CTNF 18/452,279 CTNF 101405 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. Information Disclosure Statement The information disclosure statement (“IDS”) filed on 03/06/2024, 02/28/2025, and 07/18/2025 was reviewed and the listed references were noted. Drawings The 7 page drawings have been considered and placed on record in the file. 12-151 AIA 26-51 12-51 Status of Claims 12-151-10 AIA 12-51-10 Claim s 29-58 are currently pending. Claims 1-28 have been canceled 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. 07-30-04 AIA Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function. Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. 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 Claim 58 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. Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have these limitations 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 07-20-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 29-33, 43-50, and 58 are rejected under 35 U.S.C. 103 as being unpatentable over Man et. al. ( “BEV-Guided Multi-Modality Fusion for Driving Perception” with conference date June 17-24, 2023) in view of Hester et. al. ( US 11921824 B1 with effective filing date of March 29, 2021) . Consider Claim 29, Man teaches “A device for processing image data, the device comprising: a memory configured to store one or more images of an area around a vehicle , the one or more images having been captured by one or more image sensors on the vehicle;” (Man; Figure 2 (See Figure Below); (1) Sensor pool; Section 2, Paragraph 3; “Cross-view-transformer [48] leverages transformer to learn BEV segmentation from the multi-view images.”) and a processing system implemented in circuitry and configured to: cross-attention process first image feature data corresponding to the one or more images of an area around a vehicle to produce output data;” (Man; Figure 2 (See Figure Below); Examiner notes Figure 2 shows camera images from around the vehicle is in included in the input sensor pool.; Section 1, Paragraph 5; “We present a map-guided multi-sensor cross-attention learning module that is general, sensor-agnostic, and easily extensible.”) “and self-attention process second image feature data corresponding to the output data to produce a bird's-eye view (BEV) representation of the area around the vehicle.” (Man; Figure 2 (See Figure Below); (3) BEV-Guided Fusion, (4) BEV Tasks; Abstract; “We design a BEV-guided multi-sensor attention block to take queries from BEV embeddings and learn the BEV representation from sensor-specific features.”). Man does not explicitly disclose “the device comprising: a memory configured to store one or more images of an area around a vehicle” or “and a processing system implemented in circuitry”. However, in an analogous field of endeavor, Hester discloses a method to fuse “sensor data of different modalities using a transformer” (Hester; Abstract) “to make predictions about the physical environment on the basis of such telemetry data” (Hester, Col 1, Line 15-16). Hester teaches “the device comprising: a memory configured to store one or more images of an area around a vehicle” (Hester; Col 19, Line 7-34) and “and a processing system implemented in circuitry” (Hester; Col 18, Line 36-63) . Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Man with the teachings of Hester to further implement the method disclosed in Man in a device. One of ordinary skill in the art would be motivated to combine Man and Hester to use the fused BEV perception of a surround environment for an autonomous device to increase surrounding environment awareness and detection accuracy. Accordingly, the combination of Man and Hester discloses the invention of Claim 29. PNG media_image1.png 548 1162 media_image1.png Greyscale Consider Claim 30, the combination of Man and Hester teaches “The device of claim 29, wherein to cross-attention process the first image feature data, the processing system is configured to process a set of key vectors and a set of query vectors, the first image feature data comprising the set of key vectors.” (Man; Figure 3 (See Figure Below); “the module takes queries from BEV embeddings, and takes keys and values from sensor-specific feature maps.”). PNG media_image2.png 412 1164 media_image2.png Greyscale Consider Claim 31, the combination of Man and Hester teaches “The device of claim 29, wherein to cross-attention process the first image feature data, the processing system is further configured to process an encoding vector with the first image feature data.” (Man; Figure 2 (See Figure Above); (2) Sensor-Specific Feature Extraction, (3) BEV-Guided Fusion, Encoder, Positional Encoding). Consider Claim 32, the combination of Man and Hester teaches “The device of claim 29, wherein the one or more images include one or more images captured at a first time” (Man; Figure 2 (See Figure Above); (1) Sensor pool) “and one or more images captured at a second time, the second time being different than the first time.” (Man; Section 4, Paragraph 2; “The nuScenes [4] dataset is a large-scale out door dataset collected over a variety of weather and time of-day conditions .” (emphasis added); Examiner notes the different camera, lidar and radar images associated with various times of day in the nuScenes data set is interpreted as different image sets captured at different times. ). Consider Claim 33, the combination of Man and Hester teaches “The device of claim 32, wherein the first time corresponds to a first one or more point cloud frames” (Man; Section 3.1, Paragraph 1; “In this light, we define a sensor pool, which consists of all available sensor inputs for our model– For instance, images from the cameras, point clouds from the Lidar, and points with velocities from the Radar. ” (emphasis added)) “ and the second time corresponds to a second one or more point cloud frames, the second one or more point frames being different than the first one or more point frames.” (Man; Section 4, Paragraph 2; “The nuScenes [4] dataset is a large-scale out door dataset collected over a variety of weather and time of-day conditions. It has 40,157 annotated samples, each of which consists of 6 camera images, 5 Radar point clouds of different views covering approximately the full surrounding angles of the ego-vehicle, and a 360◦ 32-beam Lidar scan.” (emphasis added)). Consider Claim 43, the combination of Man and Hester “The device of claim 29, wherein the processing system and the memory form part of an advanced driver-assistance system (ADAS).” (Hester; Col 5, Line 45-53; “For example, transformer decoders effective to perform semantic segmentation of image data (e.g., distinguishing between different objects at a pixel level), emergency stop prediction (e.g., for autonomous navigation) , BEV map prediction, pose estimation, etc.” (emphasis added)). The proposed combination as well as the motivation for combining the Man and Hester references presented in the rejection of claim 29, apply to claim 43 and are incorporated herein by reference. Thus, the device recited in claim 43 is met by Man and Hester. Consider Claim 44, the combination of Man and Hester teaches “The device of claim 29, wherein the processing system is further configured to use the BEV representation to at least partially control the vehicle.” (Hester; Col 9, Line 14-17; “In another example that is specific to controlling an autonomous vehicle/robot, an E-stop prediction transformer decoder 112 d may be included that is effective to generate an E-stop probability 120.”). The proposed combination as well as the motivation for combining the Man and Hester references presented in the rejection of claim 29, apply to claim 44 and are incorporated herein by reference. Thus, the device recited in claim 44 is met by Man and Hester. Claims 45-49 recite methods with steps corresponding to the elements recited in Claims 29-33, respectively. Therefore, the recited steps of these claims are mapped to the proposed combination in the same manner as the corresponding elements in their corresponding system claims. Additionally, the rationale and motivation to combine the Man and Hester references, presented in rejection of Claims 29, apply to these claims. Consider Claim 50, the combination of Man, Hester, and Li teaches “The method of claim 45, further comprising self-attention processing initial image feature data to form the first image feature data.” (Man; Figure 2 (See Figure Above); Sensor-Agnostic Attention). Claim 58 recites a device with elements corresponding to the elements recited in Claims 29. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding device claim. Additionally, the rationale and motivation to combine the Man and Hester references, presented in rejection of Claim 29 , apply to this claim . 07-21-aia AIA 9. Claim s 34 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Man et. al. ( “BEV-Guided Multi-Modality Fusion for Driving Perception” with conference date June 17-24, 2023) in view of Hester et. al. ( US 11921824 B1 with the effective filing date of March 29, 2021) in further view of Li et. al. (“Unifying Voxel-based Representation with Transformer for 3D Object Detection” with publishing year 2022) . Consider Claim 34, the combination of Man and Hester teaches “The device of claim 29, wherein the processing system comprises a cross- attention block configured to cross-attention process the first image feature data” (Man; Figure 2 (See Figure Above); Sensor-Agnostic Attention) “and a self- attention block configured to self-attention process the second image feature data.” The combination of Man and Hester does not explicitly disclose “and a self- attention block configured to self-attention process the second image feature data.”. However, Li teaches “and a self- attention block configured to self-attention process the second image feature data.” (Li; Figure 2 (See Figure Below); Self Attention, Cross Attention). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Man and Hester with the teachings of Li to further implement a self-attention module. One of ordinary skill in the art would be motivated to combine Man, Hester, and Li to “obtain accurate and robust predictions” (Li; Section 3.3, Paragraph 1) . Accordingly, the combination of Man, Hester, and Li discloses the invention of Claim 34. PNG media_image3.png 653 1235 media_image3.png Greyscale Consider Claim 35, the combination of Man, Hester and Li teaches “The device of claim 34, wherein the self-attention block comprises a second self-attention block,” (Hester; FIG. 3 (See Figure Below); Self-attention blocks 315) “the processing system further comprising a first self-attention block configured to self-attention process initial image feature data to form the first image feature data.” (Li; Figure 2 (See Figure Above); Self Attention, Cross Attention). The proposed combination as well as the motivation for combining the Man, Hester, and Li references presented in the rejection of claim 34, apply to claim 35 and are incorporated herein by reference. Thus, the device recited in claim 35 is met by Man, Hester, and Li . PNG media_image4.png 515 670 media_image4.png Greyscale 07-21-aia AIA 10. Claim s 36, 41-42, 51 and 56-57 are rejected under 35 U.S.C. 103 as being unpatentable over Man et. al. ( “BEV-Guided Multi-Modality Fusion for Driving Perception” with conference date June 17-24, 2023) in view of Hester et. al. ( US 11921824 B1 with the effective filing date of March 29, 2021) in further view of Xie et. al. (“AMMF: Attention-Based Multi-Phase Multi-Task Fusion for Small Contour Object 3D Detection” published on December 16, 2022) . Consider Claim 36, the combination of Man and Hester teaches “The device of claim 29, wherein the processing system further comprises: a first feature conditioning unit configured to process, based on a positional encoding model, a set of BEV position data features corresponding to position data to generate a set of conditioned BEV position data features;” (Man; Figure 2 (See Figure Above); Encoder, Camera, Positional Encoding) “and a second feature conditioning unit configured to process, based on the positional encoding model, a set of perspective image data features corresponding to the one or more images to generate a set of conditioned perspective image data features,” (Man; Figure 2 (See Figure Above); Encoder, Lidar, Positional Encoding). “ and wherein the processing system is further configured to: generate a weighted summation indicating a first relative importance of the one or more images to indicate characteristics of a three-dimensional (3D) environment comprising the area around the vehicle” (Man; Figure 3 (See Figure Above); Lidar Specific Feature Map, K) “and a second relative importance of the position data to indicate characteristics of the 3D environment,” (Man; Figure 3 (See Figure Above); Camera Specific Feature Map, K) “wherein to cross-attention process the first image feature data, the processing system is configured to use the weighted summation during the cross-attention process.”. The combination of Man and Hester does not explicitly disclose “wherein to cross-attention process the first image feature data, the processing system is configured to use the weighted summation” and “during the cross-attention process.” . However, in an analogous field of endeavor, Xie teaches “wherein to cross-attention process the first image feature data, the processing system is configured to use the weighted summation” (Xie; Section B.1.b; “The learned attention map is proposed for weighting the priorities of different sensor information. With the learned map, the adding and concatenation operations are used to fuse multi-sensor features”) “during the cross-attention process.” (Xie; Fig. 3 (See Figure Below); Section A.1; “Specifically, LF-MA is shown in Fig. 3 (a). I. Step 1, a concatenation operation is used to combine the features. Step 2, a convolution operation extracts high-representative features from the combined features. Step 3, batch normalization is used to unify the scales of features. Step 4, a matching attention is used to re- weight the balanced feature .” (emphasis added)). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Man and Hester with the teachings of Xie to further generate a weight summation as input into a cross attention module. One of ordinary skill in the art would be motivated to combine Man, Hester, and Xie to “efficiently extract spatial and contextual information” (Xie; Section A.1). Accordingly, the combination of Man, Hester, and Xie discloses the invention of Claim 36. PNG media_image5.png 424 845 media_image5.png Greyscale Consider Claim 41, the combination of Man, Hester, and Xie teaches “The device of claim 36, wherein the processing system is configured to fuse the set of BEV position data features and the set of BEV image data features to generate a fused set of BEV features, and wherein the processing system further comprises:” (Man; Figure 1; BEV- Guided Sensor Fusion) “a first decoder configured to use the fused set of BEV features to generate a set of 3D bounding boxes that indicate shapes of one or more objects within the 3D environment;” (Hester; Col 5, Line 54-57; “In various examples, transformer decoders may be added to detect two-dimensional objects (e.g., represented as bounding boxes) and/or three-dimensional objects (e.g., represented as cuboid boxes).”) “and a second decoder configured to use the fused set of BEV features to generate the BEV representation as a two-dimensional (2D) representation of the 3D environment from a perspective above the one or more objects and looking down at the one or more objects.” (Man; Figure 2; Step 4, “…(4) and the learned BEV representation is decoded for final perception tasks.”). The proposed combination as well as the motivation for combining the Man, Hester, and Xie references presented in the rejection of claim 36, apply to claim 41 and are incorporated herein by reference. Thus, the device recited in claim 41 is met by Man, Hester, and Xie. Consider Claim 42, the combination of Man, Hester, and Xie “The device of claim 36, wherein the position data comprises at least one of Light Detection and Ranging (LiDAR) data or radar data.” (Man; Figure 1 (See Figure Above); (1) Sensor Pool). Claims 51 and 56-57 recites a method with steps corresponding to the elements recited in Claims 36 and 41-42, respectively. Therefore, the recited steps of these claims are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Man, Hester, and Xie references, presented in rejection of Claim 36, apply to these claims . 07-21-aia AIA 11. Claim s 40 and 55 are rejected under 35 U.S.C. 103 as being unpatentable over Man et. al. ( “BEV-Guided Multi-Modality Fusion for Driving Perception” with conference date June 17-24, 2023) in view of Hester et. al. ( US 11921824 B1 with the effective filing date of March 29, 2021) in further view of Xie et. al. (“AMMF: Attention-Based Multi-Phase Multi-Task Fusion for Small Contour Object 3D Detection” published on December 16, 2022) in further view of Li et. al. (“Unifying Voxel-based Representation with Transformer for 3D Object Detection” with publishing year 2022) . Consider Claim 40, the combination of Man, Hester, and Xie teaches “The device of claim 36, wherein the processing system further comprises: a first encoder configured to extract, from the one or more images, the set of perspective image data features;” (Man; Figure 2; (2) Sensor-Specific Feature Extraction, Encoder, Camera) “and a second encoder configured to extract, from the position data, a set of 3D position data features,” (Man; Figure 2; (2) Sensor-Specific Feature Extraction, Encoder, Lidar) “wherein the processing system is further configured to compress the set of 3D position data features to generate the BEV representation.” The combination of Man, Hester, and Xie does not explicitly disclose “wherein the processing system is further configured to compress the set of 3D position data features to generate the BEV representation.”. However, Li teaches “wherein the processing system is further configured to compress the set of 3D position data features to generate the BEV representation.” (Li; Section Introduction, Paragraph 2; “For feature-level method, a typical approach is to transform image features as frustum and then compress to BEV space”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Man, Hester, and Xie with the teachings of Li to further compress image feature data to the BEV space. One of ordinary skill in the art would be motivated to combine Man, Hester, Xie, and Li to resolve “…inaccurate depth in pseudo point clouds and collapsed 3D geometry in range-view images” (Li; Section Introduction; Paragraph 2) . Accordingly, the combination of Man, Hester, Xie, and Li discloses the invention of Claim 40. Claim 55 recites a method with steps corresponding to the elements recited in Claims 40. Therefore, the recited steps of these claims are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Man, Hester, Xie, and Li references, presented in rejection of Claim 55, apply to these claims. Allowable Subject Matter 07-43 Claims 37-39 and 52-54 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of the limitations recited in Claims 37-39 and 52-54 with the limitations of claims they depends from. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Annie Pham whose telephone number is (571)272-1673. The examiner can be normally be reached Mon-Fri 9:00a – 5:00p. 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, Amandeep Saini can be reached on (571)272-3382. 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. /ANNIE H PHAM/Examiner, Art Unit 2662 /Siamak Harandi/Primary Examiner, Art Unit 2662 Application/Control Number: 18/452,279 Page 2 Art Unit: 2662 Application/Control Number: 18/452,279 Page 3 Art Unit: 2662 Application/Control Number: 18/452,279 Page 4 Art Unit: 2662 Application/Control Number: 18/452,279 Page 5 Art Unit: 2662 Application/Control Number: 18/452,279 Page 6 Art Unit: 2662 Application/Control Number: 18/452,279 Page 7 Art Unit: 2662 Application/Control Number: 18/452,279 Page 8 Art Unit: 2662 Application/Control Number: 18/452,279 Page 9 Art Unit: 2662 Application/Control Number: 18/452,279 Page 10 Art Unit: 2662 Application/Control Number: 18/452,279 Page 11 Art Unit: 2662 Application/Control Number: 18/452,279 Page 12 Art Unit: 2662 Application/Control Number: 18/452,279 Page 13 Art Unit: 2662 Application/Control Number: 18/452,279 Page 14 Art Unit: 2662 Application/Control Number: 18/452,279 Page 15 Art Unit: 2662 Application/Control Number: 18/452,279 Page 16 Art Unit: 2662 Application/Control Number: 18/452,279 Page 17 Art Unit: 2662 Application/Control Number: 18/452,279 Page 18 Art Unit: 2662
