DETAILED ACTION
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 .
Notice to Applicants
This office action is a response to the amendment filed on 04/14/2026.
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1-6, 8-14, and 16 are pending in the application.
Response to Amendments
The Amendment filled 04/14/2026 in response to Non-Final Office Action mailed 01/14/2026 has been entered. Claims 1 and 9 have been amended. Claims 7 and 15 have been canceled. Claims 2-6, 8-14 and 16 are original. The claim interpretation under 35 USC §112(f) is maintained. Rejection under 35 USC §103 is maintained.
Response to Arguments/Remarks
Applicant's arguments with respect to Claim Interpretation – 35 USC §112(f), filed 04/14/2026, See Remarks page 8, have been fully considered but they are not persuasive. Applicant respectfully disagrees with claim limitations interpreted under 35 USC §112(f).
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Per MPEP §2181(I) “Examiners will apply 35 U.S.C. §112(f) to a claim limitation if it meets the following 3-prong analysis:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.”
Regarding the limitations "depth estimation network" (claims 1, 7), "vulnerability output device" (claims 1, 3-5), and "training data acquisition support device" (claims 1 and 6), (A) the nonce terms “network” and “device” are (B) modified by functional language, specifically “configured to”, and (C) they are not modified by sufficient structure in the claim. Sufficient structure is found is the specification for these terms. Additionally, MPEP §2181(I)(A) lists "device for" as a non-structural generic place holder, or nonce term, that may invoke claim interpretation under 35 USC §112(f). The term “network” requires specific structure for performing the claimed functions. The standard is whether the words of the claim are understood by persons of ordinary skill in the art to have a sufficiently definite meaning as the name for structure (MPEP §2181 (I)(A)). The term “network” does not limit the scope of the claim to any specific manner or structure for performing the claimed function. Examiner respectfully submits that the claim interpretation set forth in the Non-Final Rejection Office Action, filed 01/14/2026, is maintained.
Applicant's arguments with respect to rejections under 35 USC §103, filed 04/14/2026, see Remarks pages 9-12, have been fully considered but they are not persuasive. Applicant submits that Guizilini (2022) fails to teach/suggest “perform a process of outputting probability values corresponding to default depths for pixels of the input image as the depth distribution information”, as shown below, (see Remarks, page 9).
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However, Guizilini teaches “cost volume” and “mechanism to refine these per pixel probabilities” which correspond to depths ([p1, §1, col 2, ¶3] – [p2, §1, col 1, ¶1]; “We build a cost volume between target and context image features using differentiable depth-discretized epipolar sampling, and propose a novel attention-based mechanism to refine perpixel matching probabilities. We show that the refined probabilities are sharper and more representative of the underlying 3D structure than traditional similarity metrics [81]”). The cost volume is interpreted as probabilities because it is based on a high matching confidence values that the depths of the pixels between the target and context match, and the cross-attention matching further refines these by ensuring the similarity between the target and context pixels; in other words the result is based on high probabilities of estimated or predicted depth values (Guizilini, [page 5, §3.2.2, col 2, ¶2] removing pixels with low matching confidence.). Applicant has submitted that matching probability distribution is not based on default depths, however, Guizilini teaches assumed minimum and maximum depth values, where pixels are uniformly discretized into D bins. The minimum, maximum depths and D bins are interpreted as default depths; see at least Equation 1 and [page 3, §3.2.1, col 2, ¶1], shown below.
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.
Applicant has not provided evidence of how Guizilini does not teach the limitations, therefore examiner respectfully disagrees. Accordingly, Guizilini is found to meet the claimed limitation, and the rejection is maintained.
Applicant submits that Guizilini (2022) fails to teach/suggest the limitations of claim 7, in part, applying depth estimation calculation to the beforehand training image to generate predicted depth distribution, in full as shown below (see Remarks, page 10).
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However, Guizilini (2022) teaches that depth estimation network that inputs an image and outputs a depth estimation map corresponding to the input image, where the network is a learning device that generates predicted depths, see at least Figure 5;
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Applicant has not provided evidence of how at least Figure 5 of Guizilini does not teach the limitations of claim 7, therefore examiner respectfully disagrees. Accordingly, Guizilini is found to meet the claimed limitation, and the rejection is maintained.
Applicant submits that Guizilini (2022) is silent on the claimed predicted depth distribution information to teach/suggest the limitations of claim 7, as shown below (see Remarks, page 10).
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Guizilini teaches that no ground truth depth distribution information are used when predicting depth maps ([p 9, §Appendix B, Col 2, ¶1]; training is conducted without explicit supervision from ground-truth depth maps; [p 11, §Appendix D, Col 1, ¶1]; We reiterate here that no ground-truth is used at training or inference time, only videos.). Applicant has provided evidence, therefore examiner respectfully agrees. Applicant further submits that Srinivasan fails to make up for the above deficiencies in Guizilini. However, Srinivasan teaches the use of ground truth depth distribution information in the depth loss calculation (Srinivasan, [Col 2: 51-55]; the machine0learning model being trained can determine depth data to minimize a difference between the ground truth and the generated depth data), curing the deficiency of Guizilini. Guizilini teaches that their self-supervised method would benefit from supervision in order to overcome the limitation of scale ambiguity and therefore the motivation to combine Guizilini would be to produce metrically-accurate predictions (Guizilini, [page 11, §Appendix F, col 2, ¶1]). Applicant has not provided evidence as to why Srinivasan does not teach the use of ground truth depth distribution information. Accordingly, Guizilini in view of Srinivasan meets the claimed limitation, and the rejection is maintained.
Applicant submits that Guizilini (2022) fails to teach/suggest the claimed limitation “perform back propagation of the depth loss to learn a parameter of the depth estimation network”, as shown below (see Remarks, page 11).
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However, Guizilini teaches depth loss, Ls, exhibited in Equation 10 (Guizilini [p 5, §3.4, Col 2, ¶1] – [p6, §3.4, col 1, ¶1]; we also use depth regularization … these two terms are combined to produce the final training loss which is aggregated across all predicted depth maps.
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which is incorporated in the final training loss Equation 11, included in back-propagation for training the model parameters ([page 3,§3.1, col 1, ¶1]; gradient back-propagation for end-to-end training). Applicant has not provided evidence that Guizilini does not use depth loss when performing back-propagation. Accordingly, Guizilini meets the claimed limitation, and the rejection is maintained.
The above response to applicant arguments apply to amended claim 9, reciting substantially similar limitations as independent claim 1. Additionally, dependent claims 2, 3, 6, and 8 depending from claim 1, and dependent claims 10, 11, 14, and 16 depending from claim 9 are also rendered obvious by the combination of Guizilini and Srinivasan for at least the same reasons. Accordingly, the rejection is maintained
Information Disclosure Statement
No Information Disclosure Statement (IDS) was filed; therefore, no applicant-submitted references were considered.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-3, 6-8, 9-11, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over “Guizilini” (Vitor Guizilini and Rares Ambrus and Dian Chen and Sergey Zakharov and Adrien Gaidon. “Multi-Frame Self-Supervised Depth with Transformers”, (2022). arXiv: arXiv:2204.07616v2.) in view of Srinivasan, U.S. Patent No. US 11087494 B1.
Regarding claim 1, Guizilini teaches a training data selection device, comprising: a depth estimation network configured to
apply depth estimation calculation to an input image obtained in real time to output depth distribution information corresponding to the input image (See Guizilini, FIG 5 exhibits input image into depth estimation calculation to output depth distribution information)
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a vulnerability output device configured to output depth estimation vulnerability corresponding to the input image with reference to the depth distribution information (Guizilini, FIG 4(c), shown below, exhibits Maximum attention, “normalized attention values which can be used as a measure of confidence”, [p 5, §3.1.1, Col 1, ¶1])
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a training data acquisition support device configured {to store the input image and specific point cloud data} corresponding to the input image as new training data {in a certain storage space or configured to transmit the input image and the specific point cloud data to another device}, when it is determined that the depth estimation vulnerability is greater than or equal to a predetermined threshold (Guizilini, [p 5, §3.3.1, Col 1, ¶1]; leverage this novel matching confidence metric by masking out pixels with maximum attention value below a certain threshold _min, both from the high response loss calculation and the decoded features (Figure 4d)).
wherein the depth estimation network is further configured to:
perform a process of outputting probability values corresponding to default depths (Guizilini, See Equation 1, and [page 3, §3.2.1, col 2, ¶1]; assumed minimum and maximum depth values, where pixels are uniformly discretized into D bins. The minimum, maximum depths and D bins are interpreted as default depths;
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for pixels of the input image as the depth distribution information (Guizilini, [p1, §1, col 2, ¶3] – [p2, §1, col 1, ¶1]; We build a cost volume between target and context image features using differentiable depth-discretized epipolar sampling, and propose a novel attention-based mechanism to refine perpixel matching probabilities. We show that the refined probabilities are sharper and more representative of the underlying 3D structure than traditional similarity metrics [81]).);
apply the depth estimation calculation to the input image to output the depth distribution information corresponding to the input image (Guizilini, See FIG 5, shown below, exhibits an input image processed by a depth estimation network that outputs depth maps corresponding to the input image;
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), in a state where a learning device applies the depth estimation calculation to beforehand training image to generate predicted depth distribution information corresponding to the beforehand training image;
generate a depth loss using the predicted depth distribution information {and ground truth (GT) depth distribution information} corresponding to the predicted depth distribution information (Guizilini [p 5, §3.4, Col 2, ¶1] – [p6, §3.4, col 1, ¶1]; we also use depth regularization … these two terms are combined to produce the final training loss which is aggregated across all predicted depth maps.
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and [p 8, §4.5, Col 2, ¶1]; the VKITTI2, PD, and TartainAir models are pre-trained with depth supervision (using a Smooth L1 loss) and use ground-truth relative poses. Real-world datasets (DDAD and Cityscapes) are pre-trained using the self-supervised loss described in Section 3.4). However, Guizilini teaches away from use of ground truth depth distribution information: [p 9, §Appendix B, Col 2, ¶1]; training is conducted without explicit supervision from ground-truth depth maps; [p 11, §Appendix D, Col 1, ¶1]; We reiterate here that no ground-truth is used at training or inference time, only videos.); and
perform back propagation of the depth loss to learn a parameter of the depth estimation network (Guizilini 2022, Equation 10, shown above, exhibits
L
s
which is interpreted as depth loss, that is included in the back propagation for training the parameters of the model [p 3, §3.1, Col 1, ¶1]; gradient back-propagation for end-to-end training).
Guizilini does not explicitly disclose store the input image and specific point cloud data corresponding to the input image as new training data in a certain storage space or configured to transmit the input image and the specific point cloud data to another device.
Guizilini does not explicitly disclose using ground truth depth distribution information in the limitation “generate a depth loss using the predicted depth distribution information and ground truth (GT) depth distribution information”.
However, Srinivasan, in a similar field of endeavor of depth estimation, teaches store the input image and specific point cloud data corresponding to the input image as new training data in a certain storage space or configured to transmit the input image and the specific point cloud data to another device (Srinivasan, [Col 2: 22-24]; The machine-learning model can be trained using training image data and training lidar data (i.e., point cloud) as a ground truth for training the machine-learning model; Examiner interprets that image and point cloud data are stored if they are used to train the model).
However Srinivasan teaches generate a depth loss using the predicted depth distribution information and ground truth (GT) depth distribution information (Srinivasan, [Col 2: 51-55]; the machine0learning model being trained can determine depth data to minimize a difference between the ground truth and the generated depth data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include storing point cloud data as training data as taught by Srinivasan to the invention of Guizilini. The motivation to do so would be to provide supervision for the machine-learning model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include ground truth depth data for calculating loss as taught by Srinivasan to the invention of Guizilini. While Guizilini is focused on a self-supervised model, i.e., without use of ground truth depth data, Guizilini’s method is supervised with regards to pose data (i.e., ground truth relative poses [p 8, §4.5, col 2, ¶1]) for translation/rotation. Additionally, the need for supervision, at least weak supervision, is recognized in order to overcome the limitations found in self-supervised monocular depth estimation (e.g., scale ambiguity; [page 11, §Appendix F, col 2, ¶1]), The motivation to combine would be to provide scale-aware results necessary for downstream tasks that ingest reconstructed pointclouds, such as 3D object detection, by introducing weak velocity supervision [20] or additional geometric information such as camera height [77] or multi-camera extrinsics [24], from which ground truth depth may be derived. The method of Guizilini does not address this issue, however the authors assert that their self-supervised method can directly benefit from these works to produce scale-aware estimates from which metrically-accurate predictions may be generated.
Regarding claim 2, the combination of Guizilini and Srinivasan teach the training data selection device of claim 1. Guizilini further teaches wherein the depth estimation network performs a process of outputting j_1st to j_Kth probability values corresponding to 1st to Kth default depths for a jth pixel being any one of 1st to nth pixels of the input image with respect to the 1st to nth pixels to output probability values from 1_1st to 1_Kth probability values to n_1st to n_Kth probability values for the 1st to nth pixels as the depth distribution information (Guizilini 2022, [p 4, FIG 3(c) caption]; Matching probability distribution along depth bins for different pixels relative to their depth-discretized epipolar candidates).
Regarding claim 3, the combination of Guizilini and Srinivasan teach the training data selection device of claim 2. Guizilini further teaches wherein the vulnerability output device is further configured to: generate 1st to nth predicted depth values of the 1st to nth pixels and 1st to nth offsets corresponding to the 1st to nth predicted depth values with reference to the probability values from the 1_1st to 1_Kth probability values to the n_1st to n_Kth probability values (Guizilini 2022, [p 4,§3.1.1, Col 2, ¶1]; for each pixel, puv, the argmax operation is used to find the index huv of the most probable alongside its sampled epipolar line
ε
t
→
c
u
v
. A 1-dimensional 2s+1 window is placed around huv, and a re-normalization step is applied such that its sum is 1:
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and the 1st to Kth default depths ([p 5, §3.1.1, Col 1, ¶1]; The depth value for puv is calculated by multiplying this re-normalized distribution with the corresponding depth bins:
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output the depth estimation vulnerability with reference to the 1st to nth predicted depth values and the 1st to nth offsets ([p 5, §3.1.1, Col 1, ¶1];The normalized attention values can also be used as a measure of matching confidence).
Regarding claim 6, the combination of Guizilini and Srinivasan teach the training data selection device of claim 1. Srinivasan further teaches wherein the training data acquisition support device is further configured to store point cloud data obtained in a first time interval set on the basis of a time point when the input image is obtained as the specific point cloud data (Srinivasan, [Col 2:60-64]; After training (i.e., first time point) , the machine-learned model can receive image data captured by image sensor(s) to determine depth data associated with image data. In some instances, the machine-learned model can receive captured depth data captured by depth sensors (e.g., lidar sensors)) in the certain storage space or further configured to transmit the point cloud data to the other device (Srinivasan, FIG 12 exhibits memory 1238).
Regarding claim 8, the combination of Guizilini and Srinivasan teach the training data selection device of claim 1. Guizilini teaches wherein: the {GT} depth distribution information is generated by applying point cloud data for training to an image coordinate system corresponding to the beforehand training image; {and the point cloud data is obtained in a second time interval set on the basis of a time point when the beforehand training image is obtained}. Guizilini 2022 teaches point cloud data is reconstructed (See FIG 10 and FIG 11), and that “no ground-truth is used at training or inference time, only videos” (p 11, Appendix D, Col 1, ¶1). Rather, images are used as ground truth (p 8, §4.5, Col 2, ¶1). While recognizing the need for point cloud ground truth ([p 11, Appendix F, Col 2, ¶1]; Another common limitation of self-supervised monocular depth estimation is scale ambiguity, since models trained purely on image information cannot produce metrically-accurate predictions. Scale-aware results are necessary for downstream tasks that ingest our reconstructed pointclouds), the Guizilini does not explicitly disclose the GT depth distribution information is generated by applying point cloud data for training to an image coordinate system corresponding to the beforehand training image the point cloud data is obtained in a second time interval set on the basis of a time point when the beforehand training image is obtained.
However, Srinivasan teaches the GT depth distribution information is generated by applying point cloud data for training to an image coordinate system corresponding to the beforehand training image the point cloud data is obtained in a second time interval set on the basis of a time point when the beforehand training image is obtained (Srinivasan, [Col 16:15-21]; ground truth data (e.g., from lidar data and/or other sensor data) associated with the image data 804 can be used to train the machine-learned model (i.e., beforehand training)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include ground truth point cloud data as taught by Srinivasan to the invention of Guizilini. The motivation to do so would be to provide supervision for the machine-learning model.
Claim 9 is similarly analyzed as analogous claim 1.
Claim 10 is similarly analyzed as analogous claim 2.
Claim 11 is similarly analyzed as analogous claim 3.
Claim 14 is similarly analyzed as analogous claim 6.
Claim 16 is similarly analyzed as analogous claim 8.
Claims 4-5, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Guizilini in view of Srinivasan, and further in view of Dudzik et al., US 20210150278 A, hereinafter Dudzik.
Regarding claim 4, the combination of Guizilini and Srinivasan teach the training data selection device of claim 3. Guizilini teaches wherein the vulnerability output device is further configured to: perform a process of generating a j_ith predicted depth value corresponding to an ith default depth {with reference to an ith middle value} determined on the basis of at least one default depth including the ith default depth and a j_ith probability value corresponding to the ith default depth for the jth pixel with respect to the 1st to Kth default depths to generate j_1st to j_Kth predicted depth values; and perform a process of generating a jth predicted depth value of the jth pixel with reference to the j_1st to j_Kth predicted depth values with respect to the 1st to nth pixels to generate the 1st to nth predicted depth values (Guizilini, [p 4, §3.1.1, Col , ¶1]); We use a localized high-response window [72] to estimate continuous depth values from discretized bins, thus increasing robustness to multi-modal distributions [48]. A diagram is shown in Figure 4a; See Eq 8 shown above, and [p 5, §3.1.1, Col 1, ¶1]) The depth value for puv is calculated by multiplying this re-normalized distribution with the corresponding depth bins:).
Srinivasan also teaches wherein the vulnerability output device is further configured to: perform a process of generating a j_ith predicted depth value corresponding to an ith default depth {with reference to an ith middle value} determined on the basis of at least one default depth including the ith default depth and a j_ith probability value corresponding to the ith default depth for the jth pixel with respect to the 1st to Kth default depths to generate j_1st to j_Kth predicted depth values; and perform a process of generating a jth predicted depth value of the jth pixel with reference to the j_1st to j_Kth predicted depth values with respect to the 1st to nth pixels to generate the 1st to nth predicted depth values (Srinivasan, ¶[Col 3:25-40]; the machine-learned model can determine discrete depth portions/bins associated with the image data. For example, output values falling within a range of depths (e.g., within a depth bin) can be associated with a discrete depth bin and output a discrete value.) The combination does not explicitly disclose an ith default depth with reference to an ith middle value.
However, Dudzik, similar field of endeavor of depth estimation, teaches an ith default depth with reference to an ith middle value (Dudzik, ¶[0020]; a combination of binning and offsets may be used (as may be measured from the “center” of the bin). In some instances, the machine-learned algorithm can use a loss function and/or softmax loss that is associated with a depth bin to determine the continuous offset (i.e., vulnerability depends upon the offset).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include measuring offset from the “center” of the bin as taught by Dudzik to the combined invention of Guizilini and Srinivasan. The motivation to do so would be to output a “coarse” measurement of a bin.
Regarding claim 5, the combination of Guizilini, Srinivasan, and Dudzik teach the training data selection device of claim 4.
Srinivasan further teaches wherein the vulnerability output device is further configured to: perform a process of generating a j_ith offset {corresponding to the ith default depth with reference to the ith middle value}, the jth predicted depth value, and the j_ith probability value for the jth pixel with respect to the 1st to Kth default depths to generate j_1st to j_Kth offsets; and perform a process of generating a jth offset of the jth pixel with reference to the j_1st to j_Kth offsets with respect to the 1st to nth pixels to generate the 1st to nth offsets (Srinivasan, [Col 3:44-49]; a continuous offset can be determined with respect to a binned output. Continuing with the example above, a machine-learned model may output a binned depth value of 10.5 meters with a continuous offset of positive 15 cm from the discrete depth value. In such an example, the depth value would correspond to a depth of 10.65 meters.). The combination does not explicitly disclose the ith default depth with reference to the ith middle value.
However, Dudzik teaches wherein the vulnerability output device is further configured to: perform a process of generating a j_ith offset corresponding to the ith default depth with reference to the ith middle value, the jth predicted depth value, and the j_ith probability value for the jth pixel with respect to the 1st to Kth default depths to generate j_1st to j_Kth offsets; and perform a process of generating a jth offset of the jth pixel with reference to the j_1st to j_Kth offsets with respect to the 1st to nth pixels to generate the 1st to nth offsets (Dudzik, ¶[0020]; the machine-learned model can determine discrete depth portions/bins associated with the image data; a machine-learned model can output a continuous depth value as a continuous output, … the continuous offset can provide a graduated transition of between depth values regardless of whether the discrete depth bins are used; a combination of binning and offsets may be used (e.g., the model may output a “coarse” measurement of a bin in addition to a fine-grained offset (as may be measured from the “center” of the bin)).
Claim 11 is similarly analyzed as analogous claim 4.
Claim 12 is similarly analyzed as analogous claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kwon et al., (US 20200218979 A1), teaches deep neural network (DNN) trained to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for. A confidence value may be interpreted as a probability, or as providing a relative “weight” of each detection compared to other detections. This confidence value enables the system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHANDHANA PEDAPATI whose telephone number is (571)272-5325. The examiner can normally be reached M-F 8:30am-6pm (ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 5712727409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669
/JOHN B STREGE/Primary Examiner, Art Unit 2669
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