CTNF 18/383,616 CTNF 101456 DETAILED ACTION This action is in response to the original filing on October 25, 2023. Claims 1-8 are pending and have been considered below. Claims 1 and 7 are independent claims. 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on October 25, 2023 is being considered by the examiner. Specification 06-11 AIA The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 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. Such claim limitations are: “… an image generator that obtains an image and a distance image… cuts a partial area out from the distance image… and generates an embedded image…” in claim 7. Here, an image generator is a generic placeholder (prong 1), modified by the functions “obtains… cuts… and generates” (prong 2), and not modified by sufficient structure to perform the claimed functions (prong 3). Specifically, the claimed “image generator” is not sufficient structure to perform the functions of obtaining, cutting, and generating. The image generator will be interpreted to mean code or software that performs the recited functions (Fig. 1 – 120, 124, 130, Page 7, Lines 3-8 “The functions of… embedded image generator 124… are achieved by a processor or a microcomputer, which configures information processor 120, executing a computer program stored in storage 130”). “… a trainer that trains a machine learning model, using training data…” Here, a trainer is a generic placeholder (prong 1), modified by the function “trains” (prong 2), and not modified by sufficient structure to perform the claimed functions (prong 3). Specifically, the claimed “trainer” is not sufficient structure to perform the functions of training a machine learning model. The trainer will be interpreted to mean code or software that performs the recited functions (Fig. 1 – 120, 125, 130, Page 7, Lines 3-8 “The functions of… trainer 125 are achieved by a processor or a microcomputer, which configures information processor 120, executing a computer program stored in storage 130”). 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 only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof. 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 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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 1-2 and 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Yun et al. (“CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features,” 2019, hereinafter Yun) in view of Lee et al. (“From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation” Version 5, 2020, hereinafter Lee) . Regarding claim 1 : Yun teaches a training method (Abstract: “We therefore propose the CutMix augmentation strategy… our CutMix-trained ImageNet classifier”) comprising: obtaining an image and a distance image corresponding to the image (Page 3, Col. 1, Section 3.1, ¶1 “Let x … and y denote a training image and its label, respectively. The goal of CutMix is to generate a new training sample… by combining two training samples (x A , y A ) and (x B -, y B ) … CutMix replaces an image region with a patch from another training image ”). Yun further teaches cutting a partial area out from the distance image obtained (Page 3, Col. 1, Section 3.1, ¶1 “M… denotes a binary mask indicating where to drop out and fill in from two images… CutMix replaces an image region with a patch from another training image ”). Yun further teaches generating an embedded image by pasting the partial area cut out from the distance image onto a predetermined area in the image, the predetermined area being located at a position corresponding to a position of the partial area and having a size corresponding to a size of the partial area (Page 1, Col. 2, Table 1 depicts an image produced by “CutMix,” Page 3, Col. 1, Section 3.1, ¶1-3 “Let x … and y denote a training image and its label, respectively. The goal of CutMix is to generate a new training sample … by combining two training samples (x A , y A ) and (x B -, y B ) … We define the combining operation as PNG media_image1.png 89 491 media_image1.png Greyscale Where M… denotes a binary mask indicating where to drop out and fill in from two images … To sample the binary mask M, we first sample the bounding box coordinates B = (r x , r y , r w , r h ) indicating the cropping regions on x A and x B . The region B in x A is removed and filled in with the patch cropped from B of x B … we sample rectangular masks M whose aspect ratio is proportional to the original image… With the cropping region , the binary mask … is decided by filling with 0 in the bounding box B, otherwise 1,” Col. 2, Figure 1 depicts “Original Samples” with a “Saint Bernard” or the image and a “Poodle” or the distance image and an input image or embedded image generated by pasting the partial area cut out from the distance image onto a predetermined area or “the cropping region” B in the image, the predetermined area located at a position corresponding to a position of the partial area or “where to drop out and fill in from two images” according to “the binary mask M” and having a size corresponding to a size of the partial area ). Regarding the limitation and training a machine learning model, using training data including the embedded image as input data and the distance image as correct answer data , Yun teaches and training a machine learning model, using training data including the embedded image as input data (Page 3, Col. 1, Section 3.1, ¶1 “The goal of CutMix is to generate a new training sample… by combining two training samples… The generated training sample… is used to train the model”). However, Yun fails to teach and the distance image as correct answer data . Lee, in the same field of endeavor, teaches and the distance image as correct answer data (Page 2, Col. 2, Section 2.1, ¶1 “In monocular depth estimation, supervised approaches take a single image and use depth data measured with range sensors such as RGB-D cameras or multi-channel laser scanners as ground truth for supervision in training,” Page 8, Col. 2, Section 5, ¶1 “we have presented a supervised monocular depth estimation network,” Page 7, Col. 2, ¶2 “We trained Ours-DenseNet for 50 epochs with 28,654 image-ground truth pairs sampled from the official training and validation set”). Yun and Lee are analogous art to the claimed invention as both are from the same field of endeavor of machine learning. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the monocular depth estimation of Lee with the methodology of Yun. The motivation to do so is to design a depth estimation method that “outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks” covering “both indoor and outdoor environments” (Lee, Abstract and Page 6, Col. 1, ¶1). Regarding claim 2 , Yun in view of Lee teaches the training method according to claim 1 (and thus the rejection of claim 1 is incorporated). Yun teaches wherein the predetermined area has an area size that is 25% to 75%, inclusive, of the image (Page 3, Col. 1, Section 3.1, ¶1-3 “the combination ratio λ between two data points is sampled from the beta distribution Beta(a, a). In all our experiments, we set a = 1, that is λ sampled from the uniform distribution (0, 1)… we first sample the bounding box coordinates B = (r x , r y , r w , r h ) indicating the cropping regions on x A and x B . The region B in x A is removed and filled in with the patch cropped from B of x B … The box coordinates are uniformly sampled according to: PNG media_image2.png 85 513 media_image2.png Greyscale Making the cropped area ratio r w r h /WH = 1 – λ . With the cropping region, the binary mask M… is decided,” given Equation (2) and “the cropped area ratio,” the area of “the cropping region” can be defined as r w r h = WH(1- λ), hence λ must be between 0.25 and 0.75 for the predetermined area to have an area size that is 25% to 75%, inclusive, of the image , Page 6, Col. 1, Section 4.1.3, ¶3 “’Fixed-size CutMix’ fixes the size of the cropping region (r w , r h ) at 16x16 (i.e. λ = 0.75 ),” Page 8, Fig. 5b depicts various values of “Combination ratio λ” between 0.25 and 0.75, inclusive ). Regarding claim 4 , Yun in view of Lee teaches the training method according to claim 1 (and thus the rejection of claim 1 is incorporated). Lee teaches wherein the machine learning model is trained to learn a relationship between the image and the distance image (Page 2, Col. 2, Section 2.1, ¶1 “ depth data measured with range sensors such as RGB-D cameras or multi-channel laser scanners as ground truth ,” Page 8, Col. 2, Section 5, ¶1 “a supervised monocular depth estimation network ,” Page 7, Col. 1, Section 4.3, ¶1 “KITTI provides the dataset with 61 scenes from “city,” “residential,” “road” and “campus” categories… existing works commonly use a split proposed by Eigen… for the training and test, we also follow it… 697 images covering a total of 29 scenes are used for evaluation and the remaining 32 scenes of 23,488 images are used for training,” Page 7, Col. 2, ¶2 “We… evaluated the proposed method… with a model trained using KITTI’s official split. Apart from the training set, all other settings remain the same as in the experiment using KITTI’s Eigen split. We trained Ours-DenseNet … with 28,654 image-ground truth pairs sampled from the official validation and training set ” one of ordinary skill in the art would recognize that training a model with “ground truth pairs sampled from the… training and validation set” encompasses wherein the machine learning model is trained to learn a relationship between the image and the distance image ). Yun and Lee are analogous art to the claimed invention as both are from the same field of endeavor of machine learning. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the model of Lee with the methodology of Yun. The motivation to do so is to design a depth estimation method that “outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks” covering “both indoor and outdoor environments” (Lee, Abstract and Page 6, Col. 1, ¶1). Regarding claim 5 , Yun in view of Lee teaches the training method according to claim 1 (and thus the rejection of claim 1 is incorporated). Lee teaches wherein the machine learning model is composed of an encoder network model (Page 8, Col. 1, Section 4.6, ¶1 “the proposed network adopts existing models as an encoder for dense feature extraction”) and an output layer that upsamples, to an output image (Page 4, Col. 1, Section 3.1, ¶1 “at each stage in the decoding phase where internal outputs are recovered to the full resolution with a factor of 2, we employ the proposed local planar guidance (LPG) layer to more effectively relate the features to the desired depth estimation,” Page 3, Fig. 2 depicts the “Local Planar Guidance” layers upsampling inputs with resolutions H/8, H/4, and H/2 back to an output image of resolution H ) , a low-dimensional feature representation outputted from the encoder network model (Page 4, Col. 1, Section 3.1, ¶1 “the backbone network that we use as a dense feature extractor which produces an H/8 feature map ”) , the output image having a same size as the image (Page 3, Fig. 2 depicts an “Input” image with resolution H , Page 4, Col. 1, Section 3.1, ¶1 “we follow an encoding-decoding scheme that reduces feature map resolution to H/8 then recovers back to the original resolution H ”). Yun and Lee are analogous art to the claimed invention as both are from the same field of endeavor of machine learning. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the network architecture of Lee with the methodology of Yun. The motivation to do so is to design a depth estimation method that “outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks” covering “both indoor and outdoor environments” (Lee, Abstract and Page 6, Col. 1, ¶1). Regarding claim 6 , Yun in view of Lee teaches the training method according to claim 1 (and thus the rejection of claim 1 is incorporated). Lee teaches wherein the machine learning model is composed of an encoder network model and a decoder network model (Page 8, Col. 1, Section 4.6, ¶1 “the proposed network adopts existing models as an encoder,” Page 4, Col. 1, Section 3.1, ¶1 “we follow an encoding-decoding scheme… at each stage in the decoding phase… we employ the proposed local planar guidance (LPG) layer,” Col. 2, Fig. 4 depicts the “local planar guidance layer” or a decoder network model ). Yun and Lee are analogous art to the claimed invention as both are from the same field of endeavor of machine learning. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the network architecture of Lee with the methodology of Yun. The motivation to do so is to design a depth estimation method that “outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks” covering “both indoor and outdoor environments” (Lee, Abstract and Page 6, Col. 1, ¶1) . 07-21-aia AIA Claim s 3 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Yun in view of Lee, and further in view of Yoshikuwa et al. (US 20220292702 A1, hereinafter Yoshikuwa) . Regarding claim 3 , Yun in view of Lee teaches the training method according to claim 2 (and thus the rejection of claim 2 is incorporated). Regarding the limitation wherein the partial area includes an edge portion indicating a contour of an object shown in the image , Yun teaches the partial area (Page 3, Col. 1, Section 3.1, ¶1 “a patch from another training image”) and the image (Page 3, Col. 2, Fig. 1 – “Saint Bernard” in “Original Samples”). However, Yun fails to teach wherein the partial area includes an edge portion indicating a contour of an object shown in the image . Yoshikuwa, in the same field of endeavor, teaches wherein a partial area includes an edge portion indicating a contour of an object shown in an image (Fig. 6 – 111a-g, 115, ¶108 “the embodiment includes the memory module 115 configured to store the shape of the article A as the object to be captured … the extracting module 111 a configured to extract the second image from the first image, the first image being an image of the article A … and the second image being the target range that is a partial area of the first image… the distance detecting module 111 b configured to process the second image to detect the distances from at least three parts projected within the target range … the plane estimating module 111 c configured to estimate the plane projected within the target range using the distances … the angle detecting module 111 d configured to detect the angle of the estimated plane … the contour estimating module 111 e configured to estimate, based on the shape of the article A stored in the memory module 115 and the angle of the estimated plane , the contour of the article A projected on the first image , and the controller 100 includes the identifying module 111 g configured to identify the article A on the first image based on the contour of the article A,” ¶109 “based on such a contour, the article A can accurately be identified on the first image ,” ¶112 “the controller 100 may further include the edge extracting module 111 f configured to extract an edge from the image , and the identifying module 111 g may identify the article A by comparing a shape of the edge with the contour of the article A ”). Yun and Yoshikuwa are analogous art to the claimed invention as both are in the same field of endeavor of image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the image processing modules of Yoshikuwa with the methodology of Yun. The motivation to do so is to reduce image processing and improve image processing speed and accuracy (Yoshikuwa, ¶109 “based on such a contour, the article A can accurately be identified on the first image, which reduces the processing amount required for identifying the article A… the processing amount can be reduced. As a result, the image processing accuracy and speed are improved”). Regarding claim 7 : Regarding the limitation a training device comprising: an image generator that obtains an image and a distance image corresponding to the image, cuts a partial area out from the distance image obtained, and generates an embedded image by pasting the partial area cut out from the distance image onto a predetermined area in the image, the predetermined area being located at a position corresponding to a position of the partial area and having a size corresponding to a size of the partial area , Yun teaches a training algorithm comprising: an image generator (Page 12, Algorithm A1 – “Psueo-code of CutMix,” Lines 4-16, wherein an “algorithm” or “pseudo-code” encompasses an image generator , given its interpretation under 112(f) as explained above ) that obtains an image and a distance image corresponding to the image (Page 3, Col. 1, Section 3.1, ¶1) , cuts a partial area out from the distance image obtained (Page 3, Col. 1, Section 3.1, ¶1) , and generates an embedded image by pasting the partial area cut out from the distance image onto a predetermined area in the image, the predetermined area being located at a position corresponding to a position of the partial area and having a size corresponding to a size of the partial area (Page 1, Col. 2, Table 1, Page 3, Col. 1, Section 3.1, ¶1-3 and Col. 2, Figure 1 all as explained above with respect to claim 1 ). However, Yun fails to teach a training device … Yoshikuwa teaches an imaging device for obtaining and processing images (Fig. 5 – 40, 41a-b, 111, ¶62 “The imaging controlling module 401 , the image processing module 111 , and the cameras 41 a and 41 b constitute an imaging device 40 ”). Regarding the limitation and a trainer that trains a machine learning model, using training data including the embedded image as input data and the distance image as correct answer data , Yun teaches and a trainer that trains a machine learning model (Page 12, Algorithm A1, Lines 18-19, one of ordinary skill in the art would recognize that the depicted “model_forward” and “compute_loss” functions are common in machine learning libraries such as PyTorch, TensorFlow, or Keras, that initiate model training; the lines of code that makes calls said functions encompass a trainer , given its interpretation under 112(f) as explained above ) using training data including the embedded image as input data (Page 3, Col. 1, Section 3.1, ¶1, Page 4, Col. 1, ¶1 “We analyze the effect of CutMix on stabilizing the training of deep networks,” wherein to train a network on images produced by the “CutMix” method implies that a processor, such as a CPU or GPU, is used to train models, or a trainer that trains … ). However, Yun fails to teach and the distance image as correct answer data . Lee teaches and the distance image as correct answer data (Page 2, Col. 2, Section 2.1, ¶1, Page 8, Col. 2, Section 5, ¶1, Page 7, Col. 2, ¶2). Yun, Lee, and Yoshikuwa are analogous art to the claimed invention as all are in the same field of endeavor of image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the imaging device and modules of Yoshikuwa and the ground truth of Lee with the training algorithm of Yun. The motivation to do so is to design a depth estimation method that “outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks” covering “both indoor and outdoor environments” (Lee, Abstract and Page 6, Col. 1, ¶1) and to reduce image processing and improve image processing speed and accuracy (Yoshikuwa, ¶109 “article A can accurately be identified on the first image, which reduces the processing amount required for identifying the article A… the processing amount can be reduced. As a result, the image processing accuracy and speed are improved”). Regarding claim 8 , Yun in view of Lee and further in view of Yoshikuwa teaches the training method of claim 1 (and thus the rejection of claim 1 is incorporated). Yoshikuwa teaches a non-transitory computer-readable recording medium having recorded thereon a computer program for causing a computer to execute a program (Fig. 4 – 100, 101-104, ¶51 “The memory 104 includes a storage device,” ¶52 “The program for the operation of the CPU 101 is stored in the ROM 102 or the memory 104 in advance. The CPU 101 reads the program from the ROM 102 or the memory 104 to the RAM 103 , and deploys the program”). Yun and Yoshikuwa are analogous art to the claimed invention as both are in the same field of endeavor of image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the computer-readable medium of Yoshikuwa with the training algorithm of Yun. The motivation to do so is to reduce image processing and improve image processing speed and accuracy (Yoshikuwa, ¶109 “article A can accurately be identified on the first image, which reduces the processing amount required for identifying the article A… the processing amount can be reduced. As a result, the image processing accuracy and speed are improved”). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM M LEE whose telephone number is (571)272-4761. The examiner can normally be reached Mon-Fri. 8am-5pm. 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, Cesar Paula can be reached at (571)272-4128. 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. /WILLIAM MICHAEL LEE/ Examiner, Art Unit 2145 /CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145 Application/Control Number: 18/383,616 Page 2 Art Unit: 2145 Application/Control Number: 18/383,616 Page 3 Art Unit: 2145 Application/Control Number: 18/383,616 Page 4 Art Unit: 2145 Application/Control Number: 18/383,616 Page 5 Art Unit: 2145 Application/Control Number: 18/383,616 Page 6 Art Unit: 2145 Application/Control Number: 18/383,616 Page 7 Art Unit: 2145 Application/Control Number: 18/383,616 Page 8 Art Unit: 2145 Application/Control Number: 18/383,616 Page 9 Art Unit: 2145 Application/Control Number: 18/383,616 Page 10 Art Unit: 2145 Application/Control Number: 18/383,616 Page 11 Art Unit: 2145 Application/Control Number: 18/383,616 Page 12 Art Unit: 2145 Application/Control Number: 18/383,616 Page 13 Art Unit: 2145
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