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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/04/2024, 03/03/2025, and 12/01/2025 have been considered by the examiner.
Claim Objections
Claim 1, 5 are objected to because of the following informalities:
In claim 1, lines 13-14, “via the ringing correction unit…” should read “via a ringing correction unit…”
In claim 5, line 2, “set by the matrix size setting unit…” should read “set by a matrix size setting unit…”
In claim 9, line 4, “applied by the filter unit…” should read “applied by a filter unit…”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 1 recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “an imaging unit that collects measurement data consisting of magnetic resonance signals…” [Line 2].
Claim 1; recites the limitation, “performing ringing correction on the intermediate reconstructed image via the ringing correction unit…” [Line 14].
Claim 5 and 9; recite the limitation, “matrix size set by the matrix size setting unit…” [Line 2].
Claims 9 and 10; recite the limitation, “frequency filter applied by the filter unit…” [Line 4].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 1:
“imaging unit” (Fig. 1, #10 called imaging unit, Paragraph [0021] – “Since the configuration of the imaging unit 10 is the same as a configuration of a normal MRI apparatus, a detailed description thereof will be omitted, but the imaging unit 10 comprises a static magnetic field magnet 11 that generates a static magnetic field space in which a subject 5 is placed, a gradient magnetic field coil 12 that applies a gradient magnetic field in a static magnetic field, an RF transmission coil 13, an RF receive coil 14, a gradient magnetic field power supply 15 that drives these coils and a transmitter 16, a receiver 17 to which a high-frequency receive coil is connected, and a bed device 19 that transports the subject 5 into the static magnetic field space. Further, the imaging unit comprises a sequencer 18 that controls the operations of sampling the signals via the gradient magnetic field coil 12, the RF transmission coil 13, and the RF receive coil 14 in accordance with a pulse sequence.” Thus, the imaging unit does have sufficient structure associated with it wherein it is understood to have the same configuration as an MRI apparatus.
“matrix size setting unit” (Fig. 2, #232 called matrix size setting unit, Paragraph [0012] – “The image generation unit includes a matrix size setting unit that sets a measurement matrix size of the measurement data.” Paragraph [0025] – “As shown in Fig. 2, the image processing unit 23 comprises an image generation unit 231 that performs operations such as a Fourier transform on the measurement data to generate an image having a desired reconstruction matrix size, and a ringing correction unit 233 that corrects the ringing that has occurred in the image.” Paragraph [0022] – “The computer 20 includes an imaging controller 21 that controls the operation of the imaging unit 10 via the sequencer 18, an image processing unit 23 that performs image reconstruction and other processing on measurement data (k-space data) collected by the imaging unit 10, and a display controller 25 that controls the display of the reconstructed images, a GUI, and the like.”) Thus, the matrix size setting unit does have sufficient structure associated with it wherein it is understood to be a component of a computer.
“filter unit” (Fig. 2, #239 called filter unit, Paragraph [0026] – “The image processing unit 23 further comprises a filter unit 239 that applies a k-space filter to the k-space data prior to the final reconstruction.” Paragraph [0022] – “The computer 20 includes an imaging controller 21 that controls the operation of the imaging unit 10 via the sequencer 18, an image processing unit 23 that performs image reconstruction and other processing on measurement data (k-space data) collected by the imaging unit 10, and a display controller 25 that controls the display of the reconstructed images, a GUI, and the like.”) Thus, the filter unit does have sufficient structure associated with it wherein it is understood to be a component of a computer.
“ringing correction unit” (Fig. 2, #233 called ringing correction unit, Paragraph [0024] – “The computer 20 comprises a ringing correction unit that performs ringing correction on the image reconstructed at a specific matrix ratio.”) Thus, the ringing correction unit does have sufficient structure associated with it wherein it is understood to be a computer.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 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 of this title, 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 1 is rejected under 35 U.S.C. 103 as being unpatentable over NIELSON (US 20240074671 A1), hereinafter referenced as NIELSON in view of SHOJI (US 20210333347 A1), hereinafter referenced as SHOJI.
Regarding claim 1, NIELSON teaches a magnetic resonance imaging apparatus (Fig. 1, Paragraph [0028] – NIELSON discloses an image processing apparatus 100 according to the present embodiment is connected to an MRI apparatus 200 and an image storing apparatus 300 via a network 400 so as to be able to communicate with one another.) comprising:
an imaging unit (Fig. 1, #151 called MR image acquiring function, Paragraph [0044] – NIELSON discloses the MR image acquiring function 151 is an example of the acquiring unit.)
that collects measurement data consisting of magnetic resonance signals (Fig. 1, Paragraph [0029] – NIELSON discloses MRI apparatus 200 is configured to take an image of a subject by employing magnetic resonance phenomena. Paragraph [0046] – NIELSON further discloses from the MRI apparatus 200 or the image storing apparatus 300, the MR image acquiring function 151 is configured to acquire the MR image designated by the operator through the operation and to store the acquired MR image into the storage 120.);
and one or more processors (Fig. 19, #14-17 called processing circuitry, Paragraph [0173]) that reconstruct the measurement data at a desired reconstruction matrix size (Fig. 19, Paragraph [0191] – NIELSON discloses processing circuitry 16 includes an MR image generating function 16a. More specifically, the MR image generating function 16a is configured to generate a two- or three-dimensional MR image by reading the k-space data acquired by the acquiring function 15a of the processing circuitry 15 from the storage 13 and performing a reconstruction process such as a Fourier transform on the read k-space data.),
and perform ringing correction on a reconstructed image (Fig. 19, Paragraph [0195] – NIELSON discloses the processing circuitry 17 includes a ringing correcting function 17b. In this situation, the ringing correcting function 17b is an example of the correcting unit. Paragraph [0197] – NIELSON further discloses the ringing correcting function 17b is configured to read the MR image generated by the MR image generating function 16a from the storage 13.),
Although NIELSON further teaches ringing correction (Fig. 1, Paragraph [0048] – NIELSON discloses the ringing correcting function 152 is configured to determine a shift amount from the position of the pixel to a position where the ringing artifacts will be reduced, and to perform a ringing correction to correct the ringing artifacts occurring in the MR image on the basis of the determined shift amounts.),
NIELSON fails to explicitly teach wherein one or more processors are configured to set a measurement matrix size of the measurement data, an intermediate reconstruction matrix size having a predetermined matrix ratio to the measurement matrix size, and the desired reconstruction matrix size, reconstruct the measurement data at the intermediate reconstruction matrix size to generate an intermediate reconstructed image and reconstruct k-space data of a corrected intermediate reconstructed image obtained by performing correction on the intermediate reconstructed image via the correction unit at the desired reconstruction matrix size.
However, SHOJI explicitly teaches wherein one or more processors (Fig. 3, #231 called pre-processing unit, Paragraph [0038]) are configured to set a measurement matrix size of the measurement data (Fig. 3, Paragraph [0038] – SHOJI discloses a pre-processing unit 231 that performs a pre-processing to change a matrix size of the image generated by the image generator 220 prior to the Wavelet transform.),
an intermediate reconstruction matrix size having a predetermined matrix ratio to the measurement matrix size (Fig. 9A, Paragraph [0056] – SHOJI discloses the pre-processing unit 231 acquires the sizes of the two-dimensional acquisition matrix and the reconstruction matrix from the parameters (S6121). Paragraph [0072] – SHOJI further discloses behavior of artifact is, for example, information indicating which component of the image the artifact affects, depending on the ratio of the reconstruction matrix to the acquisition matrix. See also Paragraph [0041, 0057].),
and the desired reconstruction matrix size (Fig. 9A, Paragraph [0056] – SHOJI discloses the pre-processing unit 231 acquires the sizes of the two-dimensional acquisition matrix and the reconstruction matrix from the parameters (S6121).),
reconstruct the measurement data at the intermediate reconstruction matrix size to generate an intermediate reconstructed image (Fig. 5, Paragraph [0042] – SHOJI discloses image generator 220 arranges the nuclear magnetic resonance signals in the k-space, and, as shown in FIG. 5, compensates for the insufficient area of the k-space data with zeros with respect to the reconstruction matrix (image space matrix), performs Fourier transform, and generates a reconstructed image (S403).)
and reconstruct k-space data of a corrected intermediate reconstructed image (Fig. 8, Paragraph [0051] – SHOJI discloses the post-processing unit 234 converts the noise-removed image from the image space data to k-space data by the Fourier transform (S6041), and cuts out the reconstruction matrix size that is the same as the size prior to extension in the k-space (S6042).)
obtained by performing correction on the intermediate reconstructed image via the correction unit (Fig. 8, Paragraph [0051] – SHOJI discloses post-processing unit 234 converts the noise-removed image [wherein noise-removed image is a corrected intermediate image] from the image space data to k-space data by the Fourier transform (S6041). Paragraph [0043] – SHOJI further discloses the noise remover 230 performs a process for removing noise by the Wavelet transform and the iterative operation on the image data (S404). In this situation, a processing to eliminate artifacts caused by the noise removal, or a processing to prevent artifacts are performed (S405).),
at the desired reconstruction matrix size (Fig. 3, Paragraph [0038] – SHOJI discloses a post-processing unit 234 that performs processing such as restoring the image size to the size before the pre-processing.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of NIELSON of having a magnetic resonance imaging apparatus comprising: an imaging unit that collects measurement data consisting of magnetic resonance signals; and one or more processors that reconstruct the measurement data at a desired reconstruction matrix size, and perform ringing correction on a reconstructed image, with the teachings of SHOJI having wherein one or more processors are configured to set a measurement matrix size of the measurement data, an intermediate reconstruction matrix size having a predetermined matrix ratio to the measurement matrix size, and the desired reconstruction matrix size, reconstruct the measurement data at the intermediate reconstruction matrix size to generate an intermediate reconstructed image and reconstruct k-space data of a corrected intermediate reconstructed image obtained by performing correction on the intermediate reconstructed image via the correction unit at the desired reconstruction matrix size.
Wherein NIELSON’s magnetic resonance imaging apparatus wherein one or more processors are configured to set a measurement matrix size of the measurement data, an intermediate reconstruction matrix size having a predetermined matrix ratio to the measurement matrix size, and the desired reconstruction matrix size, reconstruct the measurement data at the intermediate reconstruction matrix size to generate an intermediate reconstructed image, and reconstruct k-space data of a corrected intermediate reconstructed image obtained by performing ringing correction on the intermediate reconstructed image via the ringing correction unit, at the desired reconstruction matrix size.
The motivation behind this modification would have been to provide an enhanced method of artifact correction for magnetic resonance imaging with better precision and improved processing speed, since both NIELSON and SHOJI relate to magnetic resonance image processing techniques, wherein NIELSON relates to an image processing apparatus, an image processing method, and a magnetic resonance imaging apparatus to improve the level of precision in estimating the local amplitudes of ringing artifacts and to provide an MR image in which ringing artifacts have been corrected with a higher level of precision, and which is less blurred than an MR image processed by a conventional method for countering ringing artifacts, and SHOJI relates to a technique for eliminating artifacts after removing noise of an image, the artifacts being caused by the noise removal, whereby the Fourier transform can be processed at high speed, resulting in improvement of the processing speed in the whole noise removal processing. Please see NIELSON (US 20240074671 A1), Paragraph [0002, 0166], and SHOJI (US 20210333347 A1), Paragraph [0002, 0057].
Allowable Subject Matter
Claims 2, along with dependent claims 3, 4, and 6-12, are therefrom objected to as being dependent upon rejected base claim 1, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims, once claim objections are overcome.
Claim 5, a dependent claim, is therefrom objected to as being dependent upon rejected base claim 1, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims.
Claim 13, an independent claim, comprises of allowable subject matter and is allowed along with its dependent claim 14.
The following is a statement of reasons for the indication of allowable subject matter:
With regards to dependent claim 2, the cited prior arts fail to explicitly teach the following limitation in combination with all claim limitations:
Regarding claim 2, the prior arts fail to explicitly teach wherein the one or more processors include a CNN that has been trained by using a plurality of image sets consisting of a first image in which ringing has not occurred and a second image in which ringing has occurred, and the second image is an image obtained by changing a size of the first image and reconstructing the first image in a k-space at the predetermined matrix ratio.
With regards to dependent claim 5, the cited prior arts fail to explicitly teach the following limitation in combination with all claim limitations:
Regarding claim 5, the prior arts fail to explicitly teach wherein the desired reconstruction matrix size set by the matrix size setting unit is larger than the measurement matrix size, and the predetermined matrix ratio (intermediate reconstruction matrix size/measurement matrix size) is larger than 1.
With regards to independent claim 13, the cited prior arts fail to explicitly teach the following limitation in combination with all claim limitations:
Regarding claim 13, the prior arts fail to explicitly teach a correction step of performing ringing correction on the intermediate reconstructed image by using a CNN that has been trained by using a set including a first image in which ringing has not occurred and a second image in which ringing has occurred and that outputs an image in which ringing is corrected with respect to an input image; wherein the second image used for training the CNN is an image which is obtained by changing a size of the first image and reconstructing the first image in a k-space at a reconstruction matrix size of the first image and in which the reconstruction matrix size and a matrix size after the size change satisfy the predetermined matrix ratio.
Regarding claim 13, NIELSON teaches an image processing method of reconstructing a ringing-corrected image (Fig. 2, Paragraph [0051] – NIELSON discloses FIG. 2 is a chart illustrating examples of process flow in the ringing correction performed by the ringing correcting function 152 according to the first embodiment.)
by using measurement data consisting of magnetic resonance signals (Fig. 2, Paragraph [0052] – NIELSON discloses the ringing correcting function 152 is configured to generate an output image in which ringing artifacts have been corrected by using the MR image to be processed as an input image and performing the ringing correction on the input image. See also Fig. 19, Paragraph [0180].)
collected by a magnetic resonance imaging apparatus (Fig. 1, #200 called MRI apparatus, Paragraph [0029] – NIELSON discloses MRI apparatus 200 is configured to take an image of a subject by employing magnetic resonance phenomena. More specifically, the MRI apparatus 200 is configured to apply a Radio Frequency (RF) pulse to the subject by executing any of various types of imaging sequences on the basis of an image acquisition condition set by an operator, to receive a Nuclear Magnetic Resonance (NMR) signal radiating from the subject owing to the influence of the RF pulse, and to acquire NMR data based on the NMR signal as k-space data.),
Although NIELSON further teaches ringing correction (Fig. 1, Paragraph [0048] – NIELSON discloses the ringing correcting function 152 is configured to determine a shift amount from the position of the pixel to a position where the ringing artifacts will be reduced, and to perform a ringing correction to correct the ringing artifacts occurring in the MR image on the basis of the determined shift amounts.),
NIELSON fails to explicitly teach the image processing method comprising: a first reconstruction step of reconstructing the measurement data at a reconstruction matrix size having a predetermined matrix ratio to a measurement matrix size to generate an intermediate reconstructed image; and a second reconstruction step of transforming the intermediate reconstructed image after the correction into k-space data and reconstructing the k-space data at a desired reconstruction matrix size,
However, SHOJI explicitly teaches the image processing method comprising: a first reconstruction step of reconstructing the measurement data at a reconstruction matrix size having a predetermined matrix ratio to a measurement matrix size (Fig. 9A, Paragraph [0056] – SHOJI discloses the pre-processing unit 231 acquires the sizes of the two-dimensional acquisition matrix and the reconstruction matrix from the parameters (S6121). Paragraph [0072] – SHOJI further discloses behavior of artifact is, for example, information indicating which component of the image the artifact affects, depending on the ratio of the reconstruction matrix to the acquisition matrix.)
to generate an intermediate reconstructed image (Fig. 5, Paragraph [0042] – SHOJI discloses image generator 220 arranges the nuclear magnetic resonance signals in the k-space, and, as shown in FIG. 5, compensates for the insufficient area of the k-space data with zeros with respect to the reconstruction matrix (image space matrix), performs Fourier transform, and generates a reconstructed image (S403).);
and a second reconstruction step of transforming the intermediate reconstructed image after the correction into k-space data (Fig. 8, Paragraph [0051] – SHOJI discloses the post-processing unit 234 converts the noise-removed image [wherein the noise removed image is a corrected intermediate image] from the image space data to k-space data by the Fourier transform (S6041), and cuts out the reconstruction matrix size that is the same as the size prior to extension in the k-space (S6042).)
and reconstructing the k-space data at a desired reconstruction matrix size (Fig. 3, Paragraph [0038] – SHOJI discloses a post-processing unit 234 that performs processing such as restoring the image size to the size before the pre-processing.),
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
CHATTERJEE et al. (US 20240410965 A1)- Systems and methods are provided for reconstructing images from motion-affected k-space data. In one example, a method comprises obtaining k-space data of a spin echo magnetic resonance imaging (MRI) exam of a subject, the k-space data comprising a plurality of echo train lengths (ETLs), with each ETL comprising a subset of lines of the k-space data. The method further comprises identifying a subset of ETLs of the plurality of ETLs of the k-space data corresponding to a dominant pose of the subject, generating an undersampled version of the k-space data, the undersampled version including only the subset of ETLs, entering the undersampled version of the k-space data as input to a reconstruction model trained to output a reconstructed image based on the undersampled version of the k-space data, and displaying the reconstructed image on a display device and/or saving the reconstructed image in memory..... ...... Fig. 1. Abstract.
CAO et al. (US 20180158216 A1)- The present disclosure provides a system and method for CT image reconstruction. The method may include combining an analytic image reconstruction technique with an iterative reconstruction algorithm of CT images. The image reconstruction may be performed on or near a region of interest..... ...... Fig. 1. Abstract.
HUBER et al. (US 20240135502 A1)- A neural network is trained and implemented to simultaneously remove noise and artifacts from medical images using a Generalized noise and Artifact Reduction Network (“GARNET”) method for training a convolutional neural network (“CNN”) or other suitable neural network or machine learning algorithm. Noise and artifact realizations from phantom images are used to synthetically corrupt images for training. Corrupted and uncorrupted image pairs are used for training GARNET. Following the training phase, GARNET can be used to improve image quality of routine medical images by way of noise and artifact reduction..... ...... Fig. 1. Abstract.
MAILHE et al. (US 20170372193 A1)- For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced..... ...... Fig. 1. Abstract.
CHEN et al. (US 20190377047 A1)- For artifact reduction in a magnetic resonance imaging system, deep learning trains an image-to-image neural network to generate an image with reduced artifact from input, artifacted MR data. For application, the image-to-image network may be applied in real time with a lower computational burden than typical post-processing methods. To handle a range of different imaging situations, the image-to-image network may (a) use an auxiliary map as an input with the MR data from the patient, (b) use sequence metadata as a controller of the encoder of the image-to-image network, and/or (c) be trained to generate contrast invariant features in the encoder using a discriminator that receives encoder features...... ...... Fig. 1. Abstract.
Tolpadi, Aniket A., et al. "K2S challenge: from undersampled k-space to automatic segmentation." Bioengineering 10.2 (2023): 267.
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/BEZAWIT NOLAWI SHIMELES/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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