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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/11/2025 has been entered.
This action is non-final, and is in response to the amendments filed on 12/11/2025. Claims 1-20 are pending and have been considered. Claims 1, 11, and 18 are independent claims. Claims 1,11, 18 have been amended. No claims have been canceled.
Summary of claim rejections
Claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to judicial exception, an abstract idea of mental process, and it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. The rejections are also properly made final.
Claims 1-5, 7-15, 19-20 are rejected under U.S.C. 102 as being unpatentable over Denli et al (US 2020/0183047).
Claims 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Denli et al (US 2020/0183047) , in further view of Li et al (US 2022/0099855).
Claims 6-8, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Denli et al (US 2020/0183047) in view of Smith, Two methods for voxel detail enhancement PCGames '11: Proceedings of the 2nd International Workshop on Procedural Content Generation in Games Article No.: 6, Pages 1 – 4.
Response to Amendments and Arguments
In the amendment filed on 12/11/2025, applicant amended independent claims 1, 11, 18 to include new limitations. The amendments have been fully considered.
Some of the amendments use recitations that are not found in the original specification, for which the broadest reasonable interpretation is presenting in the following. In any further correspondence the Applicant is requested to clarify if this involves new matter or to clarify flagged differences between the original specification and the amended claims.
Applicant amended, in the independent claims, the limitation of “generating, by using the spatial context generator to generate, based on the contextual spatial information, at least one output dataset associated with the area of interest,”
The interpretation is in accordance with the amendment. However one makes of the record that that in the original specification (see at least [004] using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, where each output dataset includes simulated contextual spatial information for the area of interest; also Fig 7 step 706 shown below
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the generation is based on partial spatial information.
Regarding “the contextual spatial information providing geomorphological information identified as missing from the partial spatial information” , in broadest reasonable interpretation and in view of the specification “[0030]… where the contextual spatial information provides additional information (i.e., context) missing from the partial spatial information” the interpretation is additional information missing from the partial spatial information (without a specific emphasis on “identification”).
Similarly “identification” and “parameters” in the recited limitation “the simulated contextual spatial information comprising an identification of petrophysical parameters” No “identification” and no “parameters” are mentioned in the original specification, the only encounter of the word “parameter” is in [0086] “Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references” which is different than what the claim recites.
Regarding claim 1-20 rejected under 35 USC 101, the arguments have been considered but have not been found persuasive.
The independent claims have been amended to include limitations with additional elements intended to integrate into a practical application and provide significantly more. Unfortunately, these is not the case. Selecting well drilling based on the petrophysical parameters … continues to recite a mental process, unchanged by the word well or petrophysical parameters, a descriptor characterizing the field of use. Similarly, generating by using the spatial context generator remains a mental process, despite the use of a computer implementing the spatial context generator; the clarification that the context spatial information provides information identified as missing from the partial spatial information is simply providing clarification on the nature of context spatial information, and an eventual step of identification that is missing would itself be a mental process of observation, evaluation and decision making. The additional elements are either “apply it” or linking with a field of use, or data gathering/manipulation and do not integrate the judicial exception into a practical application and fail to provide significantly more. The detailed analysis is provided in the following.
Regarding claims 1-3, 5, 9-13, 15, and 19-20 rejected under 35 U.S.C. § 102 as being anticipated by U.S. Publication No. 2020/0183047 (hereinafter, "Denli"); the Examiner agrees that the at least the cited portions of Denli have not been shown to teach or to suggest at least "the contextual spatial information providing geomorphological information identified as missing from the partial spatial information," which is a new limitation and was not analyzed before. First, to clarify, in the embodiment cited by the Applicant, Denli does not teach that all data is available, but specifically uses the word “may” it says “may comprise available conditioning data” or “may comprise available inputs” etc. Nonetheless, Denli specifically teach the above limitation as shown in the analysis that follows. { [0100] When the generative model is introduced with the different types of surfaces, their unique labels may either be removed, maintained, or changed to provide additional context to the model} in broadest reasonable interpretation contextual spatial information is interpreted are the labels; “identified as missing” from the partial spatial information is interpreted as being additional – additional meaning it was not there before, hence they were missing, which in is consistent with the specification in the current application “[0030]… where the contextual spatial information provides additional information (i.e., context) missing from the partial spatial information” (as noted, the words “missing” and “geomorphological” were lacking in the recitation above in the specification).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 11, 18 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The amended claims recite “generating, by using the spatial context generator to generate, based on the contextual spatial information, at least one output dataset associated with the area of interest,” “
The original specification (see at least [004] using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, where each output dataset includes simulated contextual spatial information for the area of interest; also Fig 7 step 706 shown below
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the generation is based on partial spatial information.
Similarly “identification” and “parameters” in the recited limitation “the simulated contextual spatial information comprising an identification of petrophysical parameters” No “identification” and no “parameters” are mentioned in the original specification, the only encounter of the word “parameter” is in [0086] “Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references” which is different than what the claim recites.
Dependent claims 2-10, 12-17, 19-20, are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as they inherit the deficiency of the parent claims.
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 are analyzed under the Alice/Mayo framework to determine whether the claims are directed to an ineligible judicial exception. The number in the parenthesis, next to a claim number, is the number of the parent claim. Recitation of judicial exceptions are highlighted in bold font. Paraphrased language, shown in italics, is used to simplify reference. Claims with similar limitations, although not verbatim identical, that share the same rationale under Alice/Mayo steps Step 1 (S1) and Steps 2 Prongs A1, A2 and B (S2A1, S2A2, S2B) are grouped. The analysis is performed on a representative claim of each group. An additional analysis is performed if any claims in the group includes additional limitations.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, a judicial exception (abstract idea, mental process) without significantly more.
(S1) Prima facie, claims 1-20 are each directed to a statutory category of invention: process (Claims 1-10 directed to a method), machine (claims 11-17 directed to a system) and manufacture (claims 18-20 directed to a non-transitory computer readable medium).
(S2A1)
Claim 1 (representative for claims 11, 18) recites a computer-implemented method comprising: receiving an input dataset that represents partial spatial information of an area of interest within a subterranean region, the partial spatial information comprising photographic images of a geographic feature type forming a portion of a dataset at a particular location in the dataset; providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest comprising the dataset and an interpretation of the photographic images, the interpretation comprising a label of the geographic feature type, the contextual spatial information providing geomorphological information identified as missing from the partial spatial information; generating, by using the spatial context generator based on the contextual spatial information, at least one output dataset associated with the area of interest, wherein each output dataset comprises simulated contextual spatial information for the area of interest, the simulated contextual spatial information comprising an identification of petrophysical parameters; and selecting well drilling operations based on the petrophysical parameters of the at least one output dataset associated with the area of interest.
In addition, system claim 11 recites one or more processor and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors (to perform operations comprising).
In broadest reasonable interpretation and in view of the specification the claim recites the following limitations generating, based on contextual information, at least one output dataset for a region of interest and use the petrophysical characteristics in the dataset to select well drilling operations, which, when considered together as a single abstract idea for further analysis (as per the MPEP 2106.04 II. B guidance), recites a process aimed at: “deriving petrophysical characteristics of a region based on contextual data and using them to select well drilling”.
Regarding the claim elements “computer-implemented” and “using the spatial context generator” (which is an implementation of software on a generic computer), the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (MPEP § 2106.04(a)(2), subsection III)
This is a combination that, under its broadest reasonable interpretation covers performance of limitations expressing observation, evaluation, judgement and decision-making. Generating the additional information could be performed in the mind, through observation of information provided, evaluation of missing information, and judgement on generating the missing information. Making a selection also implies a mental process of evaluation of alternatives to be selected from and making a decision. A human would have been able to make a selection in the mind.
Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. These are Mental Processes – Concepts Performed in the Human Mind (MPEP § 2106.04(a)(2), subsection III).
Accordingly, claims 1, 11, 18 recite an abstract idea.
(S2A2)
The judicial exception is not integrated into a practical application, because the additional elements in the claim only amount to insignificant extra-solution activity, mere instructions to apply an exception or linking the use of a judicial exception to a particular technological environment or field of use. (see MPEP 2106.05(d); MPEP 2106.05(g); MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the additional elements ““using the spatial context generator” (which is an implementation of software on a generic computer)” recite computing elements at a high level of generality, which is equivalent to instructions to implement the abstract idea “by a computer” or “on a computer.”
Additional elements include: one or more processors configured to perform operations comprising and the memory containing instructions to execute the method includes additional elements for computing, that are Mere Instructions to Apply an Exception (MPEP § 2106.05(f)); receiving an input dataset that represents partial spatial information of an area of interest within a subterranean region, the partial spatial information comprising photographic images of a geographic feature type forming a portion of a dataset at a particular location in the dataset; includes additional elements, of data gathering and data manipulation, providing the input dataset to a spatial context generator includes additional elements, data manipulation and outputting MPEP §2106.05(d);); wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest includes additional elements, Mere Instructions to Apply an Exception (MPEP § 2106.05(f)) [R-01.2024]; wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest comprising the dataset and an interpretation of the photographic images, the interpretation comprising a label of the geographic feature type, the contextual spatial information providing geomorphological information identified as missing from the partial spatial information; includes additional elements, Mere Instructions to Apply an Exception, which can also be interpreted as linking the use of a judicial exception to a particular technological environment or field of use.; wherein each output dataset comprises simulated contextual spatial information for the area of interest, the simulated contextual spatial information comprising an identification of petrophysical parameters, additional elements linking the use of a judicial exception to a particular technological environment or field of use.
The ”wherein” modifiers above are nothing more than general links to the computing environment, which, as noted, amounts to instructions to “apply it,” or nothing more than descriptive limitations of claim elements, such as describing the nature, structure and/or content of claim elements (MPEP 2106.05(f)). These modifiers do not preclude from carrying out the identified abstract idea: “deriving petrophysical characteristics of a region based on contextual data and using them to select well drilling”. Therefore, those modifiers do not serve to integrate the identified abstract idea into a practical application.
Overall, the additional elements in the claim do not integrate the abstract idea into a practical application, because: (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment (see MPEP 2106.05 (e) and the Vanda memo).
(S2B)
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than insignificant extra-solution activity and mere instructions to apply an exception or generally linking the , which do not amount to significantly more than the abstract idea. Further, the insignificant extra-solution data gathering, data manipulation, and data transmission activities are also Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(ll) "The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii Performing repetitive calculations" ). The above Insignificant Extra Activity does not add a meaningful limitation to the process of computing the area. Similarly, additionally elements listed above that amount to Mere Instructions to Apply an Exception (MPEP § 2106.05(f)) simply recites a judicial exception with the words "apply it" (or an equivalent). As per MPEP § 2106.05(f)(1) 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 integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claim 1 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claims 2-10, 12-17 and 19-20 include the limitations of claims 1, 11, 18 which recite an abstract idea.
Claims 2, 12, 19 are also similar rejected under same rationale as cited above. These further recite: wherein the machine learning model is a conditional Generative Adversarial Network (cGAN). These dependent claims do not recite an additional abstract idea. However, they incorporate the limitations of the independent claims (1, 11, 18 respectively) which recite an abstract idea.
Step 2A Prong Two - for each of the claims, 2, 12, 19: the claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim (including those from the claim of which this claim depends) only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use. In addition to the respective limitations in the claim from which it depends, the additional elements in the claim wherein the machine learning model is a conditional Generative Adversarial Network (cGAN) only amount to nothing more than general links to the computing environment, which, as noted, amounts to instructions to “apply it,” or nothing more than descriptive limitations of claim elements, such as describing the nature, structure and/or content of claim elements (MPEP 2106.05(f)). These modifiers do not preclude from carrying out the identified abstract idea: “deriving petrophysical characteristics of a region based on contextual data and using them to select well drilling”. Therefore, those modifiers do not serve to integrate the identified abstract idea into a practical application. These additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, thus no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, which the courts found to not amount to “significantly more” than the judicial exception. The added additional element in current claim conditional Generative Adversarial Network (cGAN) is WURC as evidenced by the titles of the references in IDS filed 06/28/2024 and many in the IDS filed 05/18/2022. these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 2,12,19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claims 3, 13, 20 are also similar rejected under same rationale as cited above. These further recite: wherein the input dataset is a seismic dataset that represents the partial spatial information of the area of interest. These dependent claims do not recite an additional abstract idea. However, they incorporate the limitations of independent claims (1, 11, 18 respectively) which do recite an abstract idea.
Step 2A Prong Two - for each of the claims 3, 13, 20: the claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim (including those from the claim of which this claim depends) only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use. In addition to the respective limitations in the claim from which it depends, the additional elements in the claim wherein the input dataset is a seismic dataset that represents the partial spatial information of the area of interest only amount to Mere Instructions to Apply an Exception (MPEP § 2106.05(f)) [R-01.2024]. Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception, no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claim 3, 13, 19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 4, 14 are also similar rejected under same rationale as cited above. These further recites: wherein the input dataset is an input seismic cube that has a first dimension, and wherein each output dataset is an output seismic cube that has a second dimension larger than the first dimension. These dependent claims do not recite an additional abstract idea. However, they incorporate the limitations of independent claims (1, 11 respectively) which recite an abstract idea.
Step 2A Prong Two - for each of the claims 4, 14: the claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim (including those from the claim of which this claim depends) only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use. In addition to the respective limitations in the claim from which it depends, the additional elements in the claim wherein the input dataset is an input seismic cube that has a first dimension, and wherein each output dataset is an output seismic cube that has a second dimension larger than the first dimension only amount to linking the use of a judicial exception to a field of use (MPEP § 2106.05(h)) [R-01.2024]. Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, or linking the use of a judicial exception to a field of use, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 4, 14 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 5, 15 are also similar rejected under same rationale as cited above. These further recites: wherein the input dataset is a photographic image dataset that represents the partial spatial information of the area of interest. These dependent claims do not recite an additional abstract idea. However, they incorporate the limitations of independent claims (1, 11 respectively) which recite an abstract idea.
Step 2A Prong Two - for each of the claims 5, 15: the claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim (including those from the claim of which this claim depends) only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use. In addition to the respective limitations in the claim from which it depends, the additional elements in the claim wherein the input dataset is a photographic image dataset that represents the partial spatial information of the area of interest only amount to linking the use of a judicial exception to a field of use (MPEP § 2106.05(f)) [R-01.2024].
Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, or linking the use of a judicial exception to a field of use, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 5, 15 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 6, 16 are also similar rejected under same rationale as cited above. The claim recites:
[A] wherein the partial spatial information comprises a single 16x16x16 seismic volume and the simulated contextual spatial information for the area of interest comprises one or more 256x256x256 seismic volumes.
The elements in this dependent claim are comparable to receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea into a practical application (see MPEP 2106.05(f)(2)).
These further elements in the dependent claim do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – partial spatial information, simulated contextual spatial information and as such, cannot change the nature of the identified abstract idea (“ deriving petrophysical characteristics of a region based on contextual data and using them to select well drilling”), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, claim 6, 16 are deemed ineligible.
Claim 7 and 17 are also similar rejected under same rationale as cited above. Claim 7, dependent on Claim 6 (which is dependent on Claim 1 and incorporates all its limitations), and Claim 17, dependent on claim 16 (which is dependent on Claim 1 and incorporates all its limitations) and recite further: wherein the machine learning model is a conditional Generative Adversarial Network (cGAN), and wherein training the machine learning model comprises: training a generator network of the cGAN to generate the contextual spatial information for the area of interest based on the partial spatial information, wherein the generator network is trained based on feedback received from a discriminator network of the cGAN, and wherein the discriminator network is configured to distinguish between real data and simulated data generated by the generator network.
These dependent claims recite a judicial exception since the limitations of independent claim 1, respectively 11, which recite an abstract idea, are incorporated.
Step 2A Prong Two - for each of the claims 7, 17: the claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim (including those from the claim of which this claim depends on) only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use. In addition to the respective limitations in the claim from which it depends, the additional elements wherein the machine learning model is a conditional Generative Adversarial Network (cGAN), only amount to linking the use of a judicial exception to a field of use (MPEP § 2106.05(h)) [R-01.2024]; and wherein training the machine learning model comprises: only amounts to Mere Instructions to Apply an Exception (MPEP § 2106.05(f)) [R-01.2024]; training a generator network of the cGAN to generate the contextual spatial information for the area of interest based on the partial spatial information only amount to linking the use of a judicial exception to a field of use (MPEP § 2106.05(h)) [R-01.2024]; wherein the generator network is trained based on feedback received from a discriminator network of the cGAN, and only amounts to Mere Instructions to Apply an Exception (MPEP § 2106.05(f)) [R-01.2024];wherein the discriminator network is configured to distinguish between real data and simulated data generated by the generator network only amounts to Mere Instructions to Apply an Exception (MPEP § 2106.05(f)) [R-01.2024]. Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, or linking the use of a judicial exception to a field of use, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claim 7, 17 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 8, dependent on Claim 7 (which is dependent on Claim 6, which is dependent on Claim 1 and incorporates all respective limitations), similar rejected under the same rationale as cited above, further recites: wherein the real data and the simulated data are conditioned on training partial spatial information.
Claim 8 recites a judicial exception since it incorporates the limitations of independent claim 1, which recite an abstract idea. Furthermore, Claim 8 limitation wherein the real data and the simulated data are conditioned on training partial spatial information as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, expressing the evaluation and judgement which takes place when specifically tailoring the real and simulated data to specific conditions, such as, for example, class labels . Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (see MPEP § 2106.04(a)(2), subsection III). Accordingly, the claim recites an abstract idea under Step 2A Prong One.
Step 2A Prong Two: The claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim, all coming from Claim 1, only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use as were previously analyzed for Claim 7.
Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, or linking the use of a judicial exception to a field of use, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claim 8 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 9, dependent on Claim 1, similar rejected under same rationale as cited above, further recites: generating a model of the area of interest based on the at least one second seismic dataset.
Claim 9 recites a judicial exception since it incorporates the limitations of independent claim 1, which recites an abstract idea. Furthermore, Claim 9 limitation generating a model of the area of interest based on the at least one second seismic dataset as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, expressing the observation and analysis of the seismic dataset, identifying patterns, evaluation in the context of knowledge available, including conditioners, and judgement in the process of generating a representation, which is the model of the area of interest. Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (see MPEP § 2106.04(a)(2), subsection III). Accordingly, the claim recites an abstract idea under Step 2A Prong One.
Step 2A Prong Two: The claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim, all coming from Claim 1, only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use as were previously analyzed. Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, or linking the use of a judicial exception to a field of use, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claim 9 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 10, dependent on Claim 1, is similar rejected under the same rationale as cited above. It further recites: wherein the area of interest is at least one of a surface or subsurface.
Claim 10 does not recite an additional abstract idea. However, Claim 10 recites a judicial exception since it incorporates the limitations of independent claim 1, which recites an abstract idea.
Step 2A Prong Two: The claim as a whole does not integrate the recited judicial exception into a practical application, because the additional elements in the claim (including those from the claim 1 on which this claim depends) only amount to mere instructions to apply an exception, or adding insignificant extra-activity, or generally linking the use of judicial exception to a particular field of use. In addition to the respective limitations in the claim from which it depends, the additional elements in claim 10 wherein the area of interest is at least one of a surface or subsurface only amount to linking the use of a judicial exception to a field of use (MPEP § 2106.05(h)) [R-01.2024]. Therefore, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (including additional elements from independent claim 1) amount to no more than mere instructions to apply an exception, or to adding insignificant extra-solution activity to the judicial exception, or linking the use of a judicial exception to a field of use, which the courts found to not amount to “significantly more” than the judicial exception. Therefore, these additional elements, alone or in combination, as a whole, do not amount to significantly more than the judicial exception. Having concluded analysis within the
provided framework, claim 10 does not recite patent eligible subject matter under 35 U.S.C. § 101.
In summary, claims 1-20 are deemed ineligible under 35 U.S.C. §101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(g)(1) during the course of an interference conducted under section 135 or section 291, another inventor involved therein establishes, to the extent permitted in section 104, that before such person’s invention thereof the invention was made by such other inventor and not abandoned, suppressed, or concealed, or (2) before such person’s invention thereof, the invention was made in this country by another inventor who had not abandoned, suppressed, or concealed it. In determining priority of invention under this subsection, there shall be considered not only the respective dates of conception and reduction to practice of the invention, but also the reasonable diligence of one who was first to conceive and last to reduce to practice, from a time prior to conception by the other.
Claims 1-3, 5, 9-13, 15, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Denli et al (US 2020/0183047)
Claims 1, 11, 18 share similar limitations, with system claim 11 having one additional limitation. The analysis is performed on Claim 11. Since all limitations of Claims 1 and 18 are similar to limitations of Claim 11, Claim 1 and 18 are rejected under the same rationale presented for Claim 11.
Regarding Claim 11, Denli discloses a system comprising one or more processors and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors: { [0103…FIG. 11 is a diagram of an exemplary computer system 1300 that may be utilized to implement methods described herein. A central processing unit (CPU) 1302 is coupled to system bus 1304. The CPU 1302 may be any general-purpose CPU… while only a single CPU 1302 is shown in FIG. 11, additional CPUs may be present. [0104] The computer system 1300 may also include computer components such as non-transitory, computer-readable media..} One or more processors are interpreted as one or more CPUs. In BRI a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors is any non-transitory computer readable media.
receiving an input dataset that represents partial spatial information of an area of interest within a subterranean region, the partial spatial information comprising photographic images of a geographic feature type forming a portion of a dataset at a particular location in the dataset; { [0031] A 3-D geologic model (particularly a model represented in image form) may be represented in volume elements (voxels), in a similar way that a photograph (or 2-D geologic model) is represented by picture elements (pixels).
“[0072] Referring to the figures, FIG. 3 is a flow diagram 300 for generating multiple geological models using machine learning at one or more stages of the life cycle of oil and gas field (e.g., exploration, development and production). For example, machine learning may be used in any one, any combination, or all of: the petroleum exploration stage; the development stage; or the production stage. Exploration may include any one, any combination, or all of: analysis of geological maps (to identify major sedimentary basins); aerial photography (identify promising landscape formations such as faults or anticlines); or survey methods (e.g., seismic, magnetic, electromagnetic, gravity, gravimetric). Similarly, additional data may be generated in each of the subsequent stages of exploration; development (e.g., new densely-acquired broadband 3D seismic, well logs) or production (e.g., 4D or time-lapse seismic for monitoring reservoir).”
{Fig. 3(310), [0073] “At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. The life cycle of the oil and gas field may include any one, any combination, or all of: exploration; development; or production. As discussed above, various types of geophysical data (e.g., seismic data), various geological concepts (e.g., reservoir geological concepts, EODs or other concepts derived from experience or from the data), a set of interpreted surfaces (e.g., horizons or faults) or zones (e.g., strata, anticline structure and reservoir section), and various reservoir stratigraphic configurations (e.g., lithofacies learned from the well logs) may be used. In some or all embodiments, all of the available conditioning data relevant to the reservoir (or the target subsurface area) may be the input to a previously trained generative model.”}
In broadest reasonable interpretation and in view of the specification, this refers to partial spatial information of an area (of interest) within a subterranean region (i.e. below Earth surface), that includes any-image-like representation produced from data by an imaging process, not limited to optical photography. In the context of the application, this includes images generated from seismic data that resemble photographs (e.g cross-sectional images. It covers visual renderings of subterranean structures produced by wave reflection/refraction data, even if no light-based photography is involved. Thus the interpretation extends to analogous pictorial depictions of data.
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in broadest reasonable interpretation and in view of specification the dataset that represents partial spatial information of an area of interest, receiving is interpreted as be accessed. Denli’s disclosure includes the photograph-like representation, which is not tied with being optical photography, but other types, including for example seismic imaging or seismic photography.
This interpretation is also consistent with the use of the term in the industry, where people loosely call non-visible (i.e. non light-based) imaging “photography”, e.g. infrared photography, X-Ray photography”, which means capturing images of the underground features with waves other than visible light – and that includes, for example, seismic imaging (acoustic, not optic waves). Furthermore, petroleum exploration even uses the term “seismic photography”. A few examples from the many that can be found in an internet search: “I also had the opportunity to participate in new research work in the field of seismic photography.” https://www.kaust.edu.sa/en/news/alumni-focus--hassan-al-ismail , or, “With the aid of three-dimensional seismic photography, directional drilling… “ “https://www.heraldtribune.com/story/news/2005/03/20/drilling-for-oil-best-energy-option/28837608007/”
The Examiner interprets the limitation maps to various types of data that can be represented in a similar way that a photograph (or 2-D geologic model) is represented by picture elements (pixels). This includes optical photography and seismic, and other photographic-like image obtained at exploration, development or production, of a seismic .
of a geographic feature type forming a portion of a dataset at a particular location in the dataset {[0069] Such training will enable the generative network to learn reservoir features or patterns that correspond with the particular concept. In this way, the GAN may process different sections of the subsurface in order to analyze the potential universe of geological structures and how they comport with the given data}. In BRI a geographic feature type forming a portion of a dataset at a particular location of a dataset is interpreted as local data corresponding to a feature of the ground, and is mapped to reservoir feature or partterns, in a section of the subsurface.
providing the input dataset to a spatial context generator, { Fig.3 (310) [0073] At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed.} in broadest reasonable interpretation and in view of specification conditioning data available and for use as input which may be accessed, in interpreted as providing the input dataset, and generative network is interpreted as the spatial context generator.
wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest comprising the dataset and an interpretation of the photographic images,
the interpretation comprising a label of the geographic feature type,
{ Fig. 3(310), Fig. 3(320), Fig. 3(310); [0073] At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. …various types of geophysical data (e.g., seismic data), various geological concepts … may be used; [0031] A 3-D geologic model (particularly a model represented in image form) may be represented in volume elements (voxels), in a similar way that a photograph (or 2-D geologic model) is represented by picture elements (pixels).
[0074] At 320, machine learning is performed using the accessed data in order to train a machine learning model. At 330, one or more geological models for the respective stage of the life cycle are generated based on the machine learning model. [0100] When the generative model is introduced with the different types of surfaces, their unique labels may either be removed, maintained, or changed to provide additional context to the model} in broadest reasonable interpretation generative network is interpreted as the spatial context generator, seismic data as the partial spatial information, geological models consisting of seismic data, with labels to provide additional context, are interpreted as the contextual spatial information; photographic images interpreted as seismic images.
the contextual spatial information providing geomorphological information identified as missing from the partial spatial information; { [0100] When the generative model is introduced with the different types of surfaces, their unique labels may either be removed, maintained, or changed to provide additional context to the model} in broadest reasonable interpretation contextual spatial information is interpreted are the labels; “identified as missing” from the partial spatial information is interpreted as being additional – additional interpreted as they were not there before, hence they were missing, which in is consistent with the specification “[0030]… where the contextual spatial information provides additional information (i.e., context) missing from the partial spatial information” (words “missing” and “geomorphological” were lacking in the recitation above)
Generating, by using the spatial context generator, based on the contextual {“[0100] When the generative model is introduced with the different types of surfaces, their unique labels may either be removed, maintained, or changed to provide additional context to the model”; “[0102] FIGS. 9 and 10 illustrate respective sets of the interpreted surfaces, horizon and fault surfaces and automatically-generated reservoir model using the generative networks trained with the SEAM Foothill geological data… The corresponding outputs of the generative model trained with the paired samples from the structural framework and its seismic image (FIGS. 9(b) and 11(a) respectively of Regone et al. 2017) are shown in the second column of FIGS. 9 and 10 (1150, 1250).”} In broadest reasonable interpretation and in view of the specification the generative model is interpreted as the spatial context generator, the labels as the contextual spatial information, the outputs (1150, 1250) as the outputs sets associated with the area of interest.
wherein each output dataset comprises simulated contextual spatial information for the area of interest; the simulated contextual spatial information comprising an identification of petrophysical parameters; {[0016] FIG. 1 is a flow diagram from seismic to simulations for building reservoir models. [0062] Thus, in some implementations, machine learning generates one or more geological models, such as one or more reservoir models or one or more stratigraphic models that are consistent with applicable geological concepts and/or conditioning data (e.g., seismic and other available information useful to infer the plausible reservoir geology). In particular, machine learning may generate reservoir models (or interpret stratigraphy) that are automatically conditioned with any one, any combination, or all of: (1) seismic data; (2) interpreted surfaces; (3) geobodies; (4) petrophysical/rock physics models; (5) reservoir property models; (6) well log data; and (7) geological concepts; [0079] GANs include generative models that learn mapping from one or more inputs to an output (such as y, G: z.fwdarw.y where y is output (e.g., reservoir model)}In broadest reasonable interpretation, reservoir model built from simulations is interpreted as the simulated contextual spatial information, the output generated by machine model as the output dataset. [0031] The terms “velocity model,” “density model,” “physical property model,” or other similar terms as used herein refer to a numerical representation of parameters for subsurface regions. [0032] Subsurface model is a model (or map) associated with the physical properties of the subsurface (e.g., geophysical or petrophysical models) } the simulated contextual spatial information comprising an identification of petrophysical parameters is interpreted as the petrophysical subsurface model which is a numerical representation of the parameters for subsurface region.
Selecting well drilling operations based on the petrophysical parameters of the at least one output dataset associated with the area of interest. { [0108] For instance, methods according to various embodiments may include managing hydrocarbons based at least in part upon the one or more generated geological models and data representations (e.g., seismic images, feature probability maps, feature objects, etc.) constructed according to the above-described methods. In particular, such methods may include drilling a well, and/or causing a well to be drilled, based at least in part upon the one or more generated geological models and data representations discussed herein (e.g., such that the well is located based at least in part upon a location determined from the models and/or data representations, which location may optionally be informed by other inputs, data, and/or analyses, as well) and further prospecting for and/or producing hydrocarbons using the well.
[0031] The terms “velocity model,” “density model,” “physical property model,” or other similar terms as used herein refer to a numerical representation of parameters for subsurface regions. [0032] Subsurface model is a model (or map) associated with the physical properties of the subsurface (e.g., geophysical or petrophysical models) } which selecting drilling operations based on the at least one output dataset associated with the area of interest, In BRI and in view of the specification that regarding drilling recites only in one place (apart of this limitation, which was not in the original specification and only appeared first time in the amended claims) “[Background, 0003] The images are interpreted. This interpretation…may include decisions about field development, such locations to drill future wells”, is interpreted as the interpretation method helps make decisions about drilling which is mapped to the underlined fragment above.
Regarding Claims 2, 12, 19 – Denli discloses the limitations of Claims 1, 11, 18. Denli also discloses:
wherein the machine learning model is a conditional Generative Adversarial Network (cGAN) {[0077] As discussed above, various machine learning methodologies are contemplated. As one example, a generative adversarial network (GAN) may be used, such as illustrated in FIGS. 6A-B. In this regard, any discussion regarding the application of GAN to generate and/or evaluate geological models may likewise be applied to other machine learning methodologies. [0078] Specifically, FIG. 6A is a first example block diagram 600 of a conditional generative-adversarial neural network}
Regarding Claims 3, 13, 20 – Denli discloses the limitations of Claims 1, 11, 18. Denli also discloses:
herein the input dataset is a seismic dataset that represents the partial spatial information of the area of interest. {Fig. 3(310), [0073] “At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. The life cycle of the oil and gas field may include any one, any combination, or all of: exploration; development; or production. As discussed above, various types of geophysical data (e.g., seismic data), various geological concepts (e.g., reservoir geological concepts, EODs or other concepts derived from experience or from the data), a set of interpreted surfaces (e.g., horizons or faults) or zones (e.g., strata, anticline structure and reservoir section), and various reservoir stratigraphic configurations (e.g., lithofacies learned from the well logs) may be used. In some or all embodiments, all of the available conditioning data relevant to the reservoir (or the target subsurface area) may be the input to a previously trained generative model.”} in broadest reasonable interpretation and in view of specification seismic data is interpreted as the dataset that represents partial spatial information of an area of interest.
Regarding Claims 5, 15 – Denli discloses the limitations of Claims 1 to which 5 depends, and 11, to which 15 depends on. Denli also discloses:
wherein the input dataset is a photographic image dataset that represents the partial spatial information of the area of interest. { [0031] A 3-D geologic model (particularly a model represented in image form) may be represented in volume elements (voxels), in a similar way that a photograph (or 2-D geologic model) is represented by picture elements (pixels); [0072] Referring to the figures, FIG. 3 is a flow diagram 300 for generating multiple geological models using machine learning at one or more stages of the life cycle of oil and gas field (e.g., exploration, development and production). For example, machine learning may be used in any one, any combination, or all of: the petroleum exploration stage; the development stage; or the production stage. Exploration may include any one, any combination, or all of: analysis of geological maps (to identify major sedimentary basins); aerial photography (identify promising landscape formations such as faults or anticlines); or survey methods (e.g., seismic, magnetic, electromagnetic, gravity, gravimetric). Similarly, additional data may be generated in each of the subsequent stages of exploration; development (e.g., new densely-acquired broadband 3D seismic, well logs) or production (e.g., 4D or time-lapse seismic for monitoring reservoir).”
{Fig. 3(310), [0073] “At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. The life cycle of the oil and gas field may include any one, any combination, or all of: exploration; development; or production. As discussed above, various types of geophysical data (e.g., seismic data), various geological concepts (e.g., reservoir geological concepts, EODs or other concepts derived from experience or from the data), a set of interpreted surfaces (e.g., horizons or faults) or zones (e.g., strata, anticline structure and reservoir section), and various reservoir stratigraphic configurations (e.g., lithofacies learned from the well logs) may be used. In some or all embodiments, all of the available conditioning data relevant to the reservoir (or the target subsurface area) may be the input to a previously trained generative model.”}
In broadest reasonable interpretation and in view of the specification, photographic image dataset that represents the partial spatial information of the area of interest this refers to partial spatial information of an area (of interest) that includes any-image-like representation produced from data by an imaging process, not limited to optical photography. In the context of the application, this includes images generated from seismic data that resemble photographs (e.g cross-sectional images). Thus, it covers visual renderings of subterranean structures produced by wave reflection/refraction data, even if no light-based photography is involved. Thus the BRI is analogous pictorial depictions of data.
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Receiving is interpreted as be accessed. Denli’s disclosure includes the photograph-like representation, which is tied with a number of image sensing modalities, including for example seismic imaging or seismic photography. Thus, Examiner interprets the limitation maps to various types of data that can be represented in a similar way that a photograph (or 2-D geologic model) is represented by picture elements (pixels), and which includes optical photography and seismic, and other photographic-like image obtained at exploration, development or production, of a seismic .
Regarding Claim 9. – Denli discloses the limitations of Claims 1 to which 9 depends. Denli also discloses:
generating a model of the area of interest based on the at least one second seismic dataset.
{[0076] Thereafter, responsive to obtaining additional data responsive to reservoir development, an updated set of applicable conditioning data (e.g., second stage data) may be used in addition to the available prior conditioning data from exploration stage by the machine learning methodology in order to generate the geological models; [0073] “At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. The life cycle of the oil and gas field may include any one, any combination, or all of: exploration; development; or production. As discussed above, various types of geophysical data (e.g., seismic data); [0073] In some or all embodiments, all of the available conditioning data relevant to the reservoir (or the target subsurface area) may be the input to a previously trained generative model to generate one or more geological models in the respective stage.}. Generating the geological model interpreted as generating a model, target area is interpreted as the area of interest,
an updated set of conditioning data (second stage data), which is seismic, interpreted as based on at least a second seismic data.
Regarding Claim 10 – Denli discloses the limitations of Claims 1. Denli also discloses:
wherein the area of interest is at least one of a surface or subsurface. {[Abstract] machine learning may be used to generate one or more reservoir models that characterize the subsurface. [0007] (i) seismic data 110 is processed to generate a geophysical model 120, which may define one or more geophysical properties (e.g., compressional and shear wave velocities, density, anisotropy and attenuation) of the subsurface. [Claim 10]. The method of claim 9, wherein the one or more input geological models of the subsurface comprise simulated reservoir models of the subsurface. [0073] In some or all embodiments, all of the available conditioning data relevant to the reservoir (or the target subsurface area) may be the input to a previously trained generative model to generate one or more geological models in the respective stage.} Target area is interpreted as the area of interest, which is of the subsurface.
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 difference 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 the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Denli et al (US 2020/0183047) , in further view of Li et al (US 2022/0099855).
Regarding Claims 4, 14 – Denli discloses the limitations of Claims 1 to which 4 depends, and 11, to which 16 depends on. Denli does not disclose, however Li discloses:
wherein the input dataset is an input seismic cube that has a first dimension {[0186] FIG. 11 shows an input block 1110 for receipt of seismic data as a “cube” with appropriate dimensions in x, y and z; noting that they need not be equal (e.g., the term “cube” as applied to seismic data is to mean volumetric and not necessarily of uniform x, y and z dimensions). While the seismic data may be “raw”, it may also be or include seismic data subjected to some amount of processing such as, for example, seismic attribute processing, filtering, normalization, etc.}
wherein each output dataset is an output seismic cube that has a second dimension larger than the first dimension. {[0186] FIG. 11 shows an input block 1110 for receipt of seismic data as a “cube” with appropriate dimensions in x, y and z; noting that they need not be equal (e.g., the term “cube” as applied to seismic data is to mean volumetric and not necessarily of uniform x, y and z dimensions). While the seismic data may be “raw”, it may also be or include seismic data subjected to some amount of processing such as, for example, seismic attribute processing, filtering, normalization, etc.; [0231] As mentioned, a method can include generating a series of outputs of 2D stratigraphic units based on a slice of seismic image data from a seismic cube. In such an example, the method can include interpolating between the series of 2D stratigraphic units to generate a 3D model of stratigraphic units. As to interpolation, linear and/or nonlinear approaches may be implemented. As an example, a spline fitting approach may be implemented where constraints may be imposed, for example, based on output from a slice that may be orthogonal to the series of 2D stratigraphic units. As an example, a method can include generating a series of 2D stratigraphic units along a first dimension and generating a series of 2D stratigraphic units along a second dimension, which may be orthogonal to the first dimension. In such an example, a 3D model of stratigraphic units may be built using the two series (e.g., or more series), optionally using interpolation.}
In addition, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify to include elements of Li. Denli teaches taking seismic data as an example of spatial data from the subsurface, to be used as input for machine learning training. Denli uses the training to obtain mode context for the model. One would have been motivated to use a volumetric data with cube shape (rectangular cross-sections more precisely) since it offers advantages in computer processing, for example in structured data storage, as cube-based (or rectangular grid) aligns well with memory structures and processing algorithms used in computing; moreover, it is efficient for matrix-mased numerical solvers, allows fast spatial interpolation, and has consistent resolution in all directions. The advantage of having one additional dimension for the outputs is that it allows locations where to add the context which is what is aimed for. One would have had a reasonable expectation of success, thus predictable results, since it is a most suitable form of data format for computing and data storage. Furthermore, both prior art elements, of Denli and Li are in the same or similar context of obtaining seismic data for training machine learning models for interpretation. Examiner concludes the claimed subject matter is obvious over Denli/Li.
Claims 6-8, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Denli et al (US 2020/0183047) , in further view of Smith, Two methods for voxel detail enhancement PCGames '11: Proceedings of the 2nd International Workshop on Procedural Content Generation in Games Article No.: 6, Pages 1 – 4 https://doi.org/10.1145/2000919.20009
Regarding Claims 6, 16 – Denli discloses the limitations of Claims 1 to which 4 depends, and 11, to which 16 depends on. Denli does not disclose, however Smith discloses:
wherein the partial spatial information comprises a single 16x16x16 seismic volume and the simulated contextual spatial information for the area of interest comprises one or more 256x256x256 seismic volumes. {Figure 1. Enhancing a 16x16x16 input voxel map (left) to produce a detailed, 256x256x256 output fragment map (right) }
In addition, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Denli to include elements of Smith. Denli teaches three-dimensional (geophysical) augmentation and generating such simulated contextual spatial information. Smith is relied upon to provide dimensions for the volumetric data, One would have been motivated to use cube sizes of lateral dimension 16 (2^4) and 256 (2^8) since these are binary friendly dimensions that align with radix-2 algorithms (e.g. FFT) and memory addressing, yielding predictable speed-ups and simple indexing. A POSITA would naturally select powers of two for block and volume sizes to optimize performance. Similarly POSITA would chose 16^3 tiles since it is a cash-friendly working set for CPUs/GPU fitting common shared memory strategies. One would have had a reasonable expectation of success, thus predictable results, since it is a most suitable form of data format for computing and data storage. Furthermore, both prior art elements, of Denli and Smith are deal with methods of image enhancement/reconstruction. Examiner concludes the claimed subject matter is obvious over Denli/Smith.
Regarding Claims 7, 17 – Denli, Smith discloses the limitations of Claims 6 to which Claims 7 depends on, respectively 16 to which 17 depends on. Denli also discloses:
wherein the machine learning model is a conditional Generative Adversarial Network (cGAN) {Claim 5. The method of claim 4, wherein the machine learning model comprises a generative adversarial network (GAN) including a generator and a discriminator. [0021] FIG. 6A is a first example block diagram of a conditional generative-adversarial neural network (CGAN) }
wherein training the machine learning model comprises: {Claim 5. The method of claim 4, wherein the machine learning model comprises a generative adversarial network (GAN) ; [0069] For example, during GAN training… }
training a generator network of the cGAN to generate the contextual spatial information for the area of interest based on the partial spatial information { [0049] Generative Adversarial Network (GAN) is an artificial network system including generator (or interpreter) and discriminator network used for training the generative network model; [0021] FIG. 6A is a first example block diagram of a conditional generative-adversarial neural network (CGAN); [0069] For example, during GAN training, a section from the mask volume may be extracted. There may be multiple potential concepts (e.g., different potential geological templates) associated with the extracted section.; Fig. 3(310), Fig. 3(320), Fig. 3(310); [0073] At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. …various types of geophysical data (e.g., seismic data), various geological concepts … may be used; [0074] At 320, machine learning is performed using the accessed data in order to train a machine learning model. At 330, one or more geological models for the respective stage of the life cycle are generated based on the machine learning model. [0100] When the generative model is introduced with the different types of surfaces, their unique labels may either be removed, maintained, or changed to provide additional context to the model} in broadest reasonable interpretation generative network is interpreted as the spatial context generator, seismic data as the partial spatial information, geological models consisting of seismic data, with labels to provide additional context, are interpreted as the contextual spatial information.
wherein the generator network is trained based on feedback received from a discriminator network of the cGAN, {Claim 5. The method of claim 4, wherein the machine learning model comprises a generative adversarial network (GAN) including a generator and a discriminator. [0046] Training (machine learning) is typically an iterative process of adjusting the parameters of a neural network to minimize a loss function which may be based on an analytical function (e.g., binary cross entropy) or based on a neural network (e.g., discriminator); [0078] Specifically, FIG. 6A is a first example block diagram 600 of a conditional generative-adversarial neural network}} In broadest reasonable interpretation the generator network is the generator network and what the discriminator neural network provides to minimize the loss function, which is part of the training, is the feedback from the discriminator network. The discriminator network is a component of the GAN, which can be a cGAN.
wherein the discriminator network is configured to distinguish between real data and simulated data generated by the generator network. {[0083] The generative model G may be trained iteratively by solving an optimization problem which may be based on an objective functional involving discriminator D and a measure of reconstruction loss (e.g., an indication of the similarity of the generated data to the ground truth) and/or adversarial loss (e.g., loss related to discriminator being able to discern the difference between the generated data and ground truth).} Ground truth is interpreted to be real data, generated data be simulated data generated by generator network.
Regarding Claims 8 – Denli discloses the limitations of Claim 7 to which Claim 8 depends on. Denli also discloses:
wherein the real data and the simulated data are conditioned on training partial spatial information. { {[0083] The generative model G may be trained iteratively by solving an optimization problem which may be based on an objective functional involving discriminator D and a measure of reconstruction loss (e.g., an indication of the similarity of the generated data to the ground truth) and/or adversarial loss (e.g., loss related to discriminator being able to discern the difference between the generated data and ground truth); [0073] At 310, various conditioning data, available for a respective stage of the life cycle of an oil and gas field and for use as input to the generative network, may be accessed. The life cycle of the oil and gas field may include any one, any combination, or all of: exploration; development; or production. As discussed above, various types of geophysical data (e.g., seismic data)…}. Ground truth is interpreted to be real data, generated data be simulated data generated by generator network. Conditioning data used in training, which is seismic data as the training partial spatial information.
Prior art made of record
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
Wei et al. US 2021/0293983 FACILITATING HYDROCARBON EXPLORATION AND EXTRACTION BY APPLYING A MACHINE-LEARNING MODEL TO SEISMIC DATA
Abstract: Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.
Lai, S-H et al, US 20190236759 A1, METHOD OF IMAGE COMPLETION
Abstract: A method of image completion comprises: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types; inputting the training image corresponding to the plurality of objective types such that the plurality of conditional generative adversarial networks respectively conduct a corruption feature training; inputting the image in need of repair and respectively conducting an image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images; and judging a reasonable probability of the plurality of repaired images through a probability analyzer, choosing an accomplished image and outputting the accomplished image through an output interface.
Baumstein, A. et al 20210318458 A1 Methodology For Enhancing Properties Of Geophysical Data With Deep Learning Networks
Abstract: A method for enhancing properties of geophysical data with deep learning networks. Geophysical data may be acquired by positioning a source of sound waves at a chosen shot location, and measuring back-scattered energy generated by the source using receivers placed at selected locations. For example, seismic data may be collected using towed streamer acquisition in order to derive subsurface properties or to form images of the subsurface. However, towed streamer data may be deficient in one or more properties (e.g., at low frequencies). To compensate for the deficiencies, another survey (such as an Ocean Bottom Nodes (OBN) survey) may be sparsely acquired in order to train a neural network. The trained neural network may then be used to compensate for the towed streamer deficient properties, such as by using the trained neural network to extend the towed streamer data to the low frequencies.
Liu, W.D. et al US 11397272 B2 Data Augmentation For Seismic Interpretation Systems And Methods
Abstract: A method and apparatus for machine learning for use with automated seismic interpretation include: obtaining input data; extracting patches from a pre-extraction dataset based on the input data; transforming data of a pre-transformation dataset based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and generating augmented data from the extracted patches and the transformed data. A method and apparatus for machine learning for use with automated seismic interpretation include: a data input module configured to obtain input data; a patch extraction module configured to extract patches from a pre-extraction dataset that is based on the input data; a data transformation module configured to transform data from a pre-transformation dataset that is based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and a data augmentation module configured to augment data from the extracted patches and the transformed data.
Fuchey, Y. et al US 20240077642 A1 GEOLOGIC ANALOGUE SEARCH FRAMEWORK
Abstract: A method can include, responsive to receipt of a search instruction that includes one or more search criteria, accessing a data structure for subsurface geologic regions categorized at least in part according to parameters that describe depositional environments, where the data structure includes one or more includes virtual distances between the parameters; generating a search result using the one or more search criteria and the data structure, where the search result represents an organization of at least a portion of subsurface geologic regions as closest analogues to the one or more search criteria; and transmitting search result information for graphically rendering the search result to a display as part of an interactive graphical user interface.
Conclusion
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/A.S./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188