CTFR 18/293,382 CTFR 82476 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. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-2 and 6-16 are pending. 07-30-03-h AIA Claim Interpretation Previous interpretation of claims under 11(f) have been withdrawn in view of amendments made by the applicant. Claim Rejections - 35 USC § 101 Previous 101 rejections of claims under have been withdrawn in view of amendments made by the applicant. Response to Arguments Applicant’s amendments filed 03/13/2026 have been considered and entered, however, in view of the amendments filed and applicant’s arguments, the previously made rejections have been withdrawn and a new ground(s) of rejections have been made. 07-37 AIA Applicant's arguments filed 03/13/2026 have been fully considered but they are not persuasive. Applicant argues that cited references fail to teach the newly amended features of claims which are currently recited to incorporate some of the features of cancelled dependent claims 3-5 such as "perform segmentation of the image captured by the surgical camera to generate a plurality of segmentation regions corresponding to targets that appear in the image ... determine the region-of-interest based on a spatial relationship between at least one of the plurality of segmentation regions and the region-of-interest candidate”, see remarks, pages 17-19. In reply, examiner disagrees and asserts that new 103 combination of Jarc with Zhao and Berci teaches all the amended features of claim 1 as seemed to be argued by the applicant. For instance, it is not just one reference (Jarc) as seemed to be argued by the applicant but a 103 combination in view of Zhao and Berci which teaches all the features of the claim as a combination. For example, Jarc teaches that based on the image captured by the surgical camera to generate segmentation region corresponding to targets that appear in the image (“ endoscope 112 focuses on other quadrants in the anatomical environment, the labeled 3D location may be compensated for subsequent camera movements, or by being referenced from an external position sensor to retain the labeled location. The labeled location may be used as a reference location which could help the surgeon identify the desired surgical site efficiently and effectively” , paragraph 99 and “ movement of the 3D gaze point between the target spot and the initial spot includes a plurality of segmental movements of the 3D gaze point, each segmental movement being from an instrument spot to the target spot, and wherein the instrument spot is between the initial spot and the target spot” , paragraph 136 ); and determine the region-of-interest based on a spatial relationship ( endoscope processor of the endoscopic camera may spatially align the common feature of the magnified (segmented) image with common feature in the primary image, paragraphs 211 ) between at least one of segmentation region and the region-of-interest candidate ( eye tracker configured to measure data reflective of a gaze point of the user, and gaze point of a surgeon is displayed with an image of an operating field (left, right, which is spatial, paragraph 40) and if a surgeon gazes at a surgical site to be labeled, the current gaze point is displayed on the image display, and by pressing a button or stepping on a foot pedal, a label is applied to the position of the current gaze fixation point, thus, on the basis of the spatial relationship between a surgical site to be labeled and the gaze point, a label is applied at a particular site, paragraphs 77, 80, 99, 235 ); Wherein, it is Zhao which teaches to generate a plurality of segmentation regions corresponding to targets that appear in the image ( navigation for a medical instrument by generating a two-dimensional model of an anatomical structure of a surgical site where segmentation process is performed in real time during surgery based on generated set/plurality of two-dimensional images which are captured by an endoscope camera of the illuminated anatomical area, paragraphs 62-63, 76, 85, 93 ); And finally, Berci teaches wherein at least one imaging parameter of surgical camera includes focus or exposure ( it is possible to adjust the focus and zoom (magnification) of the endoscopic camera head using the camera control unit, paragraph 34 ). Applicant’s rest of the arguments related to other independents and corresponding dependent claims have been rendered moot as they are based on same assertions related to claim 1 which have been already successfully shown to be taught by Jarc in view of Zhao and Berci . 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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-2, 6-7, 11-12 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Jarc et al., US 2017/0172675 in view of Zhao et al., US 2017/0209071 further in view of Berci, US 2008/0303899 . Regarding claim 1, Jarc discloses a surgical system ( tele-operational medical system 10 for use in medical procedures including diagnostic, therapeutic, or surgical procedures, paragraph 42 ) comprising: a surgical camera configured to capture an image of a surgical field ( surgical tools such as endoscopic camera are mounted onto the arm to take images of the surgical site, paragraphs 52, 61, 188 ); and processing circuitry ( as shown in fig. 1 ) configured to: based on the image captured by the surgical camera to generate segmentation region corresponding to targets that appear in the image (“ endoscope 112 focuses on other quadrants in the anatomical environment, the labeled 3D location may be compensated for subsequent camera movements, or by being referenced from an external position sensor to retain the labeled location. The labeled location may be used as a reference location which could help the surgeon identify the desired surgical site efficiently and effectively” , paragraph 99 and “ movement of the 3D gaze point between the target spot and the initial spot includes a plurality of segmental movements of the 3D gaze point, each segmental movement being from an instrument spot to the target spot, and wherein the instrument spot is between the initial spot and the target spot” , paragraph 136 ); acquire a region-of-interest candidate ( gaze point ) that is a region to be a candidate corresponding to an operator input selected from at least one of a voice input, a gaze input, a gesture input, a touch input, or a foot switch operation ( position to be labeled by surgeon ) ( surgeon may use the surgeon's 3D gaze point to label and to locate the surgical site. For example, when the surgeon wants to label a location in the anatomy, the surgeon may stare at the location, so that the eye tracking unit 200 captures the surgeon's 3D gaze point and determines the 3D coordinate values using the method 300. Then the surgeon may further press a button on the surgeon's console 120, or tap a foot pedal 128, to label the location at the current gaze point on the image displays using an icon, paragraph 99 ); determine ( surgeon may further press a button on the surgeon's console 120, or tap a foot pedal 128, paragraph 99 ) the region-of-interest based on a spatial relationship ( endoscope processor of the endoscopic camera may spatially align the common feature of the magnified (segmented) image with common feature in the primary image, paragraphs 211 ) between at least one of segmentation region and the region-of-interest candidate ( eye tracker configured to measure data reflective of a gaze point of the user, and gaze point of a surgeon is displayed with an image of an operating field (left, right, which is spatial, paragraph 40) and if a surgeon gazes at a surgical site to be labeled, the current gaze point is displayed on the image display, and by pressing a button or stepping on a foot pedal, a label is applied to the position of the current gaze fixation point, thus, on the basis of the spatial relationship between a surgical site to be labeled and the gaze point, a label is applied at a particular site, paragraphs 77, 80, 99, 235 ); and control at least one imaging parameter of the surgical camera based on the determined region-of-interest ( various system parameters and characteristics of the tele-operational medical system such that parameters of endoscopic camera may be adjusted to capture an updated image having the 3D gaze point (region of interest) of the surgeon located at the center of the image, paragraphs 78, 81, 100, 105 ). Jarc fails to explicitly disclose surgical system comprising: perform segmentation of image captured by surgical camera to generate a plurality of segmentation regions; spatial relationship between at least one of the plurality of segmentation regions and candidate region; wherein at least one imaging parameter of surgical camera includes focus or exposure . However, Zhao teaches surgical system ( surgical system 100, fig. 1 ) comprising: perform segmentation of image captured by surgical camera ( monoscopic endoscope camera that obtains a temporal series of localized two dimensional images of an illuminated anatomical area, such that a surgeon may be able to visually determine that an artery or airway as viewed from a camera, paragraphs 76, 88 ) to generate a plurality of segmentation regions corresponding to targets that appear in the image ( navigation for a medical instrument by generating a two-dimensional model of an anatomical structure of a surgical site where segmentation process is performed in real time during surgery based on generated set/plurality of two-dimensional images which are captured by an endoscope camera of the illuminated anatomical area, paragraphs 62-63, 76, 85, 93 ); spatial relationship between at least one of the plurality of segmentation regions and candidate region ( model ) (t emporal or spatial data obtained from the medical instrument indicates that the instrument is within or has been within a passageway that is not indicated by the model, the model may be updated using the obtained data, paragraphs 68, 71-72 ); Jarc and Zhao are combinable because they both are in the same field of endeavor dealing with performing surgical procedures with segmentation of images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with the teachings of Zhao for the benefit of effectively and efficiently navigating the patient's anatomy and updating the model based on data obtained by the sensing tool as taught by Zhao at paragraphs 7. Jarc with Zhao fail to further teach wherein at least one imaging parameter of surgical camera includes focus or exposure . However, Berci teaches wherein at least one imaging parameter of surgical camera includes focus or exposure ( it is possible to adjust the focus and zoom (magnification) of the endoscopic camera head using the camera control unit, paragraph 34 ). Jarc and Zhao are combinable with Berci because they all are in the same field of endeavor dealing with image processing of images in surgery field which are captured by a camera. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with Zhao with the teachings of Berci for the benefit of having an efficient imaging system which uses existing operating room equipment to receive, magnify, transmit, and display images of a surgical field as taught by Berci at paragraph 22. Regarding claim 2, Combination of Jarc with Zhao further teaches wherein the processing circuitry is further configured to determine a common region between at least one of the plurality of segmentation regions and the region-of-interest candidate as the region-of-interest (Jarc, position to be labeled is the common region of the surgical site to be labeled and detected gaze fixation point, paragraph 99 and Zhao, segmentation process is performed in real time during surgery based on generated set/plurality of two-dimensional images which are captured by an endoscope camera of the illuminated anatomical area, paragraphs 62-63, 93 ). Jarc and Zhao are combinable because they both are in the same field of endeavor dealing with performing surgical procedures with segmentation of images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with the teachings of Zhao for the benefit of effectively and efficiently navigating the patient's anatomy and updating the model based on data obtained by the sensing tool as taught by Zhao at paragraphs 7. Regarding claim 6, Combination of Jarc with Zhao further teaches wherein the processing circuitry is further configured to determine the plurality of segmentation regions corresponding to an operation target organ based on surgical procedure information (Jarc, target spot includes focusing the 3D gaze point on the target spot on the 3D image display, wherein, the movement of the 3D gaze point between the target spot and the initial spot includes a plurality of segmental movements of the 3D gaze point, each segmental movement being from an instrument spot to the target spot, paragraphs 133-138, and Zhao also teaches, virtual navigational image is displayed in which the actual location of the medical instrument is registered with preoperative or concurrent images, paragraph 38, wherein, the segmentation can be done in real time during the procedure. Specifically, as the instrument navigates the passageway, it can obtain data that can then be used to perform the segmentation function and create the model during the procedure. Thus, the model and target are continually being developed as the instrument navigates the anatomy, paragraphs 93-95 ). Jarc and Zhao are combinable because they both are in the same field of endeavor dealing with performing surgical procedures with segmentation of images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with the teachings of Zhao for the benefit of effectively and efficiently navigating the patient's anatomy and updating the model based on data obtained by the sensing tool as taught by Zhao at paragraphs 7. Regarding claim 7, Combination of Jarc with Zhao further teaches determine a surgical process based on positional relationship between a surgical tool and the operation target organ, and determine the region-of-interest based on the determined result (Zhao, sensing tool of the instrument detects differences between the patient's organ such as lung and the generic images of lung, the model is updated to conform to the patient's real anatomy, for example, the sensing tool may be used to obtain the size and shape of various passageways so that model of the patient's anatomy can then be constructed in real time such that obtained data that can then be used to perform the segmentation function with region of interest and create the model during the procedure, paragraphs 93-95, claim 1 ). Jarc and Zhao are combinable because they both are in the same field of endeavor dealing with performing surgical procedures with segmentation of images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with the teachings of Zhao for the benefit of effectively and efficiently navigating the patient's anatomy and updating the model based on data obtained by the sensing tool as taught by Zhao at paragraphs 7. Regarding claim 11, Combination of Jarc with Zhao and Berci further teaches wherein the processing circuitry is further configured to present information regarding the region-of-interest to an operator during control of the surgical camera (Jarc, endoscope is caused to focus on another quadrant for surgeon to control the endoscope 112 to focus on another quadrant by looking at the image display such that information is provided relating to labeled surgical site at the time of control of the endoscope, paragraph 99 ). Regarding claim 12, Combination of Jarc with Zhao and Berci further teaches wherein the processing circuitry is further configured to change the region-of-interest based on speech of the operator made after the information regarding the region-of-interest is presented (Jarc, region of interest is located and changed to desired magnification level based on audio message sent by the surgeon who desires a certain level of magnification, paragraphs 199, 221, 231 ). Regarding claim 14, Combination of Jarc with Zhao and Berci further teaches wherein the processing circuitry is further configured to change the region-of-interest based on display magnification of the captured image (Jarc, when the surgeon wants to investigate a region of interest within a surgical area in greater detail. For example, the surgeon may wish to examine a magnified view of fine structures of a surgical area, such as nerves, blood vessels, and lesions., where the surgeon may use his or her eye gaze point or surgeon may press a button at the surgeon console, tap a foot pedal, send an audio message, or wink to locate and change the region of interest based on level of magnification desired, paragraphs 199, 215, 221, 231 ). Regarding claim 15, is a method version of claim 1 reciting similar features and thus is rejected on the same rationale as presented for claim 1. Regarding claim 16, is a program product version of claim 1 reciting similar features with having a non-transitory computer readable medium executing the program on a computer and thus is rejected on the same rationale as presented for claim 1. Note that non-transitory computer readable medium is taught by Jarc at paragraphs 42, 48 . 07-21-aia AIA Claim s 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Jarc et al., US 2017/0172675 in view of Zhao et al., US 2017/0209071 further in view of Berci, US 2008/0303899 as applied in claim 1 and 6 above and further in view of Sethuraman et al., US 2007/0008342 . Regarding claim 8, Jarc with Zhao and Berci further teaches wherein the processing circuitry is further configured to: determine , based on the surgical procedure information, target in each portion of the plurality of segmentation regions in which the operation target organ appears , and determine the region-of-interest to include a portion where the target is higher than a threshold value (Zhao, virtual navigational image is displayed in which the actual location of the medical instrument is registered with preoperative or concurrent images, paragraph 38, wherein, the segmentation can be done in real time during the procedure. Specifically, as the instrument navigates the passageway, it can obtain data that can then be used to perform the segmentation function and create the model during the procedure. Thus, the model and target are continually being developed as the instrument navigates the anatomy, paragraphs 93-95 ). Jarc and Zhao are combinable because they both are in the same field of endeavor dealing with performing surgical procedures with segmentation of images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with the teachings of Zhao for the benefit of effectively and efficiently navigating the patient's anatomy and updating the model based on data obtained by the sensing tool as taught by Zhao at paragraphs 7. Jarc with Zhao and Berci fails to explicitly teach determine , based on information, a degree of importance in each portion of the plurality of segmentation regions in which the operation is performed, and setting region so as to include a portion where the degree of importance is weighted. However, Sethuraman teaches determine , based on information, a degree of importance in each portion of the plurality of segmentation regions in which the operation is performed, and setting region so as to include a portion where the degree of importance is weighted ( optimal segmentation is defined as the segmentation that minimizes the sum of an error term plus a regularization term over all pixels in the image with a equation, where k is a regularization parameter that weights the importance of the regularization term, paragraph 36 ). Jarc and Zhao and Berci are combinable with Sethuraman because they all are in the same field of endeavor dealing with segmentation of images in medical field. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with Zhao and Berci with the teachings of Sethuraman for the benefit of providing relatively efficient segmentation process with regard to memory access as taught by Sethuraman at paragraphs 8 and 11. Regarding claim 9, Jarc with Zhao further teaches wherein the processing circuitry is further configured to: perform depth estimation based on the captured image, and based on depth information obtained from the depth estimation , perform division of the at least one of plurality of segmentation regions or coupling of two or more of the plurality of the segmentation regions (Zhao, navigation for a medical instrument by generating a two-dimensional model of an anatomical structure of a surgical site where segmentation process is performed in real time during surgery based on two- dimensional images captured by an endoscope camera of the illuminated anatomical area, paragraphs 62, 76, 93 ). Jarc and Zhao are combinable because they both are in the same field of endeavor dealing with performing surgical procedures with segmentation of images. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with the teachings of Zhao for the benefit of effectively and efficiently navigating the patient's anatomy and updating the model based on data obtained by the sensing tool as taught by Zhao at paragraphs 7. Jarc with Zhao and Berci fails to explicitly teach performing depth estimation based on captured image, and based on depth information obtained from the depth estimation , perform division of the at least one of plurality of segmentation regions or coupling of two or more of the plurality of the segmentation regions. However, Sethuraman teaches performing depth estimation based on captured image, and based on depth information obtained from the depth estimation , perform division of the at least one of plurality of segmentation regions or coupling of two or more of the plurality of the segmentation regions ( “ single depth value is estimated for each color region. This depth estimation per region has the advantage that there exists per definition a large color contrast along the region boundary. The temporal stability of color edge positions is critical for the final quality of the depth maps. When the edges are not stable over time, an annoying flicker may be perceived by the viewer when the video is shown on a 3D color television. Thus, a time-stable segmentation method is the first step in the conversion process from 2D to 3D video. Image segmentation using a constant color model achieves this desired effect. This method of image segmentation is described in greater detail below. It is based on a first set of initial segments and iterative updates resulting in a second set of updated segments. In other words the segmentation is a conversion of a first set of initial segments into a second set of updated segments ”, paragraph 35 ). Jarc and Zhao and Berci are combinable with Sethuraman because they all are in the same field of endeavor dealing with segmentation of images in medical field. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with Zhao and Berci with the teachings of Sethuraman for the benefit of providing relatively efficient segmentation process with regard to memory access as taught by Sethuraman at paragraphs 8 and 11 . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Jarc et al., US 2017/0172675 in view of Zhao et al., US 2017/0209071 further in view of Berci, US 2008/0303899 as applied in claim 1 above and further in view of Fleischman et al., US 2020/0357170 . Regarding claim 10, Jarc with Zhao and Berci fails to explicitly teach wherein the processing circuitry is further configured to: perform simultaneous localization and mapping (SLAM) processing based on the captured image, and based on SLAM information representing a result of the SLAM processing, perform division of at least one of the plurality of segmentation regions or coupling of two or more of the plurality of the segmentation regions. However, Fleischman teaches wherein the processing circuitry is further configured to: perform simultaneous localization and mapping (SLAM) processing based on the captured image, and based on SLAM information representing a result of the SLAM processing, perform division of at least one of the plurality of segmentation regions or coupling of two or more of the plurality of the segmentation regions ( SLAM module 216 receives the sequence of 360-degree images 212 and performs a SLAM algorithm to generate a first estimate 218 of the camera path. Before performing the SLAM algorithm, the SLAM module 216 can perform one or more preprocessing steps on the images 212, wherein pre-processing steps can also include a segmentation process. The segmentation process divides the sequence of images into segments based on the quality of the features in each of the images, paragraphs 43-46 ). Jarc with Zhao and Berci are combinable with Fleischman because they all are in the same field of endeavor dealing with image processing of images in surgery field which are captured by a camera. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with Zhao and Berci with the teachings of Fleischman for the benefit of having a large number of images are captured at once or where images of the same space are captured at regular time intervals in order to monitor changes within the space over a period of time as taught by Fleischman at paragraph 6 . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jarc et al., US 2017/0172675 in view of Zhao et al., US 2017/0209071 further in view of Berci, US 2008/0303899 as applied in claim 6 above and further in view of Kotian et al., US 2019/0090954 . Regarding claim 13, Jarc with Zhao and Berci fails to explicitly teach a surgical procedure information acquisition interface configured to acquire the surgical procedure information based on at least one of: information transmitted from a hospital information system (HIS); or a recognition result of speech made before start of surgery. However, Kotian teaches a surgical procedure information acquisition interface configured to acquire the surgical procedure information based on at least one of: information transmitted from a hospital information system (HIS) ( surgical site within the patient with the surveillance data acquired at the same time external to the patient in order to identify actions and events in the surgical procedure and carry out the functions as described in further detail herein. The AI enabled controller 62 is further exemplarily communicatively connected to the hospital information system (HIS) 56 through which the AI enabled controller 62 is able to access surgery, personnel, or surgery suite schedules, surgical procedure logs, patient care guidelines, and procedure standards, paragraph 39 ); or a recognition result of speech made before start of surgery ( audio data may be interpreted through speech recognition processing and techniques for the communicative content of the audio data itself, for example to interpret the discussion between clinicians in order to identify actions or events in the surgery procedure, paragraph 36 ). Jarc with Zhao and Berci are combinable with Kotian because they all are in the same field of endeavor dealing with image processing of images in surgery field. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Jarc with Zhao and Berci with the teachings of Kotian for the benefit of having improved surgery procedure performance, integration between medical devices and clinician users of those devices, as well as management of personnel and resources within the hospital of which the surgical suite is a part can be achieved but with greater leverage and integration of surgical suite audio and video resources as taught by Kotian at paragraph 3 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Murakita et al., US 2017/0196443 Zagorchev et al., US 2023/0000561 Dargis et al., US 2019/0282190 Cao et al., US 2017/0358075 Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAWANDEEP DHINGRA whose telephone number is (571) 270-1231. The examiner can normally be reached 9:00-5:00. 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, Abderrahim Merouan can be reached at (571) 270-5254. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /PAWAN DHINGRA/Examiner, Art Unit 2683 /ABDERRAHIM MEROUAN/Supervisory Patent Examiner, Art Unit 2683 Application/Control Number: 18/293,382 Page 2 Art Unit: 2683 Application/Control Number: 18/293,382 Page 3 Art Unit: 2683 Application/Control Number: 18/293,382 Page 4 Art Unit: 2683 Application/Control Number: 18/293,382 Page 5 Art Unit: 2683 Application/Control Number: 18/293,382 Page 6 Art Unit: 2683 Application/Control Number: 18/293,382 Page 7 Art Unit: 2683 Application/Control Number: 18/293,382 Page 8 Art Unit: 2683 Application/Control Number: 18/293,382 Page 9 Art Unit: 2683 Application/Control Number: 18/293,382 Page 10 Art Unit: 2683 Application/Control Number: 18/293,382 Page 11 Art Unit: 2683 Application/Control Number: 18/293,382 Page 12 Art Unit: 2683 Application/Control Number: 18/293,382 Page 13 Art Unit: 2683 Application/Control Number: 18/293,382 Page 14 Art Unit: 2683 Application/Control Number: 18/293,382 Page 15 Art Unit: 2683 Application/Control Number: 18/293,382 Page 16 Art Unit: 2683 Application/Control Number: 18/293,382 Page 17 Art Unit: 2683 Application/Control Number: 18/293,382 Page 18 Art Unit: 2683 Application/Control Number: 18/293,382 Page 19 Art Unit: 2683 Application/Control Number: 18/293,382 Page 20 Art Unit: 2683 Application/Control Number: 18/293,382 Page 21 Art Unit: 2683
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