DETAILED ACTION
This office action is in response to application filed on October 27, 2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/27/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, “the list may not be incorporated into the specification but must be submitted in a separate paper.” Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
The examiner notes that the specification refers to multiple references in the description including patent and non-patent literature, which has neither been submitted to the Office for consideration nor has been listed in an information disclosure statement.
Response to Amendment
Preliminary amendments filed on October 27, 2023 have been entered.
The drawings have been amended.
Claims 3, 5-7, 10-15 and 18-19 have been amended.
Claims 1-20 have been examined.
Specification
The disclosure is objected to because of the following informalities:
[0006]: Language “However, the salt body may cause strong diffraction that scatters away the energy that should illuminate the target sediment, or causes multiples that makes “fake structures”” should read “However, the salt body may cause strong diffraction that scatters away the energy that should illuminate the target sediment, or causes multiples that make “fake structures”” in order to correct for minor informalities.
[0007]: Language “For example, the salt environment comes with complex structures of various scales, among which the presence of carbonates, anhydrites, volcanic ridges, and deep, thin salt layers demonstrates the main challenges to detecting the correct location of salt boundary” should read “For example, the salt environment comes with complex structures of various scales, among which the presence of carbonates, anhydrites, volcanic ridges, and deep, thin salt layers demonstrate the main challenges to detecting the correct location of salt boundary” in order to correct for minor informalities.
[0028]: Language should read “The term “subsurface model” as used herein refers to a numerical, spatial representation of a specified region or properties in the subsurface” in order to correct for minor informalities.
[0029]: Language should read “The term “geologic model” as used herein refers to a subsurface model that is aligned with specified geological feature such as faults and specified horizons” in order to correct for minor informalities.
[0030]: Language “The term “reservoir model” as used herein refer to a geologic model …” should read “The term “reservoir model” as used herein refers to a geologic model …” in order to correct for minor informalities.
[0055]: Language “As a consequence, the gradient flow may pass through Channel S to Channel T (via first junction 196) and through Channel S to Channel B (via second junction 198). In this way, when optimizing the loss function based on TOS or BOS, the decoder for salt body may also being trained” should read “As a consequence, the gradient flow may pass through Channel S to Channel T (via first junction 196) and through Channel S to Channel B (via second junction 198). In this way, when optimizing the loss function based on TOS or BOS, the decoder for salt body may also be trained” in order to correct for minor informalities.
[0056]: Language “At 210, input values, such as seismic images, and corresponding output values, such as labeled output (including output values for the manually-labeled salt feature and other manually-labeled feature(s))” should read “At 210, input values, such as seismic images, and corresponding output values, such as labeled output (including output values for the manually-labeled salt feature and other manually-labeled feature(s)) are obtained” in order to correct for minor informalities.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “A computer-implemented method for performing machine learning to generate and use a salt feature model, the method comprising:” should read “A computer-implemented method for performing machine learning to generate and use a salt feature model, the computer-implemented method comprising:” in order to provide appropriate antecedence basis.
Claim language “performing machine learning in order to train the salt feature model using the input values and the output values” should read “performing the machine learning in order to train the salt feature model using the input values and the corresponding output values” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 4 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Claim language “wherein the errors between one or both of the top of salt output and the top of salt label and the bottom of salt output and the bottom of salt label are used to train the salt mask model” should read “wherein the errors between one or both of the top of salt output and the top of salt label, and the bottom of salt output and the bottom of salt label are used to train the salt mask model” in order to clarify the recited subject matter.
Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Claim language “wherein the errors between the top of salt output and the top of salt label and the bottom of salt output and the bottom of salt label are used to train the salt mask model” should read “wherein the errors between the top of salt output and the top of salt label, and the bottom of salt output and the bottom of salt label are used to train the salt mask model” in order to clarify the recited subject matter.
Appropriate correction is required.
Claim 7 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 8 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 11 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 12 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 13 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 14 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 15 is objected to because of the following informalities:
Claim language should read “The computer-implemented method of claim [[1]]14, wherein the one or more secondary image transformation networks include fewer hidden layers than the image transformation network .
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Claim language “wherein the one or more secondary image transformation networks are between the salt mask output and one or both of the TOS feature output or the BOS feature output” should read “wherein the one or more secondary image transformation networks are between the salt mask output and the one or both of the TOS feature output or the BOS feature output” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 17 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 18 is objected to because of the following informalities:
Claim language should read “The computer-implemented method of claim 1, further comprising masking the input values corresponding to data associated with one or both of the salt feature label or the at least another feature label” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 19 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 20 is objected to because of the following informalities:
Claim language “The method of claim …” should read “The computer-implemented method of claim …” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation “performing machine learning in order to train the salt feature model using the input values and the output values, the machine learning including mapping the input values to a plurality of target output values, the plurality of target output values comprising a salt feature output and at least another feature output, the salt feature model being trained based on both errors between the salt feature output and the salt feature label and between the at least another feature output and the at least another feature label” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to manipulate data and train a model (e.g., mapping information for training a model and comparing information to model results, see specification at [0038], [0052], [0059], [0062]-[0063]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer implementation (i.e., machine learning), the limitation in the context of the claim mainly refers to applying mathematical concepts to manipulate data for training a model.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application when considering the claim as a whole. In particular, the additional elements recited in the claim:
“A computer-implemented method for performing machine learning to generate and use a salt feature model” generally links the use of the judicial exception to a particular technological environment or field of use (see specification at [0004]-[0008]) (see MPEP 2106.05(h)), while adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f));
“accessing input values and corresponding output values for a salt feature label and at least another feature label” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated, see specification at [0024], [0035], [0042], [0046], [0050]) (see MPEP 2106.05(g)); and
“using the salt feature model for hydrocarbon management” appends steps at a high level of generality such that substantially all practical applications of the judicial exception are covered (see specification at [0004], [0034], [0085]) (see MPEP 2106.05(c)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim, when considered as a whole, does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., modeling salt features), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h));
append generic computer implementation used to facilitate the application of the abstract idea (i.e., machine learning), which as indicated in the MPEP: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more” (see MPEP 2106.05(f));
recite extra-solution activities (i.e., mere data gathering by selecting a particular data source/type to be manipulated), which as indicated in the MPEP: “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process” (see MPEP 2106.05(g)); and
append steps at a high level of generality such that substantially all practical applications of the judicial exception are covered (i.e., using the salt feature model for hydrocarbon management), which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)) and “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not provide significantly more because this type of recitation is equivalent to the words “apply it”” (see MPEP 2106.05(f)).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 4-6, 10, 12 and 17-20), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering using a data type or source), generic computer components/implementation and/or field of use (Claims 2-3, 5-9, 11-17 and 19), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
Subject Matter Not Rejected Over Prior Art
Claims 1-20 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Kaul (US 20210270983 A1) discloses:
A computer-implemented method for performing machine learning to generate and use a salt feature model ([0063]-[0065]: a machine-learning workflow is used to identify and delineate a salt body and its boundaries (see also [0005]-[0006])), the method comprising:
accessing input values and corresponding output values for a salt feature label and at least another feature label (Fig. 4A, items 402-408; [0066]: seismic data (input values) in the form of a seismic cube is received, the seismic data being processed to obtain crossline slices and inline slices which are combined in order to produce samples of “seismic, top of salt (TOS) label” pairs (corresponding output values for a salt feature label and at least another feature label) (see also [0078]-[0085] regarding using depth information to train a second model to predict a salt body));
performing machine learning in order to train the salt feature model using the input values and the output values (Fig. 4A, item 410; [0066]: one or more models are generated based on the label pairs, which were generated using the seismic cube (see also [0067] regarding predicting entire seismic cube in inline and crossline directions, and combining these predictions to produce a probability cube of TOS labels, and [0078]-[0079] regarding using depth information to train a second model to predict a salt body)); and
using the salt feature model for hydrocarbon management ([0086]-[0092]: second model of the seismic cube is used for locating hydrocarbons).
Wang (Detao Wang et al., “Seismic Stratum Segmentation Using an Encoder-Decoder Convolutional Neural Network”, Mathematical Geosciences, Springer Berlin Heidelberg, Berlin/Heidelberg, vol. 53, no 6, February 12, 2021, IDS reference) discloses:
“In this paper, a specific U-shaped fully convolutional network (U-Net) is established for automatic seismic stratigraphic interpretation. Specifically, this task is formulated as a semantic segmentation problem by identifying strata at the pixel level and classifying each pixel in the image into a specific stratum category” (Abstract” a U-shaped fully convolutional network is used for strata classification (see also p. 1357, par. 1 regarding using U-Net for salt-body delimitation, as well as networks based on encoder-decoder structures)).
Liu (US 20190064378 A1, IDS reference) discloses:
“Training a fully convolutional neural network requires providing multiple pairs of input seismic and target label patches or volumes. A patch refers to an extracted portion of a seismic image (2D or 3D) that represents the region being analyzed by the network. The patch should contain sufficient information and context for the network to recognize the features of interest” ([0051]: convolutional neural network is trained using pairs of input seismic and target label patches or volumes); and
“Using fully convolutional networks allows for prediction on input images that are different in size from the patch size used for training. The input image can be propagated through the trained network using a sequence of operations defined by the network (FIG. 1.) and the parameters learned during training. The networks will always generate an output label map that is the same size relative to the input image” ([0061]: convolutional networks predict label maps (see also [0042] regarding detecting slat-bodies, and [0043] regarding training models based on errors)).
The closest prior art of record, taken individually or in combination, fail to teach or suggest:
“the machine learning including mapping the input values to a plurality of target output values, the plurality of target output values comprising a salt feature output and at least another feature output, the salt feature model being trained based on both errors between the salt feature output and the salt feature label and between the at least another feature output and the at least another feature label” (the examiner submits that the prior art of record mainly uses seismic data with label data for identification or classification of salt bodies, without disclosing the details of the recited machine learning mapping and errors)
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claims 2-20.
They are also distinguished over the prior art of record due to their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CHEN; Xiaoli et al., US 20230122128 A1, UNCERTAINTY ANALYSIS FOR NEURAL NETWORKS
Reference discloses using geophysical data as input to a neural network in order to predict a geophysical structure and corresponding uncertainties.
Ji, Xu, BenHasan, Nasher, Luo, Yi, Gashawbeza, Ewenet, and Saleh M. Saleh. “Recognition of salt zones in 3D seismic images using machine learning.” Paper presented at the SEG International Exposition and Annual Meeting, Virtual, October 2020. doi: https://doi.org/10.1190/segam2020-3426105.1
Reference discloses application of encoder-decoder U-Net convolutional neural network for identifying salt/non-salt voxels in 3D seismic images.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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