DETAILED ACTION
This office action is in response to communication filed on July 22, 2026.
Response to Amendment
Amendments filed on July 22, 2026 have been entered.
The specification has been amended.
Claims 1, 5-6, 15-16 and 18 have been amended.
Claims 1-20 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 9), filed on 07/22/2026, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 9), filed on 07/22/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn.
Applicant’s arguments, see Remarks (p. 9-11), filed on 07/22/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 9-10) that When considering the claims as whole, the independent claims are directed to a process for hydrocarbon management that trains a salt feature model using at least one other feature output. The training is “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.” Although the claim includes “accessing input values and corresponding output values” for both the salt feature label and another feature label, the claims are directed to the practical applications of hydrocarbon management.
These arguments are not persuasive.
The examiner submits that the claimed invention, when considered as a whole, is directed to collecting and manipulating data using machine learning to obtain a model, while adding extra-solution activities (e.g., mere data gathering, source/type of data being evaluated), a field of use (e.g., salt feature modeling), and mere computer implementation (i.e., machine learning), which under the current Office’s guidance:
“For data, mere “manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’” has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994))” (see MPEP 2106.05(c));
“… in Parker v. Flook, the Court found that the claim recited a mathematical formula. This determination was not altered by the fact that the math was being used to solve an engineering problem (i.e., updating an alarm limit during catalytic conversion processes)” (see October 2019 Update: Subject Matter Eligibility, p. 3);
“Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering … Selecting a particular data source or type of data to be manipulated” (see MPEP 2106.05(g));
“Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: … vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)” (see MPEP 2106.05(h)); and
“For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation” (see MPEP 2106.05(h)).
The examiner further submits that the claimed invention seeks patent protection for the concept of mapping information for training a model and comparing information to model results, which can be performed either by mental processes or using mathematical concepts (e.g., identifying best fit between collected output values and corresponding model outputs), while generally reciting an intended use for the model (i.e., for hydrocarbon management), which according to the current guidance:
“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)).
Applicant also argues (p. 10) that Even assuming arguendo, that the claims are directed to a judicial exception (which they are not), “Claims that are directed to improvements in computer functionality or other technology are not abstract.” MPEP § 2106 … Applicant respectfully asserts that independent claim 1 is directed to just such an improvement in hydrocarbon management. Specifically, independent claim 1 is directed to methods for performing hydrocarbon management using salt feature models where the salt feature models are trained using, not only salt feature inputs and outputs, but also other feature inputs and outputs. By incorporating other (non-salt) feature inputs and outputs into the training of the salt feature models, hydrocarbon management may be improved given a limited amount of salt training data. This can enhance prediction quality of the salt feature model, particularly in the absence of sufficient quality labels. At the least, embodiments may improve hydrocarbon management by quantifying uncertainties in the salt feature model. See paragraphs [0038]-[0040] of the specification. As such, the claims are directed to a novel tool that uses non-salt features in the generation of a salt feature model that directly improves hydrocarbon management from a salt feature model. Moreover, independent claim 1 is explicitly directed to just such an improvement.
These arguments are not persuasive.
The examiner submits that adding machine learning to an abstract idea limited to a field of use does not render the judicial exception eligible as indicated by the current guidance: “in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described “the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’” 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of “collecting, displaying, and manipulating data.” 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it”. 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims “so result focused, so functional, as to effectively cover any solution to an identified problem”))” (see MPEP 2106.05(f)).
Additionally, the examiner submits that according to the current Office’s guidance: “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel or newly discovered, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were “‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection.” Myriad, 569 U.S. 576, 589, 106 USPQ2d at 1976, 1978 (noting that Myriad discovered the BRCA1 and BRCA1 genes and quoting Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 (“the novelty of the mathematical algorithm is not a determining factor at all”); Mayo, 566 U.S. 73-74, 78, 101 USPQ2d 1966, 1968 (noting that the claims embody the researcher’s discoveries of laws of nature). The Supreme Court’s cited rationale for considering even “just discovered” judicial exceptions as exceptions stems from the concern that “without this exception, there would be considerable danger that the grant of patents would ‘tie up’ the use of such tools and thereby ‘inhibit future innovation premised upon them.’” Myriad, 569 U.S. at 589, 106 USPQ2d at 1978-79 (quoting Mayo, 566 U.S. at 86, 101 USPQ2d at 1971). See also Myriad, 569 U.S. at 591, 106 USPQ2d at 1979 (“Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry.”). The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was “new”. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a new abstract idea is still an abstract idea”) (emphasis in original)” (see MPEP 2106.04).
Applicant further argues (p. 11) that Applicant respectfully asserts that the independent claim is not a drafting effort designed to monopolize hydrocarbon management. As noted above, the amended independent claims require “performing the machine learning in order to train the salt feature model using the input values and the corresponding output values ... 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.” Such limitations establish a meaningful limit on the practical application of hydrocarbon management.
These arguments are not persuasive.
The examiner submits that as described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence: “Even if the judicial exception is narrow (e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”).
Furthermore, the examiner submits that as explained in the MPEP: ““Likewise, Einstein could not patent his celebrated law that E=mc2; nor could Newton have patented the law of gravity.” Id. Nor can one patent “a novel and useful mathematical formula,” Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978); electromagnetism or steam power, O’Reilly v. Morse, 56 U.S. (15 How.) 62, 113-114 (1853); or “[t]he qualities of ... bacteria, ... the heat of the sun, electricity, or the qualities of metals,” Funk, 333 U.S. at 130, 76 USPQ at 281; see also Le Roy v. Tatham, 55 U.S. (14 How.) 156, 175 (1853)” (see MPEP 2106.04(b)).
Information Disclosure Statement
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.
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 the machine learning in order to train the salt feature model using the input values and the corresponding 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 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 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 the machine learning in order to train the salt feature model using the input values and the corresponding 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.
M. Alfarhan, M. Deriche and A. Maalej, “Robust Concurrent Detection of Salt Domes and Faults in Seismic Surveys Using an Improved UNet Architecture,” in IEEE Access, vol. 10, pp. 39424-39435, 2020, doi: 10.1109/ACCESS.2020.3043973
Reference discloses use of convolutional neural networks for salt domes identification.
Shi, Yunzhi, Wu, Xinming, and Sergey Fomel. “Automatic salt-body classification using a deep convolutional neural network.” Paper presented at the 2018 SEG International Exposition and Annual Meeting, Anaheim, California, USA, October 2018. doi: https://doi.org/10.1190/segam2018-2997304.1
Reference discloses use of multi-layer convolutional network for salt body detection using seismic images.
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.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW SCHECHTER can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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