DETAILED ACTION
This Office Action is in response to the claims filed on [date].
Claims 1-20 are pending.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below for the convenience of the applicant. Although
the specified citations are representative of the teachings in the art and are applied to
the specific limitations within the individual claim, other passages and figures may apply
as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. (Examiner’s interpretation), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/28/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 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.
Claims 1, 4-9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ovcharenko, Oleg, et al. "Multi-task learning for low-frequency extrapolation and elastic model building from seismic data." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-17 (hereinafter referred to as “Ovcharenko”) in view of Alwon US Patent No. 11105942 B2 (hereinafter referred to as “Alwon”).
Regarding claim 1, Ovcharenko discloses A method, comprising: obtaining a machine-learned model parameterized by a set of weights (“we apply the same trained network (i.e. machine-learned model) to synthetic and real marine streamer datasets and run an elastic FWI from the extrapolated dataset.” [Pg.1 Abstract]. The trained-network is interpreted as parameterized by a set of weights because “MTL is equivalent to training a network to simultaneously perform several tasks while partially or completely sharing trainable weights in the neural network branches leading to each of these tasks.” [Pg.3 Col.2 Sec.II); generating a first synthetic seismic dataset and associated first target (“The workflow for synthetic data generation utilizes the survey geometry, the seafloor bathymetry, and the average source wavelet derived from the target field data.” Ovcharenko [Pg.6 Col.1 P.5]); determining a first noise profile for the first synthetic seismic dataset in a frequency domain that when added, in a spatial-temporal domain, to the first synthetic seismic dataset ; adding the first noise profile to the first synthetic seismic dataset in the spatial-temporal domain forming a first noisy seismic dataset; (“To mimic realistic noise, we extract the noise imprint directly from the field data [ ] we train the network in a semisynthetic framework [ ] This implies blending synthetic data on waveforms with the real-world noise specific to the target dataset. An example of such a donor area of representative noise is shown in Fig. 2 (see below)” [Pg.7 Col.2 P.2]);
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updating the set of weights of the machine-learned model based on the
first noisy seismic dataset and the first target (Ovcharenko discloses trainable weights [Pg.3 II. Multitask Learning Framework] which the examiner interprets as updating sets of weights of the ML model based on noisy seismic dataset. Also see [Pg.3-4 A.MTL Loss Design]); receiving a seismic dataset corresponding to a subsurface (“The target 2-D marine streamer dataset was acquired in the Northwestern part of the Australian continental shelf.” Ovcharenko [Pg.6 Col.1 P.3]); processing the seismic dataset with the machine-learned model parameterized by the updated set of weights to form a predicted target for the seismic dataset (“Fig. 15 (see below). Mean amplitude spectra of (Left) synthetic and (Right) field datasets, where predicted (red line) and target (black dashed line) data are shown together with the spectrum of input data (black solid line) which was set to zero below 4 Hz.” Ovcharenko [Pg.13 Fig.15]);
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and developing a geological model for the subsurface using the predicted target (“we initiate the inversion from a predicted smooth initial velocity model and use predicted data as the target.” Ovcharenko [Pg.11 Col.2 P.4]).
Ovcharenko fails to specifically disclose reduces a performance of the machine-learned model. However, Alwon discloses reduces a performance of the machine-learned model (“seismic data (e.g., actual and/or synthetic) are utilized as the “ground-truth” for a discriminator network and seismic data are generated by the generator network (e.g., generative network) for feeding to the discriminator network such that the discriminator network can make determinations (e.g., outputs) that facilitate training of the generator network.” Alwon [Col.15 Ln.36]. The examiner interprets the use of synthetic data as reducing the performance of the machine learned model due to Applicant’s disclosure “Various reasons for the observed reduction in performance exist and may include: training of the machine-learned model using synthetically generated data, training the machine-learned model with data not representative of that being processed by the machine-learned model, and receiving data highly contaminated by noise.” [Spec. P.0068])
Ovcharenko and Alwon are analogous art as they both relate to seismic data processing in geophysics, specifically to methods and systems for generating, training, and utilizing machine-learned models. Ovcharenko discloses “we propose to jointly reconstruct LF data and a smooth background subsurface model within a multitask deep learning framework [ ] We also design a pipeline for generating synthetic data suitable for field data applications. Finally, we apply the same trained network to synthetic and real marine streamer datasets” [Abstract]. And Alwon discloses “generating seismic data; training a generator network utilizing at least a portion of the generated seismic data and a discriminator network; and outputting a trained generator network” [Abstract]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko’s seismic data multi-task learning technique to include synthetic seismic data that reduces a performance of the machine model, as Alwon discloses, in order to train the model “to produce outputs that are to some degree indistinguishable from “real” inputs via an adversarially trained discriminator D, which is trained to do as well as possible at detecting generator generated “fakes”” Alwon [Col.17 Ln.25].
Regarding claim 4, the method of claim 1, Ovcharenko discloses further
comprising: generating a second synthetic seismic dataset and associated second
target; determining a second noise profile for the second synthetic seismic dataset in a frequency domain that when added, in a spatial-temporal domain, to the second synthetic seismic dataset ; adding the second noise profile to the second synthetic seismic dataset in the spatial-temporal domain forming a second noisy seismic dataset; and updating the set of weights of the machine-learned model based on the second noisy seismic dataset and the second target (“The FWI (i.e. full waveform inversion) then iteratively updates the elastic model parameters to build the final model of the subsurface.” Ovcharenko [Pg.11 Col.2 P.2]. The examiner interprets iteratively updates as performing claim 1 limitations at least a second time, as claim 4 recites.).
Ovcharenko fails to specifically disclose reduces the performance of the machine-learned model. However, Alwon discloses reduces a performance of the machine-learned model (“seismic data (e.g., actual and/or synthetic) are utilized as the “ground-truth” for a discriminator network and seismic data are generated by the generator network (e.g., generative network) for feeding to the discriminator network such that the discriminator network can make determinations (e.g., outputs) that facilitate training of the generator network.” Alwon [Col.15 Ln.36]. The examiner interprets the use of synthetic data as reducing the performance of the machine learned model due to Applicant’s disclosure “Various reasons for the observed reduction in performance exist and may include: training of the machine-learned model using synthetically generated data, training the machine-learned model with data not representative of that being processed by the machine-learned model, and receiving data highly contaminated by noise.” [Spec. P.0068])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko’s seismic data multi-task learning technique to include synthetic seismic data that reduces a performance of the machine model, as Alwon discloses, in order to train the model “to produce outputs that are to some degree indistinguishable from “real” inputs via an adversarially trained discriminator D, which is trained to do as well as possible at detecting generator generated “fakes”” Alwon [Col.17 Ln.25].
Regarding claim 5, the method of claim 1, Ovcharenko further discloses wherein the first target represents first break picks for the first synthetic seismic dataset (“we accommodate this observation by introducing offset-flip data augmentation at the training stage.” Ovcharenko [Pg.11 Col.1 P.1]. The examiner interprets augmentation as first break picks due to Applicant’s disclosures within Spec. P.00137, and Figs.21/22).
Regarding claim 6, the method of claim 1, Ovcharenko further discloses wherein the obtained machine-learned model is a previously trained or pre-trained machine-learned model (“we apply the same trained network (i.e. machine-learned model) to synthetic and real marine streamer datasets and run an elastic FWI from the extrapolated dataset.” Ovcharenko [Pg.1 Abstract]).
Regarding claim 7, the method of claim 1, Ovcharenko further discloses wherein
the machine-learned model is a U-net type convolutional neural network (“The baseline architecture is the UNet” Ovcharenko [Pg.9 Col.1 P.3]. The baseline architecture is interpreted as a convolutional neural network because “We design a fully convolutional architecture for joint prediction of the local subsurface model together with two cascaded bands of seismic data” Ovcharenko [Pg.5 Sec. E]).
Regarding claim 8, the method of claim 1, Ovcharenko further discloses wherein determining the first noise profile comprises occluding frequency content of the first synthetic seismic dataset (“The LF (i.e. low frequency) target data are built by applying a low-pass filter (i.e. occlude) with a corner frequency of 5 Hz” Ovcharenko [Pg.7 Col.2 P.6]).
Regarding claim 9, the method of claim 1, Ovcharenko fails to specifically disclose further comprising obtaining an epsilon value, wherein the epsilon value constrains a signal-to-noise ratio of the first noisy seismic dataset. However, Alwon further discloses obtaining an epsilon value, wherein the epsilon value constrains a signal-to-noise ratio of the first noisy seismic dataset (“a method can include generating seismic data using a generator network and training that includes receiving acquired seismic data of a survey and the generated seismic data by a discriminator network. In such an example, the acquired seismic data of the survey can be characterized by a level of signal to noise (e.g., an average SNR, a local SNR, etc.) and/or the acquired seismic data of the survey can be noise attenuated seismic data.” Alwon [Col.27 Ln.45]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko to include constraining signal-to-noise ratio, as Alwon discloses, since such as approach “may provide guidance or decision making for performing one or more surveys and/or tailoring one or more survey parameters.” Alwon [Col.26 Ln.39].
Claim 17 recites substantially the same subject matter as claim 1 and is rejected under similar rationale.
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Ovcharenko, Oleg, et al. "Multi-task learning for low-frequency extrapolation and elastic model building from seismic data." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-17 (hereinafter referred to as “Ovcharenko”) in view of Alwon US Patent No. 11105942 B2 (hereinafter referred to as “Alwon”), in further view of Zhang et al. US Pub. No. 2025/0003327 A1 (hereinafter referred to as “Zhang”).
Regarding claim 2, the method of claim 1, Ovcharenko fails to specifically disclose further comprising planning a wellbore to penetrate a hydrocarbon reservoir based on the geological model, wherein the planned wellbore comprises a planned wellbore path. However, Zhang discloses further comprising planning a wellbore to penetrate a hydrocarbon reservoir based on the geological model, wherein the planned wellbore comprises a planned wellbore path (“The wellbore planning system (338) may use information regarding the reservoir (104) location to plan a well, including a wellbore trajectory (304) from the surface (124) of the earth to penetrate the reservoir (104)” Zhang [P.0044])
Zhang is analogous art as it relates to processing seismic data associated with subsurface regions, aims to improve geological modeling for hydrocarbon exploration, supports downstream applications such as wellbore planning and drilling, and involves the use of computer systems to process seismic data and generate outputs for subsurface analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko’s seismic data multi-task learning technique to include a planned wellbore path, as Zhang discloses, in order to avoid “drilling hazards, such as shallow gas pockets, over-pressure zones, and active fault planes.” Zhang [P.0045].
Regarding claim 3, the method of claim 2, Ovcharenko fails to specifically disclose further comprising a drilling the wellbore guided by the planned wellbore path.
However, Zhang discloses further comprising a drilling the wellbore guided by the planned wellbore path (“The drilling system (300) may drill the wellbore (118) along the planned wellbore trajectory (304)” Zhang [P.0045])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko’s seismic data multi-task learning technique to include drilling a wellbore guided by the planned wellbore path, as Zhang discloses, in order to avoid “drilling hazards, such as shallow gas pockets, over-pressure zones, and active fault planes.” Zhang [P.0045].
Claims 10-11, 14, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ovcharenko, Oleg, et al. "Multi-task learning for low-frequency extrapolation and elastic model building from seismic data." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-17 (hereinafter referred to as “Ovcharenko”) in view of Alwon US Patent No. 11105942 B2 (hereinafter referred to as “Alwon”), in further view of Saraiva, Marcus, et al. "Data-driven full-waveform inversion surrogate using conditional generative adversarial networks." 2021 International joint conference on neural networks (IJCNN). IEEE, 2021. (hereinafter referred to as “Saraiva”).
Regarding claim 10, the method of claim 1, Ovcharenko fails to specifically disclose wherein the determination of the first noise profile is guided by an adversarial signal. However, Saraiva further discloses wherein the determination of the first noise profile is guided by an adversarial signal (“The objective function (i.e. loss function) of a cGAN can be expressed by (1).
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(1), where the generator G tries to minimize this objective function against an adversarial discriminator D that tries to maximize it (2).
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” Saraiva [Pg.3 Col.2 P.4]. Note: The discriminator D is interpreted as an adversarial signal due to Applicant’s disclosure, “the purpose of the adversarial attacker (1002) is to produce an update to the noise δ that maximizes the loss (according to the loss function l)” [Spec. P.00109]).
Saraiva is analogous art as it relates to seismic data processing in geophysics, specifically to methods and systems for generating, training, and utilizing machine-learned models. Saraiva discloses “we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial network (cGAN) with multiple inputs” [Abstract]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko’s seismic data multi-task learning technique to include a noise profile guided by Saraiva’s disclosed adversarial signal (i.e. cGAN) since “GANs are generative models that learn to generalize a rule of how to transform a random noise vector
z
to output image
y
(
G
:
z
-->
y
)” Saraiva [Pg.3 Col.2 P.2].
Regarding claim 11, Ovcharenko discloses A system, comprising: a machine-learned model parameterized by a set of weights (“we apply the same trained network (i.e. machine-learned model) to synthetic and real marine streamer datasets and run an elastic FWI from the extrapolated dataset.” [Pg.1 Abstract]. The trained-network is interpreted as parameterized by a set of weights because “MTL is equivalent to training a network to simultaneously perform several tasks while partially or completely sharing trainable weights in the neural network branches leading to each of these tasks.” Ovcharenko [Pg.3 Col.2 Sec.II); the computer configured to: generate a first synthetic seismic dataset and associated first target (“The workflow for synthetic data generation utilizes the survey geometry, the seafloor bathymetry, and the average source wavelet derived from the target field data.” Ovcharenko [Pg.6 Col.1 P.5]); determine a first noise profile ; add the first noise profile to the first synthetic seismic dataset in a spatial-temporal domain forming a first noisy seismic dataset (“To mimic realistic noise, we extract the noise imprint directly from the field data [ ] we train the network in a semisynthetic framework [ ] This implies blending synthetic data on waveforms with the real-world noise specific to the target dataset. An example of such a donor area of representative noise is shown in Fig. 2 (see below)” Ovcharenko [Pg.7 Col.2 P.2]);
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update the set of weights of the machine-learned model based on the first noisy seismic dataset and the first target (Ovcharenko discloses trainable weights [Pg.3 II. Multitask Learning Framework] which the examiner interprets as updating sets of weights of the ML model based on noisy seismic dataset. Also see [Pg.3-4 A.MTL Loss Design]); receive a seismic dataset corresponding to a subsurface (“The target 2-D marine streamer dataset was acquired in the Northwestern part of the Australian continental shelf.” Ovcharenko [Pg.6 Col.1 P.3]); process the seismic dataset with the machine-learned model parameterized by the updated set of weights to form a predicted target for the seismic dataset (“Fig. 15 (see below). Mean amplitude spectra of (Left) synthetic and (Right) field datasets, where predicted (red line) and target (black dashed line) data are shown together with the spectrum of input data (black solid line) which was set to zero below 4 Hz.” Ovcharenko [Pg.13 Fig.15]);
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and develop a geological model for the subsurface using the predicted target (“we initiate the inversion from a predicted smooth initial velocity model and use predicted data as the target.” Ovcharenko [Pg.11 Col.2 P.4]).
Ovcharenko fails to specifically disclose a computer comprising one or more computer processors and a non-transitory computer-readable medium, a conventional noise generator that produces an initial noise profile; an in-domain adversarial attacker configured by an in-domain regularizer that updates the initial noise profile to reduce a performance of the machine-learned model, and using the in-domain adversarial attacker by updating the initial noise profile.
Alwon discloses a computer comprising one or more computer processors and a non-transitory computer-readable medium (“the system 250 includes one or more information storage devices 252, one or more computers 254, one or more network interfaces 260 and one or more sets of instructions 270. As to the one or more computers 254, each computer may include one or more processors (e.g., or processing cores) 256 and memory 258 for storing instructions (e.g., consider one or more of the one or more sets of instructions 270), for example, executable by at least one of the one or more processors.” Alwon [Col.9 Ln.55]), a conventional noise generator that produces an initial noise profile (“FIG. 7 (see below) shows example graphics 701, 705, 710 and 730 of an architecture of a computational framework that can be utilized for processing seismic data [ ] the generator network 704 can operate using random noise where a distribution may be chosen from random noise” Alwon [Col.15 Ln.10]. The examiner interprets random noise as generated from a conventional noise generator due to Applicant’s disclosure “the conventional noise generator randomly, or semi-randomly, generates noise” [Spec. P.0096])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko to include a conventional noise generator that produces a noise profile, as Alwon discloses, in order to establish “a computational framework that can be utilized for processing seismic data” Alwon [Col.15 Ln.10].
Alwon also fails to disclose an in-domain adversarial attacker configured by an in-domain regularizer that updates the initial noise profile to reduce a performance of the machine-learned model and using the in-domain adversarial attacker by updating the initial noise profile..
Saraiva discloses an in-domain adversarial attacker configured by an in-domain regularizer that updates the initial noise profile to reduce a performance of the machine-learned model; using the in-domain adversarial attacker by updating the initial noise profile. (“Through the backpropagation process, discriminator classification in real or fake images provides a signal for the generator to update its weights and bias (i.e. updates noise profile), thus improving performance. The objective function (i.e. loss function) of a cGAN can be expressed by (1).
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(1), where the generator G tries to minimize this objective function against an adversarial discriminator D that tries to maximize it (2).
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. Previous work [22] shows that L1 regularization (i.e. in-domain regularizer) is beneficial to this process, helping the generator G to create sharper images (3).” Saraiva [Pg.3 Col.2 P.4]. Note: The discriminator D is interpreted as an adversarial signal due to Applicant’s disclosure, “the purpose of the adversarial attacker (1002) is to produce an update to the noise δ that maximizes the loss (according to the loss function
l
)” [Spec. P.00109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ovcharenko’s seismic data multi-task learning technique to include Saraiva’s cGAN (i.e. in-domain adversarial attacker) since “GANs are generative models that learn to generalize a rule of how to transform a random noise vector
z
to output image
y
(
G
:
z
-->
y
)” Saraiva [Pg.3 Col.2 P.2].
Claims 14 and 16 recite substantially the same subject matter as claims 4 and 9, respectively, and are rejected under similar rationale.
Claim 19 recites substantially the same subject matter as claim 4 and is rejected under similar rationale.
Claims 12-13, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ovcharenko, Oleg, et al. "Multi-task learning for low-frequency extrapolation and elastic model building from seismic data." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-17 (hereinafter referred to as “Ovcharenko”) in view of Alwon US Patent No. 11105942 B2 (hereinafter referred to as “Alwon”), in further view of Saraiva, Marcus, et al. "Data-driven full-waveform inversion surrogate using conditional generative adversarial networks." 2021 International joint conference on neural networks (IJCNN). IEEE, 2021 (hereinafter referred to as “Saraiva”), and in further view of Zhang et al. US Pub. No. 2025/0003327 A1 (hereinafter referred to as “Zhang”).
Claims 12 and 18 recite substantially the same subject matter as claim 2 and are rejected under similar rationale.
Claim 13 recites substantially the same subject matter as claim 3 and is rejected under similar rationale.
Claims 15 and 20 recite substantially the same subject matter as claim 8 and are rejected under similar rationale.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is
considered pertinent to applicant's disclosure:
Liu, Mingliang, et al. "Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks." Geophysics 85.4 (2020): O47-O58. “We have proposed two types of deep neural network frameworks for 3D seismic facies classification calibrated from well log data. Fully supervised CNNs are recommended for mature fields with relatively abundant well data, and semisupervised GAN work well for new prospects with limited wells.” [Pg.O57 Conclusion]
Alsinan, Salma, Philippe Nivlet, and Hamad Alghenaim. "Automated facies prediction in pro-glacial channel system using deep learning." SEG International Exposition and Annual Meeting. SEG, 2021. “we demonstrate a workflow to detect channel reservoir quality within a pro-glacial channel system. The workflow is based on training a 2D UNET network on a realistic 3D geological model conditioned by the facies at the wells.” [Pg.1 Summary]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anthony Chavez whose telephone number is (571) 272-1036. The examiner can normally be reached Monday - Thursday, 8 a.m. - 5 p.m. ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at (571) 270-1104 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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