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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/23/2026 has been entered.
Response to Arguments
Applicant’s arguments, see Remarks pages 5-6, filed 03/23/2026, with respect to the rejection of claim 1 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
On Page 6 of Remarks, Applicant argues:
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Examiner respectfully disagrees.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the specific processing methods used for the determination of a three dimensional size or a gray level interval of the three-dimensional grey-level matrix, and the specific processing methods used for the transformation the CBCT file of the midpalatal suture of the maxilla into the three- dimensional gray-level matrix based on the three-dimensional size and the gray level interval.) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
As presented in the previous office action, Section 2.1.2. of Gao discloses "CBCT images were taken with NewTom VGi (Quantitative Radiology, Verona, Italy), at 2.81 mA, 110 kV, 3.6-s exposure, and a 15 x 15 cm field of view, with an axial slice thickness of 0.3 mm, and isotropic voxels (Figure 1)," wherein the disclosure of the axial slice thickness of .3 mm, along with disclosing the voxels are isotropic, thus discloses that the resolution of the captured CBCT images is contained within the file information, which also must include the number of layers sliced to a thickness of .3mm.
In addition, Section 2.3.1. of Gao discloses "The CBCT files were read and converted into three
dimensional gray matrixes and then converted into a series of axial images of 512 x 512 resolution," wherein the CBCT files, which comprise file information such as resolution and a number of layers, are processed for the creation of a three-dimensional gray matrix, thus influencing the creation, and size of the 3-D matrix.
Thus, Gao discloses: reading the CBCT file of the midpalatal suture of the maxilla to obtain file information, wherein the file information comprises a resolution and a number of layers of the CBCT file of the midpalatal suture of the maxilla; and wherein the transforming the CBCT file of the midpalatal suture of the maxilla into a three- dimensional gray-level matrix based on the file information specifically comprises the following steps: determining a three-dimensional size of the three-dimensional gray-level matrix based on the resolution and the number of layers.
As presented in the previous office action, Section 5.2.2.2 of Scarfe discloses "Contrast and brightness are the principal methods for improving overall CBCT image visibility. Contrast, also referred to as the window width (W), is adjusted to displaying only part of the available full gray value range...Brightness, also referred to as the window level (L), determines the central gray value within the window width. For example, a W/L of 1000/0 implies that gray values between -500 and +500 are considered for display, with all other values showing as black (-500) or white (+500) (Fig. 5.43) (Pauwels et al. 2015a)." Wherein Scarfe discloses that CBCT file parameters: contrast and brightness, or window width and window level, respectfully, determine the available gray value range and central gray value of the CBCT image processed, thus together disclosing the CBCT image's gray level interval.
Thus, it would have been obvious for one of ordinary skill in the art, prior to the disclosed invention's effective filing date, to incorporate the reading, processing, and modification of the window width and window level taught by Scarfe for the reading, processing, and modification of the window width and window length of the read CBCT files disclosed by Gao. Wherein, since Gao discloses the reading and processing of CBCT file information for the creation of the three-dimensional gray matrix, the incorporation of the window width and window level teachings disclosed by Scarfe, for the determination of the three-dimensional gray matrix's gray level interval, based on the CBCT image's gray level interval, would have been obvious.
In response to applicant's argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, as disclosed by Section 5.2.2.2 of Scarfe "Contrast and brightness are the principal methods for improving overall CBCT image visibility," wherein the acquired window width and window level may be adjusted upon processing a CBCT file in order to improve image visibility.
Thus, as further disclosed in the previous office action's rejection of claim 1 under 35 U.S.C. 103, Gao in view of Pauwels and Scarfe discloses the claim 1 limitations:
"reading the CBCT file of the midpalatal suture of the maxilla to obtain file information, wherein the file information comprises a resolution, a number of layers, a window width, and a window level of the CBCT file of the midpalatal suture of the maxilla;
wherein the transforming the CBCT file of the midpalatal suture of the maxilla into a three dimensional gray-level matrix based on the file information specifically comprises the following steps:
determining a three-dimensional size of the three-dimensional gray-level matrix based on the resolution and the number of layers;
determining a gray level interval of the three-dimensional gray-level matrix based on the window width and the window level; and
transforming the CBCT file of the midpalatal suture of the maxilla into the three dimensional gray-level matrix based on the three-dimensional size and the gray level interval".
Therefore, the rejection of claim 1 under 35 U.S.C. 103 is maintained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 and 4-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization) hereinafter referenced as Gao, in view of Pauwels et al. (Automated implant segmentation in cone-beam CT using edge detection and particle counting) hereinafter referenced as Pauwels, and Scarfe et al. (CBCT Use in Daily Practice) hereinafter referenced as Scarfe.
Regarding claim 1, Gao discloses: A method for reconstructing an image of a midpalatal suture based on an operator, which is implemented in a system comprising a processor, a memory and a display, wherein, the memory stores instructions for the processor (Gao: Abstract; Wherein the training of the CNN and analysis of the CBCT image fusion algorithm and image texture feature analysis algorithms implies the usage of a system comprising a processor, memory, and a display) to perform the following steps:
performing data preprocessing on a cone beam computed tomography (CBCT) file of a midpalatal suture of a maxilla to obtain a plurality of local images of the midpalatal suture (Gao: 2.3.1. Image Processing: “The CBCT files were read and converted into three-dimensional gray matrixes and then converted into a series of axial images of 512 × 512 resolution. The midpalatal suture normalized ROI of 50 × 200 resolution were extracted.”);
performing merging fusion on the plurality of local images of the midpalatal suture to obtain a reconstructed image of the midpalatal suture (Gao: 2.3.2. Image Fusion: “Image fusion was carried out in every two sections of midpalatal suture multi-slice ROI images for each CBCT file until all of the images were fused into one overall midpalatal suture image.”), wherein an operator is utilized for optimization during the merging fusion (Gao: 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture.”); and
based on the reconstructed image of the midpalatal suture, displaying an overall profile of the midpalatal suture on the display (Gao: Figure 8; 3.2. Midpalatal Suture ROI Extraction and Image Fusion Algorithm: “Then, the direct image fusion, weighted optimization, and convolution operator optimization were carried out (Figure 7 and Figure 8). The direct fusion shows poorer performance, in which the image is blurred, and the morphological characteristics of the midpalatal suture region are not clear. By adjusting the fusion weight, the image contrast increases, and the midpalatal suture structure is clearer. Furthermore, after convolution operator optimization, the fused images show clear and distinct texture, which is more conducive for clinical evaluation and subsequent model training process.” );
wherein the performing data preprocessing on a CBCT file of a midpalatal suture of a maxilla to obtain a plurality of local images of the midpalatal suture specifically comprises the following steps:
reading the CBCT file of the midpalatal suture of the maxilla to obtain file information, wherein the file information comprises a resolution and a number of layers of the CBCT file of the midpalatal suture of the maxilla (Gao: 2.1.2. CBCT Examination: “CBCT images were taken with NewTom VGi (Quantitative Radiology, Verona, Italy), at 2.81 mA, 110 kV, 3.6-s exposure, and a 15 × 15 cm field of view, with an axial slice thickness of 0.3 mm, and isotropic voxels (Figure 1).”; Wherein the field of view and axial slice thickness imply a known layer number and the isotropic voxels imply a known resolution.);
transforming the CBCT file of the midpalatal suture of the maxilla into a three-dimensional gray-level matrix based on the file information, and transforming the three-dimensional gray-level matrix into a plurality of axial CT cross-sectional images (Gao: 2.3.1. Image Processing: “The CBCT files were read and converted into three-dimensional gray matrixes and then converted into a series of axial images of 512 × 512 resolution.”); and
cutting each of the axial CT cross-sectional images containing midpalatal suture regions to obtain the plurality of local images of the midpalatal suture (Gao: 2.2. Region of Interest Labeling in Midpalatal Suture CBCT Images: “The region of interest (ROI) labeling was completed by two experienced clinical experts. The upper and lower boundaries of the CBCT axial sections for each CBCT file were located by Dolphin Imaging software (11.8, Oakdale, CA, USA) and recorded by Microsoft Excel software (2203, Redmond, WA, USA).”; 2.3.1. Image Processing: “The midpalatal suture normalized ROI of 50 × 200 resolution were extracted.”; Wherein the axial images are then cropped to ROI images.);
wherein the transforming the CBCT file of the midpalatal suture of the maxilla into a three-dimensional gray-level matrix based on the file information specifically comprises the following steps:
determining a three-dimensional size of the three-dimensional gray-level matrix based on the resolution and the number of layers (Gao: 2.1.2. CBCT Examination: “CBCT images were taken with NewTom VGi (Quantitative Radiology, Verona, Italy), at 2.81 mA, 110 kV, 3.6-s exposure, and a 15 × 15 cm field of view, with an axial slice thickness of 0.3 mm, and isotropic voxels (Figure 1).”; 2.3.1. Image Processing: “The CBCT files were read and converted into three-dimensional gray matrixes and then converted into a series of axial images of 512 × 512 resolution.”; Wherein since the 3-D matrix is converted into axial images and since the CBCT image layer numbers and resolution are known, the size of the 3D matrix is determined by the number of layers and resolution.); and
transforming the CBCT file of the midpalatal suture of the maxilla into the three-dimensional gray-level matrix based on the three-dimensional size and a gray level interval (Gao: 2.3.1. Image Processing: “The CBCT files were read and converted into three-dimensional gray matrixes and then converted into a series of axial images of 512 × 512 resolution. The midpalatal suture normalized ROI of 50 × 200 resolution were extracted.”; Wherein the CBCT file is transformed into a 3D gray-level matrix based on its 3D size/resolution).
Gao does not disclose expressly: wherein a Sobel operator is utilized for optimization during the merging fusion.
Pauwels discloses: the utilization of a Sobel operator on CBCT images (Pauwels: Figure 4; Method 2: Pre-thresholding, edge detection and particle counting: “all CBCT data sets were filtered with a Sobel operator to highlight edges. The operator uses two 3 × 3 convolution kernels (Sx and Sy) which calculate the derivative of the image in the x- and y-direction…The two derivates are then combined into one output image by calculating the square root of the sum of squares. The output image corresponds to the gradient of the edges in the original image”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the Sobel operator taught by Pauwels as the convolutional operator disclosed by Gao. The suggestion/motivation for doing so would have been “all CBCT data sets were filtered with a Sobel operator to highlight edges…The output image corresponds to the gradient of the edges in the original image” (Pauwels: Method 2: Pre-thresholding, edge detection and particle counting; Wherein the Sobel operator is able to highlight edges and textures present in the images). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Gao in view of Pauwels does not disclose expressly: wherein the file information comprises a window width and a window level of the CBCT file of the midpalatal suture of the maxilla; and
wherein the transforming the CBCT file of the midpalatal suture of the maxilla into a three-dimensional gray-level matrix based on the file information specifically comprises the following steps: determining a gray level interval of the three-dimensional gray-level matrix based on the window width and the window level; and transforming the CBCT file of the midpalatal suture of the maxilla into the three-dimensional gray-level matrix based on the three-dimensional size and the gray level interval.
Scarfe discloses: the modification of window width and window length disclosed in a CBCT file to improve the CBCT file visibility (Scarfe: 5.2.2.2 Adjust Contrast and Brightness: “Contrast and brightness are the principal methods for improving overall CBCT image visibility. Contrast, also referred to as the window width (W), is adjusted to displaying only part of the available full gray value range…Brightness, also referred to as the window level (L), determines the central gray value within the window width. For example, a W/L of 1000/0 implies that gray values between −500 and +500 are considered for display, with all other values showing as black (−500) or white (+500) (Fig. 5.43) (Pauwels et al. 2015a).”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate the adjustment of a CBCT files’ window width and window length as taught by Scarfe by modifying the window width and window length of the CBCT files disclosed by Gao in view of Pauwels in order to generate the three-dimensional gray-level matrix based on the modified gray level interval. The suggestion/motivation for doing so would have been “Contrast and brightness are the principal methods for improving overall CBCT image visibility” (Scarfe: 5.2.2.2 Adjust Contrast and Brightness). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Gao in view of Pauwels with Scarfe to obtain the invention as specified in claim 1.
Regarding claim 4, Gao in view of Pauwels and Scarfe discloses: The method according to claim 1, wherein the performing merging fusion on the plurality of local images of the midpalatal suture to obtain a reconstructed image of the midpalatal suture specifically comprises the following steps: combining the plurality of local images of the midpalatal suture in pairs to obtain a plurality of image pairs to be fused (Gao: 2.3.2. Image Fusion: “Image fusion was carried out in every two sections of midpalatal suture multi-slice ROI images for each CBCT file until all of the images were fused into one overall midpalatal suture image”),
wherein pixels of the local images of the midpalatal suture comprised in each image pair to be fused are in one-to-one correspondence with each other, and two pixels in the one-to-one correspondence are denoted as a pixel pair (Gao: 2.3.2. Image Fusion: “The pixel value of each point in the fused image was calculated by the following formula:
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Aij refers to the average gray scale value of point (i, j) in each of the two images that need to be fused”; Wherein the average gray scale value refers to the same point in both images, constituting a one-to-one correspondence.);
for each image pair to be fused, calculating a fusion weight for each pixel pair, and performing merging fusion on the image pair to be fused based on the fusion weight for each pixel pair to obtain a fused image (Gao: 2.3.2. Image Fusion: “The fusion weights were calculated and adjusted by combining the existing pixel-level image fusion algorithm with the characteristics of the midpalatal suture region… The pixel value of each point in the fused image was calculated by the following formula:
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Aij refers to the average gray scale value of point (i, j) in each of the two images that need to be fused, e refers to the total average gray scale of the images that need to be fused, and d refers to the adjustment factor based on the maximum gray scale difference of the images that need to be fused”; Wherein the pixel pairs are merged based upon fusion weights based upon variables Aij, e, and d.);
optimizing each fused image with the Sobel operator to obtain an optimized image (Gao: 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture.”; Wherein the operator is the Sobel operator taught by Pauwels.);
determining whether a number of the optimized images is 1; if yes, taking the optimized image as the reconstructed image of the midpalatal suture; and if no, taking the optimized images as the local images of the midpalatal suture in next loop, and returning to the step of combining the plurality of local images of the midpalatal suture in pairs (Gao: 2.3.2. Image Fusion: “we performed the weighted fusion of images in pairs and then continued to fuse the fused images in pairs until all of the images were fused into one.”; Wherein the image fusion process loops until all of the images are fused into a single image.).
Regarding claim 5, Gao in view of Pauwels and Scarfe discloses: The method according to claim 4, wherein the calculating a fusion weight for each pixel pair specifically comprises the following steps: calculating an overall average gray level and an adjustment factor based on gray level values of the plurality of local images of the midpalatal suture (Gao: 2.3.2. Image Fusion: “The pixel value of each point in the fused image was calculated by the following formula:
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...e refers to the total average gray scale of the images that need to be fused, and d refers to the adjustment factor based on the maximum gray scale difference of the images that need to be fused”; Wherein e and d correspond to the overall average gray level and the adjustment factor based on the gray level values of the plurality of local images to be fused.);
for each pixel pair, calculating an average value of gray level values of the image pair to be fused at the pixel pair to obtain an average gray level (Gao: 2.3.2. Image Fusion: “The pixel value of each point in the fused image was calculated by the following formula:
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Aij refers to the average gray scale value of point (i, j) in each of the two images that need to be fused”; Wherein Aij corresponds to the pixel pair’s average gray level.);
and calculating the fusion weight for the pixel pair based on the overall average gray level, the adjustment factor, and the average gray level (Gao: 2.3.2. Image Fusion: “The pixel value of each point in the fused image was calculated by the following formula:
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”; Where the formula calculates the fusion weight based upon Aij, e, and d.).
Regarding claim 6, Gao in view of Pauwels and Scarfe discloses: The method according to claim 4, wherein the performing merging fusion on the image pair to be fused based on the fusion weight for each pixel pair to obtain a fused image specifically comprises the following steps: for each pixel pair, calculating an average value of gray level values of the image pair to be fused at the pixel pair to obtain an average gray level (Gao: 2.3.2. Image Fusion: “The pixel value of each point in the fused image was calculated by the following formula:
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Aij refers to the average gray scale value of point (i, j) in each of the two images that need to be fused”; Wherein Aij corresponds to the pixel pair’s average gray level.);
and calculating a fused gray level value of the pixel pair based on the average gray level and the fusion weight to obtain the fused image (Gao: 2.3.2. Image Fusion: “The pixel value of each point in the fused image was calculated by the following formula:
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”; Wherein Pij is the fused gray level value of the pixel pair calculated by multiplying the pixel pair’s average gray value by a fusion weight).
Regarding claim 7, Gao in view of Pauwels and Scarfe discloses: The method according to claim 4, wherein the optimizing each fused image with the Sobel operator to obtain an optimized image specifically comprises the following step: for each pixel of each fused image, calculating an optimized gray level value of the pixel based on the Sobel operator to obtain the optimized image (Gao: 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture.”)
(Pauwels: Method 2: Pre-thresholding, edge detection and particle counting: “all CBCT data sets were filtered with a Sobel operator to highlight edges…The output image corresponds to the gradient of the edges in the original image. Figure 4 shows the x- and y-derivatives and the output image…As the Sobel operator rearranged grey values according to the magnitude of the edge in each voxel”; Wherein the Sobel operator modifies the gray values of the filtered images.).
Regarding claim 8, Gao in view of Pauwels and Scarfe discloses: The method according to claim 1, further comprising the following steps after obtaining the reconstructed image of the midpalatal suture: analyzing a texture feature of the reconstructed image of the midpalatal suture to adjust a weight for the Sobel operator, thereby obtaining an optimized Sobel operator (Gao: 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture. The operator weight was adjusted according to the image fusion result to make the image textures clearer.”; Wherein the fused image constitutes a reconstructed image.);
performing merging fusion on the plurality of local images of the midpalatal suture to obtain a new reconstructed image of the midpalatal suture (Gao: 2.3.2. Image Fusion: “Image fusion was carried out in every two sections of midpalatal suture multi-slice ROI images for each CBCT file until all of the images were fused into one overall midpalatal suture image”; Wherein the two ROI images fused together constitutes the plurality of local images being fused into a new reconstructed image),
wherein the optimized Sobel operator is utilized for optimization during the merging fusion (Gao: 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture.”; Wherein the fused image constitutes a reconstructed image.);
determining whether a maximum number of iterations is reached (Gao: 2.3.2. Image Fusion: “Image fusion was carried out in every two sections of midpalatal suture multi-slice ROI images for each CBCT file until all of the images were fused into one overall midpalatal suture image.”; Wherein the number of iterations to fuse all the ROI images extracted into a single image constitutes the max number of iterations.);
if yes, determining an optimal Sobel operator based on optimization of all reconstructed images, wherein the reconstructed images comprise the reconstructed image of the midpalatal suture and the new reconstructed image of the midpalatal suture (Gao: 2.3.2. Image Fusion: “Therefore, we performed the weighted fusion of images in pairs and then continued to fuse the fused images in pairs until all of the images were fused into one.”; 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture. The operator weight was adjusted according to the image fusion result to make the image textures clearer.”; Wherein the Sobel operator, disclosed by Pauwels, is optimized/adjusted by all the image pair fusions until the final image pair fusion.);
and if no, taking the new reconstructed image of the midpalatal suture as the reconstructed image of the midpalatal suture in next loop and the optimized Sobel operator as the Sobel operator in next loop, and returning to the step of analyzing a texture feature of the reconstructed image of the midpalatal suture (Gao: 2.3.2. Image Fusion: “Therefore, we performed the weighted fusion of images in pairs and then continued to fuse the fused images in pairs until all of the images were fused into one.”; 2.3.3. Fused Image Optimization: “During the image fusion process, we used the convolution operator to optimize the fused image so as to improve the clarity of the midpalatal suture. The operator weight was adjusted according to the image fusion result to make the image textures clearer.”; Wherein the all the ROI images are paired and fused until there is a single fused image left. Also, wherein the Sobel operator, disclosed by Pauwels, is optimized/adjusted according to the most recent image pair’s fusion’s texture analysis.).
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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 ANTHONY J RODRIGUEZ whose telephone number is (703)756-5821. The examiner can normally be reached Monday-Friday 10am-7pm.
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/ANTHONY J RODRIGUEZ/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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