Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The following is a final office action in response to the communication filed on 08/03/2026. Claims 4-6 and 9-11 have been amended. Claims 14 and 15 have been added. Claims 4-15 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments and remarks filed on 08/03/2026 have been fully considered.
Applicant’s amendments overcome the 35 U.S.C. 112(a) enablement issues regarding the priority document.
Applicant’s amendments overcome the objections to the specification.
Applicant’s amendments overcome the objections to the claims.
Applicant’s amendments overcome each and every 35 U.S.C. 112(b) rejection of the claims.
Applicant’s arguments provided for the 35 U.S.C. §103 rejections of claims 4-15 have been considered but moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Benefit is given to the priority document PRO 63/263,995 and the effective filing date of 11/12/2021.
Claim Rejections - 35 USC § 103
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.
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.
Claims 4, 8, 9 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bialer (US-20180120429-A1; hereinafter Bialer) in view of Adireddy et al. (US-20190213873-A1; hereinafter Adireddy).
Regarding claim 4, Bialer discloses [Note: what Bialer fails to disclose is strike-through]
A method of tracking objects (see at least Abs; “A processor is coupled to the plurality of receivers in order to determine when a plurality of detected objects correspond to a single object and to track the single object in a dynamic environment.”) in a geographical area having one or more roadways (see at least [0029]; “The transmitting radar signals subsequently contact other vehicles and/or other objects on or alongside the road on which the host vehicle 10 is travelling.”), the method comprising:
receiving a first set of sensor cluster data (see at least [0049]; “The radar systems are first operative to transmit and receive operate at a short observation time 605.”) from a plurality of radar sensors mounted (see at least Abs; “Methods and systems are provided for controlling a radar system of a vehicle. One or more transmitters are configured to transmit radar signals. A plurality of receivers are configured to receive return radar signals after the transmitted radar signals are deflected from an object proximate the vehicle.” Examiner notes that the radars are mounted on the vehicle.), the first set of sensor cluster data comprising reflections from a static object in the geographical area (see at least [0044]; “Typical scenario for automotive radar consists of multiple every strong, large size, echoes form static objects and few much weaker, small size, such as pedestrian, dynamic objects.”);
identifying a position of the static object in the geographical area based on the reflections from the static object in the first set of sensor cluster data (see at least [0049]; “The radar system then accumulates in memory, the location and velocity of the determined detections 610.” See also [0051]; “The method is then operative to estimate if objects are dynamic or static taking into account the vehicle velocity and the angular location of the objects with respect to the vehicle 630.”);
receiving a second set of sensor cluster data from the plurality of radar sensors (see at least [0051]; “The system is then operative to transmit and receive radar pulses over a longer observation time 640.”), the second set of sensor cluster data comprising reflections from the static object and reflections from a moving object in the geographical area (see at least [0051]; “Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.”);
identifying (see at least [0051]; “Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.”);
determining that the position of the static object in the geographical area in the first set of sensor cluster data is the same as the position of the static object in the geographical area in the second set of cluster data (see at least [0051]; “Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.” Thus, the position of the static object derived from the first set of data is refined by analysis of the second set of data);
filtering out the reflections from the static object from the second set of cluster data that are the same as the reflections from the static object from the first set of cluster data (see at least [0052]; “The method is then operative to determine if an object is static or dynamic 650. If an object is determined to be static, the method may either discard the object from tracking, radar echoes from the static object may be cancelled by the processor, and/or the location of the static objects may be saved to a memory 660.”); and
tracking the moving object using the filtered second sensor cluster data (see at least [0052]; “Once the static objects are identified, the remaining dynamic objects are then tracked and more accurately estimated and localized 670 due to the reduced processing requirements for object tracking.”).
Bialer does not explicitly teach identifying the position of the moving object in the second set of cluster data prior to filtering out the static objects. However, Bialer explicitly teaches identifying the position and velocity of all detections in the first set of data (see [0049]), as well as analyzing the position of the static objects and velocity of the dynamic objects in the second set of data (see [0051]). It would have been obvious to one of ordinary skill in the art to gather position and velocity of all detections in the second set of cluster data, as is taught for the first set, because the only difference taught between the first and second set of data is the observation time (see [0051]).
However, Bialer does not teach a plurality of radar sensor mounted and fixed in the geographical area. Rather, the radar sensors of Bialer are mounted and moving in the geographical area, because they are attached to a vehicle.
Bialer discloses a vehicle radar system able to detect objects and classify them as static or dynamic, and Adireddy is directed to generating a traffic heat map using a traffic monitoring system. Adireddy teaches:
A method of tracking objects in a geographical area having one or more roadways, the method comprising:
receiving a set of sensor cluster data from a plurality of radar sensors mounted and fixed in the geographical area (see at least [0018]; “In some implementations, the one or more sensors 122a-n may be positioned to capture data 124 associated with a specific area 10, where each sensor 122a-n captures data 124 associated with a portion of the area 10. As a result, the sensor data 124 associated with each sensor 122a-n includes sensor data 124 associated with the entire area 10. In some examples, the sensors 122a-n are positioned within the intersection 10, for example, each sensor 122a-n is positioned on a corner of the intersection to view the traffic participants 102 or supported by a traffic light. The sensors 120 may include, but are not limited to, Radar, Sonar, LIDAR (Light Detection and Ranging, which can entail optical remote sensing that measures properties of scattered light to find range and/or other information of a distant target), HFL (High Flash LIDAR), LADAR (Laser Detection and Ranging), cameras (e.g., monocular camera, binocular camera).”),
identifying a position of the moving object in the geographical area based on the reflections from the moving object in the set of sensor cluster data (see at least [0018]; “Each sensor 120 is positioned at a location where the sensor 120 can capture sensor data 124 associated with the traffic participants 102, 102a-c at the specific location. Therefore, the sensor system 120 analyses the sensor data 124 captured by the one or more sensors 122a-n. The analysis of the sensor data 124 includes the sensor system 120 identifying one or more traffic participants 102 and determining one or more attributes 106, 106a-n associated with each traffic participant 102. The traffic attributes 106, 106a-n, may include, but are not limited to, the location of the traffic participant 102 (e.g., in a coordinate system), a speed associated with the traffic participant 102, a type of the traffic participant 102 (e.g., vehicles 102a, pedestrians and bicyclists 102b, user devices 102c), and other attributes of each traffic participant 102 within the area 10.”), and
tracking the moving object using the filtered second sensor cluster data (see at least [0019]; “The traffic monitoring system 110 executes a heat map generator 130 that generates a heat map 200, 200a, as shown in FIG. 2A, based on the analyzed sensor data 126 received from the sensor system 120. Therefore, the sensors 122a-n capture sensor data 124 associated with an area 10, such as a road or intersection, then the sensor system 120 analyses the received sensor data 124. Following, the heat map generator 130 determines a traffic heat map 200a of the respective area based on the analyzed sensor data 126. The heat map 200a is based on an occurrence of an object or traffic participant 102, 102a-c within the specific area 10. As the number of traffic participants 102, 102a-c increases within the area 10, a heat-index associated with the area 10 increases as well.”).
Both Bialer and Adireddy teach a method of using radar to monitor moving objects in a geographical area having one or more roadways. The radars of Bialer are mounted on a vehicle, and the radars of Adireddy are mounted on fixed infrastructure. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the tracking method of Bialer using the infrastructure-mounted radars taught by Adireddy. Doing so would reduce the computational load of tracking moving objects, as taught by Bialer (see at least [0052]; “Once the static objects are identified, the remaining dynamic objects are then tracked and more accurately estimated and localized 670 due to the reduced processing requirements for object tracking.”).
Regarding claim 8, Bialer in view of Adireddy discloses the method of claim 4. Bialer further teaches:
wherein the tracking comprises creating an object list comprising the static object and the moving object (see at least [0048]; “The joint signal processor 540 is operative to process the data received from the first digital signal processor 525 and a second digital signal processor 530 in order to perform object detection, object determination and recognition and parameter estimation. The joint signal processor 540 is further operative to track the determined objects according to aspects of the exemplary embodiments. The joint signal processor 540 may then generate an object list which is stored to memory 505 and may further be operative to generate an object map used for autonomous driving and/or obstacle avoidance.”), the object list comprising attributes of the static object (see at least [0052]; “The method is then operative to determine if an object is static or dynamic 650. If an object is determined to be static, the method may either discard the object from tracking, radar echoes from the static object may be cancelled by the processor, and/or the location of the static objects may be saved to a memory 660. The static object may be represented in a mapping of the environment for autonomous driving operations.”) and attributes of the moving object (see at least [0052]; “Once the static objects are identified, the remaining dynamic objects are then tracked and more accurately estimated and localized 670 due to the reduced processing requirements for object tracking.”), and wherein the filtering comprises filtering radar cluster data of the static object from among the second set of radar cluster data based on the object list (see again [0052]; “The method is then operative to determine if an object is static or dynamic 650. If an object is determined to be static, the method may either discard the object from tracking, radar echoes from the static object may be cancelled by the processor, and/or the location of the static objects may be saved to a memory 660.”).
Regarding claim 9, Bialer teaches:
An object tracking system (see at least Fig. 2, radar control system 12) comprising:
a hardware processing unit (see at least Fig. 2, processor 240); and
non-transitory memory (see at least Fig. 2, memory 242) coupled to the hardware processing unit, the memory storing program code having instructions which, when executed by the hardware processing unit, cause the hardware processing unit to perform a method (see at least [0035]; “During operation, the processor 240 executes one or more programs 250 contained within the memory 242 and, as such, controls the general operation of the controller 104 and the computer system 232, generally in executing the processes described herein, such as those of the method 400 described further below in connection with FIGS. 4-6.”) of tracking objects (see at least Abs; “A processor is coupled to the plurality of receivers in order to determine when a plurality of detected objects correspond to a single object and to track the single object in a dynamic environment.”) in a geographical area having one or more roadways (see at least [0029]; “The transmitting radar signals subsequently contact other vehicles and/or other objects on or alongside the road on which the host vehicle 10 is travelling.”), the method comprising:
receiving a first set of sensor cluster data (see at least [0049]; “The radar systems are first operative to transmit and receive operate at a short observation time 605.”) from a plurality of radar sensors mounted (see at least Abs; “Methods and systems are provided for controlling a radar system of a vehicle. One or more transmitters are configured to transmit radar signals. A plurality of receivers are configured to receive return radar signals after the transmitted radar signals are deflected from an object proximate the vehicle.” Examiner notes that the radars are mounted on the vehicle.), the first set of sensor cluster data comprising reflections from a static object in the geographical area (see at least [0044]; “Typical scenario for automotive radar consists of multiple every strong, large size, echoes form static objects and few much weaker, small size, such as pedestrian, dynamic objects.”);
identifying a position of the static object in the geographical area based on the reflections from the static object in the first set of sensor cluster data (see at least [0049]; “The radar system then accumulates in memory, the location and velocity of the determined detections 610.” See also [0051]; “The method is then operative to estimate if objects are dynamic or static taking into account the vehicle velocity and the angular location of the objects with respect to the vehicle 630.”);
receiving a second set of sensor cluster data from the plurality of radar sensors (see at least [0051]; “The system is then operative to transmit and receive radar pulses over a longer observation time 640.”), the second set of sensor cluster data comprising reflections from the static object and reflections from a moving object in the geographical area (see at least [0051]; “The method is then operative to estimate if objects are dynamic or static taking into account the vehicle velocity and the angular location of the objects with respect to the vehicle 630. The system is then operative to transmit and receive radar pulses over a longer observation time 640.”);
identifying (see at least [0051]; “Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.”);
determining that the position of the static object in the geographical area in the first set of sensor cluster data is the same as the position of the static object in the geographical area in the second set of cluster data (see at least [0051]; “Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.” Thus, the position of the static object derived from the first set of data is refined by analysis of the second set of data);
filtering out the reflections from the static object from the second set of cluster data that are the same as the reflections from the static object from the first set of cluster data (see at least [0052]; “The method is then operative to determine if an object is static or dynamic 650. If an object is determined to be static, the method may either discard the object from tracking, radar echoes from the static object may be cancelled by the processor, and/or the location of the static objects may be saved to a memory 660.”); and
tracking the moving object using the filtered second sensor cluster data (see at least [0052]; “Once the static objects are identified, the remaining dynamic objects are then tracked and more accurately estimated and localized 670 due to the reduced processing requirements for object tracking.”).
Bialer does not explicitly teach identifying the position of the moving object in the second set of cluster data prior to filtering out the static objects. However, Bialer explicitly teaches identifying the position and velocity of all detections in the first set of data (see [0049]), as well as analyzing the position of the static objects and velocity of the dynamic objects in the second set of data (see [0051]). It would have been obvious to one of ordinary skill in the art to gather position and velocity of all detections in the second set of cluster data, as is taught for the first set, because the only difference taught between the first and second set of data is the observation time (see [0051]).
However, Bialer does not teach a plurality of radar sensor mounted and fixed in the geographical area. Rather, the radar sensors of Bialer are mounted and moving in the geographical area, because they are attached to a vehicle.
Bialer discloses a vehicle radar system able to detect objects and classify them as static or dynamic, and Adireddy is directed to generating a traffic heat map using a traffic monitoring system. Adireddy teaches:
A method of tracking objects in a geographical area having one or more roadways, the method comprising:
receiving a set of sensor cluster data from a plurality of radar sensors mounted and fixed in the geographical area (see at least [0018]; “In some implementations, the one or more sensors 122a-n may be positioned to capture data 124 associated with a specific area 10, where each sensor 122a-n captures data 124 associated with a portion of the area 10. As a result, the sensor data 124 associated with each sensor 122a-n includes sensor data 124 associated with the entire area 10. In some examples, the sensors 122a-n are positioned within the intersection 10, for example, each sensor 122a-n is positioned on a corner of the intersection to view the traffic participants 102 or supported by a traffic light. The sensors 120 may include, but are not limited to, Radar, Sonar, LIDAR (Light Detection and Ranging, which can entail optical remote sensing that measures properties of scattered light to find range and/or other information of a distant target), HFL (High Flash LIDAR), LADAR (Laser Detection and Ranging), cameras (e.g., monocular camera, binocular camera).”),
identifying a position of the moving object in the geographical area based on the reflections from the moving object in the set of sensor cluster data (see at least [0018]; “Each sensor 120 is positioned at a location where the sensor 120 can capture sensor data 124 associated with the traffic participants 102, 102a-c at the specific location. Therefore, the sensor system 120 analyses the sensor data 124 captured by the one or more sensors 122a-n. The analysis of the sensor data 124 includes the sensor system 120 identifying one or more traffic participants 102 and determining one or more attributes 106, 106a-n associated with each traffic participant 102. The traffic attributes 106, 106a-n, may include, but are not limited to, the location of the traffic participant 102 (e.g., in a coordinate system), a speed associated with the traffic participant 102, a type of the traffic participant 102 (e.g., vehicles 102a, pedestrians and bicyclists 102b, user devices 102c), and other attributes of each traffic participant 102 within the area 10.”), and
tracking the moving object using the filtered second sensor cluster data (see at least [0019]; “The traffic monitoring system 110 executes a heat map generator 130 that generates a heat map 200, 200a, as shown in FIG. 2A, based on the analyzed sensor data 126 received from the sensor system 120. Therefore, the sensors 122a-n capture sensor data 124 associated with an area 10, such as a road or intersection, then the sensor system 120 analyses the received sensor data 124. Following, the heat map generator 130 determines a traffic heat map 200a of the respective area based on the analyzed sensor data 126. The heat map 200a is based on an occurrence of an object or traffic participant 102, 102a-c within the specific area 10. As the number of traffic participants 102, 102a-c increases within the area 10, a heat-index associated with the area 10 increases as well.”).
Both Bialer and Adireddy teach a method of using radar to monitor moving objects in a geographical area having one or more roadways. The radars of Bialer are mounted on a vehicle, and the radars of Adireddy are mounted on fixed infrastructure. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the tracking method of Bialer using the infrastructure-mounted radars taught by Adireddy. Doing so would reduce the computational load of tracking moving objects, as taught by Bialer (see at least [0052]; “Once the static objects are identified, the remaining dynamic objects are then tracked and more accurately estimated and localized 670 due to the reduced processing requirements for object tracking.”).
Regarding claim 13, Bialer in view of Adireddy teaches the object tracking system of claim 9. The remaining limitations of claim 13 are analogous to those of claim 8 and are rejected for similar reasons.
Regarding claim 14, Bialer in view of Adireddy discloses the method of claim 4. Adireddy further teaches:
wherein the geographical area is one or more intersections of the one or more roadways (see at least [0017]; “The sensor system 120 includes one or more sensors 122a-n positioned at one or more roads or road intersections 10 and configured to sense one or more traffic participants 102, 102a-c.”).
It would have been obvious to combine Bialer and Adireddy for the reasons given regarding claim 4.
Regarding claim 15, Bialer in view of Adireddy teaches the object tracking system of claim 9. The remaining limitations of claim 15 are analogous to those of claim 14 and are rejected for similar reasons.
Claims 5-7 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Bialer in view of Adireddy, further in view of Liu et al. (US-20200103523-A1; hereinafter Liu).
Regarding claim 5, Bialer in view of Adireddy discloses the method of claim 4. Bialer further teaches:
wherein identifying the position of the static object comprises:
identifying zero speed sensor cluster data (see at least [0051]; “The method is then operative to estimate if objects are dynamic or static taking into account the vehicle velocity and the angular location of the objects with respect to the vehicle 630.”), the zero speed sensor cluster data corresponding to permanently fixed objects (see at least [0041]; “For a radar equipped vehicle, the typical operating environment is extremely dense, with a majority of targets being static, such as the road, buildings, parked vehicles, trees, etc.”); and
identifying the position of the static object based on the zero speed sensor cluster data (see at least [0051]; “The system is then operative to transmit and receive radar pulses over a longer observation time 640. Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.”).
However, Bialer does not explicitly teach generating a heat map.
Bialer discloses a vehicle radar system able to detect objects and classify them as static or dynamic, and Liu is directed to a vehicle radar system able to generate a spatial grid and classify grid cells as statically occupied, dynamically occupied, occluded or free space. Liu teaches:
generating a heat map based on the first set of sensor cluster data (see at least [0030]; “In at least one example, the vehicle computing device(s) 106 can utilize sensor data captured by the one or more sensors systems 104 in a spatial monitoring component 108. For example, the spatial monitoring component 108 can generate and maintain one or more spatial grids, including a radar spatial grid 110, that include cells associated with regions of the environment.”);
identifying zero speed sensor cluster data in the heat map (see at least [0030]; “Each cell of the radar spatial grid 110 can be labeled to designate whether the region of the environment corresponding to the respective cell is occupied, occluded, and/or free space.”).
Bialer tracks the location and velocity of detected radar targets (see at least [0005]). Liu uses radar to measure location and speed data in an environment, and creates a visual map representing this data across the field of view. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data representation methods used in Bialer to include a visual map as taught by Liu. One of ordinary skill would be motivated to include a visual map in order to methodically record the status of each sensed area, as demonstrated by Liu (see Liu at least [0030]).
Regarding claim 6, Bialer in view of Adireddy and Liu discloses the method of claim 5. Bialer further teaches:
wherein identifying the zero speed sensor cluster data comprises:
determining static speed sensor cluster data (see at least [0051]; “The system is then operative to transmit and receive radar pulses over a longer observation time 640.”); and
identifying the static speed sensor cluster data as the zero speed sensor cluster data (see at least [0051]; “Over the longer observation time, the dynamic clusters may spread in the Doppler and the static objects can be more accurately localized.”).
However, Bialer does not teach representing the radar data as a heat map.
Liu teaches:
determining static speed sensor cluster data in the heat map over a predetermined period of time (see at least the radar maps across time of Fig. 4).
It would have been obvious to combine Bialer and the radar map of Liu for the reasons given regarding claim 5.
Regarding claim 7, Bialer in view of Adireddy and Liu discloses the method of claim 6. Bialer further teaches:
wherein identifying the zero speed sensor cluster data further comprises outputting a static region corresponding to the static speed sensor cluster data, the static region comprising at least one of a range, an angle (see at least [0044]; “The apparatus is used to, localize, or determine the position, of the objects by determining their position either relative to the host vehicle or to some global reference coordinate. Localizing may include determining the range azimuth and elevation angles of the target with respect to the host vehicle and its velocity. Furthermore, the apparatus 500 may be operative to determine which objects are static and what are dynamic helps in scene understanding…”), a standard deviation, and a radar cross section of the static region with respect to each radar sensor among the plurality of radar sensors (Examiner notes that as radars are mounted on a vehicle, giving the location as an angle relative to the vehicle also give the location with respect to the vehicle radars).
Regarding claim 10, Bialer in view of Adireddy and Liu teaches the object tracking system of claim 9. The remaining limitations of claim 10 are analogous to those of claim 5 and are rejected for similar reasons.
Regarding claim 11, Bialer in view of Adireddy and Liu teaches the object tracking system of claim 10. The remaining limitations of claim 11 are analogous to those of claim 6 and are rejected for similar reasons.
Regarding claim 12, Bialer in view of Adireddy and Liu teaches the object tracking system of claim 11. The remaining limitations of claim 12 are analogous to those of claim 7 and are rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kale et al. (US-20230061522-A1) is considered relevant prior art as it teaches fusing data from multiple infrastructure surroundings sensors such as radar sensors, including calculating occlusions resulting from static and dynamic obstacles.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley B. Raynal whose telephone number is (703)756-4546. The examiner can normally be reached Monday - Friday, 8 AM - 4 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/OLUMIDE AJIBADE AKONAI/Primary Examiner, Art Unit 3648
