DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/12/2024 and 08/21/2025 has been considered by the examiner and an initialed copy of the IDS is hereby attached.
Specification
The disclosure is objected to because of the following informalities:
Element 590 from Figure 5 is not mentioned in the Specification.
Appropriate correction is required.
Claim Objections
Claims 7, 10, 11, and 13 are objected to because of the following informalities:
Claim 7 (point (iii), line 1) recites “a sample point”, which should recite “a second sample point”.
Claim 7 (point (iv), line 1) recites “a single sample point”, which should recite “a second single sample point”.
Claim 10 (line 2) recites “all frames”, which should recite “the plurality of frames”.
Claim 11 (line 4) recites “candidate points”, which should recite “the candidate points”.
Claim 13 (last line) recites “sample points, groups, and attributes”, which should recite “the sample points, groups, and attributes”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 9, 13, 17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Agia et al. (US 2023/0267615 A1), hereinafter Agia.
Regarding claim 1, Agia discloses,
A device comprising control circuitry that includes a set of processors coupled to memory (see col 4, lines 28-42), the control circuitry constructed and arranged to perform a method of identifying object features in an environment of a vehicle (see col 2, lines 9-27), the method including:
receiving, by an encoder that runs on the control circuitry (Examiner’s Note: Using BRI, an “encoder” takes one kind of information, perhaps a radar signal, and turns it into something else, perhaps a digital signal for processing. Therefore, a mere act of receiving a signal at an antenna could be interpreted as an "encoder"), data representing a plurality of frames, the frames of the plurality of frames providing point-in-time versions of a segmented pointed cloud (see col 13, lines 15-17, “…Each element in the sliding window buffer is a point cloud 100. At a specific given timestamp t, all the point clouds within the sliding window buffer form the dataset 212...”) derived from output of one or more radar sensors of the vehicle and including points that represent radar detections corresponding to an object in the environment at respective instants in time (see col 8, lines 9-14, “…The sensor data may include image data received from the cameras, a three-dimensional point cloud received from the LIDAR sensor, radar data received from the radar sensor,”, col 8 lines 38-40 “The camera, LIDAR sensor, radar sensor may collect information about the local external environment of the vehicle (e.g., any immediately surrounding obstacles)…”);
arranging the plurality of frames in a time-ordered queue (see col 13, lines 17-26, “… The point clouds 100 are stored chronologically (i.e., sequentially from newest to oldest), from PC.sub.t, which is the most recent point cloud 100, to PC.sub.t−w+1, which is the oldest point cloud 100, wherein w is the total number of point clouds in the sliding window buffer or dataset 212 at given time t. At t, which is known as the inference time, all the point clouds 100 within the sliding window buffer are aggregated, via sub-process 410, to generate one dense point cloud, known as the aggregated point cloud PC.sub.t_agg 415…”, further see col 13, lines 6-7, “…A sliding window buffer can be interpreted as a queue-like data structure…” ); and
processing the frames in the queue, including
(i) selecting, from among the points, a plurality of sample points that spans multiple frames of the queue (see col 12, lines 47-55, “…The aggregated point cloud PC.sub.t_agg 415 is sent to a Pillar Feature Net (PFN) neural network 430 which processes the aggregated point cloud PC.sub.t_agg 415 to generate a BEV image 500 (also referred to as a pseudo image) based on pillars (vertical columns) of the point cloud as described more fully below in connection with FIG. 5. A pillar is a voxel corresponding to a point in the aggregated point cloud PC.sub.t_agg 415 with coordinates x, y in the x-y plane and an unlimited spatial extent in the z direction…”, further see col 14, lines 47-54, “…each point in the aggregated point cloud PC.sub.t_agg 415 is represented by an array of values (x, y, z, i, xc, yc, zc, xp, yp, t.sub.lag), Here xc, yc, zc are defined as the point coordinates with respect to the arithmetic mean of all points within a pillar to which the points belongs, xp, yp each encodes a respective distance of the pillar from the vehicle origin, and t.sub.lag encodes the time lag of the respective source point cloud to the target point cloud in seconds…”),
(ii) forming a plurality of groups of points based on respective sample points of the plurality of sample points (see col 12, lines 47-55, “…The aggregated point cloud PC.sub.t_agg 415 is sent to a Pillar Feature Net (PFN) neural network 430 which processes the aggregated point cloud PC.sub.t_agg 415 to generate a BEV image 500 (also referred to as a pseudo image) based on pillars (vertical columns) of the point cloud as described more fully below in connection with FIG. 5. A pillar is a voxel corresponding to a point in the aggregated point cloud PC.sub.t_agg 415 with coordinates x, y in the x-y plane and an unlimited spatial extent in the z direction…”), and
(iii) extracting features of the object based on the plurality of sample points and the plurality of groups (see col 14, lines 60-63, “…The aggregated point cloud PC.sub.t_agg 415 is then sent to the PFN neural network 430 which extracts learned features from pillars of the aggregated point cloud PC.sub.t_agg 415 and generates the BEV image 500 based on the pillars…”).
Regarding claim 2, Agia, as shown above, discloses claim 1.
Agia further discloses,
wherein the method further includes providing the extracted features of the object to a classification head constructed and arranged to classify the object as one of a plurality of object types, the object types including one or more of (i) pedestrians, (ii) bicyclists, or (iii) motorcyclists (see Fig. 1, elements 134 and 136, further see col 8, lines 47-57,“…The perception module includes a neural network model configured performs sematic segmentation on the 3D point clouds to locate and classify objects in 3D point clouds, for example to local and classify objects in 3D point clouds with a class label such as pedestrian, building, tree, road, crosswalk, intersection, car, etc. The perception module may include any suitable neural network model which perform semantic segmentation on 3D point clouds…”).
Regarding claim 9, Agia, as shown above, discloses claim 1.
Agia further discloses,
wherein forming the plurality of groups includes providing a respective group for each of the plurality of sample points, and wherein at least one group includes points from multiple frames (see col 14, lines 8-19, “…all the point clouds in previous frames (i.e., taken earlier than the most recent point cloud PC.sub.t), may be reprojected this way to the current point cloud at timestamp t.”).
Regarding claim 13, Agia, as shown above, discloses claim 1.
Agia further discloses,
wherein extracting features of the object includes:
constructing a tensor having a first dimension for different sample points of the plurality of sample points, a second dimension for points per group of the plurality of groups, and a third dimension for attributes of points within the groups (see Fig. 4, elements 433, 435, and further see col 15, lines 33-41); and
providing the tensor as input to a neural network trained to identify object features from sample points, groups, and attributes (see Fig. 4, elements 433, 435, and further see col 15, lines 12-13, “…stacked pillar tensor 433, which is then used to extract a set of learned features 435…”).
Regarding claim 17,
Claim 17 is directed to a method.
Claim 17 recite limitations that are parallel in nature as those addressed above for claim 1 which is directed towards a device/system. Claim 17 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 19,
Claim 19 is directed to a computer program product.
Claim 19 recite limitations that are parallel in nature as those addressed above for claim 1 which is directed towards a device/system. Claim 19 is therefore rejected for the same reasons as set forth above for claim 1.
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 non-obviousness.
Claims 3, 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Agia et al. (US 2023/0267615 A1), in view of Cennamo et al. (EP 3985411 A1), hereinafter Cennamo and further in view of Remenyi et al. (EP 4246183 B1), hereinafter Remenyi.
Regarding claim 3, Agia, as shown above, teaches claim 1.
Agia does not disclose the limitation below. However, Cennamo rectifies the deficiencies of Agia by teaching,
wherein selecting the plurality of sample points includes performing a farthest point sampling (FPS), the FPS including searching for a next sample point of the plurality of sample points based on distances of other points of the plurality of frames from a current sample point of the plurality of sample points (see Fig. 1c, element 43 and further see paragraphs [0053]-[0054], “..with the shifted data points 37, a so called farthest point sampling (FPS) 43 is applied to the remaining data points…” of Cennamo),
Agia in view of Cennamo does not disclose the limitation below. However, Remenyi rectifies the
deficiencies of Agia in view of Cennamo by teaching,
wherein the distances are based on both spatial offsets and temporal offsets (see paragraph [0016] and equation 1 of Remenyi).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features as disclosed by Cennamo and Remenyi into the invention of Agia. All references are considered analogous arts to the claimed invention as they all disclose techniques for detecting motions/targets using segmenting and filtering radar data. Agia discloses in Col 9, lines 4-5 “…result in a larger separation between adjacent points with increasing…”. Therefore, Agia mentions separation between adjacent points for object detection but fails to explicitly disclose any sampling method and how separation/distance is measured as a function of spatial and temporal offsets. It would have been obvious to one of ordinary skill in the art to modify the separation between adjacent points as disclosed by Agia by incorporating the distance relation as taught by Cennamo and Remenyi. The combination of Agia with Cennamo and Remenyi would be obvious with a reasonable expectation of success in order to accurately measure the distance between two points while segmenting radar data of the vehicle (see paragraphs [0015]-[0016] of Remenyi).
Regarding claim 8, Agia in view of Cennamo and Remenyi, as shown above, teaches claim 3.
Cennamo further teaches,
wherein selecting the plurality of sample points includes selecting fewer than all of the points in the frames of the queue (see Fig. 1c, and further see paragraph [0029], “…the second stage of the neural network is configured to select a subset of the plurality of data points…” of Cennamo). Examiner’s Note: Using BRI, the claim could be interpreted as using only some of the points. This could be read on by almost any filtering operation, or even the segmenting operation that excludes points.
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features as disclosed by Cennamo into the invention of Agia in view of Cennamo and Remenyi. All references are considered analogous arts to the claimed invention as they all disclose techniques for detecting motions/targets using segmenting and filtering radar data. Agia discloses in Col 9, lines 4-5 “…The point cloud 100 includes a number of points, each of which may be represented by a set of coordinates.…”. Therefore, Agia mentions using points for object detection but fails to explicitly disclose limiting number of points. It would have been obvious to one of ordinary skill in the art to modify the points as disclosed by Agia in view of Cennamo and Remenyi by incorporating the limit on the number of points as taught by Cennamo. The combination of Agia in view of Cennamo, and Remenyi would be obvious with a reasonable expectation of success in order to reduce the computational effort for detecting objects (see paragraphs [0012]-[0013] of Cennamo).
Regarding claim 20,
Claim 20 is directed to a computer program product.
Claim 20 recite limitations that are parallel in nature as those addressed above for claim 3 which is directed towards a device/system. Claim 20 is therefore rejected for the same reasons as set forth above for claim 3.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Agia et al. (US 2023/0267615 A1), in view of Cennamo et al. (EP 3985411 A1), and Remenyi et al. (EP 4246183 B1), and further in view of Bi et al. (“Recursive spatial-temporal clustering-based target detection with millimeter-wave radar point cloud”, Measurement Science and Technology, 34, 075110, 2023), hereinafter Bi.
Regarding claim 4, Agia in view of Cennamo and Remenyi, as shown above, teaches claim 3.
Agia in view of Cennamo does not disclose the limitation below. However, Remenyi rectifies the deficiencies of Agia in view of Cennamo by teaching,
wherein the method further includes determining the distances of the other points from the current sample point of the plurality of sample points based on the spatial offsets and the temporal offsets (see paragraph [0016] and equation 1 of Remenyi),
Agia in view of Cennamo and Remenyi does not disclose the limitation below. However, Bi rectifies the deficiencies of Agia in view of Cennamo and Remenyi by teaching,
wherein determining the distances includes weighting contributions of the spatial offsets and temporal offsets using at least one tunable parameter. (see page 6, section 4.1, equations 7 and 8 of Bi)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features as disclosed by Bi into the invention of Agia in view of Cennamo and Remenyi. All references are considered analogous arts to the claimed invention as they all disclose techniques for detecting motions/targets using segmenting and filtering radar data. Agia discloses in Col 9, lines 4-5 “…result in a larger separation between adjacent points with increasing…”. Therefore, Agia mentions separation between adjacent points for object detection but fails to explicitly disclose how separation/distance is measured as a function of spatial and temporal offsets. It would have been obvious to one of ordinary skill in the art to modify the separation between adjacent points as disclosed by Agia in view of Cennamo and Remenyi by incorporating the distance relation as taught by Bi. The combination of Agia in view of Cennamo, Remenyi, and Bi would be obvious with a reasonable expectation of success in order to accurately measure the minimum distance between sampled points across both space and time (see paragraph below equation 8, section 4.1 of Bi).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Agia et al. (US 2023/0267615 A1), in view of Ding et al. (US 2023/0067322 A1), hereinafter Ding.
Regarding claim 10, Agia, as shown above, teaches claim 9.
Ding further teaches,
further comprising limiting frames from which points may be selected for a group to fewer than all frames in the queue (see Fig. 4a, steps 401-403, and further see paragraphs [0107]-[0117], “…apparatus checks whether the quantity of frames in the first point cloud data set … is consistent with the preset quantity of frames…” of Ding). Examiner’s Note: Using BRI, the claim could be interpreted as using only some of the frames. This could be read on by almost any filtering operation, or even the segmenting operation that excludes frames.
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features as disclosed by Ding into the invention of Agia. Both references are considered analogous arts to the claimed invention as they both disclose techniques for detecting motions using the point cloud data. Agia discloses in Col 13, lines 26-29 “…In some embodiments, to account for motion of the vehicle between point cloud frames, all point clouds in the sliding window buffer except the most recent point cloud.…”. Therefore, Agia mentions using frames for motion detection but fails to explicitly disclose limiting number of frames. It would have been obvious to one of ordinary skill in the art to modify the frames as disclosed by Agia by incorporating the limit on the number of frames as taught by Ding. The combination of Agia in view of Ding would be obvious with a reasonable expectation of success in order to normalize or limit the number of frames in the process of motion detection (see paragraphs [0107]-[0108] of Ding).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Agia et al. (US 2023/0267615 A1), in view of “A University of Washington CSE390B course website [online]. A webpage showing how data is stored in a memory, last modified in 2021” [retrieved on 2026-06-08]. Retrieved from the Internet: <URL: https://courses.cs.washington.edu/courses/cse390b/21sp/readings/memory.html>, hereinafter UWpage.
Regarding claim 12, Agia, as shown above, discloses claim 1.
Agia further discloses,
wherein selecting the plurality of sample points is performed by a sampling component, wherein forming the plurality of groups is performed by a grouping component, and wherein the method further includes:
storing the points and associated attributes in an array in computer memory, the array having different indices for respective points (see col 14, lines 35-46, “…In some embodiments, each point in the aggregated point cloud PC.sub.t_agg 415 is represented by an array of values (x, y, z, i, xc, yc, zc, xp, yp, t.sub.lag), Here xc, yc, zc are defined as the point coordinates with respect to the arithmetic mean of all points within a pillar to which the points belongs, xp, yp each encodes a respective distance of the pillar from the vehicle origin, and t.sub.lag encodes the time lag of the respective source point cloud to the target point cloud in seconds …”), and
Agia does not disclose the limitation below. However, UWpage rectifies the deficiencies of Agia by teaching,
identifying, by the sampling component, the plurality of sample points to the grouping component by providing array indices of the plurality of sample points but not by providing the associated attributes (see the whole webpage which describes how data is stored and retrieved from the memory and how it relates to arrays)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features as disclosed by UWpage into the invention of Agia. Both references are considered analogous arts to the claimed invention as they both disclose techniques related to data storage, memory and arrays. Agia discloses in Col 8, line 9 “…The memory also stores a variety of data.…”, and in Col 14, line 31 “…the target point cloud of reference can be stored in an array like data-structure...”. Therefore, Agia mentions using array data structure and storing data in memory but fails to explicitly disclose how these data are identified and retrieved. It would have been obvious to one of ordinary skill in the art to modify the array data storage in memory as disclosed by Agia by incorporating the conventional RAM approach to data storage and retrieval as taught by UWpage. The combination of Agia in view of UWpage would be obvious with a reasonable expectation of success in order to improve the access of data by using indices/addresses instead of the associated attributes (see paragraphs labelled ‘Quick Example’ and ‘Blocks of Memory’ of UWpage).
Claims 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Agia et al. (US 2023/0267615 A1), in view of Pronovost (US 12221134 B1), hereinafter Pronovost.
Regarding claim 16, Agia, as shown above, teaches claim 1.
Pronovost further teaches,
wherein the encoder is a hierarchical encoder that includes multiple encoder stages, the stages including:
a first encoder stage constructed and arranged to extract features of the object on a first spatial scale (see Fig. 1, element 121, Fig. 4, step 408, and further see col 9, lines 52-57, “…the first encoder machine learning model 121 may be configured to process the contextual data 102 associated with the vehicle environment and object state data…” of Pronovost); and
a second encoder stage cascaded with the first encoder stage, the second encoder stage constructed and arranged to extract features of the object on a second spatial scale different from the first spatial scale and to receive features of the object extracted by the first encoder stage as inputs (see Fig. 1, elements 141 and 122, Fig. 4, steps 410 and 412, and further see col 12, lines 42-46, “…the second encoder machine learning model 122 may be configured to process image data 132…” of Pronovost).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features as disclosed by Pronovost into the invention of Agia. Both references are considered analogous arts to the claimed invention as they both disclose techniques for generating a prediction about a vehicle environment. Agia discloses in Col 6, lines 1-3 “…such encoder-decoder neural network models have been implemented to classify pixels of an image or points of a point cloud, i.e., predict class labels for pixels of an image or points of a point cloud.…”. Therefore, Agia mentions basic function of an encoder but fails to explicitly disclose the first and the second encoders. It would have been obvious to one of ordinary skill in the art to modify the encoder model as disclosed by Agia by incorporating the first and the second encoders as taught by Pronovost. The combination of Agia in view of Pronovost would be obvious with a reasonable expectation of success in order to process multiple sets of data e.g., the contextual data, the object state data, and the image data (see col 2, lines 12-50 of Pronovost).
Regarding claim 18,
Claim 18 is directed to a method.
Claim 18 recite limitations that are parallel in nature as those addressed above for claim 16 which is directed towards a device/system. Claim 18 is therefore rejected for the same reasons as set forth above for claim 16.
Allowable Subject Matter
Claims 5, 6, 7, 11, 14 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claims 7 and 11 are also objected to because of the informalities.
The following is a statement of reasons for the indication of allowable subject matter.
Regarding claim 5, Agia in view of Cennamo and Remenyi, as shown above, teaches claim 3.
However, Agia in view of Cennamo and Remenyi, does not disclose,
wherein performing the FPS includes limiting a temporal search range within which the next sample point is selected, such that points from at least one frame in the queue are excluded as candidates for the next sample point.
Therefore, the prior art made of record individually or in any combination, fails to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 5.
Moreover, even assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Regarding claim 6, it depends on claim 5.
Regarding claim 7, it depends on claim 5.
Regarding claim 11, Agia in view of Ding, as shown above, discloses claim 10.
However, Agia in view of Ding, does not disclose,
wherein forming the plurality of groups further includes:
limiting candidate points within a current frame that may be selected for inclusion in a particular group to points within a first spatial radius of a current sample point; and
limiting candidate points within a time-adjacent frame that may be selected for inclusion in the particular group to points within a second spatial radius of the current sample point,
wherein the first spatial radius is larger than the second spatial radius.
Therefore, the prior art made of record individually or in any combination, fails to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 11.
Moreover, even assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Regarding claim 14, Agia, as shown above, discloses claim 1.
However, Agia, does not disclose,
wherein the method further includes adding frames to the queue until a total number of points in the frames of the queue meets a minimum limit.
Therefore, the prior art made of record individually or in any combination, fails to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 14.
Moreover, even assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Regarding claim 15, Agia, as shown above, discloses claim 1.
However, Agia, does not disclose,
wherein the method further includes removing at least one oldest frame from the queue responsive to a total number of points in the frames of the queue exceeding a maximum limit.
Therefore, the prior art made of record individually or in any combination, fails to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 15.
Moreover, even assuming arguendo that the features of the claims exist individually, the combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence obtained to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zapf et al. (EP 4357806 A1): discloses processing of radar reflection points and specifically related to a method and device for filtering radar reflection points.
Mei et al. (US 2020/0117947 A1): discloses a method for performing LiDAR-based vehicle tracking, involves a cluster of neighboring points and clusters of processed points based on Euclidean distance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIMISH P. HATHI whose telephone number is (571)272-9508. The examiner can normally be reached M--F 8.30 am to 5.30 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H. Desai can be reached at (571) 270 7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NIMISH P. HATHI/Examiner, Art Unit 3648
/PETER M BYTHROW/Primary Examiner, Art Unit 3648
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