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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 9, 2026 has been entered.
Response to Amendment
Claims 1, 5-6, 9-10, 14, 17 and 20 have been amended changing the scope and contents of the claim.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 10 and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see Remarks, pages 16-20, filed January 5, 2026, with respect to claims 5 and 6 have been fully considered and are persuasive. The 35 USC 103 rejections of claims 5 and 6 have been withdrawn.
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.
Claim(s) 1-4, 10-13 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet and M. Norouzi, "Image Super-Resolution via Iterative Refinement," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4713-4726, 1 April 2023, doi: 10.1109/TPAMI.2022.3204461. (hereinafter Saharia), and further in view of G. Sainarayanan, R. Nagarajan, C. D. Raman and M. Phanindranath, "Iterative image deblurring approach for coronary artery enhancement in MRI image," 2005 1st International Conference on Computers, Communications, & Signal Processing with Special Track on Biomedical Engineering, Kuala Lumpur, Malaysia, 2005, pp. 320-323, doi: 10.1109/CCSP.2005.4977216. (hereinafter Sainarayanan).
Regarding independent claim 1, Saharia discloses A method for image restoration (abstract, “We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process.”), the method comprising:
inputting the medical imaging data (Figure 1, input; though not explicitly medical, it could be any image type) into an iterative restoration network configured to solve an ill-posed inverse image restoration problem by progressively reversing image degradation through a plurality of trained restoration stages (page 4714, left column, “We are interested in learning a parametric approximation to pðyjxÞ through a stochastic iterative refinement process that maps a source image x to a target image y 2 Rd.” … “The distribution of intermediate images in the iterative refinement chain is defined in terms of a forward diffusion process that gradually adds Gaussian noise to the output via a fixed Markov chain, denoted qðyt jyt 1Þ. The goal of our model is to reverse the Gaussian diffusion process by iteratively recovering signal from noise through a reverse Markov chain conditioned on x.”), each restoration stage of the plurality of trained restoration stages trained to perform a partial restoration that reduces image corruption relative to an output of a previous stage (page 4717, left column, “SR3, by comparison, relies on a series of iterative refinement steps, each of which is trained with a regression loss. This difference permits our iterative approach to capture richer distributions.”) the iterative restoration network configured to generate a sequence of intermediate images corresponding to progressively less degraded reconstructions of the medical imaging data (page 4714, left column, “The distribution of intermediate images in the iterative refinement chain”); and
outputting, by the iterative restoration network, higher quality medical imaging data corresponding to a final restoration stage of the plurality of trained restoration stages (Page 4718, “Fig. 6 shows outputs of our face super-resolution models (64x64 -> 512x512) on three test images (provided by col leagues), again with selected patches enlarged. With the 8 magnification factor one can clearly see the detailed structure inferred”).
Saharia fails to explicitly disclose as further recited. However, Sainarayanan discloses acquiring medical imaging data (abstract, “This paper outlines the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI).”).
Saharia is directed toward, “to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image (abstract).” Sainarayanan is directed toward, “ the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI). The work is highly motivated by need to process the images after its acquisition which causes blurriness (abstract)” and “In this paper, application of two different iterative techniques which are Blind Deconvolution and Simulated Annealing algorithms are discussed (section 1. Introduction).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Saharia and Sainarayanan are directed toward similar methods of endeavor of iterative refinement of images. Further Sainarayanan allows for iterative refinement specifically of medical images, such as MRI. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sainarayanan in order to ensure medical images can also be corrected, so that accurate diagnosis can be made from the most clear image possible.
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Sainarayanan in the combination further discloses wherein the medical imaging data is acquired using a magnetic resonance imaging device (abstract, “This paper outlines the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI).”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sainarayanan in order to ensure MRI images can also have their quality increased, to allow medical diagnoses to be made from the most accurate images.
Regarding dependent claim 3, the rejection of claim 1 is incorporated herein. Additionally, Saharia in the combination further discloses wherein the iterative restoration network is configured to reconstruct a high-resolution image from a low-resolution image (Page 4718, “Fig. 6 shows outputs of our face super-resolution models (64x64 -> 512x512) on three test images (provided by col leagues), again with selected patches enlarged. With the 8 magnification factor one can clearly see the detailed structure inferred;”).
Regarding dependent claim 4, the rejection of claim 1 is incorporated herein. Additionally, Saharia further discloses wherein the iterative restoration network is configured to denoise the medical imaging data (abstract, “performs super-resolution through a stochastic iterative denoising process”).
Regarding independent claim 10, the rejection of claim 1 applies directly. Additionally, Saharia discloses A system for image restoration (abstract, “We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process;” the system si read as the SR3 algorithm being executed on a computer to be ran), the system comprising:
an iterative restoration network configured to solve an ill-posed inverse image restoration problem by progressively reversing image degradation through a plurality of different restoration stages, each restoration stage of the plurality of different restoration stages (page 4714, left column, “We are interested in learning a parametric approximation to pðyjxÞ through a stochastic iterative refinement process that maps a source image x to a target image y 2 Rd.” … “The distribution of intermediate images in the iterative refinement chain is defined in terms of a forward diffusion process that gradually adds Gaussian noise to the output via a fixed Markov chain, denoted qðyt jyt 1Þ. The goal of our model is to reverse the Gaussian diffusion process by iteratively recovering signal from noise through a reverse Markov chain conditioned on x.”) trained using machine learning to perform a partial restoration that reduces image corruption relative to an output of a previous stage of the plurality of different restoration stages (page 4717, left column, “SR3, by comparison, relies on a series of iterative refinement steps, each of which is trained with a regression loss. This difference permits our iterative approach to capture richer distributions;” page 4714, left column, “The distribution of intermediate images in the iterative refinement chain”)), each stage of the plurality of different restoration stages configured to generate an intermediate image of a sequence of intermediate images corresponding to progressively less degraded reconstructions of the medical imaging data (page 4714, left column, “The distribution of intermediate images in the iterative refinement chain”); and
Saharia fails to explicitly disclose as further recited. However, Sainarayanan discloses a medical imaging device configured to acquire medical imaging data of a patient (abstract, “This paper outlines the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI). ;” section 3.1 Blind Deconvolution, “This method has no or very little priori information about the image and the degradation process affected. Cardiac MRI images which are with very little priory information would be best processed using this technique of deblurring.”);
a processor configured to apply the iterative restoration network to medical imaging data from the medical imaging device (abstract, “This paper outlines the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI)”) and to provide a representation of the patient based on an output of the iterative restoration network (section 4, conclusion, “The method of Blind deconvolution is an iterative approach in restoring a blurred image.”… “ Blind deconvolution tends to be a better method in deblurring cardiac MRI images as it does not require any prior information;” Figure 3).
Saharia is directed toward, “to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image (abstract).” Sainarayanan is directed toward, “ the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI). The work is highly motivated by need to process the images after its acquisition which causes blurriness (abstract)” and “In this paper, application of two different iterative techniques which are Blind Deconvolution and Simulated Annealing algorithms are discussed (section 1. Introduction).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Saharia and Sainarayanan are directed toward similar methods of endeavor of iterative refinement of images. Further Sainarayanan allows for iterative refinement specifically of medical images, such as MRI. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sainarayanan in order to ensure medical images can also be corrected, so that accurate diagnosis can be made from the most clear image possible.
Regarding dependent claim 11, the rejection of claim 10 is incorporated herein. Additionally, Sainarayanan in the combination further discloses wherein the medical imaging device comprises a magnetic resonance imaging device (abstract, “This paper outlines the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI).”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sainarayanan in order to ensure MRI images can also have their quality increased, to allow medical diagnoses to be made from the most accurate images.
Regarding dependent claim 12, the rejection of claim 10 is incorporated herein. Additionally, Saharia in the combination further discloses wherein the iterative restoration network is configured to reconstruct a high-resolution image from a low-resolution image (Page 4718, “Fig. 6 shows outputs of our face super-resolution models (64x64 -> 512x512) on three test images (provided by col leagues), again with selected patches enlarged. With the 8 magnification factor one can clearly see the detailed structure inferred”).
Regarding dependent claim 13, the rejection of claim 10 is incorporated herein. Additionally, Saharia in the combination further discloses wherein the iterative restoration network is configured to denoise the medical imaging data (abstract, “performs super-resolution through a stochastic iterative denoising process”).
Regarding independent claim 17, the rejection of claim 1 applies directly. Additionally, Saharia discloses A non-transitory computer readable storage medium comprising a set of computer-readable instructions stored thereon which (abstract, “We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process;” SR3 is read as an algorithm which is programmed to execute the iterative refinement processes), when executed by at least one processor cause the processor to:
input the medical imaging data into an iterative restoration network (Figure 1, input; though not explicitly medical, it could be any image type), the iterative restoration network configured to solve an ill-posed inverse image restoration problem by progressively reversing image degradation through a plurality of trained restoration stages (page 4714, left column, “We are interested in learning a parametric approximation to pðyjxÞ through a stochastic iterative refinement process that maps a source image x to a target image y 2 Rd.” … “The distribution of intermediate images in the iterative refinement chain is defined in terms of a forward diffusion process that gradually adds Gaussian noise to the output via a fixed Markov chain, denoted qðyt jyt 1Þ. The goal of our model is to reverse the Gaussian diffusion process by iteratively recovering signal from noise through a reverse Markov chain conditioned on x.”), each restoration stage of the plurality of trained restoration stages trained using machine learning to perform a partial restoration that reduces image corruption relative to an output of a previous stage of the plurality of trained restoration stages (page 4717, left column, “SR3, by comparison, relies on a series of iterative refinement steps, each of which is trained with a regression loss. This difference permits our iterative approach to capture richer distributions.”), the iterative restoration network configured to generate a sequence of intermediate images corresponding to progressively less degraded reconstructions of the medical imaging data (page 4714, left column, “The distribution of intermediate images in the iterative refinement chain”) using a plurality of incremental steps that provide a sequence of less corrupted images, wherein each of the incremental steps is provided by a different stage trained to incrementally improve an image quality of the medical imaging data based on an output from a previous stage (page 4714, left column, “The distribution of intermediate images in the iterative refinement chain;” Page 4718, “Fig. 6 shows outputs of our face super-resolution models (64x64 -> 512x512) on three test images (provided by col leagues), again with selected patches enlarged. With the 8 magnification factor one can clearly see the detailed structure inferred”).
Saharia fails to explicitly disclose as further recited. However, Sainarayanan discloses acquire medical imaging data (abstract, “This paper outlines the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI).”);
output, by the iterative restoration network, higher quality medical imaging data corresponding to a final restoration stage of the plurality of trained restoration stages (section 4, conclusion, “The method of Blind deconvolution is an iterative approach in restoring a blurred image.”… “ Blind deconvolution tends to be a better method in deblurring cardiac MRI images as it does not require any prior information;” Figure 3).
Saharia is directed toward, “to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image (abstract).” Sainarayanan is directed toward, “ the digital image enhancement techniques applied on cardiac Magnetic Resonance Images (MRI). The work is highly motivated by need to process the images after its acquisition which causes blurriness (abstract)” and “In this paper, application of two different iterative techniques which are Blind Deconvolution and Simulated Annealing algorithms are discussed (section 1. Introduction).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Saharia and Sainarayanan are directed toward similar methods of endeavor of iterative refinement of images. Further Sainarayanan allows for iterative refinement specifically of medical images, such as MRI. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sainarayanan in order to ensure medical images can also be corrected, so that accurate diagnosis can be made from the most clear image possible.
Regarding dependent claim 18, the rejection of claim 17 is incorporated herein. Additionally, Saharia in the combination further discloses further comprising instructions to:
display the higher quality medical imaging data (Figure 1, middle column).
Regarding dependent claim 19, the rejection of claim 17 is incorporated herein. Additionally, Saharia in the combination further discloses wherein the higher quality medical imaging data includes less noise (abstract, “performs super-resolution through a stochastic iterative denoising process”), is less blurry (page 4721, right column, “As noted above, our Regression baseline produces results consistent to the input, however they are typically quite blurry. By comparison, the SR3 results are consistent with the input and contain more detailed image structure;” the regression being quite blurry is read that the SR3 results are not blurry), or includes less noise and is less blurry than the input image data (see both above).
Claim(s) 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Saharia further in view of Sainarayanan as applied to claims 1 and 10 respectively above, and further in view of Y. Wang, Y. Deng, Y. Shen and H. Jin, "A New Concept of Multiple Neural Networks Structure Using Convex Combination," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 4968-4979, Nov. 2020, doi: 10.1109/TNNLS.2019.2962020. (hereinafter Wang).
Regarding dependent claim 8, the rejection of claim 1 is incorporated herein. Additionally, Saharia discloses wherein the iterative restoration network is trained by optimizing a loss of (page 4713, right column, “In contrast to GANs, which require inner-loop maximization, we minimize a well-defined loss function. Unlike autoregressive models, SR3 uses a constant number of inference steps regardless of output resolution.”).
Saharia and Sainarayanan in the combination as a whole fails to explicitly disclose as further recited. However, Wang discloses a convex combination of input and ground truth images (Section 1. Introduction, “In this article, we propose a convex-combined multiple NN structure to improve model performance in terms of training speed and accuracy by bypassing the vanishing gradient issue through a global loss function and reducing the negative effects of nonconvex optimization;” see also SECTION III. Convex-Combined Multiple Neural Network Structure). Wang does not disclose the convex combination with respect to an iterative restoration network, however one of ordinary skill in the art before the effective filing date of the claimed invention would be aware of how to apply the convex combination with the previously cited iterative restoration network.
As noted above Saharia and Sainarayanan are directed toward similar methods of endeavor of iterative image quality enhancement. Wang is directed toward, “a convex-combined multiple NN structure to improve model performance in terms of training speed and accuracy by bypassing the vanishing gradient issue through a global loss function and reducing the negative effects of nonconvex optimization (page 4969, right column).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Saharia, Sainarayanan and Wang are directed toward similar methods of endeavor of utilizing neural networks to perform tasks. Further, it evidenced in Wang there are benefits to the convex combination such as “a convex-combined multiple NN structure to improve model performance in terms of training speed and accuracy by bypassing the vanishing gradient issue through a global loss function and reducing the negative effects of nonconvex optimization (page 4969, right column).” Being that Saharia utilizes trained networks, and often users have a desire for the most efficient system for image processing, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Wang in order to ensure optimal model performance in terms of both training speed and accuracy.
Regarding dependent claim 16, the rejection of claim 10 is incorporated herein. Additionally, Saharia discloses wherein the iterative restoration network is trained by optimizing a loss of (page 4713, right column, “In contrast to GANs, which require inner-loop maximization, we minimize a well-defined loss function. Unlike autoregressive models, SR3 uses a constant number of inference steps regardless of output resolution.”).
Saharia and Sainarayanan in the combination as a whole fails to explicitly disclose as further recited. However, Wang discloses a convex combination of input and ground truth images (Section 1. Introduction, “In this article, we propose a convex-combined multiple NN structure to improve model performance in terms of training speed and accuracy by bypassing the vanishing gradient issue through a global loss function and reducing the negative effects of nonconvex optimization;” see also SECTION III. Convex-Combined Multiple Neural Network Structure). Wang does not disclose the convex combination with respect to an iterative restoration network, however one of ordinary skill in the art before the effective filing date of the claimed invention would be aware of how to apply the convex combination with the previously cited iterative restoration network.
As noted above Saharia and Sainarayanan are directed toward similar methods of endeavor of iterative image quality enhancement. Wang is directed toward, “a convex-combined multiple NN structure to improve model performance in terms of training speed and accuracy by bypassing the vanishing gradient issue through a global loss function and reducing the negative effects of nonconvex optimization (page 4969, right column).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Saharia, Sainarayanan and Wang are directed toward similar methods of endeavor of utilizing neural networks to perform tasks. Further, it evidenced in Wang there are benefits to the convex combination such as “a convex-combined multiple NN structure to improve model performance in terms of training speed and accuracy by bypassing the vanishing gradient issue through a global loss function and reducing the negative effects of nonconvex optimization (page 4969, right column).” Being that Saharia utilizes trained networks, and often users have a desire for the most efficient system for image processing, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Wang in order to ensure optimal model performance in terms of both training speed and accuracy.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Saharia and Sainarayanan as applied to claim 1 above, and further in view of CN 111127356 A (a machine translation obtained from Google Patents; hereinafter CN ‘356).
Regarding dependent claim 9, the rejection of claim 1 is incorporated herein. Additionally, Saharia and Sainarayanan in the combination as a whole fails to explicitly disclose wherein an amount of white noise is added to an output of each restoration stage.
However, CN ‘356 discloses wherein an amount of white noise is added to an output of each restoration stage (page 10, “In this embodiment, as shown in FIG. 2, noise blindly network module uses encoder-decoder network structure connected with the jump. the input of the noise blindly network module is a uniform noise and in the iterative process of adding white Gaussian noise as the disturbance.”).
As noted above Saharia and Sainarayanan are directed toward similar methods of endeavor of iterative image quality enhancement. CN ‘356 is directed toward, “an image blind denoising system (abstract).” As can be easily seen by one of ordinary skill in the art at the time of filing the claimed invention, Saharia, Sainarayanan and CN ‘356 are directed toward similar methods of endeavor of image denoising. Further, it evidenced in CN ‘356 “the present invention provides an image blind denoising system, which optimizes a DIP method through transfer learning, determines the number of iterations required by the DIP method through blind image quality evaluation, and reduces the number of iterations required to generate an optimal reconstructed image (page 6).” Being that Saharia discloses an iterative denoising method, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of CN ‘356 to determine the number of iterations needed to obtain an image of optimal quality so that processing power needs are understood.
Allowable Subject Matter
Claims 5-7, 14-15 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 5:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of image restoration using iterative methods to generate incremental quality increases. However, none of them alone or in any combination teaches generating intermediate images of incremental quality increases, where each stage for incremental restoration includes a CNN and a data consistency layer.
The closest prior art being Saharia discloses “We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process.”
However, Saharia fails to disclose generating intermediate images of incremental quality increases, where each stage for incremental restoration includes a CNN and a data consistency layer.
Claims 6-7, 14-15 and 20:
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of image restoration using iterative methods to generate incremental quality increases. However, none of them alone or in any combination teaches determining a rate of restoration at each restoration stage, that is controlled by a predefined parameter.
The closest prior art being Saharia discloses iterative refinement of images using SR3 which is used to perform super-resolution. Further, Saharia discloses, “SR3 uses a constant number of inference steps regardless of output resolution (page 4713).” However, these steps don’t determine the rate of restoration, only how many stages there are as a whole. This does not define the impact or aggressiveness of the change each stage performs.
Thus, Saharia fails to disclose determining a rate of restoration at each restoration stage, that is controlled by a predefined parameter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2021/0390694 to Mao et al. discloses, “methods and systems for image quality optimization (abstract)”
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661