CTNF 18/151,945 CTNF 94282 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. 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/09/2026 has been entered. 12-151 AIA 26-51 12-51 Status of Claims This is a Non-Final Action for Request for Continued Examination (RCE) application Serial No. 18/151,945. Claim(s) 64-72 and 74-92 have been examined and fully considered. Claim(s) 64, 66, 76, 83, 84, and 88 have be amended. Claim(s) 64-72 and 74-92 are pending in Instant Application. Response to Arguments/Rejections 07-38-02 AIA Applicant’s arguments, see Remarks , filed 01/09/2026 , with respect to the rejection(s) of claim(s) 64, 69-72, 74, and 78-84 under 35 U.S.C. § 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Fridman (Pub. No.: US 2018/0025235) . 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim (s) 64-72, 74-75, 77-84, and 91 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rempala Averilla (EP3647733A1; previously recorded) hereinafter, referred to as “Averilla” in view of Fridman (Pub. No.: US 2018/0025235) . Regarding [claim 64] , Averilla discloses a system for navigating a host vehicle (see at least Abstract; Paragraph [0023]), the system comprising: at least one processor comprising circuitry and a memory (see at least Figure 3, Paragraph [0075]), wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to : receive a plurality of images acquired by a camera onboard the host vehicle (see at least Paragraph [0087] and [0128]); generate, based on analysis of the plurality of images, a road geometry model for a segment of road forward of the host vehicle (see at least Paragraph [0034]); based on analysis of the road geometry model, detect a presence of a local horizon represented in the road geometry model, wherein the local horizon is associated with an obscured road segment beyond the local horizon (see at least Paragraph [0138]); and based on the road geometry model and in response to the detected local horizon represented by the road geometry model, generate an output for causing one or more navigational actions to be taken by the host vehicle (see at least Paragraph [0111]). *** Examiner notes that Averilla reference discloses an autonomous vehicle that is capable of navigating on roadways capturing the roadway features elevation of the drivable area and automatically annotating the environmental features in a map during navigation of a vehicle *** Averilla does not explicitly discloses …. identify, based on a location of the local horizon , a portion of a map representative of the obscured road segment; and based on the road geometry model and a trajectory of the obscured road segment represented in the map, generate an output for causing one or more navigational actions to be taken by the host vehicle relative to the obscured road segment. However, Fridman teaches … identify, based on a location of the local horizon , a portion of a map representative of the obscured road segment (see, Paragraph [0003]: “ the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon . Autonomous vehicles may need to take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination. For example , an autonomous vehicle may need to process and interpret visual information (e.g., information captured from a camera ) and may also use information obtained from other sources (e.g., from a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc.). At the same time , in order to navigate to a destination, an autonomous vehicle may also need to identify its location within a particular roadway (e.g., a specific lane within a multi-lane road ), navigate alongside other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and travel from on road to another road at appropriate intersections or interchanges. ”; and [0200]: “ For example, rather than including detailed representations of a road, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with a road segment, the disclosed embodiments of the sparse map may require relatively little storage space and relatively little bandwidth when portions of the sparse map are transferred to a vehicle ) but may still adequately provide for autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, discussed in further detail below, may be achieved in some embodiments by storing representations of road-related elements that require small amounts of data but still enable autonomous navigation. ”; [0201]: “ Thus, rather than storing (or having to transfer) details regarding the physical nature of the road to enable navigation along the road, using the disclosed sparse maps , a vehicle may be navigated along a particular road segment without, in some cases, having to interpret physical aspects of the road , but rather , by aligning its path of travel with a trajectory (e. g., a polynomial spline) along the particular road segment. In this way, the vehicle may be navigated based mainly upon the stored trajectory (e. g., a polynomial spline) that may require much less storage space than an approach involving storage In of roadway images, road parameters, road layout, etc. ”); and based on the road geometry model and a trajectory of the obscured road segment represented in the map, generate an output for causing one or more navigational actions to be taken by the host vehicle relative to the obscured road segment (see, Paragraphs [0197]-[0198]; and [0199]: “ an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may distribute a sparse map for generating a road navigation model for an autonomous vehicle and may navigate an autonomous vehicle along a road segment using a sparse map and/or a generated road navigation model. Sparse maps consistent with the present disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that autonomous vehicles may traverse as they move along associated road segments .”; and [0246]: “ As shown in FIG . 11C , sparse map 800 may include a local map 1140 including a road model for assisting with autonomous navigation of vehicles within geographic region 1111. For example, local map 1140 may include target trajectories for one or more lanes associated with road segments 1120 and/or 1130 within geographic region 1111. For example, local map 1140 may include target trajectories 1141 and/or 1142 that an autonomous vehicle may access or rely upon when traversing lanes 1122. Similarly, local map 1140 may include target trajectories 1143 and/or 1144 that an autonomous vehicle may access or rely upon when traversing lanes 1124. Further, local map 1140 may include target trajectories 1145 and / or 1146 that an autonomous vehicle may access or rely upon when traversing road segment 1130 .”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the autonomous vehicle navigation and a sparse map for autonomous vehicle navigation of Fridman to provide, with a reasonable expectation of success, wherein the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon. Autonomous vehicles may need to take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination (see, Fridman Paragraph [0003]). As to [claim 65], Averilla in view of Fridman teaches the system of claim 64. Averilla discloses wherein local horizon corresponds to a local maxima in elevation of the road segment (see at least Paragraphs [0172] and [0179]). As to [claim 66] , Averilla in view of Fridman teaches the system of claim 64 . Fridman teaches wherein the output includes an indicator of distance from a current location of the host vehicle to the location of the local horizon (see, Figure 35, “DETERMINE, BASED ON THE AT LEAST ONE IMAGE, A DISTANCE FROM THE VEHICLE TO THE AT LEAST ONE LANE MARKING 3520 ”; and Paragraphs [0217]: “ Sparse map 800 may also include data relating to a plurality of predetermined landmarks 820 associated with particular road segments, local maps, etc. As discussed in greater detail below, these landmarks may be used in navigation of the autonomous vehicle. For example, in some embodiments, the landmarks may be used to determine a current position of the vehicle relative to a stored target trajectory. With this position information, the autonomous vehicle may be able to adjust a heading direction to match a direction of the target trajectory at the determined location. ”; and [0220]: “ Moreover, in some embodiments, lane markings may be used for localization of the vehicle during landmark spacings. By using lane markings during landmark spacings, the accumulation of during navigation by dead reckoning may be minimized. In particular, such localization is discussed below with respect to FIG . 35 .”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the autonomous vehicle navigation and a sparse map for autonomous vehicle navigation of Fridman to provide, with a reasonable expectation of success, wherein the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon. Autonomous vehicles may need to take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination (see, Fridman Paragraph [0003]). As to [claim 67] , Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the output includes an indicator of at least one characteristic of the segment of road leading to the detected local horizon (see at least Paragraph [0125]). As to [claim 68] , Averilla in view of Fridman teaches the system of claim 67 . Averilla discloses wherein the indicator of at least one characteristic of the segment of road includes an average incline slope of at least a portion of the segment of road leading to the detected local horizon (see at least Paragraph [0143]). As to [claim 69] , Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wh erein the one or more navigational actions to be taken by the host vehicle include a change in speed of the host vehicle ( see at least Paragraph [0100]). As to [claim 70] , Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the one or more navigational actions to be taken by the host vehicle include a change in throttle level associated with the host vehicle (see at least Paragraph [0090] and [0110]). As to [claim 71], Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the one or more navigational actions to be taken by the host vehicle include a change in braking level associated with the host vehicle (see at least Paragraph [0090] and [0110]). As to [claim 72] , Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the one or more navigational actions to be taken by the host vehicle include a change of gears (see at least Paragraph [0061]). As to [claim 74] , Averilla in view of Fridman teaches the system of claim 73 . Averilla discloses wherein the map stores a target trajectory for the host vehicle, and wherein the target trajectory is represented in the map as a three-dimensional spline (see at least Paragraph [0026]; and [0034] and [0068]). As to [claim 75] , Averilla in view of Fridman teaches the system of claim 73 . Averilla discloses wherein the map stores three-dimensional spline representations of one or more of a lane marking, a lane boundary, or a road edge associated with the obscured road segment (see at least Paragraph [0191]). As to [claim 77] , Averilla in view of Fridman teaches the system of claim 73 . Averilla discloses wherein the output is dependent upon a determined elevation characteristic associated with the obscured road segment (see at least Paragraph [0042]). As to [claim 78] , Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the road geometry model is a three-dimensional road geometry model (see at least Paragraph [0042]). As to [claim 79] , Averilla in view of Fridman teaches the system of claim 64. Averilla discloses wherein the road geometry model includes a three-dimensional representation of a road surface associated with the segment of road (see at least Paragraph [0139]). As to [claim 80] , Averilla in view of Fridman teaches the system of claim 64 . Stephen discloses wherein the road geometry model includes a three-dimensional representation of one or more lane markings associated with the segment of road (see at least Paragraph [0053]). As to [claim 81] . Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the road geometry model includes a three-dimensional representation of one or more road edges associated with the segment of road (see at least Paragraph [0105]). As to [claim 82] , Averilla in view of Fridman teaches the system of claim 64 . Averilla discloses wherein the analysis of the plurality of images used to generate the road geometry model includes a structure in motion analysis (see at least Paragraph [0137]). Regarding [claim 83] , recites analogous limitations that are present in claim 64, therefore claim 84 would be rejected for the same/similar premise above. Regarding [claim 84] , recites analogous limitations that are present in claim 64, therefore claim 84 would be rejected for the same/similar premise above. As to [claim 91], Averilla in view of Fridman teaches the non-transitory computer readable medium of claim 84. Fridman teaches wherein the map stores a target trajectory for the host vehicle, and wherein the target trajectory is represented in the map as a three-dimensional spline (see, Paragraph [0054]: “ FIG . 13 illustrates an example autonomous vehicle road navigation model represented by a plurality of three dimensional splines ”; and [0201]: “ a vehicle may be navigated along a particular road segment without, in some cases, having to interpret physical aspects of the road, but rather, by aligning its path of travel with a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, the vehicle may be navigated based mainly upon the stored trajectory (e.g., a polynomial spline) that may require much less storage space than an approach involving storage In of roadway images, road parameters, road layout, etc. ”; and [0215]: “ As noted, sparse map 800 may include representations of a plurality of target trajectories 810 for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as three-dimensional splines. The target trajectories stored in sparse map 800 may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along a particular road segment , for example , as discussed with respect to FIG. 29. ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the autonomous vehicle navigation and a sparse map for autonomous vehicle navigation of Fridman to provide, with a reasonable expectation of success, wherein the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon. Autonomous vehicles may need to take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination (see, Fridman Paragraph [0003]) . 07-21-aia AIA Claim (s) 76 and 92 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averilla in view of Fridman, and in view of Jung et al. (Pub. No.: US 2018/0131924; previous recorded), hereinafter, referred to as “Jung” . As to [claim 76] , Averilla in view of Fridman teaches the system of claim 64. As Averilla disclose an autonomous vehicle that is capable of navigating on roadways capturing the roadway features elevation of the drivable area and automatically annotating the environmental features in a map during navigation of a vehicle and Fridman disclose a system for generating a sparse map for autonomous vehicle navigation along a road segment may comprise at least one processing device. The at least one processing device may be configured to receive a plurality of images acquired as one or more vehicles traverse the road segment; identify, based on the plurality of images , at least one line representation of a road surface feature extending along the road segment; and identify, based on the plurality of images, a plurality of landmarks associated with the road segment (see, Fridman Paragraph [0011]). In combination, Jung additionally teaches wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: determine, based on the trajectory of the obscured road segment represented in the map, a length of the obscured road segment; and wherein the output is dependent upon the determined length of the obscured road segment (see, Paragraphs [0045]; and [0046]-[0048] and [0092]: “ The information acquirer 910 acquires a depth value of a front of the vehicle during the driving. The information acquirer 910 calculates the depth value of the front of the vehicle based on various methods, for example, a stereo matching method and a radar or lidar-based method and acquires a depth map based on the depth value. Also, the information acquirer 910 acquires location information of the vehicle during the driving .”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for automatic annotation of environmental features in a map during navigation of a vehicle of Averilla to provide, with a reasonable expectation of success, wherein the road geometry model includes a three-dimensional representation of a road surface associated with the segment of road, as taught by Jung, to provide accurate route information to a user. (see, Jung Paragraph [0041]). As to [claim 92] , Averilla in view of Fridman teaches the non-transitory computer readable medium of claim 84. Jung teaches wherein the output is dependent upon a determined length of the obscured road segment (see, Paragraphs [0045]; and [0046]-[0048] and [0092]: “ The information acquirer 910 acquires a depth value of a front of the vehicle during the driving. The information acquirer 910 calculates the depth value of the front of the vehicle based on various methods, for example, a stereo matching method and a radar or lidar-based method and acquires a depth map based on the depth value. Also, the information acquirer 910 acquires location information of the vehicle during the driving .”) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for automatic annotation of environmental features in a map during navigation of a vehicle of Averilla to provide, with a reasonable expectation of success, wherein the road geometry model includes a three-dimensional representation of a road surface associated with the segment of road, as taught by Jung, to provide accurate route information to a user. (see, Jung Paragraph [0041]) . 07-21-aia AIA Claim (s) 85-89 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averilla in view of Fridman, and in view of Englard et al. (Pub. No.: US 2019/0180502; previous recorded), hereinafter, referred to as “Englard” . As to [claim 85] , Averilla in view of Fridman teaches the system of claim 64. Neither Averilla nor Fridman teaches wherein detecting the presence of the local horizon includes identifying a change in pitch in the segment leading to the local horizon. However, Englard teaches wherein detecting the presence of the local horizon includes identifying a change in pitch in the segment leading to the local horizon (see, Paragraph [0268]: “ block 1046 includes identifying, based on the signals generated at block 1044, an area of road in front of the vehicle, and/or the horizon (or a “local” horizon) in front of the vehicle, for example, and identifying the area(s) of interest based on a position of that area of road and/or the horizon ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for automatic annotation of environmental features in a map during navigation of a vehicle of Averilla in view of Jung to provide, with a reasonable expectation of success, One would be motivated to make this modification in order to improve the safety/performance of an autonomous vehicle , to generate alerts for a human driver , or simply to collect data relating to a particular driving trip (e.g., to record how many other vehicles or pedestrians were encountered during the trip, etc.). As to [claim 86] ., Averilla in view of Fridman teaches the system of claim 64. Englard further teaches wherein detecting the presence of the local horizon includes determining a difference in depth between: a first pixel in at least one of the plurality of images representing a portion of the segment of road before the local horizon; and a second pixel in the at least one of the plurality of images representing a portion of the segment of road after the local horizon (see, Paragraph [0085]: “ By scanning the lidar system 200 across a field of regard, the system can be used to map the distance to a number of points within the field of regard. Each of these depth-mapped points may be referred to as a pixel or a voxel. A collection of pixels captured in succession (which may be referred to as a depth map, a point cloud, or a point cloud frame) may be rendered as an image or may be analyzed to identify or detect objects or to determine a shape or distance of objects within the field of regard. For example, a depth map may cover a field of regard that extends 60° horizontally and 15° vertically, and the depth map may include a frame of 100-2000 pixels in the horizontal direction by 4-400 pixels in the vertical direction ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for automatic annotation of environmental features in a map during navigation of a vehicle of Averilla in view of Fridman to provide, with a reasonable expectation of success, One would be motivated to make this modification in order to improve the safety/performance of an autonomous vehicle , to generate alerts for a human driver , or simply to collect data relating to a particular driving trip (e.g., to record how many other vehicles or pedestrians were encountered during the trip, etc.). As to [claim 87] , Averilla in view of Fridman teaches the system of claim 64. Englard further teaches wherein detecting the presence of the local horizon includes detecting an edge associated with the local horizon in at least one of the plurality of images (see, Paragraph [0132]: “ sensor direction 704 may represent the center of the field of regard of a sensor (e.g., lidar device, camera, etc.), or the center of a bottom edge of the field of regard, etc. Alternatively, the sensor direction 704 may represent an area of highest focus within the field of regard (e.g., a densest concentration of horizontal scan lines for a lidar or radar device). ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for automatic annotation of environmental features in a map during navigation of a vehicle of Averilla to provide, with a reasonable expectation of success, One would be motivated to make this modification in order to improve the safety/performance of an autonomous vehicle , to generate alerts for a human driver , or simply to collect data relating to a particular driving trip (e.g., to record how many other vehicles or pedestrians were encountered during the trip, etc.). As to [claim 88] . Averilla in view of Fridman teaches system of claim 64. Englard further teaches wherein the trajectory of the obscured road segment is identified in the map based on an indicator of distance from a current location of the host vehicle to a location associated with the detected local horizon (see, Paragraph [0134]: “ the vehicle's downward trajectory, combined with the approaching hill, causes the sensing distance to be greatly limited. In scenarios 700C and 700D, the vehicle's upward trajectory results in a long sensing distance (e.g., equal to the theoretical range), but with the sensor direction 704 aiming into the sky where few or no objects of concern are likely to be located. Moreover, and particularly in scenario 700D, this may result in terrestrial objects that are relatively near to the vehicle 702 (e.g., the vehicle 706) being outside of the sensor field of regard, or only partially captured in the field of regard ”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for automatic annotation of environmental features in a map during navigation of a vehicle of Averilla to provide, with a reasonable expectation of success. One would be motivated to make this modification in order to improve the safety/performance of an autonomous vehicle , to generate alerts for a human driver , or simply to collect data relating to a particular driving trip (e.g., to record how many other vehicles or pedestrians were encountered during the trip, etc.). As to [claim 89] , recites analogous limitations that are present in claim 86, therefore claim 89 would be rejected for the same/similar premise above. As to [claim 90] , recites analogous limitations that are present in claim 85, therefore claim 90 would be rejected for the same/similar premise above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to BAKARI UNDERWOOD whose telephone number is (571)272-8462. The examiner can normally be reached M - F 8:00 TO 4:30. 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, Abby Flynn can be reached on (571) 272-9855 . 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. /B.U./Examiner, Art Unit 3663 /JAMES M MCPHERSON/Examiner, Art Unit 3663 Application/Control Number: 18/151,945 Page 2 Art Unit: 3663 Application/Control Number: 18/151,945 Page 3 Art Unit: 3663 Application/Control Number: 18/151,945 Page 4 Art Unit: 3663 Application/Control Number: 18/151,945 Page 5 Art Unit: 3663 Application/Control Number: 18/151,945 Page 6 Art Unit: 3663 Application/Control Number: 18/151,945 Page 7 Art Unit: 3663 Application/Control Number: 18/151,945 Page 8 Art Unit: 3663 Application/Control Number: 18/151,945 Page 9 Art Unit: 3663 Application/Control Number: 18/151,945 Page 10 Art Unit: 3663 Application/Control Number: 18/151,945 Page 11 Art Unit: 3663 Application/Control Number: 18/151,945 Page 12 Art Unit: 3663 Application/Control Number: 18/151,945 Page 13 Art Unit: 3663 Application/Control Number: 18/151,945 Page 14 Art Unit: 3663 Application/Control Number: 18/151,945 Page 15 Art Unit: 3663 Application/Control Number: 18/151,945 Page 16 Art Unit: 3663 Application/Control Number: 18/151,945 Page 17 Art Unit: 3663 Application/Control Number: 18/151,945 Page 18 Art Unit: 3663 Application/Control Number: 18/151,945 Page 19 Art Unit: 3663 Application/Control Number: 18/151,945 Page 20 Art Unit: 3663
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