CTFR 18/698,757 CTFR 100278 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 that application is a National Stage application of PCT/US2021/054721. Priority to PCT/US2021/054721 with a priority date of 10/13/2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78. Response to Amendment The amendment filed 04/27/2026 has been entered. Applicant’s amendments to the specification and claims have overcome each and every objection, 35 U.S.C. 112 rejections, and 35 U.S.C. 101 rejections previously set forth in the Non-Final Office Action mailed 01/28/2026. Claims 1-2, 4-10, and 12-22 remain pending in the application, with claims 3 and 11 having been cancelled and claims 16-22 being newly added. Response to Arguments In light of the cancellation of claim 3, claims 4 and 5 have overcome the 35 U.S.C. 112(b) rejections. In light of the amendments to claim 1, claims 1-4 and 6 have overcome the 35 U.S.C. 101 rejections. Applicant’s arguments, in the Remarks filed 04/27/2026, with respect to claims 1-2, 4-10, and 12-22 have been considered but are moot because the new ground of rejection does not rely on any combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 2, 4, and 6 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Wang et al. (CN Patent No. 113240581 A), hereinafter Wang . Regarding claim 1, Wang teaches a computing device, comprising: a processor resource; and a non-transitory memory resource storing machine-readable instructions (Wang, para n0042: “i9-8700k processor, 16GB of RAM, an NVIDIA GeForce GTX1080Ti 8GB graphics card, and a Windows operating system”) that, when executed, cause the processor resource to: identify a base resolution of a captured image having a base image quality (Wang, resolution and quality of low-resolution images input to the model, para n0013: “low-resolution image LR for training the network”; see low-resolution image, LR, in FIG. 1, top image attached below ) ; and perform, via an individual neural network (Wang, super-resolution network in Figure 1, para n0023, para n0038 ) , a plurality of neural network calculations on the captured image (Wang, steps outlined in para n0038 ) to form an enhanced image (Wang, para n0014: “output a high-resolution RGB image”; see high-resolution image, SR, in FIG. 1 ) having: an increased resolution that is higher than the base resolution (Wang, para n0017: “This invention proposes a real-world image super-resolution network for images with unknown fuzzy kernels, which is applicable to amplifying and improving the resolution of real-world images”) ; and an increased image quality that is higher than the base image quality, wherein the individual neural network is to form the enhanced image having the increased image quality by reduction of noise in the captured image (Wang, para n0052: “Since the degradation process involves blurring and noise injection, the signal power and noise power are significantly improved”; para n0010: “The resulting noise-free image is considered the high-resolution image”; see RRDB-SFT results in Figure 3, bottom image attached below ) . PNG media_image1.png 285 1000 media_image1.png Greyscale PNG media_image2.png 572 1000 media_image2.png Greyscale Regarding claim 2 (dependent on claim 1), Wang teaches wherein the processor resource is to perform the plurality of neural network calculations during inference (Wang, see para n0042-n0052 describing experiments using the models ) . Regarding claim 4 (dependent on claim 1), Wang teaches wherein the neural network is a convolutional neural network (CNN) and wherein the plurality of neural network calculations are a plurality of CNN calculations (Wang, convolutional calculations described in para n0038 ) . Regarding claim 6 (dependent on claim 1), Wang teaches wherein the increased resolution is 1.5 times or greater than the base resolution (Wang, see RRDB-SFT results in the right column of Figure 3, attached with claim 1 ) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 5, 7, 8, 13, 14, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Yang et al. (U.S. Patent No. 2021/0287342 A1), hereinafter Yang . Regarding claim 5 (dependent on claim 4), Wang teaches wherein the CNN calculations include CNN calculations to: segment, via a convolution layer of the CNN, the captured image into image dimensions having a given height and a given width (Wang, first convolutional layer in Figure 1, para n0041: “first convolution”) ; extract features from the image dimensions (Wang, para n0038: “The second part is the nonlinear mapping base block, which adopts the Dense Block and is connected in an RRDB structure”) ; and perform subpixel convolution based on the extracted features and the image dimensions to form the enhanced image (Wang, para n0038: “The third part uses subpixel convolution to amplify the feature map and then convolutions to generate an RGB three-channel image”) . Wang fails to explicitly teach pool, via a pooling layer of the CNN, the image dimensions to form pooled image dimensions. However, Yang teaches a convolutional neural network for increasing image quality ( see abstract ) , disclosing pool, via a pooling layer of the CNN, the image dimensions to form pooled image dimensions (Yang, para 49: “The image pooling layer 328 computes global features based on the input features (e.g., computes a global average or a global maximum over all of the features in the input to the ASPP 320)”) . Features are then extracted from the pooled image dimensions using residual dense components (Yang, para 55: “feature maps from earlier modules via the residual connections to compute an intermediate feature map, which is concatenated with the output of the concatenation module 330 of the ASPP module 320 and compresses the concatenated result using a cony 1×1 layer. The output of the concatenation module 358 is added to the input feature map 302 by an adder 360 to compute an output feature map 392 of the MRDB”) . The residual dense blocks of Yang utilize atrous spatial pyramid pooling to compute multi-scale features. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the dimension pooling operations taught by Yang with the system of Wang in order to improve the feature extraction performed by the RRDB blocks of Wang. Capturing global and finer details of the image before deeper feature extraction and mapping by the RRDB blocks improves feature extraction and subsequent image reconstruction. Implementing pooling after the first convolutional layer of Wang results in subsequent processing steps being performed on the pooled image dimensions, such as extract features from the pooled image dimensions and perform subpixel convolution based on the pooled image dimensions. Regarding claim 7 (dependent on claim 5), Wang in view of Yang teaches wherein the processor resource is to pool the image dimensions using atrous spatial pyramid pooling (ASPP) (Yang, para 47: “atrous spatial pyramid pooling (ASPP) module”) . Regarding claim 8, Wang teaches a non-transitory memory resource storing machine-readable instructions (Wang, para n0042: “i9-8700k processor, 16GB of RAM, an NVIDIA GeForce GTX1080Ti 8GB graphics card, and a Windows operating system”) that, when executed, cause a processor resource to: identify a base resolution of a captured image having a base image quality (Wang, resolution and quality of low-resolution images input to the model, para n0013: “low-resolution image LR for training the network”; see low-resolution image, LR, in FIG. 1 ) ; and perform, via an individual convolutional neural network (CNN) (Wang, super-resolution network in Figure 1, para n0023, convolutional calculations described in para n0038 ) , a plurality of CNN calculations on the captured image to form an enhanced image having an increased resolution that is higher than the base resolution (Wang, para n0017: “This invention proposes a real-world image super-resolution network for images with unknown fuzzy kernels, which is applicable to amplifying and improving the resolution of real-world images”) and an increased image quality that is higher than the base image quality (Wang, increase quality by removing noise from input images, para n0052: “Since the degradation process involves blurring and noise injection, the signal power and noise power are significantly improved”; para n0010: “The resulting noise-free image is considered the high-resolution image”; see RRDB-SFT results in Figure 3 ) , the CNN calculations including CNN calculations to: segment the captured image into image dimensions having a given height and a given width (Wang, first convolutional layer in Figure 1, para n0041: “first convolution”) ; extract features from the image dimensions using a residual-in-residual dense block (RRDB) (Wang, para n0038: “The second part is the nonlinear mapping base block, which adopts the Dense Block and is connected in an RRDB structure”) ; and perform subpixel convolution based on the extracted features and the image dimensions to form the enhanced image (Wang, para n0038: “The third part uses subpixel convolution to amplify the feature map and then convolutions to generate an RGB three-channel image”) . Wang fails to explicitly teach pool the image dimensions to form pooled image dimensions. However, Yang teaches a convolutional neural network for increasing image quality ( see abstract ) , disclosing pool image dimensions to form pooled image dimensions (Yang, para 49: “The image pooling layer 328 computes global features based on the input features (e.g., computes a global average or a global maximum over all of the features in the input to the ASPP 320)”) . Features are then extracted from the pooled image dimensions using residual dense components (Yang, para 55: “feature maps from earlier modules via the residual connections to compute an intermediate feature map, which is concatenated with the output of the concatenation module 330 of the ASPP module 320 and compresses the concatenated result using a cony 1×1 layer. The output of the concatenation module 358 is added to the input feature map 302 by an adder 360 to compute an output feature map 392 of the MRDB”) . The residual dense blocks of Yang utilize atrous spatial pyramid pooling to compute multi-scale features. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the dimension pooling operations taught by Yang with the system of Wang in order to improve the feature extraction performed by the RRDB blocks of Wang. Capturing global and finer details of the image before deeper feature extraction and mapping by the RRDB blocks improves feature extraction and subsequent image reconstruction. Implementing pooling after the first convolutional layer of Wang results in subsequent processing steps being performed on the pooled image dimensions, such as extract features from the pooled image dimensions and perform subpixel convolution based on the pooled image dimensions. Regarding claim 13, Wang teaches a computing device (Wang, para n0042: “i9-8700k processor, 16GB of RAM, an NVIDIA GeForce GTX1080Ti 8GB graphics card, and a Windows operating system”) , comprising: an image capture device to capture an image with a base resolution and a base image quality (Wang, para n0042: “The DPED dataset contains 5,614 images taken by the iPhone 3 camera. This dataset consists of unprocessed real-world images, including low-quality issues such as noise and blur”) . All further claim limitations are met and rendered obvious by Wang in view of Yang because the steps performed by the processor resource in claim 13 are the same as those in claim 8. Regarding claim 14 (dependent on claim 13), Wang in view of Yang teaches wherein the processor resource is to further to: pool the image dimensions to form the pooled image dimensions using atrous spatial pyramid pooling (ASPP) (Yang, para 47: “atrous spatial pyramid pooling (ASPP) module”) . Regarding claim 17 (dependent on claim 5), Wang in view of Yang teaches wherein the processor resource is to extract the features from the pooled image dimensions using a residual-in-residual dense block (RRDB) (Wang, para n0038: “The second part is the nonlinear mapping base block, which adopts the Dense Block and is connected in an RRDB structure”; pooled image dimensions taught in combination with Yang in claim 5 ) . Regarding claim 19 (dependent on claim 8), Wang in view of Yang teaches wherein the processor resource is to pool the image dimensions to form the pooled image dimensions using atrous spatial pyramid pooling (ASPP) (Yang, para 47: “atrous spatial pyramid pooling (ASPP) module”) . 07-21-aia AIA Claim s 9-10, 15, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Yang , in further view of Caballero et al. (cited in IDS: U.S. Patent No. 10,701,394 B1), hereinafter Caballero . Regarding claim 9 (dependent on claim 8), Wang in view of Yang fails to explicitly teach wherein the captured image has undergone lossy image compression to reduce an image resolution of the captured image from an original resolution to the base resolution. However, Caballero teaches an image processing method wherein the captured image has undergone lossy image compression to reduce an image resolution of the captured image from an original resolution to the base resolution (Caballero, col 44, ln 38-40: “The compression of the scene can be implemented using any well-known lossy image/video compression algorithm”) . Wang in view of Yang discloses a base method for increasing the resolution and quality of low-resolution images, but does not specify specific methods for how the image was compressed from its original resolution. Caballero teaches the known technique of a lossy image compression method. A person having ordinary skill in the art, before the effective filing date of the claimed invention, could have applied the known technique, as taught by Caballero, in the same way to the method performed by the device of Wang in view of Yang and achieved predictable results of utilizing a well-known image compression technique to reduce file size and preserve memory resources as needed. Regarding claim 10 (dependent on claim 9), Wang in view of Yang and Caballero teaches wherein the individual CNN is to form the enhanced image having the increased image quality by: sharpening feature boundaries dulled by the lossy image compression (Wang, see sharper feature boundaries in SR image versus input LR in Figure 1 and in Figure 3; removing noise from an image also sharpens features; lossy image compression taught by Caballero in claim 9 ) . Regarding claim 15 (dependent on claim 13), Wang in view of Yang fails to explicitly teach wherein the computing device further comprises a first computing device that is communicatively coupled to and in a teleconference with a second computing device, and wherein the processor resource is to: receive, during the teleconference, the image data of the image from an image capture device of the first computing device; and provide the enhanced image to the second computing device during the teleconference. However, Caballero teaches an image processing method for increasing image resolution ( see Figure 16 below ) including: a first computing device that is communicatively coupled to and in a teleconference with a second computing device ( see citations below ) , and wherein the processor resource is to: receive, during the teleconference, the image data of the image from an image capture device of the first computing device (Caballero, col 36, ln 25-40: “a technique 1500 is used to increase the resolution of visual data will now be described in detail. These embodiments can be used in combination with other embodiments described elsewhere in this specification. Received video data 1540 is provided into a decoder system and is a lower-resolution video encoded in a standard video format… The system then separates video data 1510 into single frames at step 1520, i.e. into a sequence of images at the full resolution of the received video data 310”) ; and provide the enhanced image to the second computing device during the teleconference (Caballero, col 38, ln 7-20: “Further, output video 1570 in process 1500 or 1600 can be output directly to a display or may be stored for viewing on a display on a local or remote storage device, or forwarded to a remote node for storage or viewing as required. The input video concerned may be media for playback, such as recorded video or live streamed video, or it can be videoconference video or any other video source such as video recorded or being recorded on a portable device such as a mobile phone or a video recording device such as a video camera or surveillance camera”) . PNG media_image3.png 330 612 media_image3.png Greyscale It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the communications between devices, as taught by Caballero above, with the system of Wang in view of Yang in order to output images with improved quality and resolution to improve their use in real-world applications, such as teleconference ( See col 38 citation from Caballero above ) . Regarding claim 20 (dependent on claim 9), Wang in view of Yang teaches wherein the individual CNN is to form the enhanced image having the increased image quality by: reduction of noise imparted by the lossy image compression ( See claim 8 rejection wherein the method of Wang reduces noise from the image; lossy image compression taught by Caballero in claim 9 ) . Regarding claim 21 (dependent on claim 9), Wang in view of Yang teaches wherein the individual CNN is to form the enhanced image having the increased image quality by: reduction of compression artifacts imparted by the lossy image compression ( Reducing noise and increasing resolution, taught by Wang in claim 8, reduces compression artifacts, which can include noise; lossy image compression taught by Caballero in claim 9 ) . Regarding claim 22 (dependent on claim 13), Wang in view of Yang fails to explicitly teach wherein the captured image has undergone lossy image compression, and wherein the individual CNN is to form the enhanced image having the increased image quality by: reduction of compression artifacts imparted by the lossy image compression. However, Caballero teaches an image processing method wherein the captured image has undergone lossy image compression to reduce an image resolution of the captured image from an original resolution to the base resolution (Caballero, col 44, ln 38-40: “The compression of the scene can be implemented using any well-known lossy image/video compression algorithm”) . Wang discloses a base method for increasing the resolution and quality of low-resolution images, but does not specify specific methods for how the image was compressed from its original resolution. Caballero teaches the known technique of a lossy image compression method. A person having ordinary skill in the art, before the effective filing date of the claimed invention, could have applied the known technique, as taught by Caballero, in the same way to the method performed by the device of Wang in view of Yang and achieved predictable results of utilizing a well-known image compression technique to reduce file size and preserve memory resources as needed. The method of Wang in view of Yang reduces noise in the low-resolution image ( See claim 1 rejection ) . In combination with the compressed image data taught by Caballero, the invention of Wang in view of Yang and Caballero reduces artifacts imparted by compression by increasing resolution and removing noise ( Compression artifacts include noise; see image improvements in Figures 1 and 3 ) . 07-21-aia AIA Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Yang , in further view of El-Khamy et al. (U.S. Patent No. 2020/0090305 A1), hereinafter El-Khamy . Regarding claim 12 (dependent on claim 8), Wang in view of Yang fails to explicitly teach wherein the processor resource is to determine: an upsampling factor; and utilize the upsampling factor to form the enhanced image. However, El-Khamy teaches a super resolution method ( See abstract ) , including an upsampling factor; and utilize the upsampling factor to form the enhanced image (El-Khamy, para 59: “the first individual super resolution network S.sub.1 receives the LR input image 202, or alternatively, a bicubic upsampled version of the LR input image 202, where the upsampling ratio is according to a target upsampling ratio”) . It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the upsampling factor of El-Khamy with the device of Wang in view of Yang in order to control the spatial resolution of the inputs/outputs of the model (El-Khamy, see para 59 and equation ) . 07-21-aia AIA Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Caballero . Regarding claim 16 (dependent on claim 1), Wang fails to explicitly teach wherein the captured image has undergone lossy image compression, and wherein the individual neural network is to form the enhanced image having the increased image quality by: reduction of compression artifacts imparted by the lossy image compression. However, Caballero teaches an image processing method wherein the captured image has undergone lossy image compression to reduce an image resolution of the captured image from an original resolution to the base resolution (Caballero, col 44, ln 38-40: “The compression of the scene can be implemented using any well-known lossy image/video compression algorithm”) . Wang discloses a base method for increasing the resolution and quality of low-resolution images, but does not specify specific methods for how the image was compressed from its original resolution. Caballero teaches the known technique of a lossy image compression method. A person having ordinary skill in the art, before the effective filing date of the claimed invention, could have applied the known technique, as taught by Caballero, in the same way to the method performed by the device of Wang and achieved predictable results of utilizing a well-known image compression technique to reduce file size and preserve memory resources as needed. The method of Wang reduces noise in the low-resolution image ( See claim 1 rejection ) . In combination with the compressed image data taught by Caballero, the invention of Wang in view of Caballero reduces artifacts imparted by compression by increasing resolution and removing noise ( Compression artifacts include noise; see image improvements in Figures 1 and 3 ) . 07-21-aia AIA Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Yang , in further view of Su et al. (U.S. Patent No. 2020/0402205 A1), hereinafter Su . Regarding claim 18 (dependent on claim 5), Wang in view of Yang fails to explicitly teach wherein forming the enhanced image comprises: generating first upscaled image data based on upsampling the captured image; generating second upscaled image data based on performing the subpixel convolution based on the extracted features and the pooled image dimensions; and combining the first upscaled image data and the second upscaled image data to form the enhanced image. However, Su teaches a method for increasing image resolution ( See abstract ) , including generating first upscaled image data based on upsampling the captured image (Su, Bicubic Upsampling in Fig. 3, attached below; para 76: “base HR image 312”) ; generating second upscaled image data based on performing the subpixel convolution based on the extracted features (Su, output from NN model in Fig. 3; para 75: “HR residual image 310”) ; and combining the first upscaled image data and the second upscaled image data to form the enhanced image (Su, Step 320 in Fig. 3; para 76: “The adder 320 may comprise suitable circuitry, logic, interfaces, or code and is configured to add the base HR image 312 with the HR residual image 310 to generate an HR image 314 as an output image corresponding to the input LR image 302”) . PNG media_image4.png 212 518 media_image4.png Greyscale It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the combination method, as taught by Su, with the model taught by Wang in view of Yang in order to increase the capacity of the neural network model and its efficiency (Su, para 77: “By splitting the generation of the HR image 314 into generating the base HR image 312 and the HR residual image 310, the amount of data that flows through the convolutional layers of the NN model 306 is reduced, which increases the effective capacity of the model and its inference efficiency for reconstructing an HR image from an input LR image”) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Le-Tien, T., Nguyen-Thanh, T., Xuan, H. P., Nguyen-Truong, G., & Ta-Quoc, V. (Nov 2020). Deep learning based approach implemented to image super-resolution. Journal of Advances in Information Technology Vol. 11, No. 4. Chen, C., & Qi, F. (2018, October). Single image super-resolution using deep CNN with dense skip connections and inception-resnet. In 2018 9th international conference on information technology in medicine and education (ITME) (pp. 999-1003). IEEE. Garber, B., Grossman, A., & Johnson-Yu, S. (2020). Image super-resolution via a convolutional neural network. Stanford University. Lee, T. B., & Heo, Y. S. (2020, October). Single image super resolution using convolutional neural networks for noisy images. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 195-199). IEEE. Lim, B., Son, S., Kim, H., Nah, S., & Mu Lee, K. (2017). Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 136-144). Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2472-2481). Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMA E DRYDEN whose telephone number is (571)272-1179. The examiner can normally be reached M-F 9-5 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW BEE can be reached at (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /EMMA E DRYDEN/Examiner, Art Unit 2677 /ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677 Application/Control Number: 18/698,757 Page 2 Art Unit: 2677 Application/Control Number: 18/698,757 Page 3 Art Unit: 2677 Application/Control Number: 18/698,757 Page 4 Art Unit: 2677 Application/Control Number: 18/698,757 Page 5 Art Unit: 2677 Application/Control Number: 18/698,757 Page 6 Art Unit: 2677 Application/Control Number: 18/698,757 Page 7 Art Unit: 2677 Application/Control Number: 18/698,757 Page 8 Art Unit: 2677 Application/Control Number: 18/698,757 Page 9 Art Unit: 2677 Application/Control Number: 18/698,757 Page 10 Art Unit: 2677 Application/Control Number: 18/698,757 Page 11 Art Unit: 2677 Application/Control Number: 18/698,757 Page 12 Art Unit: 2677 Application/Control Number: 18/698,757 Page 13 Art Unit: 2677 Application/Control Number: 18/698,757 Page 14 Art Unit: 2677 Application/Control Number: 18/698,757 Page 16 Art Unit: 2677 Application/Control Number: 18/698,757 Page 17 Art Unit: 2677 Application/Control Number: 18/698,757 Page 18 Art Unit: 2677 Application/Control Number: 18/698,757 Page 19 Art Unit: 2677 Application/Control Number: 18/698,757 Page 20 Art Unit: 2677
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