CTNF 18/873,205 CTNF 99663 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. Claim Objections 07-29-01 AIA Claim 18 objected to because of the following informalities: typo wherein last limitation should read “determining the reference style image to be applied based on the selected reference style image to be selected .” . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 19 and 26 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 19 recites the limitation "a style texture feature" in line 2. There is insufficient antecedent basis for this limitation in the claim. This is because it is unclear if this style texture feature is the same instance as that of the parent claim and further recitations of “a style texture feature” further add to this confusion as well as “the style texture feature” making it unclear which instance of style texture feature is being referred to. Claims 26 rejected under 35 U.S.C. 112(b) since it depend on a claim that is rejected under rejected under 35 U.S.C. 112(b). Note. Most likely these claims depend on some dependent claim or are missing elements. In order to fix this issue, dependency should be reviewed and any first instance of an element should be made clear that it’s a first instance and should be referred to as “a” or “an” instead of “the”, and if multiple instances exist, further instances should be further distinguished for example by saying “first”, “second”, and/or “third” etc. 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) 16-18, 20-21, 24, 28-30, 31-32, 33 and 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (U.S. Patent Application Publication No. 2019/0026870), hereinafter referenced as Hu, in view of Shen et al. (U.S. Patent Application Publication No. 2022/0044054), hereinafter referenced as Shen . Regarding claim 16, Hu teaches A method for processing image, comprising: obtaining an image to be processed (fig. 6 and paragraph 45 teach “step 601, a content image representative of the user's desired content is selected by the user”) ; determining an object structural feature within the image to be processed corresponding to a target object (paragraph 33 teaches “Encoder network 332 first generates a tentative content feature vector (i.e., “tent. content features” 352) from the content image 322”); tentative content features shows object structural features which is within image to be processed as shown in fig. 3; and determining a style texture feature corresponding to a reference style image to be applied (paragraph 33 and fig. 3 teach “tentative style feature vector (i.e., “tent. style features” 354) from the style image 324”, and paragraph 39 teaches “the output image to capture the colors and textures of the style image 324”); this shows style features (which have textures thus style texture feature) are determined from (thus corresponding to) reference style image 324 ; and determining a target style image corresponding to the image to be processed based on the object structural feature and the style texture feature (paragraph 43 teaches “transforming into a stylized output image (e.g., output image 326).”); stylized output image shows style image, corresponds to input image (image to be processed), and is based on object structural features (tentative content feature aforementioned) and style texture features. However, Hu fails to explicitly teach target style image. However, Shen explicitly teaches target style image (Shen, paragraph 8 teaches “third image is generated according to the content feature and the target style feature, so that the third image has a content corresponding to the content feature and a style corresponding to the target style feature”); this shows the third/output image would be target style image since it has style corresponding to target style feature. Shen is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of target style features and images generated based on such. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hu's invention with the target style transfer techniques of Shen to improve the fidelity of a style migrated image, and can improve the detection accuracy of the object in the image (Shen, paragraph 43). This would be done due to having target style image based on object features and style texture features. Regarding claim 17, the combination of Hu and Shen teache s wherein the obtaining the image to be processed comprises: in response to detecting that an effect processing operation is triggered, collecting the image to be processed; or determining at least one video frame within an uploaded video to be processed as the image to be processed (Shen, paragraph 138 teaches “front camera and/or a rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, for example, a photographing mode or a video mode”); this shows image/data would be video frame within a uploaded video. The same motivations used in claim 16 apply here in claim 17. Regarding claim 18, the combination of Hu and Shen teache s wherein the reference style image to be applied is determined by: determining a predetermined style image as the reference style image to be applied; or in response to detecting that an effect processing operation is triggered or an uploaded video to be processed is received, displaying at least one reference style image to be selected on a display interface (Hu, paragraph 41 teaches “user can choose a desired style image” and paragraph 42 teaches “user can activate a first button 421 to upload a content image, and can activate a second button 422 to upload a style image or select a preset artistic style. A display region 423 at the right of the screen can display the uploaded content”); this shows preset/predetermined style image which would be used as aforementioned reference style image ; determining the reference style image to be applied based on the selected reference style image to be selected (Hu, paragraph 41 teaches “user can then select one of these artistic styles, as shown in screen 403. In the case where the user selects a preset artistic style…from which the style feature vector can be generated”); this shows determining the reference style image based on selected reference style image . Regarding claim 20, the combination of Hu and Shen teache s wherein the determining the object structural feature within the image to be processed corresponding to the target object and determining the style texture feature corresponding to the reference style image to be applied comprises: extracting, based on a pre-trained encoder, the object structural feature within the image to be processed corresponding to the target object; (paragraph 33 teaches “Encoder network 332 first generates a tentative content feature vector (i.e., “tent. content features” 352) from the content image 322”); this shows using pre-trained encoder 352 to extract tentative content features which shows object structural features which are within image to be processed corresponding to target object as shown in fig. 3; and determining the reference style image to be applied based on a trigger operation on at least one reference style image to be selected, (Hu, paragraph 41 teaches “user can then select one of these artistic styles, as shown in screen 403. In the case where the user selects a preset artistic style…from which the style feature vector can be generated”); this shows determining reference style image (the one that is selected) which is based on trigger/selecting on at least one reference style image by the user; and retrieving a pre-stored style texture feature corresponding to the reference style image to be applied (Hu, paragraph 42 teaches “user can activate a first button 421 to upload a content image, and can activate a second button 422 to upload a style image or select a preset artistic style. A display region 423 at the right of the screen can display the uploaded content”); selecting preset artistic style shows a pre-stored style texture feature which corresponds to selected style image and this is retrieved because it is displayed . Regarding claim 21, the combination of Hu and Shen teache s wherein the determining the object structural feature within the image to be processed corresponding to the target object and determining the style texture feature corresponding to the reference style image to be applied comprises: obtaining the reference style image to be applied that is selected on a display interface (Hu, fig. 4 and paragraph 42 teach “As shown in screen 411, a user can activate a first button 421 to upload a content image, and can activate a second button 422 to upload a style image or select a preset artistic style. A display region 423 at the right of the screen can display the uploaded content images. Screen 412 depicts the uploading of a first content image, which is added to the display region 423 in screen 413”); this shows as uploaded image, the reference style image (that is selected on a display interface) is obtained ; and inputting the reference style image to be applied and the image to be processed into a pre- trained encoder (Hu, fig. 3 shows style image 324 and content image 322 input to the encoder network 332); this shows reference style image 324 and content image (image to be processed) 322 input into pre-trained encoder; to obtain the object structural feature of the image to be processed and the style texture feature of the reference style image to be applied (Hu, fig.3 shows tentative style features 354 are extracted based on encoder network 332 from style image 324, and content features 352 are extracted based on encoder network 332 from content image 322); this shows using pre-trained encoder to obtain style texture feature 354 of reference style image 324 and obtain content/structural feature 352 of image to be processed 322 . Regarding claim 24, the combination of Hu and Shen teache s wherein the determining the target style image corresponding to the image to be processed based on the object structural feature and the style texture feature comprises: reconstructing, based on a target generator, the object structural feature and the style texture feature to obtain the target style image (Hu, fig. 3 and paragraph 34 teach “a loss module 334 receives the tentative content feature vector 352 and tentative style feature vector 354 from the encoder network 332. The loss module 334 is configured to compute a content loss with regard to the tentative content feature vector 352 and a style loss with respect to the tentative style feature vector 354. As a result, the refined stylized pixels 360 are generated to produce the output image 326” and paragraph 93 teaches “the target style feature, and random noise are input into the trained image generator to obtain a second target image”; computing loss and refining pixels to compute output image shows reconstructing to obtain target style image, this is done for both content feature (object structural feature) and style feature (style texture feature), and is based on target generator since target images in Shen are generated using image generator. Regarding claim 28, the device claim 28 recites similar limitations as method claim 16, and thus is rejected under similar rationale. In addition, Hu, paragraph 52 teaches “instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system” and fig. 10 teaches device 10 with processor 11 and memory 12 to execute such. Regarding claim 29, the device claim 29 recites similar limitations as method claim 17, and thus is rejected under similar rationale. Regarding claim 30, the device claim 30 recites similar limitations as method claim 18, and thus is rejected under similar rationale. Regarding claim 31, the device claim 31 recites similar limitations as method claim 20, and thus is rejected under similar rationale. Regarding claim 32, the device claim 32 recites similar limitations as method claim 21, and thus is rejected under similar rationale. Regarding claim 33, the device claim 33 recites similar limitations as method claim 24, and thus is rejected under similar rationale. Regarding claim 35, the non-transitory storage medium claim 35 recites similar limitations as method claim 16, and thus is rejected under similar rationale. In addition, Hu, claim 20 teaches “non-transitory computer-readable medium including instructions, which when executed by one or more processors, cause the processors to perform a method” . 07-22-aia AIA Claim (s) 19 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu and Shen as applied to claim 18 above, and further in view of Slesarev et al. (U.S. Patent Application Publication No. 2018/0293439), hereinafter referenced as Slesarev . Regarding claim 19, the combination of Hu and Shen teache s further comprising: extracting, based on a pre-trained encoder, a style texture feature of the at least one reference style image to be selected, (Hu, fig.3 shows tentative style features 354 are extracted based on encoder network 332, paragraph 32 teaches “content image 322 and style image 324 are received by an encoder network 332”, and paragraph 33 teaches “Encoder network 332 first generates… a tentative style feature vector”); this shows using pre-trained encoder to extract style texture feature 354 of reference style image 324; a nd storing the style texture feature in a target cache location, so as to retrieve a corresponding style texture feature from the target cache location in response to determining a style texture feature corresponding to the reference style image to be applied (Hu, paragraph 41 teaches “In the case where the user selects a preset artistic style, the system may load the style feature vector (or tentative style feature vector) from memory, rather than generating the style feature vector from the style image itself. In addition, a preset artistic style may have one or more associated preset parameters (e.g., training parameters)”); this shows the style texture feature / vector (tentative style feature vector) is stored in a location and retrieved from that location in response to user selecting/determining the style image with associated parameters (thus determining style texture feature from parameters corresponding to style image) . However, the combination of Hu and Shen fails to teach and storing the style texture feature in a target cache location, so as to retrieve a corresponding style texture feature from the target cache location. However, Slesarev teaches and storing the style texture feature in a target cache location, so as to retrieve a corresponding style texture feature from the target cache location (Slesarev paragraph 61 teaches “extracted object (which may be temporarily stored as a separate image file), using the third set of graphical features, so that it effectively has the same features (i.e., “style”) as the base image”); this shows object with style features (thus style texture feature from above combination) stored temporarily (then later retrieved) which one of ordinary skill in the art would understand as a cache location. Slesarev is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of temporary storage of stylized features. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hu and Shen with the style feature temporary storage techniques of Slesarev so that it can effectively be blended (Slesarev, paragraph 61). This would lead to more efficient system due to faster accessing of resources because of temporary/cache storage to blend the style into the image. Regarding claim 26, the combination of Hu, Shen and Slesarev teache s wherein the pre-trained encoder comprises at least two branch structures, (Hu, fig. 2 shows two arrows to and from encoder network 232); these arrows act as branch structures; a first branch structure is used for extracting structural features, (Hu, fig. 2 shows first arrow output of content features 252); these features are structural features (since of content) and correspond to first branch; a second branch structure is used for extracting texture features, (Hu, fig. 2 shows second/bottom arrow output of style features 254); this shows second branch used for extracting texture features since style features have and correspond to texture as explained in claim 1 above; the structural features comprise object structural features and style structural features, (Hu, paragraph 28 teaches “Encoder network 232 can extract the feature vectors from both the content image and the style image by applying a series of non-linear transformations. As a result, encoder network 232 generates a content feature vector (i.e., “content features” 252”); this shows content/structural features 252 having both object structural and style structural features since feature vectors are taken from both style and object; the texture features comprise object texture features and style texture features, (Hu, fig. 2 style image 224 shows an object leading to style features 254); this shows that object information (such as object texture) from the image and the style (and texture thereof) are both included in the aforementioned texture feature; and the branch structures comprise at least one convolutional layer (Hu, paragraph 28 teaches “Encoder network 232 can comprise a deep convolutional neural network of multiple neural layers 231”); this shows the arrows/branch structure comprises convolution layer since it has convolutional neural network . 07-22-aia AIA Claim (s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu and Shen as applied to claim 21 above, and further in view of Aggarwal et al. (U.S. Patent Application Publication No. 2023/0360294), hereinafter referenced as Aggarwal . Regarding claim 22, the combination of Hu and Shen teaches wherein the inputting the reference style image to be applied and the image to be processed into the pre-trained encoder to obtain the object structural feature of the image to be processed and the style texture feature of the reference style image to be applied comprises: and extracting, based on the pre-trained encoder and in accordance with the identification attributes , the object structural feature of the image to be processed and the style texture feature of the reference style image to be applied (Hu, fig.3 shows tentative style features 354 are extracted based on encoder network 332 from style image 324, and content features 352 are extracted based on encoder network 332 from content image 322); this shows using pre-trained encoder to obtain/extract style texture feature 354 of reference style image 324 and obtain/extract content/structural feature 352 of image to be processed 322 . However, the combination of Hu and Shen fails to teach determining identification attributes of the reference style image to be applied and the image to be processed, respectively; and extracting, based on the pre-trained encoder and in accordance with the identification attributes, the object structural feature of the image to be processed and the style texture feature of the reference style image to be applied. However, Aggarwal teaches determining identification attributes of the reference style image to be applied and the image to be processed, respectively (Aggarwal, abstract teaches “identify target style attributes and target structure attributes”); this shows determining identification of attributes which would be of reference style image since are target style attributes and also would be of image to be processed since are target structure attributes ; and extracting , based on the pre-trained encoder and in accordance with the identification attributes, the object structural feature of the image to be processed and the style texture feature of the reference style image to be applied (Aggarwal, paragraph 135 teaches “include training a swapping autoencoder (SAE) model by swapping structure attributes and style attributes of a first training image and a second training image”); this shows the identification attributes being used for pre-trained encoder thus the extraction from above would be based on such . Aggarwal is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of identifying attributes, taking style as well as structure of images and compositing images. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hu and Shen with the identification of attributes techniques of Aggarwal to efficiently and accurately perform image generation based on source images (Aggarwal, paragraph 2). This would be due to the identification of attributes so later they can be used easily . 07-22-aia AIA Claim (s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu and Shen as applied to claim 21 above, and further in view of Jeon et al. (U.S. Patent No. 12,217,395), hereinafter referenced as Jeon . Regarding claim 23, the combination of Hu and Shen teache s and the inputting the reference style image to be applied and the image to be processed into the pre-trained encoder to obtain the object structural feature of the image to be processed and the style texture feature of the reference style image to be applied comprises: obtaining the object structural feature and the style texture feature (Hu, fig.3 shows tentative style features 354 are extracted based on encoder network 332 from style image 324, and content features 352 are extracted based on encoder network 332 from content image 322); this shows using pre-trained encoder to obtain style texture feature 354 of reference style image 324 and obtain content/structural feature 352 of image to be processed 322 . However, the combination of Hu and Shen fails to teach wherein the pre-trained encoder comprises a first encoder and a second encoder; extracting, based on the first encoder, features of the image to be processed to obtain the object structural feature and an object texture feature; and extracting, based on the second encoder, features of the reference style image to be applied to obtain the style texture feature and a style structural feature; However, Jeon teaches wherein the pre-trained encoder comprises a first encoder and a second encoder (Jeon, col. 7, lines 9-11 teach “In one embodiment, machine learning model 225 includes image encoder 230, alignment component 235, image decoder 240, and modulation encoder 245”); this shows first/image encoder 230 and second/modulation encoder 245 meaning the pre-trained encoder from Hu would comprise both when viewed in combination ; extracting, based on the first encoder, features of the image to be processed to obtain the object structural feature and an object texture feature (Jeon, col. 17, lines 46-49 teach “image encoder takes a content-style image pair {I.sup.1,I.sup.2} and extracts the corresponding latent vectors or latent codes using a CNN-based encoder” and col. 4, line 9 teaches “Object structure and content information is preserved”); image/first encoder extracting latent vectors shows features of image to be processed (content image) being extracted and this would be to obtain object structural feature and object texture feature when viewed in combination with the above references and also because the object structure and content information is preserved ; and extracting, based on the second encoder, features of the reference style image to be applied to obtain the style texture feature and a style structural feature (Jeon, col. 20, line 29 teaches “modulation encoder” and lines 36-38 teaches “preserve the structure from the content image…to manage the extent of texture transfer from the style image to the content image”); this shows second/modulation encoder and managing extent of texture transfer shows for extracting features of reference style image such as style texture feature and also obtaining style structural feature since it’s preserving structure. Jeon is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of creating a hybrid image based on content image and style image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hu and Shen with the multi-encoder techniques of Jeon to ensure improved image processing system that can efficiently and accurately perform object appearance transfer (Jeon, col. 1, lines 23-25). This is due to the multiple encoders used and extraction of specific features . 07-22-aia AIA Claim (s) 25 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu and Shen as applied to claim s 16 and 28 above, and further in view of Bai (CN 114331820 A), hereinafter referenced as Bai . Regarding claim 25, the combination of Hu and Shen fails to teach further comprising: in response to detecting a captured effect video or receiving an uploaded screen recording video, respectively determining a plurality of video frames in the captured effect video or the uploaded screen recording video as the images to be processed, and determining a target style image corresponding to each image to be processed; and joining a plurality of target style images corresponding to images to be processed to obtain a target effect video. However, Bai teaches further comprising: in response to detecting a captured effect video or receiving an uploaded screen recording video , respectively determining a plurality of video frames in the captured effect video or the uploaded screen recording video as the images to be processed, (Bai, paragraph 33 teaches “disclosed technical solution can be applied to any image style conversion scenario. For example, it can convert a captured still image into an image with a certain theme style, such as Japanese style, Korean style, or any theme style designed by a designer. It can be applied to special effects video shooting scenarios, such as converting a user in the frame or all users in the entire frame into a video with a certain theme style” and paragraph 34 teaches “the user in each video frame to be processed can be displayed according to the corresponding style theme, or the entire video frame can be converted into a certain theme style”); this shows captured effect video (due to special effects video shooting scenarios mentioned) and it respectively determines each video frame in the video as images to be processed; and determining a target style image corresponding to each image to be processed (Bai, paragraph 36 teaches “used to convert the image screen into a target style type”); this shows the aforementioned each video frame of video would have a target style type determined for it ; and joining a plurality of target style images corresponding to images to be processed to obtain a target effect video (Bai, paragraph 48 teaches “to obtain a target effect image conversion model that can perform this technical solution.”); this shows plurality of target style images (which correspond to images to be processed) are joined to obtain target effect video (video and joined since all images undergo this target style as aforementioned) . Bai is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of special effects with videos alongside stylization. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hu and Shen with the video styling techniques of Bai to quickly respond to the image to be processed, thereby obtaining the corresponding target special effects image, which improves image processing efficiency (Bai, paragraph 50). This would be due to the video as input with multiple frames instead of one by one picture inputted and processed. Regarding claim 34, the device claim 34 recites similar limitations as method claim 25, and thus is rejected under similar rationale . 07-22-aia AIA Claim (s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu and Shen as applied to claim 16 above, and further in view of Li et al. (U.S. Patent Application Publication No. 2007/0058865), hereinafter referenced as Li . Regarding claim 27, the combination of Hu and Shen fails to teach wherein the style texture feature of the reference style image to be applied corresponds to at least one of: a comic style texture feature, an epoch style texture feature, or a regional style texture feature . However, Li teaches wherein the style texture feature of the reference style image to be applied corresponds to at least one of: a comic style texture feature, an epoch style texture feature, or a regional style texture feature (Li, paragraph 167 teaches “Huang's method combines the regional characteristics (Mumford-Shah style texture term) and edge properties (shape) of the image into a single model”); this shows the style texture feature of reference style image (from above) would be regional style texture feature. Li is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of having regional style texture feature. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hu and Shen with the regional style texture techniques of Li to improve both the speed and memory efficiency (Li, paragraph 172). This would be done this would be due to combining the regional characteristics and edge properties into a single model when using regional characteristics . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Simons et al. (U.S. Patent Application Publication No. 2018/0350030), paragraph 100 teaches “a stylized image is generated or created by applying a style or texture of the particular style feature to the particular target feature”; this shows style and texture features used for target style image generation . Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAUMAN U AHMAD whose telephone number is (703)756-5306. The examiner can normally be reached Monday - Friday 9:00am - 5:00pm. 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, Kee Tung can be reached at (571) 272-7794. 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. /KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611 /N.U.A./Examiner, Art Unit 2611 Application/Control Number: 18/873,205 Page 2 Art Unit: 2611 Application/Control Number: 18/873,205 Page 3 Art Unit: 2611 Application/Control Number: 18/873,205 Page 4 Art Unit: 2611 Application/Control Number: 18/873,205 Page 5 Art Unit: 2611 Application/Control Number: 18/873,205 Page 6 Art Unit: 2611 Application/Control Number: 18/873,205 Page 7 Art Unit: 2611 Application/Control Number: 18/873,205 Page 8 Art Unit: 2611 Application/Control Number: 18/873,205 Page 9 Art Unit: 2611 Application/Control Number: 18/873,205 Page 10 Art Unit: 2611 Application/Control Number: 18/873,205 Page 11 Art Unit: 2611 Application/Control Number: 18/873,205 Page 12 Art Unit: 2611 Application/Control Number: 18/873,205 Page 13 Art Unit: 2611 Application/Control Number: 18/873,205 Page 14 Art Unit: 2611 Application/Control Number: 18/873,205 Page 15 Art Unit: 2611 Application/Control Number: 18/873,205 Page 16 Art Unit: 2611 Application/Control Number: 18/873,205 Page 18 Art Unit: 2611 Application/Control Number: 18/873,205 Page 19 Art Unit: 2611
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