CTFR 18/828,334 CTFR 100876 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 The following claims have been rejected or allowed for the following reasons: Claim(s) 1-20 is rejected under 35 USC § 103 Priority 02-27 AIA Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2024-0001034 , filed on 1/3/24 . 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. 07-23-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) 1-4, 6- 14, 16-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over as appl ied to Fedorov (US 20220324489 A1), in further view of Giovanardi (US 20220324421 A1); in further view of Ishii (JP 6738377 B2). In further view of Li (US 20130338921 A1); Regard ing claim 1 Fedorov teaches An apparatus comprising: a sensor; and a processor configured to: obtain, via the sensor, at least one point representing an external environment of a vehicle; (Fedorov [0069] reads “In the non-limiting embodiments of the present technology, the electronic device 210 comprises or has access to a sensor system 230. According to these embodiments, the sensor system 230 may comprise a plurality of sensors allowing for various implementations of the present technology. Examples of the plurality of sensors include but are not limited to: cameras, LIDAR sensors, and RADAR sensors, etc. The sensor system 230 is operatively coupled to the processor 110 for transmitting the so-captured information to the processor 110 for processing thereof, as will be described in greater detail herein below.”); determine, among the at least one point, a plurality of path points representing positions on a ground, wherein the plurality of path points correspond to a path along which the vehicle is expected to travel; (Fedorov [0172] reads “In one example, if the SDC 220 is intended to be travelling along the reference path 530, the trajectory calculation module 308 may continuously observe the potential future location points along with their future instances of time and the associations potential future location points with the plurality of road lanes (e.g., the plurality of road lanes 502, 504, 506, 508) on the section of the road map 500.”); classify each path point of the plurality of path points into at least one group of a plurality of groups, based on a distance between the vehicle and a position, on the ground, of each path point of the plurality of path points; (Fedorov [0128] reads “In certain non-limiting embodiments, in order to predict a trajectory (e.g., the predicted trajectory 526), the trajectory prediction module 304 may determine a distance horizon (or a “prediction horizon”). For example, the distance horizon may be 200 m long—which means that the first 200 m from the object (e.g., the object 512) may be taken into account to predict the trajectory (e.g., the predicted trajectory 526). Also, the distance horizon may include a “sub-step”. For example, the distance horizon may further be indicative of a sub-set of 0.25 m—which means that the first 200 m of the reference path may be split into smaller sections via potential future location points separated by a distance of 0.25 m. In some non-limiting embodiments of the present technology, the prediction horizon can be implemented as a “lane path” horizon.”); and output control, based on the determined road profile, a signal associated with autonomous driving control of the vehicle. (Fedorov [0104] reads “To this end, in various non-limiting embodiments of the present technologies, the plurality of trajectories associated with the objects may be predicted without consideration of the road lanes on the road. In doing so, the SDC 220 may have some additional degree of freedom for maneuver and may allow altering a current trajectory of the SDC 220 and/or to control operation of the SDC 220 such that in the above situations, the risk of collisions with objects is reduced.”); Fedorov does not teach determine a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative corresponding to the representative point, wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, a road profile comprising at least one of elevation information of the ground or contour information of the ground; Giovanardi in analogous art, teaches a road profile comprising at least one of elevation information of the ground or contour information of the ground; (Giovanardi [0009] reads “In some implementations, the road profile information comprises at least one of road slope information, road roughness information, road frequency content, road friction information, road curvature, or road grip information.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov with that of Giovanardi to include capabilities for viewing an monitoring the road ahead. These improved sensing capabilities that would allow the vehicle to better adapt to changes in the road or terrain that it may face. (Giovanardi [0003] reads “Advanced vehicle features such as, for example, advanced driver assistance systems, active suspension systems, and/or autonomous or semi-autonomous driving may rely on highly accurate localization of a vehicle. Localization systems based on, for example, global navigation satellite systems (GNSS), may not provide sufficient accuracy or resolution for such features.”); F edorov/Giovanardi does not teach determine a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point, wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups. Ishii in analogous art, teaches determine a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point; (Ishii page 3 paragraph 5 reads “Further preferably, the curvature information calculation unit divides the predetermined route from the starting point at a second predetermined interval and averages the curvature radii of the measurement points belonging to the same section to calculate the curvature information of the same section. In this case, by averaging the curvature radii for each section, it is possible to obtain highly reliable curvature information for each section.” And page 6 paragraph 10 reads “In this way, the control unit 36a extracts a plurality of different measurement point groups from the plurality of measurement points, and calculates the curvature radius as the curvature information for each of the extracted plurality of measurement point groups.”); determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, (Ishii page 6 paragraph 12 reads “The curvature information is calculated for each of the plurality of extracted measurement point groups. Therefore, the curvature information of the predetermined route P can be continuously obtained, and the shape of the predetermined route P can be easily grasped.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi with that of Ishii to provide a vehicle system that can map and interpret a plurality of path points ahead of a vehicle. This system would then allow the vehicle to better monitor the road ahead. (Ishii page 3 paragraph 1 reads “Therefore, a main object of the present invention is to provide a curvature information calculation device capable of calculating curvature information of a predetermined route regardless of the presence/absence of a guide line drawn on a road, and an autonomous vehicle including the curvature information calculation device.”); Fedorov/Giovanardi/Ishii does not teach wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; Li in analogous art, teaches wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; (Li [0012] reads “The elevation data provided for the map information is computed based on mesh elevation data sets. In the present specification, the mesh elevation data sets indicate elevations of points on the map information, which points correspond to grid intersection points of a predetermined-size mesh. In other words, elevation data at an arbitrary point on the map is determined based on linear interpolation using the above mesh elevation data sets for four points surrounding the arbitrary point.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi/Ishii with that of Li to include a method that would allow the system to better interpret elevation and road data coming from its sensor. This would allow the system to make more accurate autonomous driving control commands. (Li [0004 – 0005] reads “When the above erroneous elevation is used, for example, the road gradient within the tunnel, which is computed by using the elevation, may generate such error. Thus, in order to highly accurately estimate the road elevation, a vehicular road elevation estimating device is proposed (Patent Document 1). The vehicular road elevation estimating device computes a reliability of a surface elevation value that is computed from surface elevation data. Then, the vehicular road elevation estimating device executes a filtering process for weighing the data with higher reliability to estimate the road elevation of a value closer to an actual road elevation.”); Regarding claim 2 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the processor is configured to determine the plurality of path points by: determining a type of an object corresponding to each of the at least one point representing the external environment of the vehicle; (Giovanardi [0075] reads “The selection of the desired angle may be guided at least in part by looking at the projected trajectory of the vehicle, or by using a predicted path based on map data, or by sensors that detect road path changes, for example cameras or lidar systems. The selection may also be at least partially guided by sensors that indirectly or directly measure the position of the vehicle with respect to the road.”); and determining the plurality of path points based on the type of the object and a position of the object. (Giovanardi [0188] reads “As described previously, an accurate estimate of a vehicle's current position may be made, for example using terrain-matching of road features, road profile information, and/or events from the high-definition map. In some implementations, an accurate estimate of a vehicle's current position may be mad using feature matching for landmarks in the road profile or the environment, and/or using high precision global navigation system signals in addition to or in place of terrain-based localization.”); Regarding claim 3 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 2, wherein the at least one point is included in input data of an artificial neural network (ANN), (Fedorov [0122] reads “In order to do so, the trajectory prediction module 304 may executed a Machine Learning Algorithm (MLA), which has been trained to predict the trajectories of various objects based on their current state, their kinematic characteristics, the road information, and the like.”); and wherein the processor is configured to determine the type of the object by: determining the type of the object based on output data of the ANN. (Giovanardi [0155] reads “First, a physical model may be used, the physical model being based on road event information contained in road data in a database that may be locally stored on the vehicle or may be retrieved from the cloud at appropriate intervals. The road event information may include an event type (e.g., pothole, speed bump, frost heave, etc.), an event size (e.g., a large event, a medium event, a small event, etc.), an event length, (e.g., a length of a pothole, a length of a speed bump), an event height (e.g., a height of a speed bump, a depth of a pothole, etc.), etc.”); Regarding claim 4 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the processor is further configured to determine a degree of confidence of the representative point for each group of the plurality of groups, (Giovanardi [0206] reads “The terrain-based autonomous driving trajectory planning system may calculate an error between a trajectory indicated by the vision-based system and a trajectory determined based on information contained in the high-definition map. This error may be used in multiple ways. For example, in situations where there is high confidence in the road information contained in the high-definition map, due to , for example, the existence of data from many previous drives, or low confidence in the visual data, for example due to weather conditions, sensor obscurement, etc., the trajectory determined based on the road information data in the high-definition map may be used as a replacement, thus applying all of the calculated error as a correction to the original command. If, on the other hand, the confidence in the high-definition map data is low or there are no indications of problems with the vision data, a more cautious approach may be warranted where either only part of the error or none of the error is applied as a correction signal.”); based on at least one of: a type of an object corresponding to classified path points included in a group corresponding to the representative point, a quantity of the classified path points included in the group corresponding to the representative point, or an elevation of the classified path points included in the group corresponding to the representative point. (Giovanardi [0216] reads “A calculation may be made as to the current angle of the vehicle with respect to the upcoming road by projecting the slope of the road under the vehicle (as provided by a road preview elevation map and knowing the precise location of the vehicle or as provided by a sensor installed on the vehicle) forward to calculate its intercept with the roadway ahead of the vehicle.”); Regarding claim 6 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the processor is configured to classify each point of the plurality of path points by: classifying, into a first group, a first path point of the plurality of path points based on a first distance from a first position, on the ground, corresponding to the first path point to the vehicle satisfying a first distance range; and classifying, into a second group, a second path point of the plurality of path points based on a second distance from a second position, on the ground, corresponding to the second path point to the vehicle satisfying a second distance range that is different from the first distance range. (Giovanardi [0120] reads “The road segments of predetermined equal lengths or unequal lengths may be referred to as “slices”. In certain embodiments, consecutive road segments may be arranged in a contiguous fashion such that the end point of one road segment approximately coincides with the starting point of a subsequent road segment. In some embodiments, the consecutive road segments may be non-overlapping, such that an end point of one road segment coincides with a starting point of a subsequent road segment. Alternatively, in some embodiments, road segments may overlap, such that the start point of a subsequent road segment may be located within the boundaries of a previous road segment. Road segments may be, for example, any appropriate length, including, but not limited to, ranges between any combination of the following lengths: 20 meters, 40 meters, 50 meters, 60 meters, 80 meters, 100 meters, 120 meters, 200 meters or greater. In some embodiments, a road segment may have a length between 20 and 200 meters, 20 and 120 meters, 40 and 80 meters, 50 and 200 meters, and/or any other appropriate range of lengths.“); Regarding claim 7 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the processor is further configured to: determine, based on a steering angle of the vehicle, an expected trajectory of two front wheels with respect to a travel direction; and determine the path based on the expected trajectory. (Giovanardi [0082] reads “In addition, a projected tire path, of at least one tire of the vehicle, may also be shown relative to the road-surface feature. The method may further include adjusting the steering angle of a steering wheel of the vehicle to avoid the road-surface feature. This adjustment may be based on the projected tire path of at least one tire of the vehicle relative to the road-surface feature. In some implementations the feature may be a pothole or a bump.”); Regarding claim 8 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the processor is configured to determine the representative point for each group of the plurality of groups by: determining the representative point based on at least one of: an average longitudinal position of the one or more path points, of the plurality of path points , that are classified into a the group corresponding to the representative point; and or an average lateral position of the one or more path points that are classified into the group corresponding to the representative point, or an average elevation of the one or more path points that are classified into the group corresponding to the representative point. (Ishii page 3 paragraph 3 reads “According to the present invention, the measurement point group including the three measurement points at the first predetermined interval is extracted from the plurality of measurement points stored in the storage unit. Then, based on the position information of the three measurement points included in the extracted measurement point group, the curvature information regarding the radius of curvature of the arc passing through the three measurement points is calculated. Therefore, the curvature information of the predetermined route can be calculated regardless of the presence or absence of the guide line drawn on the road.”); Regarding claim 9 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the path has a specified length. (Giovanardi [0116] reads “Each road segment may have a predetermined length, such that a road is broken into multiple road segments. As a vehicle approaches an end point of a road segment, a road profile of the road segment may be compared with the last portion of a measured road profile with an approximately equivalent length. In this manner, a vehicle may verify its precise position based on terrain once per road segment of a predetermined length, a method which is less computationally and network bandwidth intensive.” I would be understood by one with ordinary skill in the art that the overall path would have a specified length is each of the segments have a specified length.); Regarding claim 10 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, wherein the processor is configured to determine the representative point for each group of the plurality of groups (Ishii page 3 paragraph 5 reads “Further preferably, the curvature information calculation unit divides the predetermined route from the starting point at a second predetermined interval and averages the curvature radii of the measurement points belonging to the same section to calculate the curvature information of the same section. In this case, by averaging the curvature radii for each section, it is possible to obtain highly reliable curvature information for each section.” And page 6 paragraph 10 reads “In this way, the control unit 36a extracts a plurality of different measurement point groups from the plurality of measurement points, and calculates the curvature radius as the curvature information for each of the extracted plurality of measurement point groups.”); by determining the representative point based on a quantity of classified path points included in the group corresponding to the representative point being greater than a threshold quantity. (Giovanardi [0137] reads “In some embodiments, rather than considering each cluster to correspond to a track, only clusters having a number of road profiles that exceed a threshold number of road profiles are considered to correspond to tracks.”); Regarding claim 11 Fedorov teaches A method comprising: obtaining, via a sensor, at least one point representing an external environment of a vehicle; (Fedorov [0069] reads “In the non-limiting embodiments of the present technology, the electronic device 210 comprises or has access to a sensor system 230. According to these embodiments, the sensor system 230 may comprise a plurality of sensors allowing for various implementations of the present technology. Examples of the plurality of sensors include but are not limited to: cameras, LIDAR sensors, and RADAR sensors, etc. The sensor system 230 is operatively coupled to the processor 110 for transmitting the so-captured information to the processor 110 for processing thereof, as will be described in greater detail herein below.”); determining, among the at least one point, a plurality of path points representing positions on a ground, wherein the plurality of path points correspond to a path along which the vehicle is expected to travel; (Fedorov [0172] reads “In one example, if the SDC 220 is intended to be travelling along the reference path 530, the trajectory calculation module 308 may continuously observe the potential future location points along with their future instances of time and the associations potential future location points with the plurality of road lanes (e.g., the plurality of road lanes 502, 504, 506, 508) on the section of the road map 500.”); classifying each path point of the plurality of path points into at least one group of a plurality of groups, based on a distance between the vehicle and a position, on the ground, of each path point of the plurality of path points; (Fedorov [0128] reads “In certain non-limiting embodiments, in order to predict a trajectory (e.g., the predicted trajectory 526), the trajectory prediction module 304 may determine a distance horizon (or a “prediction horizon”). For example, the distance horizon may be 200 m long—which means that the first 200 m from the object (e.g., the object 512) may be taken into account to predict the trajectory (e.g., the predicted trajectory 526). Also, the distance horizon may include a “sub-step”. For example, the distance horizon may further be indicative of a sub-set of 0.25 m—which means that the first 200 m of the reference path may be split into smaller sections via potential future location points separated by a distance of 0.25 m. In some non-limiting embodiments of the present technology, the prediction horizon can be implemented as a “lane path” horizon.”); and outputting controlling, based on the determined road profile, a signal associated with autonomous driving control of the vehicle (Fedorov [0104] reads “To this end, in various non-limiting embodiments of the present technologies, the plurality of trajectories associated with the objects may be predicted without consideration of the road lanes on the road. In doing so, the SDC 220 may have some additional degree of freedom for maneuver and may allow altering a current trajectory of the SDC 220 and/or to control operation of the SDC 220 such that in the above situations, the risk of collisions with objects is reduced.”); Fedorov does not teach determining a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point, wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; determining, based on an interpolation between representative points of two adjacent groups of the plurality of groups, and a road profile comprising at least one of elevation information of the ground or contour information of the ground; Giovanardi in analogous art, teaches a road profile comprising at least one of elevation information of the ground or contour information of the ground; (Giovanardi [0009] reads “In some implementations, the road profile information comprises at least one of road slope information, road roughness information, road frequency content, road friction information, road curvature, or road grip information.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov with that of Giovanardi to include capabilities for viewing an monitoring the road ahead. These improved sensing capabilities that would allow the vehicle to better adapt to changes in the road or terrain that it may face. (Giovanardi [0003] reads “Advanced vehicle features such as, for example, advanced driver assistance systems, active suspension systems, and/or autonomous or semi-autonomous driving may rely on highly accurate localization of a vehicle. Localization systems based on, for example, global navigation satellite systems (GNSS), may not provide sufficient accuracy or resolution for such features.”); Fedorov/Giovanardi does not teach determining a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point, wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point ; determining, based on an interpolation between representative points of two adjacent groups of the plurality of groups, Ishii in analogous art, teaches determining a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point ; (Ishii page 3 paragraph 5 reads “Further preferably, the curvature information calculation unit divides the predetermined route from the starting point at a second predetermined interval and averages the curvature radii of the measurement points belonging to the same section to calculate the curvature information of the same section. In this case, by averaging the curvature radii for each section, it is possible to obtain highly reliable curvature information for each section.” And page 6 paragraph 10 reads “In this way, the control unit 36a extracts a plurality of different measurement point groups from the plurality of measurement points, and calculates the curvature radius as the curvature information for each of the extracted plurality of measurement point groups.”); determining, based on an interpolation between representative points of two adjacent groups of the plurality of groups, (Ishii page 6 paragraph 12 reads “The curvature information is calculated for each of the plurality of extracted measurement point groups. Therefore, the curvature information of the predetermined route P can be continuously obtained, and the shape of the predetermined route P can be easily grasped.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi with that of Ishii to provide a vehicle system that can map and interpret a plurality of path points ahead of a vehicle. This system would then allow the vehicle to better monitor the road ahead. (Ishii page 3 paragraph 1 reads “Therefore, a main object of the present invention is to provide a curvature information calculation device capable of calculating curvature information of a predetermined route regardless of the presence/absence of a guide line drawn on a road, and an autonomous vehicle including the curvature information calculation device.”); Fedorov/Giovanardi/Ishii does not teach wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; Li in analogous art, teaches wherein the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point; (Li [0012] reads “The elevation data provided for the map information is computed based on mesh elevation data sets. In the present specification, the mesh elevation data sets indicate elevations of points on the map information, which points correspond to grid intersection points of a predetermined-size mesh. In other words, elevation data at an arbitrary point on the map is determined based on linear interpolation using the above mesh elevation data sets for four points surrounding the arbitrary point.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi/Ishii with that of Li to include a method that would allow the system to better interpret elevation and road data coming from its sensor. This would allow the system to make more accurate autonomous driving control commands. (Li [0004 – 0005] reads “When the above erroneous elevation is used, for example, the road gradient within the tunnel, which is computed by using the elevation, may generate such error. Thus, in order to highly accurately estimate the road elevation, a vehicular road elevation estimating device is proposed (Patent Document 1). The vehicular road elevation estimating device computes a reliability of a surface elevation value that is computed from surface elevation data. Then, the vehicular road elevation estimating device executes a filtering process for weighing the data with higher reliability to estimate the road elevation of a value closer to an actual road elevation.”); Regarding claim 12 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11, wherein the determining of the plurality of path points comprise: determining a type of an object corresponding to each of the at least one point representing the external environment of the vehicle; (Giovanardi [0075] reads “The selection of the desired angle may be guided at least in part by looking at the projected trajectory of the vehicle, or by using a predicted path based on map data, or by sensors that detect road path changes, for example cameras or lidar systems. The selection may also be at least partially guided by sensors that indirectly or directly measure the position of the vehicle with respect to the road.”); and determining the plurality of path points based on the type of the object and a position of the object. (Giovanardi [0188] reads “As described previously, an accurate estimate of a vehicle's current position may be made, for example using terrain-matching of road features, road profile information, and/or events from the high-definition map. In some implementations, an accurate estimate of a vehicle's current position may be mad using feature matching for landmarks in the road profile or the environment, and/or using high precision global navigation system signals in addition to or in place of terrain-based localization.”); Regarding claim 13 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 12, wherein the at least one point is included in input data of an artificial neural network (ANN), (Fedorov [0122] reads “In order to do so, the trajectory prediction module 304 may executed a Machine Learning Algorithm (MLA), which has been trained to predict the trajectories of various objects based on their current state, their kinematic characteristics, the road information, and the like.”); and wherein the determining of the type of the object comprises: determining the type of the object based on output data of the ANN. (Giovanardi [0155] reads “First, a physical model may be used, the physical model being based on road event information contained in road data in a database that may be locally stored on the vehicle or may be retrieved from the cloud at appropriate intervals. The road event information may include an event type (e.g., pothole, speed bump, frost heave, etc.), an event size (e.g., a large event, a medium event, a small event, etc.), an event length, (e.g., a length of a pothole, a length of a speed bump), an event height (e.g., a height of a speed bump, a depth of a pothole, etc.), etc.”); Regarding claim 14 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11, further comprising: determining a degree of confidence of the representative point for each group of the plurality of groups, (Giovanardi [0206] reads “The terrain-based autonomous driving trajectory planning system may calculate an error between a trajectory indicated by the vision-based system and a trajectory determined based on information contained in the high-definition map. This error may be used in multiple ways. For example, in situations where there is high confidence in the road information contained in the high-definition map, due to , for example, the existence of data from many previous drives, or low confidence in the visual data, for example due to weather conditions, sensor obscurement, etc., the trajectory determined based on the road information data in the high-definition map may be used as a replacement, thus applying all of the calculated error as a correction to the original command. If, on the other hand, the confidence in the high-definition map data is low or there are no indications of problems with the vision data, a more cautious approach may be warranted where either only part of the error or none of the error is applied as a correction signal.”); based on at least one of: a type of an object corresponding to classified path points included in a group corresponding to the representative point, a quantity of the classified path points included in the group corresponding to the representative point, or an elevation of the classified path points included in the group corresponding to the representative point. (Giovanardi [0216] reads “A calculation may be made as to the current angle of the vehicle with respect to the upcoming road by projecting the slope of the road under the vehicle (as provided by a road preview elevation map and knowing the precise location of the vehicle or as provided by a sensor installed on the vehicle) forward to calculate its intercept with the roadway ahead of the vehicle.”); Regarding claim 16 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11, wherein the classifying of each point of the plurality of path points comprises: classifying, into a first group, a first path point of the plurality of path points based on a first distance from a first position, on the ground, corresponding to the first path point to the vehicle satisfying a first distance range; and classifying, into a second group, a second path point of the plurality of path points based on a second distance from a second position, on the ground, corresponding to the second path point to the vehicle satisfying a second distance range that is different from the first distance range. (Giovanardi [0120] reads “The road segments of predetermined equal lengths or unequal lengths may be referred to as “slices”. In certain embodiments, consecutive road segments may be arranged in a contiguous fashion such that the end point of one road segment approximately coincides with the starting point of a subsequent road segment. In some embodiments, the consecutive road segments may be non-overlapping, such that an end point of one road segment coincides with a starting point of a subsequent road segment. Alternatively, in some embodiments, road segments may overlap, such that the start point of a subsequent road segment may be located within the boundaries of a previous road segment. Road segments may be, for example, any appropriate length, including, but not limited to, ranges between any combination of the following lengths: 20 meters, 40 meters, 50 meters, 60 meters, 80 meters, 100 meters, 120 meters, 200 meters or greater. In some embodiments, a road segment may have a length between 20 and 200 meters, 20 and 120 meters, 40 and 80 meters, 50 and 200 meters, and/or any other appropriate range of lengths.“); Regarding claim 17 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11, further comprising: determining, based on a steering angle of the vehicle, an expected trajectory of two front wheels with respect to a travel direction; and determining the path based on the expected trajectory. (Giovanardi [0082] reads “In addition, a projected tire path, of at least one tire of the vehicle, may also be shown relative to the road-surface feature. The method may further include adjusting the steering angle of a steering wheel of the vehicle to avoid the road-surface feature. This adjustment may be based on the projected tire path of at least one tire of the vehicle relative to the road-surface feature. In some implementations the feature may be a pothole or a bump.”); Regarding claim 18 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11, wherein the determining of the representative point for each group of the plurality of groups comprises: determining the representative point based on at least one of: an average longitudinal position of the one or more path points, of the plurality of path points , that are classified into a the group corresponding to the representative point; and or an average lateral position of the one or more path points that are classified into the group corresponding to the representative point, or an average elevation of the one or more path points that are classified into the group corresponding to the representative point. (Ishii page 3 paragraph 3 reads “According to the present invention, the measurement point group including the three measurement points at the first predetermined interval is extracted from the plurality of measurement points stored in the storage unit. Then, based on the position information of the three measurement points included in the extracted measurement point group, the curvature information regarding the radius of curvature of the arc passing through the three measurement points is calculated. Therefore, the curvature information of the predetermined route can be calculated regardless of the presence or absence of the guide line drawn on the road.”); Regarding claim 20 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11, wherein the determining of the representative point for each group of the plurality of groups comprises: (Ishii page 3 paragraph 5 reads “Further preferably, the curvature information calculation unit divides the predetermined route from the starting point at a second predetermined interval and averages the curvature radii of the measurement points belonging to the same section to calculate the curvature information of the same section. In this case, by averaging the curvature radii for each section, it is possible to obtain highly reliable curvature information for each section.” And page 6 paragraph 10 reads “In this way, the control unit 36a extracts a plurality of different measurement point groups from the plurality of measurement points, and calculates the curvature radius as the curvature information for each of the extracted plurality of measurement point groups.”); determining the representative point based on a quantity of classified path points included in the group corresponding to the representative point being greater than a threshold quantity. (Giovanardi [0137] reads “In some embodiments, rather than considering each cluster to correspond to a track, only clusters having a number of road profiles that exceed a threshold number of road profiles are considered to correspond to tracks.”); Regarding claim 21 Fedorov teaches A vehicle comprising: a sensor; a processor; a driving control circuit; a memory storing at least one instruction configured to: obtain, via the sensor, at least one point representing an external environment of the vehicle; (Fedorov [0069] reads “In the non-limiting embodiments of the present technology, the electronic device 210 comprises or has access to a sensor system 230. According to these embodiments, the sensor system 230 may comprise a plurality of sensors allowing for various implementations of the present technology. Examples of the plurality of sensors include but are not limited to: cameras, LIDAR sensors, and RADAR sensors, etc. The sensor system 230 is operatively coupled to the processor 110 for transmitting the so-captured information to the processor 110 for processing thereof, as will be described in greater detail herein below.”); determine, among the at least one point, a plurality of path points representing positions on a ground, wherein the plurality of path points correspond to a path along which the vehicle is expected to travel; (Fedorov [0172] reads “In one example, if the SDC 220 is intended to be travelling along the reference path 530, the trajectory calculation module 308 may continuously observe the potential future location points along with their future instances of time and the associations potential future location points with the plurality of road lanes (e.g., the plurality of road lanes 502, 504, 506, 508) on the section of the road map 500.”); classify each path point of the plurality of path points into at least one group of a plurality of groups, based on a distance between the vehicle and a position, on the ground, of each path point of the plurality of path points; (Fedorov [0128] reads “In certain non-limiting embodiments, in order to predict a trajectory (e.g., the predicted trajectory 526), the trajectory prediction module 304 may determine a distance horizon (or a “prediction horizon”). For example, the distance horizon may be 200 m long—which means that the first 200 m from the object (e.g., the object 512) may be taken into account to predict the trajectory (e.g., the predicted trajectory 526). Also, the distance horizon may include a “sub-step”. For example, the distance horizon may further be indicative of a sub-set of 0.25 m—which means that the first 200 m of the reference path may be split into smaller sections via potential future location points separated by a distance of 0.25 m. In some non-limiting embodiments of the present technology, the prediction horizon can be implemented as a “lane path” horizon.”); and control, based on the determined road profile, autonomous driving of the vehicle. (Fedorov [0104] reads “To this end, in various non-limiting embodiments of the present technologies, the plurality of trajectories associated with the objects may be predicted without consideration of the road lanes on the road. In doing so, the SDC 220 may have some additional degree of freedom for maneuver and may allow altering a current trajectory of the SDC 220 and/or to control operation of the SDC 220 such that in the above situations, the risk of collisions with objects is reduced.”); Fedorov does not teach determine a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point, wherein the representative point for each group of the plurality of groups is associated with an elevation of one or more path points that are classified into the group corresponding to the representative point; determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, a road profile comprising elevation information of the ground; Giovanardi in analogous art, teaches wherein the representative point for each group of the plurality of groups is associated with an elevation of one or more path points that are classified into the group corresponding to the representative point; (Giovanardi [0009] reads “In some implementations, the road profile information comprises at least one of road slope information, road roughness information, road frequency content, road friction information, road curvature, or road grip information.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov with that of Giovanardi to include capabilities for viewing an monitoring the road ahead. These improved sensing capabilities that would allow the vehicle to better adapt to changes in the road or terrain that it may face. (Giovanardi [0003] reads “Advanced vehicle features such as, for example, advanced driver assistance systems, active suspension systems, and/or autonomous or semi-autonomous driving may rely on highly accurate localization of a vehicle. Localization systems based on, for example, global navigation satellite systems (GNSS), may not provide sufficient accuracy or resolution for such features.”); Fedorov/Giovanardi does not teach determine a representative point for each group of the plurality of groups, based on a position of each classified path point in a group, of the plurality of the groups, corresponding to the representative point, determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, a road profile comprising elevation information of the ground; Ishii in analogous art, teaches (Ishii page 3 paragraph 5 reads “Further preferably, the curvature information calculation unit divides the predetermined route from the starting point at a second predetermined interval and averages the curvature radii of the measurement points belonging to the same section to calculate the curvature information of the same section. In this case, by averaging the curvature radii for each section, it is possible to obtain highly reliable curvature information for each section.” And page 6 paragraph 10 reads “In this way, the control unit 36a extracts a plurality of different measurement point groups from the plurality of measurement points, and calculates the curvature radius as the curvature information for each of the extracted plurality of measurement point groups.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi with that of Ishii to provide a vehicle system that can map and interpret a plurality of path points ahead of a vehicle. This system would then allow the vehicle to better monitor the road ahead. (Ishii page 3 paragraph 1 reads “Therefore, a main object of the present invention is to provide a curvature information calculation device capable of calculating curvature information of a predetermined route regardless of the presence/absence of a guide line drawn on a road, and an autonomous vehicle including the curvature information calculation device.”); Fedorov/Giovanardi/Ishii does not teach determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, a road profile comprising elevation information of the ground; Li in analogous art, teaches determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, a road profile comprising elevation information of the ground; (Li [0012] reads “The elevation data provided for the map information is computed based on mesh elevation data sets. In the present specification, the mesh elevation data sets indicate elevations of points on the map information, which points correspond to grid intersection points of a predetermined-size mesh. In other words, elevation data at an arbitrary point on the map is determined based on linear interpolation using the above mesh elevation data sets for four points surrounding the arbitrary point.”); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi/Ishii with that of Li to include a method that would allow the system to better interpret elevation and road data coming from its sensor. This would allow the system to make more accurate autonomous driving control commands. (Li [0004 – 0005] reads “When the above erroneous elevation is used, for example, the road gradient within the tunnel, which is computed by using the elevation, may generate such error. Thus, in order to highly accurately estimate the road elevation, a vehicular road elevation estimating device is proposed (Patent Document 1). The vehicular road elevation estimating device computes a reliability of a surface elevation value that is computed from surface elevation data. Then, the vehicular road elevation estimating device executes a filtering process for weighing the data with higher reliability to estimate the road elevation of a value closer to an actual road elevation.”); 07-21-aia AIA Claim (s) 5, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Fedorov/Giovanardi/Ishii/Li, in further view of Ross ( US 20210107537 A1 ) . Regarding claim 5 Fedorov/Giovanardi/Ishii/Li teaches The apparatus of claim 1, and wherein a third representative point corresponding to a third group has a greater degree of confidence than a fourth representative point corresponding to a fourth group, (Giovanardi [0206] reads “The terrain-based autonomous driving trajectory planning system may calculate an error between a trajectory indicated by the vision-based system and a trajectory determined based on information contained in the high-definition map. This error may be used in multiple ways. For example, in situations where there is high confidence in the road information contained in the high-definition map, due to , for example, the existence of data from many previous drives, or low confidence in the visual data, for example due to weather conditions, sensor obscurement, etc., the trajectory determined based on the road information data in the high-definition map may be used as a replacement, thus applying all of the calculated error as a correction to the original command. If, on the other hand, the confidence in the high-definition map data is low or there are no indications of problems with the vision data, a more cautious approach may be warranted where either only part of the error or none of the error is applied as a correction signal.”); wherein a maximum elevation value of classified path points in the third group is not greater than an average elevation value of the classified path points included in the third group by at least a specified amount, wherein a maximum value of classified path points in the fourth group is greater than an average elevation of the classified path points included in the fourth group by at least the specified amount. (Giovanardi [0206] reads “The decision of weighting or selecting a source of trajectory planning data, whether visual, terrain-based, or some combination of the two, may be made, for example, by the trajectory planning controller. The trajectory planning controller may be a component of an autonomous or a semi-autonomous driving system of the vehicle. In one implementation, the trajectory planning controller may calculate the error and look for large discrepancies.” It would be appreciated that by one with ordinary skill in the art that these large discrepancies in the path would result in a change in maximum of average values that would differ between road segments.); Fedorov/Giovanardi/Ishii/Li does not teach wherein a first representative point corresponding to a first group, having greater than a specified quantity of classified path points, has a greater degree of confidence than a second representative point corresponding to a second group having less than the specified quantity of classified path points. Ross in analogous art, teaches wherein a first representative point corresponding to a first group, having greater than a specified quantity of classified path points, has a greater degree of confidence than a second representative point corresponding to a second group having less than the specified quantity of classified path points, (Ross [0132] reads “It is noted that as there may be some error between the current (and recently stored) values and those stored in the correlation database, the window of measurement points and the number of compared values may be increased (i.e., the sample of compared values increased) in order to improve the accuracy in terms of the data compared, and to improve the confidence and reliability in any identified match of data.” It would be understood by one with ordinary skill in the art that when comparing groups the group with the increased amount of data would have a higher confidence level.); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi/Ishii/Li with that of Ross to include a method of increasing the amount of confidence that a vehicle has in it understanding of its environment. This would allow for the vehicle to better react to changes in its environment. (Ross [0002] reads “However, current systems in place to accomplish this are limited in their ability and function as they typically involve inadequate technology, technology that is cost-prohibitive, or technology that is subject to being compromised (e.g., radio navigation systems, such as satellite-based global positioning systems and others, being spoofed, delegitimized or simply jammed), thus resulting in the still frequent occurrence of accidents, many of which come at a high cost, not only in terms of expense, but also in terms of human lives lost.”); Regarding claim 15 Fedorov/Giovanardi/Ishii/Li teaches The method of claim 11 and wherein a third representative point corresponding to a third group has a greater degree of confidence than a fourth representative point corresponding to a fourth group, (Giovanardi [0206] reads “The terrain-based autonomous driving trajectory planning system may calculate an error between a trajectory indicated by the vision-based system and a trajectory determined based on information contained in the high-definition map. This error may be used in multiple ways. For example, in situations where there is high confidence in the road information contained in the high-definition map, due to , for example, the existence of data from many previous drives, or low confidence in the visual data, for example due to weather conditions, sensor obscurement, etc., the trajectory determined based on the road information data in the high-definition map may be used as a replacement, thus applying all of the calculated error as a correction to the original command. If, on the other hand, the confidence in the high-definition map data is low or there are no indications of problems with the vision data, a more cautious approach may be warranted where either only part of the error or none of the error is applied as a correction signal.”); wherein a maximum elevation value of classified path points in the third group is not greater than an average elevation value of the classified path points included in the third group by at least a specified amount, and wherein a maximum value of classified path points in the fourth group is greater than an average elevation of the classified path points included in the fourth group by at least the specified amount. (Giovanardi [0206] reads “The decision of weighting or selecting a source of trajectory planning data, whether visual, terrain-based, or some combination of the two, may be made, for example, by the trajectory planning controller. The trajectory planning controller may be a component of an autonomous or a semi-autonomous driving system of the vehicle. In one implementation, the trajectory planning controller may calculate the error and look for large discrepancies.” It would be appreciated that by one with ordinary skill in the art that these large discrepancies in the path would result in a change in maximum of average values that would differ between road segments.); Fedorov/Giovanardi/Ishii/Li does not teach wherein a first representative point corresponding to a first group, having greater than a specified quantity of classified path points, has a greater degree of confidence than a second representative point corresponding to a second group having less than the specified quantity of classified path points. Ross in analogous art, teaches wherein a first representative point corresponding to a first group, having greater than a specified quantity of classified path points, has a greater degree of confidence than a second representative point corresponding to a second group having less than the specified quantity of classified path points, (Ross [0132] reads “It is noted that as there may be some error between the current (and recently stored) values and those stored in the correlation database, the window of measurement points and the number of compared values may be increased (i.e., the sample of compared values increased) in order to improve the accuracy in terms of the data compared, and to improve the confidence and reliability in any identified match of data.” It would be understood by one with ordinary skill in the art that when comparing groups the group with the increased amount of data would have a higher confidence level.); It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Fedorov/Giovanardi/Ishii/Li with that of Ross to include a method of increasing the amount of confidence that a vehicle has in it understanding of its environment. This would allow for the vehicle to better react to changes in its environment. (Ross [0002] reads “However, current systems in place to accomplish this are limited in their ability and function as they typically involve inadequate technology, technology that is cost-prohibitive, or technology that is subject to being compromised (e.g., radio navigation systems, such as satellite-based global positioning systems and others, being spoofed, delegitimized or simply jammed), thus resulting in the still frequent occurrence of accidents, many of which come at a high cost, not only in terms of expense, but also in terms of human lives lost.”); Response to arguments Applicant argues < In setting forth the rejection of previous claim 8, the Office appears to ignore the claim language of "determining based on at least one of: , or an average elevation of the one or more path points that are classified into the group corresponding to the representative point" because of the "or" language of previous claim 8. See Office Action at 16. However, the rejection is now moot because of the present amendment to claim 1, and the Office should not ignore the language of "the representative point for each group of the plurality of groups is configured to be determined based on an average elevation of one or more path points that are classified into the group corresponding to the representative point," as presently recited in claim 1.> [Page 17 first paragraph]. The examiner respectfully disagrees. The current rejection of record relies upon Li to teach limitations that are directed towards determining and understanding the elevation of road segments ahead of a given vehicle. These limitations include determining the elevation of a given contour and averaging that elevation with values around the given point. (Li [0012] reads “The elevation data provided for the map information is computed based on mesh elevation data sets. In the present specification, the mesh elevation data sets indicate elevations of points on the map information, which points correspond to grid intersection points of a predetermined-size mesh. In other words, elevation data at an arbitrary point on the map is determined based on linear interpolation using the above mesh elevation data sets for four points surrounding the arbitrary point.”); Therefore, the combination teaches the claimed invention. Applicant argues < Second, the alleged combination also fails to disclose or suggest at least "determine, based on an interpolation between representative points of two adjacent groups of the plurality of groups, a road profile comprising at least one of elevation information of the ground or contour information of the ground," as recited in claim 1. The Office relies on Ishii in an attempt to remedy the admitted deficiencies of Fedorov and Giovanardi (see Office Action at 12), however, none of the cited references teaches or suggests at least the claimed interpolation feature (i.e., "based on an interpolation between representative points of two adjacent groups of the plurality of groups") to determine "a road profile comprising at least one of elevation information of the ground or contour information of the ground," as recited in claim 1.> [Page 17 spanning paragraph]. The examiner respectfully disagrees. The current rejection of record relies upon Ishii to teach the interpolation of path points ahead of the given vehicles. Ishii discusses that the information it captures is captured at a given frame rate which would leave the system with discreate locations or timestamps of information. Ishii then discusses how those points are then calculated into a continuous path that the vehicle will follow. Ishii page 5 12 th paragraph reads “When creating the position information, first, while the automatic traveling vehicle 10 travels on the predetermined route P, the imaging unit 34 continuously captures images of the front of the automatic traveling vehicle 10 at a predetermined frame rate. Thereby, the imaging data can be obtained by the imaging unit 34 for each of the plurality of measurement points.” And Ishii page 3 fifth paragraph reads “Preferably, the curvature information calculation unit extracts a plurality of different measurement point groups from the plurality of measurement points and calculates curvature information for each of the extracted plurality of measurement point groups. In this case, the curvature information of the default route can be continuously obtained, and the shape of the default route can be easily grasped.”); Similarly, though not used in the rejection of record, Li teaches the interpolation of data surrounding the current vehicle. (Li [0012] reads “The elevation data provided for the map information is computed based on mesh elevation data sets. In the present specification, the mesh elevation data sets indicate elevations of points on the map information, which points correspond to grid intersection points of a predetermined-size mesh. In other words, elevation data at an arbitrary point on the map is determined based on linear interpolation using the above mesh elevation data sets for four points surrounding the arbitrary point.”); Therefore, the combination teaches the claimed invention. Other references not Cited Throughout examination other references were found that could read onto the prior art. Though these references were not used in this examination they could be used in future examination and could read on the contents of the current disclosure. These references are, Pham (US 20200410254 A1); Wang (US 12145617 B2); Unnikrishnan (US 12158518 B2). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN MARTIN O'MALLEY whose telephone number is (571)272-6228. The examiner can normally be reached Mon - Fri 9 am - 5 pm. 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, Ramon Mercado can be reached at (571) 270 - 5744. 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. /JOHN MARTIN O'MALLEY/Examiner, Art Unit 3658 /Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658 Application/Control Number: 18/828,334 Page 2 Art Unit: 3658 Application/Control Number: 18/828,334 Page 3 Art Unit: 3658 Application/Control Number: 18/828,334 Page 4 Art Unit: 3658 Application/Control Number: 18/828,334 Page 5 Art Unit: 3658 Application/Control Number: 18/828,334 Page 6 Art Unit: 3658 Application/Control Number: 18/828,334 Page 7 Art Unit: 3658 Application/Control Number: 18/828,334 Page 8 Art Unit: 3658 Application/Control Number: 18/828,334 Page 9 Art Unit: 3658 Application/Control Number: 18/828,334 Page 10 Art Unit: 3658 Application/Control Number: 18/828,334 Page 11 Art Unit: 3658 Application/Control Number: 18/828,334 Page 12 Art Unit: 3658 Application/Control Number: 18/828,334 Page 13 Art Unit: 3658 Application/Control Number: 18/828,334 Page 14 Art Unit: 3658 Application/Control Number: 18/828,334 Page 15 Art Unit: 3658 Application/Control Number: 18/828,334 Page 16 Art Unit: 3658 Application/Control Number: 18/828,334 Page 17 Art Unit: 3658 Application/Control Number: 18/828,334 Page 18 Art Unit: 3658 Application/Control Number: 18/828,334 Page 19 Art Unit: 3658 Application/Control Number: 18/828,334 Page 20 Art Unit: 3658 Application/Control Number: 18/828,334 Page 21 Art Unit: 3658 Application/Control Number: 18/828,334 Page 22 Art Unit: 3658 Application/Control Number: 18/828,334 Page 23 Art Unit: 3658 Application/Control Number: 18/828,334 Page 24 Art Unit: 3658 Application/Control Number: 18/828,334 Page 25 Art Unit: 3658 Application/Control Number: 18/828,334 Page 26 Art Unit: 3658 Application/Control Number: 18/828,334 Page 27 Art Unit: 3658 Application/Control Number: 18/828,334 Page 28 Art Unit: 3658 Application/Control Number: 18/828,334 Page 29 Art Unit: 3658 Application/Control Number: 18/828,334 Page 30 Art Unit: 3658 Application/Control Number: 18/828,334 Page 31 Art Unit: 3658 Application/Control Number: 18/828,334 Page 32 Art Unit: 3658 Application/Control Number: 18/828,334 Page 33 Art Unit: 3658
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