CTNF 18/906,691 CTNF 96839 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. Examiner’s Note For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims . See MPEP 2141.02 VI. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck , 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories , 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC , 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-3, 6-8, and 18-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hester et al. (US 11,921,824 B1 “HESTER”) . Regarding claim 1, HESTER discloses a system for perceiving an object in a scene, comprising: a processor; and a memory having instructions stored thereon that, when executed by the processor ( the one or more computing devices 102 may communicate with non-transitory computer-readable memory 103 [col. 7, lines 4-6] ), cause the system to: collect features of a first radar image of the scene captured from a first sensor and a second radar image of the scene captured from a second sensor, each of the first radar image and the second radar image includes depth data ( examples of sensors may include different RGB image sensors (and/or other image sensors) having different fields-of-view, various depth sensors (e.g., stereoscopic camera pairs, time of flight (TOF) sensors), light detection and ranging (Lidar), radar, offline map data, navigation waypoints, etc. [col. 4, lines 59-63] ); ( the various sensors may be used to capture information about a physical environment of a robot and/or to control the movements of the robot based on the physical environment. Information from different sensors may be fused [col. 5, lines 1-4] ) process selected features of the collected features with a transformer neural network having a transformer architecture with self-attention over the selected features and cross-attention between object queries and the selected features to produce 2D+ embeddings of the object ( a transformer based neural network that uses attention across features from multiple sensor modalities to fuse the sensor data and for making more accurate predictions for perception-related tasks [col. 4, lines 55-58] ); ( each of the task-specific decoders of the architecture described herein may apply self-attention on its input query embeddings, and cross-attention on the encoder output embeddings [col. 5, lines 9-12] ) process the 2D+ embeddings with a detection neural network to perceive the object and produce an image of the scene with markings of the perceived object; and output the image of the scene with the markings of the perceived object ( the task-specific decoders may perform object detection and semantic segmentation [col. 5, lines 15-16] ); ( there may be a 2D detection transformer encoder 112 c effective to output 2D detections 118 (e.g., bounding boxes for different classes of objects for which the 2D detection transformer decoder has been trained) [col. 9, lines 9-12] ); ( the birds-eye-view (BEV) map and/or semantic segmentation mask may be a transformed, top-down view of the surroundings of an autonomous vehicle and may be generated based on 2D image data and/or 3D data collected by sensors of various modalities (e.g., depth sensors and image sensors) [col. 9, lines 24-29] ). Regarding claim 2, HESTER discloses the system of claim 1, wherein the markings of the perceived object include a two dimensional bounding box around the object ( there may be a 2D detection transformer encoder 112 c effective to output 2D detections 118 (e.g., bounding boxes for different classes of objects for which the 2D detection transformer decoder has been trained) [col. 9, lines 9-12], cited and incorporated in the rejection of claim 1 ), and wherein the two dimensional bounding box specifies at least one of a location of the object, a dimension of the object, and a velocity of the object ( the position embeddings 304 may represent the spatial locations within the 2D frame of image data [col. 3, lines 11-12] ). Regarding claim 3, HESTER discloses the system of claim 1, wherein the first sensor and the second sensor are arranged such that a plane of view of the first sensor defining an orientation of the first radar image is different from a plane of view of the second sensor defining an orientation of the second radar image ( examples of sensors may include different RGB image sensors (and/or other image sensors) having different fields-of-view, various depth sensors (e.g., stereoscopic camera pairs, time of flight (TOF) sensors), light detection and ranging (Lidar), radar, offline map data, navigation waypoints, etc. [col. 4, lines 59-63], cited and incorporated in the rejection of claim 1 ). Regarding claim 6, HESTER discloses the system of claim 1, wherein the first and the second sensors are of different modalities such that a multi-view image of the scene is multimodal ( a transformer based neural network that uses attention across features from multiple sensor modalities to fuse the sensor data and for making more accurate predictions for perception-related tasks [col. 4, lines 55-58], cited and incorporated in the rejection of claim 1 ). Regarding claim 7, HESTER discloses the system of claim 6, wherein the first sensor is a camera ( multiple RGB image sensors (e.g., cameras) [col. 7, lines 22-23] ), and the second sensor is a radar ( sensor data 106g (e.g., radar data [col. 8, line 11] ); ( any sensors may be included according to the desired implementation [col. 6, lines 16-17] ). Regarding claim 8, HESTER discloses the system of claim 6, wherein the first sensor is a camera ( multiple RGB image sensors (e.g., cameras) [col. 7, lines 22-23] ), and the second sensor is a lidar ( sensor data 106f (e.g., a lidar point cloud [col. 8, lines 10-11] ); ( the sensors 130 include at least a front camera 130 a , a rear camera 130 b , and lidar 130 c . However, any sensors may be included according to the desired implementation [col. 6, lines 14-17] ). Regarding claim 18, HESTER discloses the system of claim 1, wherein the markings of the perceived object include a segmentation of the object ( the task-specific decoders may perform object detection and semantic segmentation [col. 5, lines 15-16], cited and incorporated in the rejection of claim 1 ). Regarding claim 19, HESTER discloses a method for perceiving an object in a scene, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor carry out steps of the method ( the one or more computing devices 102 may communicate with non-transitory computer-readable memory 103 [col. 7, lines 4-6] ), comprising: collecting features of a first radar image of the scene captured from a first sensor and a second radar image of the scene captured from a second sensor, each of the first radar image and the second radar image includes depth data ( examples of sensors may include different RGB image sensors (and/or other image sensors) having different fields-of-view, various depth sensors (e.g., stereoscopic camera pairs, time of flight (TOF) sensors), light detection and ranging (Lidar), radar, offline map data, navigation waypoints, etc. [col. 4, lines 59-63] ); ( the various sensors may be used to capture information about a physical environment of a robot and/or to control the movements of the robot based on the physical environment. Information from different sensors may be fused [col. 5, lines 1-4] ) processing selected features of the collected features with a transformer neural network having a transformer architecture with self-attention over the selected features and cross-attention between object queries and the selected features to produce 2D+ embeddings of the object ( a transformer based neural network that uses attention across features from multiple sensor modalities to fuse the sensor data and for making more accurate predictions for perception-related tasks [col. 4, lines 55-58] ); ( each of the task-specific decoders of the architecture described herein may apply self-attention on its input query embeddings, and cross-attention on the encoder output embeddings [col. 5, lines 9-12] ) processing the 2D+ embeddings with a detection neural network to perceive the object and produce an image of the scene with markings of the perceived object; and outputting the image of the scene with the markings of the perceived object ( the task-specific decoders may perform object detection and semantic segmentation [col. 5, lines 15-16] ); ( there may be a 2D detection transformer encoder 112 c effective to output 2D detections 118 (e.g., bounding boxes for different classes of objects for which the 2D detection transformer decoder has been trained) [col. 9, lines 9-12] ); ( the birds-eye-view (BEV) map and/or semantic segmentation mask may be a transformed, top-down view of the surroundings of an autonomous vehicle and may be generated based on 2D image data and/or 3D data collected by sensors of various modalities (e.g., depth sensors and image sensors) [col. 9, lines 24-29] ). Regarding claim 20, HESTER discloses a non-transitory computer-readable storage medium embodied thereon a program executable by a processor for performing a method for perceiving an object in a scene ( the one or more computing devices 102 may communicate with non-transitory computer-readable memory 103 [col. 7, lines 4-6] ), the method comprising: collecting features of a first radar image of the scene captured from a first sensor and a second radar image of the scene captured from a second sensor, each of the first radar image and the second radar image includes depth data ( examples of sensors may include different RGB image sensors (and/or other image sensors) having different fields-of-view, various depth sensors (e.g., stereoscopic camera pairs, time of flight (TOF) sensors), light detection and ranging (Lidar), radar, offline map data, navigation waypoints, etc. [col. 4, lines 59-63] ); ( the various sensors may be used to capture information about a physical environment of a robot and/or to control the movements of the robot based on the physical environment. Information from different sensors may be fused [col. 5, lines 1-4] ) processing selected features of the collected features with a transformer neural network having a transformer architecture with self-attention over the selected features and cross-attention between object queries and the selected features to produce 2D+ embeddings of the object ( a transformer based neural network that uses attention across features from multiple sensor modalities to fuse the sensor data and for making more accurate predictions for perception-related tasks [col. 4, lines 55-58] ); ( each of the task-specific decoders of the architecture described herein may apply self-attention on its input query embeddings, and cross-attention on the encoder output embeddings [col. 5, lines 9-12] ) processing the 2D+ embeddings with a detection neural network to perceive the object and produce an image of the scene with markings of the perceived object; and outputting the image of the scene with the markings of the perceived object ( the task-specific decoders may perform object detection and semantic segmentation [col. 5, lines 15-16] ); ( there may be a 2D detection transformer encoder 112 c effective to output 2D detections 118 (e.g., bounding boxes for different classes of objects for which the 2D detection transformer decoder has been trained) [col. 9, lines 9-12] ); ( the birds-eye-view (BEV) map and/or semantic segmentation mask may be a transformed, top-down view of the surroundings of an autonomous vehicle and may be generated based on 2D image data and/or 3D data collected by sensors of various modalities (e.g., depth sensors and image sensors) [col. 9, lines 24-29] ) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim (s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over HESTER, in view of Cattle (US 2019/0324134 A1 “CATTLE”) . Regarding claim 4, HESTER ( Examiner’s note: What HESTER does not disclose is strike-through ) discloses the system of claim 1, wherein the first sensor and the second sensor are arranged such that a plane of view of the first sensor defining an orientation of the first radar image is perpendicular to a plane of view of the second sensor defining an orientation of the second radar image . In a same or similar field of endeavor, CATTLE teaches that the frequency-scanned radar imaging system can include second one or more antennas in a second orientation toward the area of interest that is orthogonal to the first orientation. The second one or more antennas can be configured to perform a second transformation of a second representation of the plurality of radar signals completing the first transformation. Additionally, the frequency-scanned radar imaging system can include a first focusing module configured to focus the first representation of the first plurality of radar signals by performing a frequency scan of the plurality of responses. The first focusing module can also be configured to identify first spatial data at first spatial locations along a first dimension corresponding to the unique beam angles using the first representation. Further, the frequency-scanned radar imaging system can include a second focusing module configured to focus the second representation of the plurality of radar signals. The second focusing module can also be configured to identify second spatial data at second spatial locations along a second dimension distinct from the first dimension using the second representation [0044] . Additionally, CATTLE teaches that the dual-frequency ranging technique described herein, e.g. with respect to the range determination module 220, is suitable for measuring the Doppler shift of moving targets, which can be used to identify velocities of the targets [0102] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of HESTER to include the teachings of CATTLE, because doing so would improve resolution and identify enough meaningful scattering properties of objects for performing 3D scene detection and recognition , as recognized by CATTLE. Regarding claim 5, HESTER/ CATTLE discloses the system of claim 4, wherein the first sensor is a radar arranged to produce a horizontal view image of the scene including at least one of Radio Frequency (RF) reflectivity, phase, depth, and velocity information, and wherein the second sensor is a radar arranged to produce a vertical view image of the scene including at least one of RF reflectivity, phase, depth, and velocity information ( the second focusing module can also be configured to identify second spatial data at second spatial locations along a second dimension distinct from the first dimension using the second representation [CATTLE 0044], cited and incorporated in the rejection of claim 4 ); ( the dual-frequency ranging technique described herein, e.g. with respect to the range determination module 220, is suitable for measuring the Doppler shift of moving targets, which can be used to identify velocities of the targets [CATTLE 0102], cited and incorporated in the rejection of claim 4 ) . 07-21-aia AIA Claim (s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over HESTER, in view of Das et al. (US 12,313,727 B1 “DAS”) . Regarding claim 9, HESTER discloses the system of claim 1, wherein the selected features correspond to the most relevant features of the features of the first radar image and the second radar image selected by applying top-K selection on the features of the first radar image and the second radar image . In a same or similar field of endeavor, DAS teaches that at 704, for each object class, the query generator can identify a set of top candidates, and therefore the corresponding locations of these candidates, from the heatmap. The total number K of top candidates can vary depending on class and may be a configurable hyperparameter. Thus, the query generator may identify the top Ki candidates from the H×W sized heatmap Si of the i-th class [col. 14, lines 41-47] . At 706, the features associated with the candidates identified in 704 are selected from the lidar/radar based feature map in 702 for use in generating lidar or radar queries. In this manner, a subset of embedding vectors (N in total) can be extracted from the lidar/radar based feature map to compute the queries [col. 14, lines 59-64] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of HESTER to include the teachings of DAS, because doing so would improve detection quality and reduce computation time , as recognized by DAS . 07-21-aia AIA Claim (s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over HESTER, in view of CATTLE, and further in view of DAS . Regarding claim 10, HESTER/ CATTLE discloses the system of claim 5, wherein the processor is further configured to: generate features of the horizontal view image and the vertical view image by processing the horizontal view image and the vertical view image with a shared backbone neural network; and select the most relevant features from the features of the horizontal view image and the vertical view image by applying top-K selection on the features of the horizontal view image and the vertical view image . In a same or similar field of endeavor, DAS teaches that the feature extractors 112, 114, 116 may operate as backbone feature extractors that perform some preliminary processing of the sensor data to identify features of interest [col. 3, lines 32-35] . Furthermore, DAS teaches that at 704, for each object class, the query generator can identify a set of top candidates, and therefore the corresponding locations of these candidates, from the heatmap. The total number K of top candidates can vary depending on class and may be a configurable hyperparameter. Thus, the query generator may identify the top Ki candidates from the H×W sized heatmap Si of the i-th class [col. 14, lines 41-47] . At 706, the features associated with the candidates identified in 704 are selected from the lidar/radar based feature map in 702 for use in generating lidar or radar queries. In this manner, a subset of embedding vectors (N in total) can be extracted from the lidar/radar based feature map to compute the queries [col. 14, lines 59-64] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of HESTER to include the teachings of DAS, because doing so would improve detection quality and reduce computation time , as recognized by DAS . 07-21-aia AIA Claim (s) 14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over HESTER, in view of DAS, and further in view of Holzel et al. (US 2022/0269900 A1 “HOLZEL”) . Regarding claim 14, HESTER discloses the system of claim 1, wherein the processor is further configured to: estimate a three dimensional bounding box around the object in radar coordinate based on the 2D+ embeddings; convert the estimated three dimensional box in the radar coordinate to a three dimensional bounding box in camera coordinate, based on a radar-camera transformation; and project the three dimensional bounding box in the camera coordinate onto a two dimensional (2D) image plane to determine a two dimensional bounding box around the object . In a same or similar field of endeavor, DAS teaches that the 2D grid may be oriented along the lateral and longitudinal axes (e.g., x and y) of a vehicle in which the sensors are mounted. However, each cell may carry additional information about the height (e.g., z) dimension. As such, the 3D bounding boxes 250 can be determined when the fused features 132 are in top-down view [col. 9, lines 7-13] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of HESTER to include the teachings of DAS, because doing so would improve detection quality and reduce computation time , as recognized by DAS. HESTER, as modified by DAS, discloses the invention as set forth above, but does not disclose convert the estimated three dimensional box in the radar coordinate to a three dimensional bounding box in camera coordinate, based on a radar-camera transformation; and project the three dimensional bounding box in the camera coordinate onto a two dimensional (2D) image plane to determine a two dimensional bounding box around the object. In a same or similar field of endeavor, HOLZEL teaches that for the camera based fusion system, the rigid body transformation may be followed by an additional step of intrinsic transformation to convert a 3D point (x, y, z) to 2D pixel (u, v) in the image frame of the camera system. In this way, the corresponding pixels in the camera frame are also grouped together. Either a 2D bounding box or a polygon is constructed enclosing the projected points [0069] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of HESTER to include the teachings of HOLZEL, because doing so would give accurate and tight-fitting predictions. Grouping together of points also enables efficient noise-removal based on the cluster size and bounding box dimensions, an essential pre-processing step to preserve useful and salient information , as recognized by HOLZEL. Regarding claim 16, HESTER/ DAS/ HOLZEL discloses the system of claim 14, wherein the processor is further configured to project the three dimensional bounding box in the radar coordinate onto a 2D horizontal radar plane, a 2D vertical radar plane, and the 2D image plane ( the 2D grid may be oriented along the lateral and longitudinal axes (e.g., x and y) of a vehicle in which the sensors are mounted. However, each cell may carry additional information about the height (e.g., z) dimension. As such, the 3D bounding boxes 250 can be determined when the fused features 132 are in top-down view [DAS col. 9, lines 7-13], cited and incorporated in the rejection of claim 14 ); ( a 2D bounding box or a polygon is constructed enclosing the projected points [HOLZEL 0069], cited and incorporated in the rejection of claim 14 ) . 07-21-aia AIA Claim (s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over HESTER, in view of DAS, and HOLZEL, and further in view of Xiao et al. (US 2022/0189051 A1 “XIAO”) . Regarding claim 15, HESTER/ DAS/ HOLZEL discloses the system of claim 14, wherein the radar-camera transformation is a learnable transformation via reparameterization on a rotation matrix of the radar-camera transformation while preserving an orthonormal structure of the rotation matrix . In a same or similar field of endeavor, XIAO teaches that the orthogonal matrix may be trained either by parameterizing the orthogonal matrix in terms of a skew symmetric training matrix using the Cayley representation/transform, or by Riemann gradient descent on a Stiefel matrix manifold [0048] . The entries of kernel matrix 428 may represent the learnable and/or trainable parameters of cost volume model 400 [0085] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of HESTER to include the teachings of XIAO, because doing so would improve imaging and detection processing , as recognized by XIAO . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim (s) 11-13, and 17 is/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 . 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: HESTER discloses techniques for fusing sensor data of different modalities using a transformer. In various examples, first sensor data may be received from a first sensor and second sensor data may be received from a second sensor. A first feature representation of the first sensor data may be generated using a first machine learning model and a second feature representation of the second sensor data may be generated using a second machine learning model. In some examples, a modified first feature representation of the first sensor data may be generated based at least in part on a self-attention mechanism of a transformer encoder. The modified first feature representation may be generated based at least in part on the first feature representation and the second feature representation. A computer vision task may be performed using the modified first feature representation. Furthermore, CATTLE discloses antennas oriented at a first orientation toward an area of interest can transform radar signals through a first transformation that physically maps the plurality of radar signals with a plurality of unique beam angles corresponding to a plurality of unique frequencies. Antennas oriented at a second orientation toward the area of interest can transform radar signals through a second transformation completing the first transformation. A frequency scan can be performed on a first plurality of responses to first radar signals to identify first spatial data along a first dimension. Second spatial data at second spatial location along a second dimension can be created from a second plurality of responses corresponding to the second transformation. An image can be generated using the first spatial data and the second spatial data while a range value of the area of interest can be determined using the first plurality of responses. Further still, DAS discloses techniques for combining data using transformer-based machine learning models. In some examples, a first transformer is used to combine a first dataset with a second dataset. The results are then combined with a third dataset, using a second transformer. Each dataset can represent data from a different sensor modality. The transformers compute scores based on queries and apply the scores to values. The first dataset can be used to generate queries for the first transformer, and the values for the first transformer can be derived from the second dataset. Similarly, the third dataset can be used to generate queries for the second transformer, and the values for the second transformer can be derived from the output of the first transformer. The output of the second transformer is therefore a combination of all three datasets and can be used for object detection, for example, determining three-dimensional boundaries of objects. However, Applicant’s claim also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Neither HESTER, CATTLE, nor DAS anticipates or renders fairly obvious, alone, or in combination, to teach all the additional limitations as cited in claim 11, within the context of Applicant' s claimed invention as a whole, that is, “ wherein the processor is further configured to: compute positional embedding by tuning a dimension ratio that changes dimensions between depth positional embedding and angular positional embedding while keeping a total dimension of the positional embedding constant; and concatenate the positional embedding with the selected features to produce a sequence of input features ”. Claim(s) 12-13 would be allowable by virtue of their dependence on claim 11. Similarly, Applicant’s claim also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Neither HESTER, CATTLE, nor DAS anticipates or renders fairly obvious, alone, or in combination, to teach all the additional limitations as cited in claim 17, within the context of Applicant' s claimed invention as a whole, that is, “ wherein the processor is further configured to determine a tri-plane bounding box loss based on a sum of 2D bounding box losses over the 2D horizontal radar plane, the 2D vertical radar plane, and the 2D image plane ”. Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.” Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAILEY R LE whose telephone number is (571)272-4910. The examiner can normally be reached 9:00 AM - 5:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VLADIMIR MAGLOIRE can be reached at (571) 270-5144. 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. /Hailey R Le/Examiner, Art Unit 3648 June 11, 2026 Application/Control Number: 18/906,691 Page 2 Art Unit: 3648 Application/Control Number: 18/906,691 Page 3 Art Unit: 3648 Application/Control Number: 18/906,691 Page 4 Art Unit: 3648 Application/Control Number: 18/906,691 Page 5 Art Unit: 3648 Application/Control Number: 18/906,691 Page 6 Art Unit: 3648 Application/Control Number: 18/906,691 Page 7 Art Unit: 3648 Application/Control Number: 18/906,691 Page 8 Art Unit: 3648 Application/Control Number: 18/906,691 Page 9 Art Unit: 3648 Application/Control Number: 18/906,691 Page 10 Art Unit: 3648 Application/Control Number: 18/906,691 Page 11 Art Unit: 3648 Application/Control Number: 18/906,691 Page 12 Art Unit: 3648 Application/Control Number: 18/906,691 Page 13 Art Unit: 3648 Application/Control Number: 18/906,691 Page 14 Art Unit: 3648 Application/Control Number: 18/906,691 Page 15 Art Unit: 3648 Application/Control Number: 18/906,691 Page 16 Art Unit: 3648 Application/Control Number: 18/906,691 Page 17 Art Unit: 3648 Application/Control Number: 18/906,691 Page 18 Art Unit: 3648
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