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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/30/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 (and by extension the dependents) have been considered but are 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.
As discussed in the interview the previous combination does not teach the order steps cluster of the point cloud prior to the feature extraction; Hoelscher’s cluster filtering would not occur prior to the feature extraction as is required by the amended claim limitations.
An updated search and consideration was performed in view of the amendments, a reference US 11079488 B2, Elijarat et al, was found which teaches a vehicle perception system in which prior to feature extraction of sensor data clustering and filtering of point cloud data is performed, in that the raw point cloud is clustered wherein as part of the clustering potential clusters are determined and then determined as valid (Corresponding to actual objects in environment) or invalid (noise) based on the number of points within a potential clustering, with potential clusters whose points are below a threshold are not registered with the final group of clusters with the final group of clusters being used for object detection. (Column 2, lines 26-41, “In addition to one or more of the features describe herein, the processor is further configured to adjust the density parameter by adjusting at least one of a gate size of a clustering algorithm and a minimum points requirement of the clustering algorithm. The processor is further configured to adjust the gate size and minimum points requirement by multiplying the gate size and minimum points requirement by the overlap value for the region. The processor is further configured to adjust the at least one of a gate size and the minimum points requirement by a sensor alignment score. The processor is further configured to reduce a number of false noise signals from the clustering algorithm. The processor is further configured to determine the object from the valid clustering. In various embodiments, the processor is configured to perform an action at the vehicle based on the determined object.”) Updated rejection appears below.
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.
Claim(s) 1-2, 6-9, and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200249353 A1, “MERGING DATA FROM MULTIPLE LIDAR DEVICES”, Di Cicco et al and further in view of Case law In Re Japikse and further in view of US 20210323572 A1, He et al, “A POINT CLOUDS REGISTRATION SYSTEM FOR AUTONOMOUS VEHICLES” and further in view of US 11079488 B2, “DBSCAN Parameters As Function Of Sensor Suite Configuration”, Elijarat
Regarding Claim 1, Di Cicco teaches “system comprising: a first LiDAR and a second LiDAR ( [0139] The processor receives 1802 first LiDAR point cloud information from a first LiDAR device and second LiDAR point cloud information from a second LiDAR device. In an embodiment, the LiDAR devices are the LiDAR devices 1302, 1304 shown in FIG. 13, and the LiDAR point cloud information is the point clouds 1622, 1624 shown in FIG. 16.);” a LiDAR data merge unit receiving data from the first LiDAR and the second LiDAR, aligning LiDAR times through time synchronization, and then converting the data into a point cloud type and merging the data;”( [0133] FIG. 16 shows components of a system used to generate a consolidated point cloud 1600. Each LiDAR device 1602, 1612 has a processor 1604, 1614 (e.g., microprocessor, microcontroller) each of which is configured with a respective starting angle 1606, 1616 and frequency 1608, 1618. In use, the LiDAR devices 1602, 1612 generates point clouds 1622, 1624 that are received by a processor 1626 (e.g., an implementation of or component of the perception module 402 shown in FIG. 4). The points of the point clouds 1622, 1624 are associated with timestamp data 1628, 1630. The processor 1626 uses the starting angles 1606, 1616, frequencies 1608, 1618, and timestamp data 1628, 1630 to generate the consolidated point cloud 1600. ” The lidars’ data outputs are aligned/synchronized via their timestamps and a merged/consolidated point cloud is created);” an electronic control unit (ECU) providing inertial data of the vehicle for correcting the data merged in the LiDAR data merge unit; and an ( [0093] “In some embodiments, outputs 504a-d are combined using a sensor fusion technique. Thus, either the individual outputs 504a-d are provided to other systems of the AV 100 (e.g., provided to a planning module 404 as shown in FIG. 4), or the combined output can be provided to the other systems, either in the form of a single combined output or multiple combined outputs of the same type (e.g., using the same combination technique or combining the same outputs or both) or different types type (e.g., using different respective combination techniques or combining different respective outputs or both) In some embodiments, an early fusion technique is used. An early fusion technique is characterized by combining outputs before one or more data processing steps are applied to the combined output. In some embodiments, a late fusion technique is used. A late fusion technique is characterized by combining outputs after one or more data processing steps are applied to the individual outputs.” Here teaches outputs are fused (merged) into a single output; earlier in [0089] it is known that the outputs 504a-d correspond to the inputs of 502a-d which come from the sensor(s) 121, which earlier in [0060] these sensors include an IMU used to infer the vehicles position/state);””( [0087] The planning module 404 also receives data representing the AV position 418 from the localization module 408. The localization module 408 determines the AV position by using data from the sensors 121 and data from the database module 410 (e.g., a geographic data) to calculate a position.” The localization module uses the sensors 121 (i.e. lidars and IMU) to determine (Extract) location + [0085] teaches the extracting (planning) of a traveling route within the route based on the data from the perception module (i.e. lidar data and imu data)); and additionally Di Cicco teaches that its various modules are implemented via processors ([0059])
Di Cicco however does not teach that the lidars are implemented on the bumper (“using a bumper-mounted dual LiDAR, “) and that the vehicle control and navigation (lidar data processing and recognition) is implemented in a SLAM system (it teaches the localization portion “L” as taught above but not necessarily the mapping “M”). (i.e. “to output data for map creation and location recognition;” and “generating a 3D map of a road on which the vehicle travels, and extracting a location and a traveling route of the vehicle inside a road.”
Regarding the first difference in where the lidars are positioned. Di Cicco does teach that the lidars can generally be mounted anywhere on the vehicle ([0038] + [0115] “Each of the two LiDAR devices 1302, 1304 is positioned at a different location on the AV 1300. In an embodiment, one of the devices 1302 is attached (e.g., welded, affixed, or mounted) at one position 1312, and the other device 1304 is attached at another position 1314. While some attachment techniques (e.g., welding) are semi-permanent and are unlikely to change during the life of the AV 1300, other attachment techniques (e.g., magnetic attachment) enable the LiDAR devices 1302, 1304 to be removed (e.g., for maintenance or replacement) or moved to a different position at a different time.”) however it does not explicitly teach two lidars mounted onto the bumper of the vehicle.
However as Di Cicco teaches generally any placement of the lidars on the vehicle, and at the level of generality currently recited regarding the placement of the lidars and their functioning the specific implementation of the lidars onto the bumper unpatentable as a simple Rearragnment of Parts as set forth by In Re Japikse.
Currently the specific placement (on a bumper of a vehicle) as opposed to elsewhere (E.g. on the roof of the vehicle) does not change the underlying principles of operation of the Lidar. And from Di Cicco [0116]-[0118] the specific placement of the lidar only changes their (positional) relationship to a common reference point in which the lidar data/point clouds are merged relative to and is something already envisioned by Di Cicco and within the capabilities of one of ordinary skill in the art to account for. Thus the difference/implementing of the lidars on the bumper is only one of placement and not of function and the specific placement of lidar onto the bumpers of Di Cicco does not change the underlying principles of operation of Di Cicco and therefore this difference of the placement in the lidars’ locations is unpatentable as a simple matter of design choice not affecting the operation of the device.
As modified to have its lidar(s) on the bumper of the vehicle, Di Cicco would still not teach the generation of a three dimensional map while also localizing the vehicle (i.e. Di Cicco does not teach the “S” and “AM” portions of “SLAM”)
He teaches an autonomous vehicle control system (He Abstract + [0001] Teaches that the system is for autonomous vehicles) which includes an Onboard SLAM system which utilizes lidar point cloud data and IMU data to create a three dimensional map of the area the vehicle is traveling through. (He [0081] IMU and lidar (point cloud) are used to generate an HD map which from [0004] is known to be in 3D) and additionally teaches “wherein the LiDAR odometry is obtained using the point cloud data of the first LiDAR and the second LiDAR, and the odometry is calculated through matching between scans using features detected in LiDAR scans,”(He [0108] Based on the extracted segments, segment based registration process 1103 utilizes a limited set of points from each segment type of the frame and applies an optimization algorithm such as ICP (as part of algorithms/models 313 of FIG. 9) to find matches from the same segment types from the previous immediate frame in the buffer. If initial frame is the only frame, e.g., the first frame, the initial frame can be established as the reference frame and the corresponding pose for the initial frame can be a reference pose.” The current frame’s features are matched to the previous (Reference) frames features);”
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to modify Di Cicco to include the SLAM (map generation in addition to the localization) as taught by He et al. One would be motivated to implement SLAM (the mapping portions thereof) into Di Cicco’s localization to allow for the vehicle to navigate through new areas where the high definition maps of Di Cicco do not currently exist. This motivation/improvement of being able to travel through new (unmapped) areas as taught in He ([0007] In a first aspect, embodiments of the disclosure provide a computer-implemented method to register point clouds for autonomous driving vehicles (ADV), the method including: receiving a plurality of point clouds and corresponding poses from ADVs equipped with LIDAR sensors capturing point clouds of a navigable area to be mapped, wherein the point clouds correspond to a first coordinate system; partitioning the point clouds and the corresponding poses into one or more loop partitions based on navigable loop information captured by the point clouds; for each of the loop partitions, applying an optimization model to point clouds corresponding to the loop partition to register the point clouds, including transforming the point clouds from the first coordinate system to a second coordinate system; and merging the one or more loop partitions together using a pose graph algorithm, wherein the merged partitions of point clouds are utilized to perceive a driving environment surrounding the ADV.” i.e. from the “to be mapped” is it known that the area doesn’t have a HD map, thus the system allows for new HD maps to be created/for the vehicle to enter new areas not previously map)
As modified, modified Di Cicco (Di Cicco + He) does not teach “clustering of the received point cloud is performed in order to reduce the number of point clouds used for matching and minimize a load in an embedded board. “and “wherein, in clustering, a cluster with less than a set number of points is not trusted and not registered, and through this process, a discontinuous noise point is filtered out and only a reliable point is left.”
Elijarat teaches a Lidar point cloud clustering system/method which includes “clustering of the received point cloud is performed in order to reduce the number of point clouds used for matching and minimize a load in an embedded board. “and ” in clustering, a cluster with less than a set number of points is not trusted and not registered, and through this process, a discontinuous noise point is filtered out and only a reliable point is left”(Column 5, lines 11-49, “FIG. 2 shows an illustrative radar detection field 200 received at a sensor of the radar system of the vehicle 10 of FIG. 1. The radar detection field 200 includes a plurality of detections (indicated by dots 202) obtained by the sensor. The radar detection field 200 further shows a clustering algorithm being applied to the plurality of detections in order to form clusters among detections related to a same object. In various embodiments, the clustering algorithm is a DBSCAN (density-based spatial clustering of applications with noise) algorithm.
The clustering algorithm groups detections using two density parameters: a gate size ε, and a minimum point minPts. These parameters define a minimum required density of detections to qualify the detections as part of a cluster. The gate size is generally a radius of a circle 204 or sphere surrounding a selected detection and indicates a search region in which to look for neighboring detections. The processor 34 searches for neighboring detections within the circle 204 or sphere indicated by the gate size and provides a count of the number of neighboring detection within the circle or sphere. The minPts parameter indicates a threshold for the count above which the algorithm defines a valid cluster of detections. In other words, when the number of neighboring detections within the circle 204 or sphere is less than minPts, the clustering algorithm considers the detections to be unrelated.
…
FIG. 2, the value of the minPts parameter is set to 3. However any suitable value for minPts can be used in alternate embodiments. As a result of the clustering algorithm with minPts=3, a group of detections at the top right of the detection field 200 is categorized as a first cluster C1 and a group of detections at the lower left of the detection field 200 is categorized as a second cluster C2. A single detection in the top middle of the detection field 200 has no immediate neighboring detections and is therefore categorized as noise ‘N’.” Here teaches that raw point clouds are clustered as part of the DBSCAN algorithm with potential clusters with less that the minimum number of points/detections are considered unrelated (discontinuous) and not registered),
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to modify Di Cicco to include the dbscan clustering to removal of noise points as taught by Elijarat. One would be motivated to implement this filtering to remove detections which correspond to sensor noise which should be ignored improving subsequent object detection and analysis(Column 2, lines 35-39, “e. The processor is further configured to reduce a number of false noise signals from the clustering algorithm. The processor is further configured to determine the object from the valid clustering.”)
Regarding Claim 2, modified Di Cicco teaches “wherein raw data of each of the first LiDAR and the second LiDAR is expressed as one integrated coordinate through point cloud merge, and a relative position difference between sensors is obtained in an integrated coordinate system and applied to align all point clouds with the sensors in a corrected coordinate system as an origin.”( Di Cicco [0127] As indicated above, if each LiDAR device 1302, 1304 generates its own point cloud, the two point clouds 1316 and 1318 are consolidated, e.g., for use by the perception module 402 (FIG. 4) according to the techniques described below. A consolidated point cloud includes the points from both point clouds 1316 and 1318. In an embodiment, the two point clouds 1316 and 1318 are consolidated or merged or amalgamated or blended together as soon as each of the LiDAR device 1302 and 1304 start generating the point clouds 1316 and 1318 respectively. In an embodiment, the two point clouds 1316 and 1318 are merged after the LiDAR devices 1302 and 1304 finish generating point clouds 1316 and 1318 respectively. One technique for consolidating the point clouds is to normalize the coordinates of each of the points to a common point of reference, e.g., a particular location 1320 on the AV 1300. In this manner, the consolidated point cloud (sometimes referred to as a merged point cloud) is defined using the particular location 1320 as the origin (e.g., coordinates 0,0,0 on Cartesian x-y-z axes, or an origin defined using polar coordinates as described in more detail below). The points from the two point clouds 1316 and 1318 are translated to the consolidated point cloud by normalizing the coordinates to the common origin. In other words, if one point from one cloud and another point from the other cloud were detected at approximately the same location in the environment 190, their coordinates are changed so that they both have approximately the same coordinates and thus occupy approximately the same location in the consolidated point cloud.” The sensor’s individual point clouds are aligned based the relative positions of the sensors to an origin/reference point.)
Regarding Claim 6, modified Di Cicco teaches “The SLAM system of claim 4, wherein, after clustering the point cloud data, a scan area is divided into a set number of sub-areas and edge and planar extraction is performed for each area to uniformly extract the features, and thereafter, correspondence of the features between two consecutive scans is calculated to obtain the lidar odometry.”(He “ [0109] In one embodiment, features extraction process 1104 extracts features or features representations from each segment of the current frame, and loop detection process 1105 compares the extracted features to features of previous frames. Features extraction is a dimensionality reduction process, where an initial set segment is reduced to a group of features for processing, while still accurately and completely describing the original segments. Examples of features include smoothness, linearity, and continuity of points for a segment (e.g., patterns). If the comparison provides a number of matching features above a predetermined threshold (e.g., quantity or percentage), then a loop closure is detected. Here, features are compared instead of segments because objects may be blocked or partially visible in a current field of view, different from a previous field of view. In one embodiment, features include eigenvalue based features. Eigen value based features can include linearity, planarity, scattering, omnivariance (e.g., characteristics of a volumetric point distribution), etc. features.” Here He teaches that each segment (which correspond to Clusters, i.e. after clustering) (is set sub area) which has features extracted for comparison with previous frames these features being matched include both smoothness and planarity, and from He [0107] lidar frames use image based feature extractors which in view of He [0145] plane extraction + He [0086] edge extraction includes both edge and planar features of the point cloud)
Regarding Claim 7, modified Di Cicco teaches “The SLAM system of claim 6, wherein the Lidar odometry is obtained by calculating a transform matrix between the features having correspondence, and at this time, in order to solve the transform matrix as an optimization problem, optimization is performed with edge correspondence and planar correspondence as costs.”( He [0108] Based on the extracted segments, segment based registration process 1103 utilizes a limited set of points from each segment type of the frame and applies an optimization algorithm such as ICP (as part of algorithms/models 313 of FIG. 9) to find matches from the same segment types from the previous immediate frame in the buffer. If initial frame is the only frame, e.g., the first frame, the initial frame can be established as the reference frame and the corresponding pose for the initial frame can be a reference pose.” Here teaches that the optimization to find correspondence (matching) features between a current and the previous/reference frame which earlier when discussing the ICP algorithm includes a transformation matrix between potential point cloud pairs (He [0088]-[0089]))
Regarding Claim 8, modified Di Cicco teaches “in the optimization process, a change of a z-axis in the Lidar odometry of the vehicle and a roll and pitch are measured through matching between the scans measured by LiDAR,”(He [0114] FIG. 11B is a flow chart illustrating an example of a loop closure localization according to one embodiment. Operations 1110 can be performed by loop detection process 1105 of FIG. 11A. Referring to FIG. 11B, when a loop closure is detected 1111, target map or segments map generation process 1112 can generate a target map based on previously extract segments for the frames of the loop. Segments map or target map can be a database, a struct or, a class object storing a list of segments of the frames. The segments can be stored as a number of points corresponding to the segments. FIG. 13 illustrates an example of a target map according to one embodiment. Referring back to FIG. 11B, based on the target map and previous registration results performed by process 1104 of FIG. 11A, process 1113 updates the initial pose by searching a best candidate pose for the initial pose. In one embodiment, based on the updated initial pose, process 1113 applies an iterative method to the initial pose and the registration results to determine a transformation to be further applied to the registration results to reduce a drift caused by SLAM. In this case, the SLAM drift can be reduced because the loop closure provides a second indication for the position and orientation of the initial pose. An example iterative method can be random sample consensus (RANSAC). RANSAC is an iterative method to fit a model from a set of observed data that contains outlier data points, when outlier data points should be accorded no influence on the model to be fitted. Once, the points clouds are registered, an HD point clouds map can be generated using the registration results.” Here teaches pose optimization of the lidar matching (i.e. yaw and pitch) in the HD map (i.e. x , y, and z axis/planes));” when calculating a movement of the vehicle in x and y directions on the road, a route estimation value is provided using inertial measurement unit (IMU) data of the vehicle to complement the Lidar odometry calculation, and data on longitudinal acceleration, lateral acceleration , and yaw rate are output from the ECU of the vehicle, based on which T.x, T.y, and Theta.yaw, which are x, y-axis movement and yaw rotation of the vehicle, are corrected.” (He [0081] FIG. 5 is a block diagram illustrating an example of an HD map generation system according to one embodiment. HD map generation system 500 illustrates an overview for HD map generation. HD map generation system 500 may be part of HD map generation engine 125 of FIG. 1. Referring to FIG. 5, in one embodiment, HD map generation system 500 includes point cloud registration subsystem 501 and HD map generation subsystem 502. Point cloud registration subsystem 501 can receive an IMU signal, a GPS signal, and LIDAR images 503 (e.g., from IMU 213, GPS unit 212, and LIDAR unit 215 respectively) as inputs and generates HD poses 504 (or aligns the poses for the LIDAR images 503) based on the received inputs. HD map generation subsystem 502 can then receive LIDAR images 503 and HD poses 504 as inputs and generate HD map 505 based on the inputs.” Here teaches IMU is used in addition to the lidar in the point cloud registration (i.e. part of the optimization in He [0088]) as part of localization , the use of both (imu and lidar) is teaching a fusion technique (i.e. correcting of one sensors (lidar) estimates/outputs using the other (IMU) ))
Regarding Claim 9 it is a roughly a method equivalent of the SLAM system of claim 1, it has the same grounds of rejection, combination, and motivation for combination as claim 1 for the equivalent limitations. Claim 9 has the additional limitation of “to the obtained odometry by performing SLAM using acceleration data output in a CAN format from an electronic control unit (ECU) inside the vehicle in order to increase precision of odometry and reduce the time required for calculation.”(As modified in claim 1, the SLAM system of He is implemented into Di Cicco, He teaches the use of acceleration data for HD map (lidar odometry data) ( in [0057])) and He teaches that the system/sensors of the vehicle are connected via a CAN bus (i.e. communicate in a CAN format) (HE [0056] Components 110-115 may be communicatively coupled to each other via an interconnect, a bus, a network, or a combination thereof. For example, components 110-115 may be communicatively coupled to each other via a controller area network (CAN) bus. A CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer. It is a message-based protocol, designed originally for multiplex electrical wiring within automobiles, but is also used in many other contexts.)
.Claims 12-14 are method equivalents to system claims 6-8 above. They have the same grounds of rejection as their respective equivalents.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over modified Di Cicco as applied to claim 1 above, and further in view of WO 2020142924 A1, “COMMUNICATION METHOD FOR LASER RADAR, LASER RADAR, AND HOST DEVICE”, Long et al.
Regarding Claim 3, modified Di Cicco teaches “The SLAM system of claim 1, wherein the raw data generated by the first LiDAR and the second LiDAR (Di Cicco + [0127] “As indicated above, if each LiDAR device 1302, 1304 generates its own point cloud, the two point clouds 1316 and 1318 are consolidated, e.g., for use by the perception module 402 (FIG. 4) according to the techniques described below. A consolidated point cloud includes the points from both point clouds 1316 and 1318. In an embodiment, the two point clouds 1316 and 1318 are consolidated or merged or amalgamated or blended together as soon as each of the LiDAR device 1302 and 1304 start generating the point clouds 1316 and 1318 respectively. In an embodiment, the two point clouds 1316 and 1318 are merged after the LiDAR devices 1302 and 1304 finish generating point clouds 1316 and 1318 respectively. “ Here the merging/consolidating after completing the lidar scans teaches the storing of the lidars’ raw data in a buffer until the scan. “ + Di Cicco [0133] FIG. 16 shows components of a system used to generate a consolidated point cloud 1600. Each LiDAR device 1602, 1612 has a processor 1604, 1614 (e.g., microprocessor, microcontroller) each of which is configured with a respective starting angle 1606, 1616 and frequency 1608, 1618. In use, the LiDAR devices 1602, 1612 generates point clouds 1622, 1624 that are received by a processor 1626 (e.g., an implementation of or component of the perception module 402 shown in FIG. 4). The points of the point clouds 1622, 1624 are associated with timestamp data 1628, 1630. The processor 1626 uses the starting angles 1606, 1616, frequencies 1608, 1618, and timestamp data 1628, 1630 to generate the consolidated point cloud 1600. In an embodiment, the LiDAR devices 1602, 1612 are synchronized, e.g., operate according to a common time reference and/or have synchronized clocks. In an embodiment, the processors 1604, 1614 share a common clock 1632 so that their timestamps are generated from a common reference point. In other words, a point having timestamp of t=x generated by one of the LiDAR devices will have been detected at the same time as a point having a timestamp of t=x generated by the other synchronized LiDAR device(s). In an embodiment, the processor 1626 configures the starting angles 1606, 1616 and/or the frequencies 1608, 1618.” The lidars are synchronized based on their time-stamps to a common reference time/point into a merged point cloud)
Modified Di Cicco however does not explicitly teach that the communications of the lidar data is through a UDP format.
Long et al teaches that UDP communication/format is a known/common data transfer communication format for Lidar (Long Background (paragraph [02]): “At present, the common communication method of lidar is Ethernet connection, and uses User Datagram Protocol (User Datagram Protocol, UDP) for communication.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to modify Di Cicco to communicate the Lidar data in a UDP format as taught by Long. One would be motivated to implement the UDP format as it is data efficient format. (Long Background (paragraph [02]) “…UDP is an efficient but unreliable communication method, and the sender of the data cannot know the data being sent. Whether it was received normally.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH MICHAEL DUNNE whose telephone number is (571)270-7392. The examiner can normally be reached Mon-Thurs 8:30-6:30.
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/KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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