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 .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/28/2025 and 07/25/2025 are considered and attached.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of:
KR10-2024-0045985, filed in Korea on 04/04/2024.
Copies of certified papers required by 37 CFR 1.55 have been retrieved.
35 USC § 101 Consideration
In regards to Claim 10, the Examiner has reviewed and analyzed the claim for subject matter eligibility using the Alice/Mayo test (see MPEP 2106.03). The Examiner decides that a rejection under 35 U.S.C. 101 is not necessary.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The examiner strongly suggested that appropriate corrections be made to clarify the claim scope.
The claim recites the following, each of which renders the claim indefinite:
“the preprocessing” on line 1 (unclear to what this refers, the Examiner finds “first preprocessing” and “second preprocessing” before this term. For the purpose of the examination, the Examiner interprets this as “the first preprocessing”);
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 and 10 are rejected under 35 U.S.C. 102 (a)(1) & (2) as being anticipated by Kogut et al. (US-20220319188-A1, hereinafter Kogut)
CLAIM 1
Regarding Claim 1, Kogut teaches a sensor fusion apparatus for a transportation apparatus (Kogut, ¶ [0016]: “a computing device … to determine an occupancy grid representing the vehicle environment … from first positioning information obtained from a first sensor system … and second positioning information associated with the semantic information, the second positioning information obtained from a second sensor system”), the sensor fusion apparatus comprising:
two or more sensors, each of the two or more sensors having different sensing characteristics, each of the two or more sensors sensing an object, respectively (Kogut, ¶ [0032]: “the device 20 includes one or more radar sensors, lidar sensors, ultrasonic sensors and/or imaging sensors (such as a stereo-camera system or RGB camera) for detecting objects in the surroundings of the vehicle 10.”);
one or more processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the one or more processors (Kogut, ¶ [0089]: “The computer program product may be a storage medium or media, instruction store(s), or storage device(s), having instructions stored thereon or therein which can be used to control, or cause, a computer or computer processor to perform any of the procedures of the example embodiments described herein”) to:
perform first preprocessing (Kogut, ¶ [0051-0054]: “The occupancy sensor modelling module 260 uses the detection data 261 to generate occupancy evidence 262 based on the detection data 261 obtained from the respective occupancy sensors 265 (for example, from one or more of a lidar, radar or ultrasound system). Occupancy evidence 262 may take the form of an occupancy grid having a grid of probability values illustrating areas in the vehicle environment occupied by one or more objects …” Kogut teaches processing sensing data regarding detected objects (lidar, radar or ultrasonic sensor) to generate an occupancy map) and second preprocessing (Kogut, ¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections … Such a grid 405 is shown in FIG. 4B. In this example a detected planar object 406 is projected on to the grid 405”, ¶ [0061-0062]: “A separate grid 405 may be provided for each classification. Such a grid may be termed a classification grid. The classification grid may be thought of as a semantic grid for a particular classification type”. Kogut teaches detecting and classifying objects using 2d image data, then generate a semantic grid of each object type), the first preprocessing and second preprocessing comprising grid mapping for each object sensed by the two or more sensors, respectively (Kogut, ¶ [0051-0054]: “The occupancy sensor modelling module 260 uses the detection data 261 to generate occupancy evidence 262 based on the detection data 261 obtained from the respective occupancy sensors 265 (for example, from one or more of a lidar, radar or ultrasound system). Occupancy evidence 262 may take the form of an occupancy grid having a grid of probability values illustrating areas in the vehicle environment occupied by one or more objects …”) (Kogut, ¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections … Such a grid 405 is shown in FIG. 4B. In this example a detected planar object 406 is projected on to the grid 405”, ¶ [0061-0062]: “A separate grid 405 may be provided for each classification. Such a grid may be termed a classification grid. The classification grid may be thought of as a semantic grid for a particular classification type”.); and
perform sensor fusion through clustering (Kogut, ¶ [0078]: “The occupancy grid thus obtained may then be transformed into a list of objects using a segmentation algorithm such as a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means, Expectation-Maximization using Gaussian Mixture Models (GMM) or any other suitable algorithm known in the art”) using an integrated grid map for each grid-mapped object. (Kogut, ¶ [0073-0075 and 0081-0084]: “the classified occupancy grid determiner 253 combines information contained in the occupancy grid with information contained in the transformed semantic grid … For any given cell, the semantic confidence value obtained from that cell of the transformed semantic grid and the occupancy probability obtained from that cell of the occupancy grid may be multiplied together. The result of this multiplication is a probability value of the cell being occupied and classified within a specific category.
p(A|occupancy) = p(occupancy)·pig(A)
A classified occupancy grid is thus determined including an array of classified occupancy probability values.” See FIG. 3. Kogut teaches a sensor fusion: processing ultrasonic sensor to obtain occupancy grid; processing image sensor to obtain classified semantic grid; then combining them into a classified occupancy grid; the final combined grid contains each object detected by both sensor system)
CLAIM 2
Regarding Claim 2, Kogut teaches the apparatus of Claim 1. In addition, Kogut teaches performing Time of Flight (TOF) preprocessing (Kogut, ¶ [0036-0037 and 0050]: “The occupancy sensor modelling module 260 may convert a delay between emitting the signal(s) and detecting the return signal(s) into range data indicating the distance between an object and the vehicle, and the direction of the object is also calculated from the return signal(s)”) and the grid mapping for a first object detected through an ultrasonic sensor of the two or more sensors (Kogut, ¶ [0051-0054]: “The occupancy sensor modelling module 260 uses the detection data 261 to generate occupancy evidence 262 based on the detection data 261 obtained from the respective occupancy sensors 265 (for example, from one or more of a lidar, radar or ultrasound system). Occupancy evidence 262 may take the form of an occupancy grid having a grid of probability values illustrating areas in the vehicle environment occupied by one or more objects …” Kogut teaches processing sensing data regarding detected objects (lidar, radar or ultrasonic sensor) to generate an occupancy map), and wherein the second preprocessing further comprises: performing object detection (OD) preprocessing and the grid mapping for a second object detected through a camera sensor of the two or more sensors. (Kogut, ¶ [0038-0039]: “ The camera system 215 may include an RGB camera …”;¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections … Such a grid 405 is shown in FIG. 4B. In this example a detected planar object 406 is projected on to the grid 405”, ¶ [0061-0062]: “A separate grid 405 may be provided for each classification. Such a grid may be termed a classification grid. The classification grid may be thought of as a semantic grid for a particular classification type”. Kogut teaches detecting and classifying objects using 2d image data, then generate a semantic grid of each object type)
CLAIM 10
Regarding Claim 10, Kogut teaches a method (Kogut, Abstract: “A computer-implemented method and device for mapping a vehicle environment of a vehicle are disclosed”), the method comprising:
performing, by a processor (Kogut, ¶ [0089]: “The computer program product may be a storage medium or media, instruction store(s), or storage device(s), having instructions stored thereon or therein which can be used to control, or cause, a computer or computer processor to perform any of the procedures of the example embodiments described herein”), preprocessing and grid mapping corresponding to a first sensor (Kogut, ¶ [0051-0054]: “The occupancy sensor modelling module 260 uses the detection data 261 to generate occupancy evidence 262 based on the detection data 261 obtained from the respective occupancy sensors 265 (for example, from one or more of a lidar, radar or ultrasound system). Occupancy evidence 262 may take the form of an occupancy grid having a grid of probability values illustrating areas in the vehicle environment occupied by one or more objects …” Kogut teaches processing sensing data regarding detected objects (lidar, radar or ultrasonic sensor) to generate an occupancy map) and a second sensor (Kogut, ¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections … Such a grid 405 is shown in FIG. 4B. In this example a detected planar object 406 is projected on to the grid 405”, ¶ [0061-0062]: “A separate grid 405 may be provided for each classification. Such a grid may be termed a classification grid. The classification grid may be thought of as a semantic grid for a particular classification type”. Kogut teaches detecting and classifying objects using 2d image data, then generate a semantic grid of each object type), respectively; and
performing, by the processor, sensor fusion through clustering (Kogut, ¶ [0078]: “The occupancy grid thus obtained may then be transformed into a list of objects using a segmentation algorithm such as a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means, Expectation-Maximization using Gaussian Mixture Models (GMM) or any other suitable algorithm known in the art”) using an integrated grid map for each preprocessed and grid-mapped object information. (Kogut, ¶ [0073-0075 and 0081-0084]: “the classified occupancy grid determiner 253 combines information contained in the occupancy grid with information contained in the transformed semantic grid … For any given cell, the semantic confidence value obtained from that cell of the transformed semantic grid and the occupancy probability obtained from that cell of the occupancy grid may be multiplied together. The result of this multiplication is a probability value of the cell being occupied and classified within a specific category.
p(A|occupancy) = p(occupancy)·pig(A)
A classified occupancy grid is thus determined including an array of classified occupancy probability values.” See FIG. 3. Kogut teaches a sensor fusion: processing ultrasonic sensor to obtain occupancy grid; processing image sensor to obtain classified semantic grid; then combining them into a classified occupancy grid; the final combined grid contains each object detected by both sensor system)
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 3
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kogut in view of Zheng et al. (Zheng, Xinhu, et al. "Multivehicle multisensor occupancy grid maps (MVMS-OGM) for autonomous driving." IEEE, hereinafter Zheng).
In regards to Claim 3, Kogut teaches the apparatus of Claim 1.
Kogut does not explicitly disclose clustering, through an intersection of an object calculated through the first preprocessing and performing object box gating of an object calculated through the second preprocessing, by using the integrated grid map.
Zheng is in the same field of art of sensor fusion in autonomous vehicle. Further, Zheng teaches clustering, through an intersection of an object calculated through the first preprocessing and performing object box gating of an object calculated through the second preprocessing (Zheng, page 22948, right col, section B: “The critical step of the single-vehicle multisensor fusion process is to map the bounding box in the image to the global coordinate and fuse the camera data and the LiDAR data together ... Zc is needed, which is the distance from the optical center to the object. The depth information is not presented in the camera data, the Zc of the points in the bounding box can be obtained based on the corresponding LiDAR point cloud data … the image bounding box is divided into grids as shown in Fig. 5, and then the point cloud data is mapped into the camera coordinate system … Based on the Zc values of the points collected by LiDAR, the Zc value of every grid in the bounding box can be estimated by calculating the average Zc value of the points that fall into the grid”, see modified FIG. 5 below. Zheng teaches clustering data points obtained from range sensor that fall within the object bounding box obtained from image sensor),
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by using the integrated grid map. (Zheng, pages 22949-22950, section IV. MVMS-OGMCONSTRUCTION: “The data of multiple sensors are now combined with spatial and temporal consistency. While the density of point clouds can be improved by fusing LiDAR and camera data, the LiDAR point cloud is discretely distributed in nature and there is no guarantee that every grid occupied by the vehicle has some data points. In other words, the fused result will only contain isolated points in space rather than continuous areas... To this end, discrete sensing data are converted into a continuous PDF by mapping those data into continuous areas on the grid map. Finally, the multivehicle maps are fused.”, see modified FIG. 10 for the integrated multi-vehicle grid map)
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Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kogut by incorporating sensor fusion algorithm that is taught by Zheng, to make a method of clustering range sensor data points using object bounding box calculated from image data; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the range and accuracy of the system (Zheng, page 22946, right col, section C. Summary of Our Contribution: “The raw data of sensors are first converted into an occupancy probability density model, and then the multiview OGMs are merged at the spatial and probability levels, thereby expanding the perception range of single-vehicle sensing as well as improving the perception accuracy”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 8
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kogut in view of Zhang et al. (US-20230106188-A1, hereinafter Zhang).
In regards to Claim 8, Kogut teaches the apparatus of Claim 1. In addition, Kogut teaches the sensor fusion further comprises: acquiring object detection information from image data detected through a second sensor of the two or more sensors through the second preprocessing (Kogut, ¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections …”);
Kogut does not explicitly disclose responsive to objects being confirmed to be the same through a comparison of previous and current values of the object detection information: correcting a coordinate value and an area value based on a specified parameter by using a specified filter.
Zhang is in the same field of art of vision-based object tracking in autonomous vehicle. Further, Zhang teaches responsive to objects being confirmed to be the same through a comparison of previous and current values of the object detection information (Zhang, ¶ [0021-0022]: “compares newly detected objects with previously valid and tracked objects to see whether one newly detected object belongs to one of the previously detected valid objects … When the assignment exists (i.e., the new object matches, to a threshold degree, a previously detected object), the attributes of the previously detected valid object may be updated with the assigned newly detected object.”, claim 1, last paragraph: “the vehicular vision system, responsive to determining that the first object and the second object are the same object, merges the attribute of the first object with the attribute of the second object” ): correcting a coordinate value and an area value (Zhang, ¶ [0022 and 0024]: “ the attributes of the previously detected valid object may be updated with the assigned newly detected object. The attributes from the newly detected object can include parameters such as an object distance (x, y), bounding box size, object confidence (e.g., a confidence the system has in detection and/or classification of the object), object heights…” Zhang teaches object information that get updated includes object coordinate and object size) based on a specified parameter (Zhang, ¶ [0022]: “When the assignment exists (i.e., the new object matches, to a threshold degree, a previously detected object)” Zhang teaches matching two objects to a threshold to determine if they are the same object) by using a specified filter. (Zhang, ¶ [0022]: “Further post-processing (e.g., Kalman filter processing) may be performed to filter these attributes to avoid sudden jumps between previous attributes and new attributes.” Zhang teaches updating object information using Kalman filter)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kogut by incorporating the tracking method that is taught by Zhang, to make an object detection system that can also track object; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve performance of detection and tracking (Zhang, ¶ [0026]: “Thus, the vehicular vision system includes tracking-by-detection (e.g., via KCF) to provide an alternative solution for missed detection/classification of objects from conventional machine learning and deep learning algorithms. This improves the detection/classification rates of target objects and enhances the performance of the vehicular vision system. The system may additionally or alternatively provide temporal filtering. The temporal filtering operator (such as Kalman filter) may, in a final step, reduce temporal position variations of final valid target objects. Temporal filtering improves temporal consistency of outputted target objects”).
The combination of Kogut and Zhang then teaches performing grid mapping in a form of a box. (Kogut, ¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections … Such a grid 405 is shown in FIG. 4B. In this example a detected planar object 406 is projected on to the grid 405”; ¶ [0078]: “The objects in the occupancy grid may be listed as contours, clusters, or bounding boxes.” See modified FIG. 4B below, box 407 in
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grid 405)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 9
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kogut in view of Fukuman et al. (US-20160116586-A1, hereinafter Fukuman).
In regards to Claim 9, Kogut teaches the apparatus of Claim 1.
Kogut does not explicitly disclose an intersection area of direct and indirect waves among TOF values acquired through a first sensor.
Fukuman is in the same field of art of distance sensor based object detection system. Further, Fukuman teaches an intersection area of direct and indirect waves among TOF values acquired through a first sensor. (Fukuman, ¶ [0030]: “The sensors 21 to 24 transmit ultrasonic waves”; ¶ [0039]: “FIG. 3 is a diagram for explaining the stable detection area R1. The stable detection area R1 is set for each of the distance sensors 20. The stable detection area R1 shown in FIG. 3 is the one set for the center sensors 21 and 22. The stable detection area R1 is set in a part of an overlapped detection area which is an area of overlap between the detection area by the direct waves and the detection area by the indirect waves. Incidentally, the overlapped detection area is an area in which the triangulation method using the direct detection sensor and the indirect detection sensor holds”)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kogut by incorporating method to determine detection area for ultrasonic sensor that is taught by Fukuman, to make a object detection system that can determine a good/stable detection area; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve accuracy of the system (Fukuman, ¶ [0007]: “, for each distance sensor, there is an area in which the distance sensor can correctly detect the position of an object even in an environment severe in temperature or humidity, and an area in which the distance sensor is likely to erroneously detect the position of an object, and cannot detect the position at all in a severe environment. If the position of an object is erroneously detected by the distance sensor, it may occur that control to increase the running safety of a vehicle is triggered unnecessarily, or is not triggered in error.” Fukuman teaches an intersection/overlapped area of direct and indirect waves from ultrasonic sensors).
The combination of Kogut and Kutomi then teaches integrating grid mapping information (Kogut, ¶ [0051-0054]: “The occupancy sensor modelling module 260 uses the detection data 261 to generate occupancy evidence 262 based on the detection data 261 obtained from the respective occupancy sensors 265 (for example, from one or more of a lidar, radar or ultrasound system). Occupancy evidence 262 may take the form of an occupancy grid having a grid of probability values illustrating areas in the vehicle environment occupied by one or more objects …” Kogut teaches processing sensing data regarding detected objects (lidar, radar or ultrasonic sensor) to generate an occupancy map) on an intersection area of direct and indirect waves among TOF values acquired through a first sensor of the two or more sensors (Fukuman, ¶ [0030]: “The sensors 21 to 24 transmit ultrasonic waves”; ¶ [0039]: “FIG. 3 is a diagram for explaining the stable detection area R1. The stable detection area R1 is set for each of the distance sensors 20. The stable detection area R1 shown in FIG. 3 is the one set for the center sensors 21 and 22. The stable detection area R1 is set in a part of an overlapped detection area which is an area of overlap between the detection area by the direct waves and the detection area by the indirect waves. Incidentally, the overlapped detection area is an area in which the triangulation method using the direct detection sensor and the indirect detection sensor holds”. Fukuman teaches an intersection/overlapped area of direct and indirect waves from ultrasonic sensors);
grid mapping information in a form of an object box on object detection information detected through a second sensor of the two or more sensors (Kogut, ¶ [0056-0058]: “Detection data 271 may be obtained by analyzing image data obtained from the camera system 215. Detection data 271 relating to objects observed in camera images may be further analyzed to provide positioning information of one or more object detections … Such a grid 405 is shown in FIG. 4B. In this example a detected planar object 406 is projected on to the grid 405”, ¶ [0061-0062]: “A separate grid 405 may be provided for each classification. Such a grid may be termed a classification grid. The classification grid may be thought of as a semantic grid for a particular classification type”.); and
mapping all the information on a single integrated grid map. (Kogut, ¶ [0073-0075 and 0081-0084]: “the classified occupancy grid determiner 253 combines information contained in the occupancy grid with information contained in the transformed semantic grid … For any given cell, the semantic confidence value obtained from that cell of the transformed semantic grid and the occupancy probability obtained from that cell of the occupancy grid may be multiplied together. The result of this multiplication is a probability value of the cell being occupied and classified within a specific category.
p(A|occupancy) = p(occupancy)·pig(A)
A classified occupancy grid is thus determined including an array of classified occupancy probability values.” See FIG. 3. Kogut teaches a sensor fusion: processing ultrasonic sensor to obtain occupancy grid; processing image sensor to obtain classified semantic grid; then combining them into a classified occupancy grid; the final combined grid contains each object detected by both sensor system)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claims 4-7 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHUT HUY (JEREMY) PHAM whose telephone number is (703)756-5797. The examiner can normally be reached Mo - Fr. 8:30am - 6pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, O'Neal Mistry can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NHUT HUY PHAM/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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