DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 10/29/2025 has been entered. Claim(s) 1, 4-11, 13-20 is/are now pending in the application. Applicant's amendments have addressed all informalities as previously set forth in the non-final action mailed on 07/29/2025.
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.
Claim(s) 15-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 15 recites
generating a plurality of synthetic datasets,
wherein
each synthetic dataset comprises an input seismic dataset with a first bandwidth and an associated target seismic dataset with a second bandwidth,
wherein
the second bandwidth is broader, by extending to higher frequencies, than the first bandwidth,
wherein
generating a given synthetic dataset of the plurality of synthetic datasets comprises:
obtaining, for the given synthetic dataset, synthetic data generation parameters,
initializing a time domain as a collection of traces organized according to a simulated location and aligned along a time axis,
each trace initialized with a value of zero or a random value at each time comprises by the time axis,
determining, using the synthetic data generation parameters, a number of events to be added into the time domain,for each event;
determining, using the data generation parameters, a geometric shape, shape parameters, and an event location;
and
superimposing the event into the time domain by altering the value of each trace at each time that intersects the event based on the geometric shape, shape parameters, and event location,
selecting, using the data generation parameters, a wavelet and wavelet parameters,
duplicating the time domain to form a first time domain and a second time domain,
setting the selected wavelet to have a first width and convolving the selected wavelet with the traces of the first time domain to form an input seismic dataset of the given synthetic dataset,
and
setting the selected wavelet to have a second width and convolving the selected wavelet with the traces of the second time domain to form a target seismic dataset of the given synthetic dataset,
wherein
the second width is smaller than the first width;
splitting the plurality of synthetic datasets into a training set;
selecting the machine-learned model type and architecture;
training the machine-learned model using the training set,
wherein
the machine-learned model is trained to
receive a seismic dataset
and
output another seismic dataset with extended bandwidth relative to the seismic dataset
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation].
In combination with Independent Claim(s) 15, Claim(s) 16-20 recite(s)
wherein
generating the given synthetic dataset of the plurality of synthetic datasets further comprises
adding noise to the given synthetic dataset.
further comprising:
splitting the plurality of synthetic datasets into a test set; and
estimating a machine-learned model generalization error using the test set.
further comprising:
applying an amplitude function to each event in each synthetic dataset;
applying random noise to each synthetic dataset; and
applying static perturbations to each synthetic data set.
wherein
the machine-learned model is
a convolutional neural network.
further comprising:
splitting the plurality of synthetic datasets into a validation set;
evaluating the machine-learned model on the validation set; and
adjusting the machine-learned model type and architecture according to the evaluation of the machine-learned model on the validation set
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
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)) (i.e. A computer-implemented method);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output, displaying, storing, etc.); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)).
Examiner’s Note - 35 USC § 101
Examiner advises applicant that incorporating the amended language of independent claim(s) 1 (e.g., “planning a wellbore to penetrate the hydrocarbon reservoir based on the location, wherein the planned wellbore comprises a planned wellbore path; and drilling the wellbore guided by the planned wellbore path”) into independent claim(s) 15 would add a practical application in a particular technical field, therefore making the claim(s) patent eligible under 35 USC 101.
Allowable Subject Matter
Claim(s) 1, 4-11, 13, 14 is/are allowed.
Allowable Subject Matter (over Prior Art)
The following is a statement of reasons for the indication of allowable subject matter over prior art:
None of the cited prior art alone or in combination provides motivation to explicitly teach:
collecting a seismic dataset from a seismic survey conducted over a subterranean region of interest;
generating a plurality of synthetic datasets, wherein each synthetic dataset comprises an input seismic dataset with a first bandwidth and an associated target seismic dataset with a second bandwidth,
wherein
the second bandwidth is broader, by extending to higher frequencies, than the first bandwidth,
wherein
generating a given synthetic dataset of the plurality of synthetic datasets comprises:
obtaining, for the given synthetic dataset, synthetic data generation parameters,
initializing a time domain as a collection of traces organized according to a simulated location and aligned along a time axis,
each trace initialized with a value of zero or a random value at each time comprised by the time axis,
determining, using the synthetic data generation parameters, a number of events to be superimposed into the time domain,
for each event:
determining, using the data generation parameters, a geometric shape, shape parameters, and an event location;
and
superimposing the event into the time domain by altering the value of each trace at each time that intersects the event based on the geometric shape, shape parameters, and event location,
selecting, using the data generation parameters, a wavelet and wavelet parameters,
duplicating the time domain to form a first time domain and a second time domain,
setting the selected wavelet to have a first width and convolving the selected wavelet with the traces of the first time domain to form an input seismic dataset of the given synthetic dataset,
and
setting the selected wavelet to have a second width and convolving the selected wavelet with the traces of the second time domain to form a target seismic dataset of the given synthetic dataset,
wherein
the second width is smaller than the first width;
splitting the plurality of synthetic datasets into a training set;
selecting a first machine-learned model with a first architecture;
training the first machine-learned model using the training set,
wherein
the first machine- learned model is trained to
receive the seismic dataset
and
output another seismic dataset with extended bandwidth relative to the seismic dataset;
using the first machine-learned model to produce an extended bandwidth seismic dataset from the seismic dataset;
determining a location of a hydrocarbon reservoir in the subterranean region of interest using the extended bandwidth seismic dataset;
planning a wellbore to penetrate the hydrocarbon reservoir based on the location,
wherein
the planned wellbore comprises a planned wellbore path; and
drilling the wellbore guided by the planned wellbore path
of claim(s) 1;
a drilling system configured to drill a wellbore guided by a wellbore plan;
and
a computer configured to:
receive a seismic dataset,
generate a plurality of synthetic datasets,
wherein
each synthetic dataset comprises an input seismic dataset with a first bandwidth and an associated target seismic dataset with a second bandwidth,
wherein
the second bandwidth is broader, by extending to higher frequencies, than the first bandwidth
wherein
generating a given synthetic dataset of the plurality of synthetic datasets comprises:
obtaining, for the given synthetic dataset, synthetic data generation parameters,
initializing a time domain as a collection of traces organized according to a simulated location and aligned along a time axis, each trace initialized with a value of zero or a random value at each time comprised by the time axis;
determining, using the synthetic data generation parameters, a number of events to be superimposed into the time domain;
for each event:
determining, using the data generation parameters, a geometric shape, shape parameters, and an event location,
and
superimposing the event into the time domain by altering the value of each trace at each time that intersects the event based on the geometric shape, shape parameters, and event location;
selecting, using the data generation parameters, a wavelet and wavelet parameters;
duplicating the time domain to form a first time domain and a second time domain;
setting the selected wavelet to have a first width and convolving the selected wavelet with the traces of the first time domain to form an input seismic dataset of the given synthetic dataset;
and
setting the selected wavelet to have a second width
and
convolving the selected wavelet with the traces of the second time domain to form a target seismic dataset of the given synthetic dataset,
wherein
the second width is smaller than the first width,
split the plurality of synthetic datasets into a training set,
train a machine-learned model using the training set,
wherein
the machine-learned model is trained to
receive the seismic dataset
and
output another seismic dataset with extended bandwidth relative to the seismic dataset,
use the trained machine-learned model
to
produce an extended bandwidth seismic dataset from the seismic dataset,
and
construct the wellbore plan using the extended bandwidth seismic dataset
of claim(s) 11;
generating a plurality of synthetic datasets,
wherein
each synthetic dataset comprises an input seismic dataset with a first bandwidth and an associated target seismic dataset with a second bandwidth,
wherein
the second bandwidth is broader, by extending to higher frequencies, than the first bandwidth,
wherein
generating a given synthetic dataset of the plurality of synthetic datasets comprises:
obtaining, for the given synthetic dataset, synthetic data generation parameters,
initializing a time domain as a collection of traces organized according to a simulated location and aligned along a time axis,
each trace initialized with a value of zero or a random value at each time comprises by the time axis,
determining, using the synthetic data generation parameters, a number of events to be added into the time domain,for each event;
determining, using the data generation parameters, a geometric shape, shape parameters, and an event location;
and
superimposing the event into the time domain by altering the value of each trace at each time that intersects the event based on the geometric shape, shape parameters, and event location,
selecting, using the data generation parameters, a wavelet and wavelet parameters,
duplicating the time domain to form a first time domain and a second time domain,
setting the selected wavelet to have a first width and convolving the selected wavelet with the traces of the first time domain to form an input seismic dataset of the given synthetic dataset,
and
setting the selected wavelet to have a second width and convolving the selected wavelet with the traces of the second time domain to form a target seismic dataset of the given synthetic dataset,
wherein
the second width is smaller than the first width;
splitting the plurality of synthetic datasets into a training set;
selecting the machine-learned model type and architecture;
training the machine-learned model using the training set,
wherein
the machine-learned model is trained to
receive a seismic dataset
and
output another seismic dataset with extended bandwidth relative to the seismic dataset
of claim(s) 15.
Response to Arguments
Applicant’s amendments, filed on 10/29/2025, have been entered and fully considered. In light of the applicant’s amendments changing the scope of the claimed invention, the rejection(s) have been withdrawn or updated. However, upon further consideration, a new or updated ground(s) of rejection(s) have been made, and applicant's argument(s)/remark(s) pertaining to the amended language have been rendered moot.
Applicant's argument(s)/remark(s), see page(s) 11-12, filed 10/29/2025, with respect to the double patenting rejection(s) has/have been fully considered.
-Applicant states
“Double Patenting
Claims 1-8, 11-15, and 17-20 are provisionally rejected under 35 U.S.C. § 101 for statutory double patenting for claiming the same invention as that of claims 1-8, 11-15, and 17-20 of co-pending Application No. 18/191,193. As discussed in an interview for the co-pending application, the statutory double patenting rejection is not applicable because the independent claims of this application reference a bandwidth extension "to higher frequencies" whereas the co-pending application references an extension "to lower frequencies." To the extent that a nonstatutory double patenting rejection may be contemplated, such a rejection is respectfully traversed as follows.
By way of this reply, claims 2, 3, and 12 have been canceled such that this rejection is now moot with respect to these claims. Further, independent claims 1, 11, and 15 have been amended to incorporate the subject matter of dependent claims 10 and 16, respectively. Claims 10 and 16 were acknowledged by the Examiner to be patentably distinct from the co-pending application. As such, independent claims 1, 11, and 15 are now patentably distinct from the co-pending application. Dependent claims 4-8, 13-14, and 17-20 depend from independent claims 1, 11, and 15, respectively, and are thus patentably distinct from the co-pending application by virtue of their dependency. Accordingly, withdrawal of this rejection is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
Applicant's argument(s)/remark(s), see page(s) 12, filed 10/29/2025, with respect to the 112 rejection(s) has/have been fully considered.
-Applicant states
“Rejection under AIA 35 U.S.C. § 112
Claims 11-14 are rejected under 35 U.S.C. § 112(b) as indefinite. Specifically, the Examiner states that in independent claim 11 it is unclear whether the computer stores the trained machine-learned model. By way of this reply, independent claim 11 has been amended to clarify the relationship between the computer and the machine-learned model. Applicant believes that the submitted amendments resolve the issue cited by the Examiner. Accordingly, withdrawal of this rejection is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
Applicant's argument(s)/remark(s), see page(s) 12-25, filed 10/29/2025, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“Rejection under AIA 35 U.S.C. § 101
Claims 1, 2, 4-11, and 13-20 are rejected under AIA 35 U.S.C. § 101 as being directed to a judicial exception without significantly more. See Office Action, p. 3. To the extent that this rejection may still apply to the claims, as amended, the rejection is respectfully traversed as follows.”.
-Applicant states
“Claims 1, 2, and 4-10
In the Office Action, the Examiner has acknowledged that dependent claim 3 contains patent eligible subject matter. See Office Action, pp. 3 and 8. By way of this reply, independent claim 1 has been amended to incorporate the subject matter of claim 3 and intervening claim 2. Consequently, claims 2 and 3 have been canceled. Thus, amended independent claim 1 is directed toward patent eligible subject matter and dependent claims 4-10 should likewise be found patentable by virtue of their dependency. Accordingly, withdrawal of this rejection for claims 1 and 4-10 is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
-Applicant states
“Claims 11, 13, and14
In the Office Action, the Examiner has acknowledged that dependent claim 12 contains patent eligible subject matter. See Office Action, pp. 3 and 8. By way of this reply, independentclaim 11 has been amended to incorporate the subject matter of claim 12. Consequently, claim 12 has been canceled. Thus, amended independent claim 11 is directed toward patent eligible subject matter and dependent claims 13-14 should likewise be found patentable by virtue of their dependency. Accordingly, withdrawal of this rejection for claims 11, 13, and 14 is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
-Applicant states
“Claims 15-20
In Alice Corp. v. CLS Bank Int'l, 573 U.S. 208 (2014) (hereinafter "Alice"), the Supreme Court reiterated a two-part analysis for analyzing subject matter eligibility under 35 U.S.C. § 101. In the two-part analysis, it is determined whether the claimed subject matter is directed towards a judicial exception, such as an abstract idea, (i.e., "the first step" or "Step 2A") and if so, whether the elements of a claim, both individually and as an ordered combination, are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself (i.e., "the second step" or "Step 2B"). See id.
Furthermore, the USPTO issued guidance for Examiners revising the procedures for determining whether claims are directed to a judicial exception under Step 2A in "2019 Revised Patent Subject Matter Eligibility Guidance." See "2019 Revised Patent Subject Matter Eligibility Guidance," 84 Federal Register 4 (7 January 2019), pp. 50 - 57 (hereinafter "Revised Step 2A Guidance"). Under the Revised Step 2A Guidance, Examiners are tasked with making a "two-prong inquiry" regarding whether claims are directed to a judicial exception. See Revised Step 2A Guidance, p. 54. Specifically, "[i]n Prong One, examiners evaluate whether the claim recites a judicial exception" using "subject matter groups of abstract ideas in Section I [of the Revised Step 2A Guidance]." Id. In Prong Two, "examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception." See id., p. 16. Accordingly, "[w]hen the exception is so integrated, then the claim is not directed to a judicial exception (STEP 2A: NO) and is eligible" and thus "concludes the eligibility analysis." See id. Moreover, Prong Two "specifically excludes consideration of whether the additional elements represent well-understood, routine, conventional activity." See id., p. 55 (emphasis added).
Turning to the rejection, the Examiner contends that independent claim 15 is drawn to an abstract idea in the form of a "mental process" and/or "mathematical concepts." See Office Action, p. 5. However, Applicant respectfully submits the following: (a) that the claims do not recite judicial exception because they contain limitations that cannot be performed by the human mind; (b) assuming arguendo that the claims would recite a judicial exception, amended independent claim 15 integrates any proposed judicial exception into a practical application in the claims; and (c) assuming arguendo that the claims would be directed toward a judicial exception, amended independent claim 15 recites significantly more than any proposed judicial exception.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
See below.
-Applicant states
“With Regard to Step 2A
Regarding Step 2A Prong One, the Examiner contends that independent claim 15 falls into a judicial exception to statutory subject matter. Specifically, the Examiner appears to allege that independent claim 15 is drawn to either a mathematical or mental concept. See Office Action, p. 5. Applicant respectfully asserts that independent claim 15 directed to neither of these.
Regarding the potential allegation that independent claim 15 is drawn to a mathematical concept, MPEP § 2106.04(a)(2) states, "[a] claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. See, e.g., Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017)." Independent claim 15 simply involves mathematical concepts. For example, independent claim 15 recites, in part, convolving a selected wavelet with traces. Thus, while the mathematical concept of convolution is involved in independent claim 15, when considered as a whole independent claim 15 is not directed solely to a mathematical concept. Independent claim 15 is directed to a computer-implemented method of training a machine-learning model that involves mathematical concepts in generating synthetic datasets. Therefore, consistent with MPEP § 2106.04(a)(2), independent claim 15 does not distill down to a mathematical concept by mere virtue of the fact that it includes elements based on mathematical concepts.
Regarding whether the claims are directed to a mental concept, MPEP § 2106.04(a)(2) states, "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019)." Additionally, Applicant notes the Revised Step 2A Guidance included updated examples including Example 39. Example 39 discusses a claim that outlines a method for training a neural network. Notably, in Example 39 it was determined under Step 2A Prong One that the recited claim did not recite a judicial exception because "[w]hile some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, the claim does not recite a mental process because the steps are not practically performed in the human mind." (Emphasis added.) Independent claim 15 requires the limitation "training the machine-learned model using the training set" and further specifies that "the machine-learned model is trained to receive a seismic dataset and output another seismic dataset with extended bandwidth relative to the seismic dataset." Applicant respectfully contends that the process of training a machine-learned model "to receive a seismic dataset and output another seismic dataset with extended bandwidth relative to the seismic dataset" as required independent claim 15, as amended, cannot be practically performed in the human mind even with the aid of pen and paper.
Furthermore, the Examiner has not clarified whether the alleged abstract idea is a mathematical or mental concept. Nonetheless, Applicant has provided a complete rebuttal to the notion that the present invention could be characterized as either a mathematical or mental concept. As noted above, MPEP guidance provides that a claim does not fall within the mathematical concept grouping if it is only based on or involves a mathematical concept. The pending claims do not only involve a mathematical concept, and as such, do not fall withing the mathematical concept grouping. Regarding whether the claims are directed to a mental concept, as noted above, MPEP guidance provides that a claim does not recite a mental process when they contain limitations that cannot practically be performed in the human mind.
In view of the above, it logically follows that the analysis should end and the subject matter of independent claim 15, as amended, should be deemed patent eligible. In particular, amended independent claim 15 does not recite an allegedly abstract idea and thus does not fall into a judicial exception to patent eligible subject matter.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Examiner’s BRI of the claimed inventions is generic computer structure being used as a tool to create and utilize a mathematical model to mathematically process/analyze seismic datasets.
When examining step 2A Prong 1, Examiner determines if there is an abstract idea present. One skilled in the art can at least perform the identified abstract idea utilizing Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation. The arguments, in light of the specification, fail to convince the Examiner that utilizing Mathematical Concepts does not fit within the scope of the identified abstract limitations.
-Applicant states
“Regarding Step 2A Prong Two, even assuming, arguendo, independent claim 15 could be considered to recite an abstract idea under Step 2A Prong One, this claim, as amended, is rooted in a practical application. In support of the assertion that amended independent claim 15 is rooted in a practical application, Applicant notes that limitations of amended independent claim 15 generate a plurality of synthetic seismic datasets and train a machine-learned model using a portion of the generated synthetic seismic datasets. Applicant respectfully contends that generating the plurality of synthetic datasets as specified in independent claim 15 and using the synthetic datasets to train a machine-learned model represent an improvement to "the functioning of the computer itself or any other technology or technical field." MPEP § 2106.05(a) states: "This consideration has also been referred to as the search for a technological solution to a technological problem." And, "[a]n indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim." Paragraph [0062] of the specification clearly indicates at least one technical problem addressed by the invention, namely, that synthetic data formed through conventional means using a forward model "are dependent on some prior knowledge of the subterranean region of interest (102) and the prior knowledge may be inaccurate leading to poor synthetic seismic datasets. Further, synthetic datasets generated through these modeling processes are known to have differences in the character of both signal and noise from real seismic datasets making machine-learned models trained with these synthetic seismic datasets unable to generalize to real seismic data." In contrast, the synthetic datasets of independent claim 15 better represent real-world datasets as they can have time- varying spectral content and non-stationary wavelet behavior. See Application, paras. [0083]-[0084]. Thus, the described invention provides a method for training a machine-learned model by augmenting training data with synthetic datasets, the synthetic datasets being improved over synthetic datasets formed using a forward model because they are "generated directly in a time domain with geometric shapes" and "[t]raining data generated in this manner is not limited to any range of frequencies, can cover a broad range of geological features, and can mimic real seismic datasets in terms of signal and noise character." See Application, para. [0063]. Paragraph [0044] of the specification outlines some of the improvements to the functioning of the computer itself or any other technology or technical field. Portions of paragraph [0044] are given as follows.
…
Notably, paragraph [0044] indicates the aspects of the invention that realize these improvements, namely, that the resultant synthetic datasets are produced from "a composition of geometric shapes" directly in the time domain, each shape being convolved with a wavelet. Amended independent claim 15 clearly recites the features of the invention that realize the improvements. For example, amended independent claim 15 specifies the initialization of a time domain, construction of events and their superimposition into the time domain, and convolution with one or more wavelets; the one or more wavelets specifically selected to have different widths for the first and second time domains.
Further, figures 12-15 and their associated description clearly demonstrate that the plurality of synthetic datasets generated according to the limitations of independent claim 15 have improved characteristics such as spatial feature resolution and incorporation of real-world behavior not achievable using conventional means such as time-varying bandwidth and non-stationary wavelets.
In summary, Applicant respectfully contends that the limitations of amended independent claim 15 cover the solution (and do not merely claim the idea of a solution) for training a machine-learned model for bandwidth extension using synthetic datasets that 1) do not require prior knowledge of the subterranean region of interest, and 2) mimic real-world behavior not achievable using synthetic datasets generated using a forward model. Thus, amended independent claim 15 represents an improvement as providing a solution to, at least, an issue of generating realistic synthetic seismic data and further improves the technologies of machine learning and seismic data processing.
In view of the above, amended independent claim 15 is not directed to a judicial exception to patent eligible subject matter. Specifically, under Step 2A of the two-part analysis for analyzing subject matter eligibility under AIA 35 U.S.C. § 101, the claimed invention is not directed toward an abstract idea (e.g., mental process, mathematical concepts) and is also sufficiently anchored in a practical application. Thus, the analysis should end and the subject matter of amended independent claim 15 should be deemed patent eligible.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
When examining step 2A Prong 2, Examiner examines the additional elements to determine if the identified abstract idea has been practically applied in a particular way in a particular technology. Limitations that are not indicative of integration into a practical application: 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)); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)); or Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). The additional elements, when viewed individually and in combination with the identified abstract idea, do not add anything beyond mere instructions to implement an abstract idea on a computer, adding generic ‘apply it’ language, and generically linking the identified abstract idea to a technological environment or field of use.
It is important to note, the judicial exception alone cannot provide the improvement. An improved abstract idea is still an abstract idea.
-Applicant states
“With Regard to Step 2B
Furthermore, amended independent claim 15 clearly recites subject matter that is significantly more in Step 2B of the Alice test than the proposed judicial exception.
For Step 2B of the USPTO's subject matter eligibility test, the Federal Circuit has "distinguished ineligible abstract-idea-based solutions implemented with generic technical components in a conventional way from the eligible technology-based solution and software-based invention that improves the performance of the computer system itself." See Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F. 3d 1288, 1299 (Fed. Cir. 2016) (internal citations removed and emphasis added). Moreover, the Federal Circuit clarified the second step of the Alice test in Berkheimer v. HP Inc., 881 F. 3d 1360 (Fed. Cir. 2018)(hereinafter "Berkheimer 1') such that "[t]he question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question offact." Berkheimer I, p. 1368. In other words, "[w]hether a claim element or combination of elements would have been well-understood, routine, and conventional to a skilled artisan in the relevant field at a particular point in time may require weighing evidence, making credibility judgments, and addressing narrow facts that utterly resist generalization." Berkheimer v. HP Inc., 890 F. 3d 1369, 1370 (Fed. Cir. 2018) (hereinafter "Berkheimer II") (Moore, J., concurring in the denial of the en banc rehearing).
Turning to amended independent claim 15, amended independent claim 15 recites an unconventional combination of steps to generate synthetic datasets, each dataset having a pair of associated datasets, namely, an input seismic dataset and a target seismic dataset differing in their frequency content. The paired datasets are then used to train a machine-learned model to extend the bandwidth of a given seismic dataset to higher frequencies. Using the unconventional combination recited in amended independent claim 15, the claimed invention provides a technological solution that overcomes current synthetic data generation procedures that are "dependent on some prior knowledge of the subterranean region of interest (102) and the prior knowledge may be inaccurate leading to poor synthetic seismic datasets" and "are known to have differences in the character of both signal and noise from real seismic datasets making machine-learned models trained with these synthetic seismic datasets unable to generalize to real seismic data." See Application, para. [0062]. Further support in the originally-filed specification for embodiments of the claimed invention providing a technical solution to a technical problem are provided below (emphasis added in the following excerpts):
…
Continuing with this line of reasoning, Applicant further contends that amended independent claim 15 should be found patent eligible when compared to Subject Matter Eligibility Example 36 (hereafter "Inventory Management Example"). Portions of independent claim 3 of the Inventory Management Example are provided below for the Examiner's convenience.
…
In the Inventory Management Example, the USPTO states that "[i]ndividually, the camera array, memory and processor limitations do not amount to significantly more . . . [because] these components are used in this invention for their well-understood, routine, conventional functions of acquiring, processing and storing information." Business Method Examples, p. 17. However, the USPTO contends that "[i]n combination, however, the limitations do amount to significantly more than the abstract idea of inventory management ... [because] the combination of the camera array's acquisition of high resolution image sequences, and the processor's performance of step (b)'s extracting contour and character information from the images to create feature vectors, step (c)'s recognizing and tracking items of inventory using the feature vectors and a recognition model, and step (d)'s determining the physical location of the recognized items using the position of the item in the image sequence(s) is not well-understood, routine, conventional activity in this field." Id. (Emphasis added.) As such, "[t]his combination of limitations provides a hardware and software solution that improves upon previous inventory management techniques by avoiding the cumbersome use of RFID and GPS transmitters and the inaccuracy issues that plagued previous computer vision solutions." Id. (Emphasis added.) Therefore, the USPTO contends that claim 3 of the Inventory Management Example recites significantly more than any alleged judicial exception, and is thereby patent eligible under Step 2B.
As shown above, independent claim 15 recites a claimed solution to overcome a problem arising in seismic data processing, specifically that synthetic datasets are poor approximations of real datasets and hinder training of machine-learned models. The solution is realized, at least in part, using an unconventional method of generating synthetic data unlike anything known in the art by directly generating the synthetic datasets in the time domain by initializing a time domain and populating it with events formed of a geometric shape and a wavelet. Applicant respectfully argues that this combination of superimposing geometric shapes in a time domain and convolving the shapes with a wavelet is not well-understood, routine, or conventional under Step 2B. This technological solution in training a machine-learned model using specifically generated synthetic datasets is no different than improving upon previous inventory management techniques as discussed by the USPTO in the Inventory Management Example. As such, independent claim 15 recites a specific combination that is significantly more than any proposed abstract idea.
Thus, amended independent claim 15 recites significantly more than any alleged abstract idea according to the USPTO's Business Method Examples.
Furthermore, Applicant notes that this type of technological solution is no less technological than the electronic spreadsheet improvements in Data Engine Technologies LLC v. Google LLC, 906 F.3d 999 (Fed. Cir. 2018), or the solution to "an accounting and billing problem faced by network service providers" described in Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288 (Fed. Cir. 2016). Both Data Engine Technologies and Amdocs courts found claims patent eligible. Therefore, amended independent claim 15 recites significantly more than any proposed judicial exception. Accordingly, under Step 2B of the two-part analysis for analyzing subject matter eligibility under 35 U.S.C. § 101, the claimed invention recites significantly more.
For at least the above reasons, Applicant respectfully asserts that amended independent claim 15 is directed to patent eligible subject matter under Step 2A and Step 2B of the USPTO Subject Matter Eligibility Test. Claims 16-20 depend from independent claim 15 and thus are also directed towards patent eligible subject matter by virtue of their dependency. Accordingly, withdrawal of this rejection is respectfully requested.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
When examining step 2B, Examiner examines the additional elements to determine if they amount to significantly more than the abstract idea. The only additional element(s) is/are the generic computer structure being used as a tool to perform the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Claim 15 fails to provide any additional elements beyond the identified abstract idea being ‘computer-implemented’.
It is important to note, the judicial exception alone cannot provide the improvement. An improved abstract idea is still an abstract idea.
Applicant's argument(s)/remark(s), see page(s) 25-33, filed 10/29/2025, with respect to the art rejection(s) has/have been fully considered.
-Applicant states
“Rejection under AIA 35 U.S.C. § 102
Claims 11-14 are rejected under AIA 35 U.S.C. § 102(a)(1) as being anticipated by US 20210318458 Al("Baumstein"). By way of this reply, claim 12 has been canceled such that this rejection is now moot with respect to this claim. The rejection is respectfully traversed as follows.
M.P.E.P. § 2131 states that "[a] claim is anticipated only if each and every element as set forth in the claim is found, either expressly or inherently described, in a single prior art reference." Verdegaal Bros. v. Union Oil Co. of California, 814 F.2d 628, 631, 2 USPQ2d 1051, 1053 (Fed. Cir. 1987). "The identical invention must be shown in as complete detail as is contained in the ... claim." Richardson v. Suzuki Motor Co., 868 F.2d 1226, 1236, 9 USPQ2d 1913, 1920 (Fed. Cir. 1989). Moreover, "[e]very element of the claimed invention must be literally present, arranged as in the claim." Id.
Independent claim 11 has been amended to include limitations directed to generating synthetic datasets directly in a time domain. With respect to independent claim 1, the Examiner has acknowledged that Baumstein does not disclose this feature. See Office Action, p. 14. Thus, independent claim 11 is not anticipated by Baumstein. Claims 13 and 14 depend from independent claim 11 and are likewise not anticipated by Baumstein by virtue of their dependency. Accordingly, withdrawal of this rejection is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
-Applicant states
“Rejections under AIA 35 U.S.C. § 103
Claims 1-3, 6-10, 15-16, 18, and 19
Claims 1-3, 6-10, 15-16, 18, and 19 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Baumstein in view of US 20190383965 Al ("Salman"). See Office Action, p. 12. Claims 2 and 3 have been canceled such that this rejection is now moot with respect to these claims. This rejection is respectfully traversed as follows.
The specification describes a seismic dataset as a "collection of seismic traces organized according to their simulated location and aligned according to time or depth." Further, a synthetic dataset is formed by initializing a time domain "such that all the traces have a value of zero for every time or depth" (see Application, para. [0066]). Further, figure 7 of the application and associated paragraphs ([0066]-[0074]) clearly describe the process of generating a seismic dataset directly in the time domain with one or more events by superimposing a geometric shape in the initialized time domain at a location specific to a given event. That is, the synthetic seismic dataset is generated by initializing a time domain as an empty collection of traces (see figure 7, element 705) and then populating this initial time domain with events. The base of an event is a geometric shape, such as a line or a hyperbola, that is injected into, or drawn onto, the initial time domain. The events, or geometric shapes, are then convolved with a wavelet to form a synthetic collection of traces. The width of the wavelet is altered to form an input-target pair of seismic datasets having different bandwidth characteristics.
The application describes that conventional methods for generating synthetic seismic datasets involve the use of a forward model (e.g., wave equation) applied to a proposed model of the subsurface (e.g. velocity model) to simulate traces received at seismic receivers; the collection of these traces forming the synthetic seismic dataset. See Application, paras. [0060]-[0062]. However, and as described in the application, this method may be considered disadvantageous because: the process is dependent on prior knowledge of the subsurface (e.g., velocity model); results in seismic datasets having differences in the character of both signal and noise compared to real seismic datasets; and, if used to train a machine-learned model, results in a machine-learned model that does not generalize well to real seismic data. See Application, para. [0062].
Amended independent claim 1 recites, in part:
…
Applicant summarizes the prior art as follows. Baumstein discusses a methodology for extending the bandwidth of seismic datasets - to both higher and lower frequencies - using a neural network. Salman teaches a method for generating synthetic geophysical data for training a deep learning framework. Liu is directed to a method and apparatus for automated interpretation of seismic data using machine learning. Fu discusses a method for mapping fiber optic distributed acoustic sensing measurements to particle motion using machine learning.
The above limitation of amended independent claim 1 requires that a synthetic dataset is generated directly in a time domain by creating "events" in a time-domain representation of a seismic dataset. None of the applied art shows or suggests generation of seismic data directly in the time domain using events comprising a geometric shape and a wavelet. In the Office Action, the Examiner states that Baumstein teaches limitations related to a geometric shape and wavelet of an event. However, the cited portions of Baumstein merely describe a domain, such as that for a velocity model, being two- or three-dimensional and a seismic data post-processing technique involving wavelet shaping. The Examiner further acknowledges that Baumstein does not teach generating the synthetic dataset directly in the time domain and instead relies upon Salman to allegedly read on this feature. See Office Action, pp. 20-22. Salman states that "rather than merely placing an object (e.g., a geobody, an interface, etc.) in a seismic image, an algorithm can include generating seismic trace data (e.g., as time series data with respect to one or more spatial dimensions) as associated with an object in a geologic environment." See Salman, para. [0067]. However, Salman does not disclose how an algorithm can be used to generate a seismic trace and how such an algorithm would do more than "merely place an object in a seismic image." Further, Applicant's understanding is that a generation process like that of Salman makes use of a forward modelling procedure due to its apparent distinction of the terms "seismic image" and "seismic trace data." That is, objects such as a geobody or interface are placed in a seismic image, or subsurface model, which is then processed with a forward model to form seismic trace data. Other references to a time domain in Salman relate to transformations on data in the time domain, like a Fourier transform, and do not disclose the generation of a seismic dataset directly in the time domain.
In view of the above, Applicant respectfully asserts that Baumstein and Salman are each silent with respect to the above-listed limitation of independent claim 1. As such, none of Baumstein, Salman, or their combination can render the above-listed limitation of independent claim 1 obvious.
Further, even assuming arguendo that a skilled person were to combine Baumstein and Salman in the manner suggested by the Examiner, the combination would still fail to render amended independent claim 1 obvious, because a person of ordinary skill in the art would have had no motivation to supply the missing elements without the benefit of Applicant's own disclosure as a guide.
In view of the above, independent claim 1 is patentable over the combination of Baumstein and Salman. By way of this reply, independent claim 15 has been amended to recite substantially similar limitations as independent claim 1. Thus, independent claim 15 should be found patentable over Baumstein and Salman for at least the same reasons. Claims 6-10 depend from independent claim 1 and are patentable over the combination of Baumstein and Salman by virtue of their dependency. Claims 16, 18 and 19 depend from independent claim 15 and are patentable over the combination of Baumstein and Salman by virtue of their dependency. Accordingly, withdrawal of this rejection is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
-Applicant states
“Claim 4
Claim 4 is rejected under AIA 35 U.S.C. § 103 as being unpatentable over Baumstein and Salman and further in view of US 20200183035 Al ("Liu") and US 20230408327 Al ("Fu"). See Office Action, p. 22. This rejection is respectfully traversed as follows.
Claim 4 depends from independent claim 1. As argued above, independent claim 1 is patentable over Baumstein and Salman because these references fail to disclose, at least, the above- listed limitation of amended independent claim 1 directed to generating a plurality of synthetic datasets. Applicant respectfully asserts that Liu and Fu fail to supply that which Baumstein and Salman lack with respect to, at least, this feature. While these references may discuss machine learning and, more specifically, an automated method for interpretation of seismic data or mapping of acoustic sensing measurement to particle motion, these references are each silent with respect to synthetic data generation directly in the time domain as required by the above-listed limitation of independent claim 1. It necessarily follows that the combination of Baumstein, Salman, Liu, and Fu cannot lead one of ordinary skill in the art to arrive at the above-listed limitation. As such, independent claim 1 is patentable over Baumstein, Salman, Liu, and Fu and claim 4 is likewise patentable over these references by virtue of its dependency. Accordingly, withdrawal of this limitation is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
-Applicant states
“Claims 5 and 17
Claims 5 and 17 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Baumstein and Salman and further in view of Fu. See Office Action, p. 25. This rejection is respectfully traversed as follows.
Claims 5 and 17 depend from independent claims 1 and 15. As argued above, independent claims 1 and 15 are patentable over Baumstein and Salman because these references fail to disclose, at least, the above-listed limitation directed to generating a plurality of synthetic datasets. Applicant respectfully asserts that Fu fails to supply that which Baumstein and Salman lack with respect to, at least, this feature. Because Fu is silent with respect to synthetic data generation, it necessarily follows that the combination of Baumstein, Salman, and Fu cannot lead one of ordinary skill in the art to arrive at the above-listed limitation. As such, independent claims 1 and 15 are patentable over Baumstein, Salman, and Fu and claims 5 and 17 are likewise patentable over these references by virtue of their dependency. Accordingly, withdrawal of this limitation is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
-Applicant states
“Claim 20
Claim 20 is rejected under AIA 35 U.S.C. § 103 as being unpatentable over Baumstein and Salman and further in view of Liu. See Office Action, p. 26. This rejection is respectfully traversed as follows.
Claim 20 depends from independent claim 15. As argued above, independent claim 15 is patentable over Baumstein and Salman because these references fail to disclose, at least, the above-listed limitation directed to generating a plurality of synthetic datasets. Applicant respectfully asserts that Liu fails to supply that which Baumstein and Salman lack with respect to, at least, this feature. Because Liu is silent with respect to synthetic data generation, it necessarily follows that the combination of Baumstein, Salman, and Liu cannot lead one of ordinary skill in the art to arrive at the above-listed limitation. As such, independent claim 15 is patentable over Baumstein, Salman, and Liu and claim 20 is likewise patentable over these references by virtue of its dependency. Accordingly, withdrawal of this limitation is respectfully requested.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit