mishev, Col. 11, lines 51–62), meaning the computed outputs are checked against a defined accuracy threshold before being used in reservoir analysis.
Neither Revelo nor Mishev appears to explicitly disclose “drilling a well in the given subterranean formation or performing hydraulic fracturing in the given subterranean formation based on the estimate of the values of the moment tensor components.”
However, Le Calvez discloses “drilling a well in the given subterranean formation or performing hydraulic fracturing in the given subterranean formation based on the estimate of the values of the moment tensor components.” Specifically, Le Calvez teaches “receiving microseismic data of microseismic events as acquired by sensors during hydraulic fracturing of a geologic region” (Le Calvez, Col. 23, lines 58–60), directly tying the acquired microseismic data to ongoing hydraulic fracturing operations. Le Calvez further explains that “downhole-based passive microseismic monitoring can include one or more arrays of 3C sensors to be lowered to appropriate distances in one or more monitoring boreholes” (Le Calvez, Col. 20, lines 41–45), confirming that the microseismic measurements are acquired from borehole-deployed sensors within the same subterranean formation undergoing treatment. Le Calvez further teaches that the resulting microseismic analysis is used to improve the fracture treatment and support drilling operations, thereby linking the analyzed subsurface outputs directly to operational decisions involving hydraulic fracturing and well development in the formation being monitored.
Revelo, Mishev, and Le Calvez are analogous art because they are each directed to computational analysis and interpretation of subsurface geophysical or reservoir data for characterizing subterranean formations and improving evaluation of subsurface conditions.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Revelo, Mishev, and Le Calvez before them, to modify the moment tensor estimation workflow of Revelo in view of the accuracy validation framework of Mishev to incorporate the operational hydraulic fracturing and drilling framework of Le Calvez, such that the estimated moment tensor component outputs generated from the trained machine learning model are used to guide drilling operations or hydraulic fracturing activities in the same subterranean formation from which the microseismic data was acquired.
The motivation for doing so would have been that Revelo already teaches using a trained neural network to generate moment tensor estimates that describe fracture behavior and subsurface seismic activity, while Mishev teaches confirming that those computational results satisfy a required accuracy threshold before they are relied upon in reservoir analysis. Le Calvez then shows that microseismic analysis results are used during actual hydraulic fracturing and drilling operations to improve fracture treatment and support wellsite decisions. A person having ordinary skill in the art would therefore have been motivated to combine these teachings because using the more accurate and validated moment tensor estimates from Revelo and Mishev within the operational workflow of Le Calvez would improve how drilling and hydraulic fracturing operations are evaluated and adjusted in the field. In particular, it would provide more reliable information about fracture behavior and subsurface conditions in the same formation being monitored, leading to better informed operational decisions during reservoir development (Le Calvez, Col. 20, lines 41–45).
Regarding Claim 5:
Revelo teaches “generating training data from the seismogram data comprises simulating seismic wave propagation from the sources to the receivers for one or more known values of the moment tensor components.” In particular, Revelo teaches that “the seismograms used to train the network were generated through the discrete-wavenumber method” (Abstract), which is a numerical wave-propagation technique used to model elastic wave propagation in layered media. This directly establishes that the training data is not observational only, but is instead constructed through forward simulation of seismic wave behavior in a subterranean formation model.
Revelo further details the underlying physical propagation modeling used to generate these training seismograms, explaining that for a layered earth model “for a stack of three layers (z1 < z < z3), the reflection and transmission matrices can be calculated” (Appendix Section 2). This shows that the seismogram generation is driven by explicit wave physics across subsurface layers, where source energy propagates through the modeled formation to the receivers.
Revelo further ties this simulated wave propagation directly to known moment tensor values used to construct labeled training data. Revelo explains that “for every set of angles, the true moment tensor is calculated and stored for the training phase”, and further that “the generated waveforms for every source in the square have information generated by different sets of angles so that the network can recognize many fault orientations” (Section 3.2).
Regarding Claim 6:
Revelo teaches “training the machine learning model using the training data comprises setting weight values of nodes represented in a neural network of the machine learning model.” In particular, Revelo explains the underlying neural network structure, stating that “the structure of an ANN resembles the way a biological brain collects and processes information… through the nerve system” (Section 1.2), which establishes a node-based computational architecture where information is processed through interconnected units. Revelo further clarifies how individual nodes operate within this architecture, explaining that “a neuron’s output y can be described as the weighted sum of the data observations… and a set of parameters… often referred to as weights” (Section 2.2). This directly ties each node’s output behavior to adjustable weight parameters, confirming that training involves configuring node-level weights. Revelo then explicitly describes the training mechanism used to set these weights, stating that “the training set is used to update the parameters of the network that minimizes the difference between the predicted and true values” (Section 3.2). This teaches iterative adjustment of network parameters during training, where the weights associated with nodes are continuously updated to reduce prediction error. Revelo further supports this node-based weight configuration by summarizing the neural network structure, noting that “the number of hidden layers, units, and activation functions per layer of the proposed ANN are summarized” (Section 2.2). Since the “units” correspond to the nodes whose outputs depend on weighted parameters, this confirms that the training process involves setting and adjusting weight values across those nodes within the neural network.
Regarding Claims 7-12:
Claims 7-12 have similar limitations as claims 1-6 as rejected above. Therefore, claims 7-12 are also rejected under the same rationale. In addition, claims 7-12 are directed to “a system … the system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations”. Revelo teaches a processor and memory in Section 3.7 by disclosing “The inversion results presented in this chapter were carried out using the following software and hardware specifications: Operating system: CentOS 7-9 ... CPU(S): 40 ... Memory (RAM): 256 GB.”
Regarding Claims 13-18:
Claims 13-18 have similar limitations as claims 1-6 as rejected above. Therefore, claims 13-18 are also rejected under the same rationale. In addition, claims 13-18 are directed to “one or more non-transitory computer-readable media storing instructions”. Revelo teaches One or more non-transitory computer-readable media storing instructions in Section 3.7 by disclosing “The inversion results presented in this chapter were carried out using the following software and hardware specifications: Operating system: CentOS 7-9 ... CPU(S): 40 ... Memory (RAM): 256 GB.”
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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