CTNF 18/962,333 CTNF 100565 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 2. 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 3. 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 4. Claim s 1, 3-6, 11, 13-14, 16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaiifar (US 20190266789 A1), hereinafter Rezaiifar, in view of Basso (US 7231099 B1), hereinafter Basso . Regarding claim 1, Rezaiifar teaches a method, comprising: for at least a first image: assigning depth values to at least a foreground portion of the first image (Fig. 2, paragraph 83, wherein generating a dense depth map of an object after segmenting the foreground from the background so that only the foreground is processed is interpreted as assigning depth values for the foreground portion of the first image); estimating surface normals for at least the foreground portion of the first image based, at least in part, on the assigned depth values (Fig. 5, paragraph 94, wherein generating a normal map consisting of vectors perpendicular to a surface based on the depth map of the foreground of the image interpreted as estimating surface normals for the foreground based on the assigned depth values); and augmenting the first image with at least a first visual effect based, at least in part, on the estimated surface normals, wherein the first visual effect comprises a specified virtual lighting effect (Fig. 5, paragraph 96, wherein determining lighting values based on parameters such as light position, direction color or intensity and using previously generated data including the estimated normals is interpreted as augmenting the image with a first visual effect based on the estimated surface normals). Rezaiifar does not teach obtaining, at a first electronic device, a video image stream comprising a plurality of images of a scene captured by a first image capture device; and transmitting the first augmented output image to a second electronic device. Basso teaches obtaining, at a first electronic device, a video image stream comprising a plurality of images of a scene captured by a first image capture device (Fig. 1, Col. 3 line 61 - Col. 4 line 2, wherein image capture module 4 receives video scene data from a camera for processing, which is interpreted as obtaining video image stream captured by a first image capture device); and transmitting the first augmented output image to a second electronic device (Fig. 1, Fig. 12, Col. 4 lines 7-13, wherein processed video data can be transmitted to a destination video conferencing device, which is interpreted as transmitting augmented output image data to a second electronic device). It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Rezaiifar with the teachings of Basso for this method of augmenting an image with virtual lighting effects. Rezaiifar discusses determining dynamic lighting effects for specific objects in an image involving determining depth maps and normal maps for the object, for the purposes of allowing for easy adjustments of lighting effects on the image. Similarly, Basso discusses digitally generating lighting effects for real-time video effects involving generating a normal map and tracking and illuminating a specific object in the video. Both references discuss adding dynamic virtual lighting effects for lighting objects, specifically a person’s face. Both references additionally discuss adding lighting effects to dynamic video data. As both references discuss analogous art, it would be obvious to combine them. Regarding claim 3, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Rezaiifar teaches the method of claim 1, wherein the steps of: assigning, estimating, augmenting, and transmitting are further performed for each image of the video stream (paragraph 126, wherein the techniques for detecting objects in an image as part of the lighting process is performed for video frames of video data; paragraph 130, wherein a video encoder may be configured to apply and output the disclosed techniques as applied to a source video data, which suggests that the processes of assigning depth values, estimating surface normals, augmenting the image, and transmitting the image are performed for each image frame of a given video stream). Regarding claim 4, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Basso teaches the method of claim 1, wherein the first augmented output image is transmitted to a second electronic device as part of a videoconferencing application (Fig. 1, Fig. 12, Col. 4 lines 7-13, wherein processed video data can be transmitted to a destination video conferencing device is interpreted as transmitting augmented output image data as part of a videoconferencing application). The motivation to combine would be the same as that set forth for claim 1. Regarding claim 5, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Rezaiifar teaches the method of claim 1, wherein the foreground portion of the first image comprises at least one human subject (paragraph 73-74, wherein the object, which corresponds to the foreground of the image, can be the face of a person or the user, which is interpreted as comprising at least one human subject). Regarding claim 6, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Rezaiifar teaches the method of claim 1, wherein the specified virtual lighting effect comprises a specification of at least one of: (a) a color of a virtual light source being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the color of the virtual light); (b) an intensity level of a virtual light source being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the intensity of the virtual light); (c) an angle of a virtual light source being added to the scene with respect to the first image capture device (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the direction of the virtual light, which is interpreted as its direction); (d) a number of virtual light sources being added to the scene ; or (e) a position of one or more virtual light sources being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the position of the virtual light). Regarding claim 11, Rezaiifar teaches an electronic device, comprising: a first image capture device (Fig. 1, paragraph 76, camera 101); a memory; and one or more processors operatively coupled to the memory (paragraph 76, computing device may contain memory and processors), wherein the one or more processors are configured to execute instructions causing the one or more processors to: for at least a first image: assigning depth values to at least a foreground portion of the first image (Fig. 2, paragraph 83, wherein generating a dense depth map of an object after segmenting the foreground from the background so that only the foreground is processed is interpreted as assigning depth values for the foreground portion of the first image); estimating surface normals for at least the foreground portion of the first image based, at least in part, on the assigned depth values (Fig. 5, paragraph 94, wherein generating a normal map consisting of vectors perpendicular to a surface based on the depth map of the foreground of the image interpreted as estimating surface normals for the foreground based on the assigned depth values); and augmenting the first image with at least a first visual effect based, at least in part, on the estimated surface normals, wherein the first visual effect comprises a specified virtual lighting effect (Fig. 5, paragraph 96, wherein determining lighting values based on parameters such as light position, direction color or intensity and using previously generated data including the estimated normals is interpreted as augmenting the image with a first visual effect based on the estimated surface normals). Rezaiifar does not teach obtaining, at a first electronic device, a video image stream comprising a plurality of images of a scene captured by a first image capture device; and transmitting the first augmented output image to a second electronic device. Basso teaches obtaining, at a first electronic device, a video image stream comprising a plurality of images of a scene captured by a first image capture device (Fig. 1, Col. 3 line 61 - Col. 4 line 2, wherein image capture module 4 receives video scene data from a camera for processing, which is interpreted as obtaining video image stream captured by a first image capture device); and transmitting the first augmented output image to a second electronic device (Fig. 1, Fig. 12, Col. 4 lines 7-13, wherein processed video data can be transmitted to a destination video conferencing device, which is interpreted as transmitting augmented output image data to a second electronic device). The motivation to combine would be the same as that set forth for claim 1. Regarding claim 13, Rezaiifar in view of Basso discloses the device of claim 11. Additionally, Rezaiifar teaches the device of claim 11, wherein the steps of: assigning, estimating, augmenting, and transmitting are further performed for each image of the video stream (paragraph 126, wherein the techniques for detecting objects in an image as part of the lighting process is performed for video frames of video data; paragraph 130, wherein a video encoder may be configured to apply and output the disclosed techniques as applied to a source video data, which suggests that the processes of assigning depth values, estimating surface normals, augmenting the image, and transmitting the image are performed for each image frame of a given video stream). Regarding claim 14, Rezaiifar in view of Basso discloses the device of claim 11. Additionally, Rezaiifar teaches the device of claim 11, wherein the specified virtual lighting effect comprises a specification of at least one of: (a) a color of a virtual light source being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the color of the virtual light); (b) an intensity level of a virtual light source being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the intensity of the virtual light); (c) an angle of a virtual light source being added to the scene with respect to the first image capture device (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the direction of the virtual light, which is interpreted as its direction); (d) a number of virtual light sources being added to the scene ; or (e) a position of one or more virtual light sources being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the position of the virtual light). Regarding claim 16, Rezaiifar teaches a non-transitory computer-readable medium (CRM) comprising computer readable instructions executable by one or more processors (paragraph 76, computing device may contain memory and processors) to: for at least a first image: assigning depth values to at least a foreground portion of the first image (Fig. 2, paragraph 83, wherein generating a dense depth map of an object after segmenting the foreground from the background so that only the foreground is processed is interpreted as assigning depth values for the foreground portion of the first image); estimating surface normals for at least the foreground portion of the first image based, at least in part, on the assigned depth values (Fig. 5, paragraph 94, wherein generating a normal map consisting of vectors perpendicular to a surface based on the depth map of the foreground of the image interpreted as estimating surface normals for the foreground based on the assigned depth values); and augmenting the first image with at least a first visual effect based, at least in part, on the estimated surface normals, wherein the first visual effect comprises a specified virtual lighting effect (Fig. 5, paragraph 96, wherein determining lighting values based on parameters such as light position, direction color or intensity and using previously generated data including the estimated normals is interpreted as augmenting the image with a first visual effect based on the estimated surface normals). Rezaiifar does not teach obtaining, at a first electronic device, a video image stream comprising a plurality of images of a scene captured by a first image capture device; and transmitting the first augmented output image to a second electronic device. Basso teaches obtaining, at a first electronic device, a video image stream comprising a plurality of images of a scene captured by a first image capture device (Fig. 1, Col. 3 line 61 - Col. 4 line 2, wherein image capture module 4 receives video scene data from a camera for processing, which is interpreted as obtaining video image stream captured by a first image capture device); and transmitting the first augmented output image to a second electronic device (Fig. 1, Fig. 12, Col. 4 lines 7-13, wherein processed video data can be transmitted to a destination video conferencing device, which is interpreted as transmitting augmented output image data to a second electronic device). The motivation to combine would be the same as that set forth for claim 1. Regarding claim 18, Rezaiifar in view of Basso discloses the computer-readable medium of claim 16. Additionally, Rezaiifar teaches the computer-readable medium of claim 16, wherein the steps of: assigning, estimating, augmenting, and transmitting are further performed for each image of the video stream (paragraph 126, wherein the techniques for detecting objects in an image as part of the lighting process is performed for video frames of video data; paragraph 130, wherein a video encoder may be configured to apply and output the disclosed techniques as applied to a source video data, which suggests that the processes of assigning depth values, estimating surface normals, augmenting the image, and transmitting the image are performed for each image frame of a given video stream). Regarding claim 19, Rezaiifar in view of Basso discloses the computer-readable medium of claim 16. Additionally, Rezaiifar teaches the computer-readable medium of claim 16, wherein the specified virtual lighting effect comprises a specification of at least one of: (a) a color of a virtual light source being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the color of the virtual light); (b) an intensity level of a virtual light source being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the intensity of the virtual light); (c) an angle of a virtual light source being added to the scene with respect to the first image capture device (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the direction of the virtual light, which is interpreted as its direction); (d) a number of virtual light sources being added to the scene ; or (e) a position of one or more virtual light sources being added to the scene (Fig. 5, paragraph 96, wherein lighting values for the object are determined, wherein the tunable inputs are interpreted as specifications of the virtual lighting effect, and includes the position of the virtual light) . 07-22-aia AIA 5. Claim s 2, 12, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaiifar in view of Basso as applied to claim s 1, 11, 16 above, and further in view of Herman (US 20230230267 A1), hereinafter Herman . Regarding claim 2, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Herman teaches the method of claim 1, wherein estimating the surface normals comprises using a machine learning (ML) or artificial intelligence (AI)-based model (paragraph 49-50, wherein object surface normals can be estimated from image data using deep neural networks). It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Rezaiifar in view of Basso with the teachings of Herman for this method of augmenting an image with virtual lighting effects. Rezaiifar discusses segmenting an object from the background as part of the process of determining dynamic lighting effects for an image involving determining depth maps and normal maps for the object, for the purposes of allowing for easy adjustments of lighting effects on the image. Similarly, Basso discusses tracking and illuminating a specific object in a video in order to provide real-time lighting effects to that object, where the process involves determining surface normals of that object. Additionally, Herman discloses a method for determining surface normals for an object using neural networks, for the purposes of obtaining the precise identification and location of an object in a scene. Both Rezaiifar and Basso discuss adding dynamic virtual lighting effects for a specific detected object, involving calculating surface normals for that object, while Herman discloses both an efficient way to determine surface normals using neural networks, and a way to detect a specified object in an image. As Rezaiifar and Basso disclose analogous art, and Herman discloses a non-limiting way to help those two references determine surface normals and detect objects in an image, it would be obvious to combine these references. Regarding claim 12, Rezaiifar in view of Basso discloses the device of claim 11. Additionally, Herman teaches the device of claim 11, wherein estimating the surface normals comprises using a machine learning (ML) or artificial intelligence (AI)-based model (paragraph 49-50, wherein object surface normals can be estimated from image data using deep neural networks). The motivation to combine would be the same as that set forth for claim 1. Regarding claim 17, Rezaiifar in view of Basso discloses the computer-readable medium of claim 16. Additionally, Herman teaches the computer-readable medium of claim 16, wherein estimating the surface normals comprises using a machine learning (ML) or artificial intelligence (AI)-based model (paragraph 49-50, wherein object surface normals can be estimated from image data using deep neural networks). The motivation to combine would be the same as that set forth for claim 1 . 07-22-aia AIA 6. Claim s 7, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaiifar in view of Basso as applied to claim 1 above, and further in view of Vyas (US 20230290108 A1), hereinafter Vyas . Regarding claim 7, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Vyas teaches the method of claim 1, wherein the specified virtual lighting effect comprises a virtual light source that is modeled as being added to the scene at an infinity distance (paragraph 21-22, wherein performing a desired lighting modification includes adding a directional lighting to the scene, where a directional light is known in the art to be a light source that models an infinite number of parallel rays from an infinitely far source, and wherein adding a directional light to the scene suggests adding a light source at an infinity distance). It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Rezaiifar in view of Basso with the teachings of Vyas for this method of augmenting an image with virtual lighting effects. Rezaiifar discusses determining dynamic lighting effects for specific objects in an image involving determining depth maps and normal maps for the object, for the purposes of allowing for easy adjustments of lighting effects on the image. Similarly, Basso discusses digitally generating lighting effects for real-time video effects involving generating a normal map and tracking and illuminating a specific object in the video. Furthermore, Vyas also teaches real-time modifications of lighting in an image utilizing effective machine-learning models, wherein the process includes determining surface normals for a specified object. All three references discuss adding dynamic virtual lighting effects for lighting objects, specifically a person’s face. All three references additionally discuss adding lighting effects to dynamic video data. As all three references discuss non-limiting methods for analogous art, it would be obvious to combine them. Regarding claim 9, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Vyas teaches the method of claim 1, wherein the first visual effect comprises automatically determining an angle of a virtual light source being added to the scene with respect to the first image capture device (Fig. 4, paragraph 40, wherein the method for modifying illumination in images may be performed automatically, which is interpreted as adding a first visual effect automatically; paragraph 38, wherein directional illumination modification may be added, wherein adding directional lighting is interpreted as determining an angle of an added virtual light source to the image; paragraph 30, wherein the modified source image is initially captured by a camera, which is interpreted as modifying the scene with respect to a first image capture device). The motivation to combine would be the same as that set forth for claim 7 . 07-22-aia AIA 7. Claim s 8, 15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaiifar in view of Basso as applied to claim s 1, 11, 16 above, and further in view of Gao (X. Gao et al., "Low-lighting video enhancement using constrained spatial-temporal model for real-time mobile communication," 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA, 2017, pp. 1-4, doi: 10.1109/ISCAS.2017.8050384.), hereinafter Gao . Regarding claim 8, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Gao teaches the method of claim 1, wherein the first visual effect comprises an application of one or more temporal stability constraints (Section 3 C, wherein applying lighting enhancements to a video frame involves setting a temporal consistency constraint, which is interpreted as applying a temporal stability constraint as part of adding a visual effect). It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Rezaiifar in view of Basso with the teachings of Vyas for this method of augmenting an image with virtual lighting effects. Rezaiifar discusses determining dynamic lighting effects for specific objects in an image involving determining depth maps and normal maps for the object, for the purposes of allowing for easy adjustments of lighting effects on the image. Similarly, Basso discusses digitally generating lighting effects for real-time video effects involving generating a normal map and tracking and illuminating a specific object in the video. Additionally, Gao also discusses methods to apply real-time lighting enhancements, for specifically enhancing low-light videos, and focusing on maintaining lighting accuracy and temporal consistency. As both Rezaiifar and Basso discuss real-time lighting enhancements involving isolating a person’s face within the image frame, and Gao discloses non-limiting real-time lighting enhancements focused on maintaining accuracy and consistency, it would be obvious to combine these three references. Regarding claim 15, Rezaiifar in view of Basso discloses the device of claim 11. Additionally, Gao teaches the device of claim 11, wherein the first visual effect comprises an application of one or more temporal stability constraints (Section 3 C, wherein applying lighting enhancements to a video frame involves setting a temporal consistency constraint, which is interpreted as applying a temporal stability constraint as part of adding a visual effect). The motivation to combine would be the same as that set forth for claim 8. Regarding claim 19, Rezaiifar in view of Basso discloses the computer-readable medium of claim 16. Additionally, Gao teaches the computer-readable medium of claim 16, wherein the first visual effect comprises an application of one or more temporal stability constraints (Section 3 C, wherein applying lighting enhancements to a video frame involves setting a temporal consistency constraint, which is interpreted as applying a temporal stability constraint as part of adding a visual effect). The motivation to combine would be the same as that set forth for claim 8 . 07-22-aia AIA 8. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Rezaiifar in view of Basso as applied to claim 1 above, and further in view of Tangeland (US 20220070389 A1), hereinafter Tangeland . Regarding claim 10, Rezaiifar in view of Basso discloses the method of claim 1. Additionally, Tangeland teaches the method of claim 1, wherein the specified virtual lighting effect comprises modeling an effect of a virtual light source being added to the scene on a virtual background for the scene (Fig. 4, paragraph 54-55, wherein modifying the virtual background's brightness in response to changing light exposure characteristics, including adding light sources, of the image is interpreted as modelling an effect of a virtual light source being added to the scene on a virtual background). It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Rezaiifar in view of Basso with the teachings of Tangeland for this method of augmenting an image with virtual lighting effects. Rezaiifar discusses determining dynamic lighting effects for specific objects in an image involving determining depth maps and normal maps for the object, for the purposes of allowing for easy adjustments of lighting effects on the image. Similarly, Basso discusses digitally generating lighting effects for real-time video effects involving generating a normal map and tracking and illuminating a specific object in the video. Furthermore, Tangeland also discusses adding dynamic lighting effects for an object isolated from its background, as well as generating a virtual background and simulating lighting effects for that virtual background. All three references discuss dynamically modifying lighting of videos, and particularly videos of a user’s face. Rezaiifar and Basso discuss similar methods of isolating an object from the background by calculating surface normals, while Basso and Tangeland both discuss adding lighting effects for specifically videoconferencing. As all three references discuss analogous art for adding virtual lighting to real-time video, it would be obvious to combine these references. Conclusion 9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN W YICK whose telephone number is (571)272-4063. The examiner can normally be reached M-F 8-5. 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, Said Broome can be reached at (571) 272-2931. 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. /JORDAN WAN YICK/Examiner, Art Unit 2612 /Said Broome/Supervisory Patent Examiner, Art Unit 2612 Application/Control Number: 18/962,333 Page 2 Art Unit: 2612 Application/Control Number: 18/962,333 Page 3 Art Unit: 2612 Application/Control Number: 18/962,333 Page 4 Art Unit: 2612 Application/Control Number: 18/962,333 Page 5 Art Unit: 2612 Application/Control Number: 18/962,333 Page 6 Art Unit: 2612 Application/Control Number: 18/962,333 Page 7 Art Unit: 2612 Application/Control Number: 18/962,333 Page 8 Art Unit: 2612 Application/Control Number: 18/962,333 Page 9 Art Unit: 2612 Application/Control Number: 18/962,333 Page 10 Art Unit: 2612 Application/Control Number: 18/962,333 Page 11 Art Unit: 2612 Application/Control Number: 18/962,333 Page 12 Art Unit: 2612 Application/Control Number: 18/962,333 Page 13 Art Unit: 2612 Application/Control Number: 18/962,333 Page 14 Art Unit: 2612 Application/Control Number: 18/962,333 Page 15 Art Unit: 2612 Application/Control Number: 18/962,333 Page 16 Art Unit: 2612
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