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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/21/2024 has been made record of and considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: #126 of FIG. 1, see [0126]; #202-1, 202-2 (possibly the motion blurred image 201-1, 201-2 of [0130]) of FIG. 2, see [0130]; #114-1, 114-2 of FIG. 2, see [0130]-[0134]; #503-2 of FIG. 5A, see [0159]-[0162]; #509 of FIG. 5B, see [0161]; #603-1, 603-2, see [0165] – [0167]; #605-1 of FIG. 6A; #604-2, 605-2, 606-2, 609-2 of FIG. 6B. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: #201-1, 201-2, see [0130]; #710 of FIG. 7 appears to be mistitled, a redundancy of #702, see [0168]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “neutralization system” in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 5 is objected to because of the following informalities: the limitation “wherein at least one of the motion data is determined” is grammatically awkward and could introduce ambiguity. Appropriate correction is required.
Claim 11 is objected to because of the following informalities: claim 11 does not specify a first capture time. The examiner respectfully recommends aligning the language of claim 11 with that of claims 21.
Claim 6 is objected to because of the following informalities: “generate at least one Gaussian Receptive Fields (GRFs)” is a typo and should be corrected to “generate at least one or more Gaussian Receptive Fields (GRFs).” Appropriate correction is required.
Claim 7 is objected to because of the following informalities: “wherein execution of the instructions cause the” is a typo and should be corrected to “wherein execution of the instructions causes the.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claims 1-22 are 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.
Claims 1, 20, and 22 recite the limitation “the one or more targeted objects comprising less than 1/100th pixels of a total number of pixels in the first image frame.” This could be interpreted to mean that each separate target object comprises less than 1/100th, or that the one or more targeted objects in total comprise less than 1/100th of a total number of pixels in the first image frame. The specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claim 7 is similarly rejected. Appropriate correction is required. Claims 2-19 and 21 are similarly rejected, as they depend on claims 1, 20, and 22.
Further, claim 22 recites “comprising less than 1/100th pixels of a total number of pixels in the image frame.” It is unclear to the examiner whether “the image frame” refers to the first, second, or another image frame. Appropriate correction is required.
Claim 5 recites the limitation “the color data comprises at least one threshold value determined based on a comparison of at least one value for each pixel of the first image frame and a threshold value.” It is unclear to the examiner if “a threshold value” corresponds to “at least one threshold value.” Appropriate correction is required.
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.
Claims 1, 6, 9, 11, 17, 20, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang (“Video Tiny-Object Detection Guided by the Spatial-Temporal Motion Information”).
Consider claims 1 and 20, Yang discloses an object detection system [claim 20: method for detecting an object in an image frame] (Abstract; “a motion-guided video tiny-object detection method (MG-VTOD), in which the spatial-temporal motion strength maps play an important role in object searching and locating”), comprising:
an image capture system configured to obtain [claim 20: real-time] image data comprising at least a first image frame and a second image frame (1; “Tiny/small object detection is an important task in many realistic scenarios, e.g., locating unauthorized flying targets around airports through cameras”; 4.1.1; “dataset is comprised of 140 high-quality full HD thermal infrared video sequences”; 4.2; “Our method can process 28 frames per second, achieving a real-time object target detection”); and
a memory storing instructions that, when executed by one or more processors, cause the one or more processors to (4.1.2; “The algorithm is performed on a PC configured with AMD EPYC 7502 32-Core Processor, A100-PCI-E-40GB GPU.”):
process a first set of image data based on the first image frame (3.3; “the original video frames are preprocessed to improve the visual quality”),
process a second set of image data based on the first image frame and the second image frame (3.1; “implement the motion-processing pathway computation model based on the adjacent frames that have gone by. These adjacent frames can be reshaped into a sequential frame cuboid”; 3.3; “The motion computational module outputs the visual motion strength, yielding a one-channel grayscale map, in which the motion areas have large intensity (e.g., 1), while the static backgrounds have small intensity values (e.g., 0), and
execute a network model configured to detect one or more targeted objects from a plurality of potential objects in the first image frame based on an input comprising the first set of image data and the second set of image data (FIG. 3, 3.3, 4.1; appearance and motion; “utilize the motion strength to guide the object detection process… Following the working mechanism of the YOLOv5 method, all the input channels are sliced and sent to the convolutional layers. Subsequently, the convolutional responses of the motion strength maps and the preprocessed video frames are concatenated together.”), the one or more targeted objects comprising less than 1/100th pixels of a total number of pixels in the first image frame (3.3; “multi-scale prediction scheme and pre-defined anchor boxes are used to improve the detection ability for targets with different sizes; 4.1.1; “the target sizes in the Anti UAV-2021 dataset are mostly less than 2500 pixels, according to the statistical data. Compared to the field-of-view of the frame (327680 pixels), most of the targets only occupy a small region”… 1/100 of 327680 is 3,277 pixels… most targets are 2500 pixels, which is < 3,277 pixels).
Consider claim 8, Yang discloses the claimed invention wherein the network model comprises a modified You Only Look Once (YOLO) architecture (Yang 3.3, 4).
Consider claim 9, Yang discloses the claimed invention wherein execution of the instructions causes the one or more processors to determine a confidence score for the one or more targeted objects and further comprising a display configured to display the first image frame, the one or more targeted objects, and features corresponding to each of the one or more targeted objects (Yang FIG. 3; 4; 4.1.2),
wherein execution of the instructions causes the one or more processors to determine the features comprising a bounding box surrounding the one or more targeted objects and the confidence score for the one or more targeted objects (Yang FIG. 3; Section 2.2;4; 4.1.2; 4.2.1).
Consider claim 11, Yang discloses the claimed invention wherein the one or more processors are further caused to detect the one or more targeted objects and the corresponding features in 500 milliseconds or less (Yang 4.2.1; Table 1).
Consider claim 17, Yang discloses the claimed invention wherein execution of the instructions causes the one or more processors to train the network model based on training data comprising the plurality of potential objects in various scenarios (Yang 4.1.1).
Consider claim 21, Yang discloses the claimed invention wherein the real-time image data is received at a first time, and the one or more targeted objects in the first image frame are detected at a second time, the second time being 500 milliseconds or less after the first time (Yang 4.2.1; Table 1).
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yang.
Consider claim 15, Yang discloses the claimed invention wherein the network model is configured to determine at least one loss function associated with the detection of the one or more targeted objects (Yang 2.1).
While not explicitly part of Yang’s cited method, in their introduction, Yang discloses: “In the YOLOv4 method [3], the authors used multi-anchors to recognize a single object, easing the imbalance problem between positive and negative samples. Besides, the YOLOv4 method employs the complete intersection-over-union loss to compute the cost function, which can better describe the difference between the detection result and the ground truth.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the loss of Yolov3 in Yang’s introduction into the cited method of Yang to compute cost and improve accuracy (Yang Introduction).
Consider claim 16, Yang discloses the claimed invention wherein execution of the instructions causes the one or more processors to determine a confidence score for the detection of the one or more targeted objects, the confidence score being associated with at least one of the at least one loss function or an image quality associated with the image data (Yang FIG. 3, Sections 2, 3, 4).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Yang, in further view of Yao (‘Interactive Object Detection’), and in further view of Lee (‘Interactive Multi-Class Tiny-Object Detection’).
Consider claim 22, Yang discloses a method for training an object detection system, the method comprising, by one or more processors:
inputting training data comprising a plurality of potential objects in various scenarios into a network model, the plurality of potential objects comprising one or more targeted objects (1; “Tiny/small object detection is an important task in many realistic scenarios, e.g., locating unauthorized flying targets around airports through cameras”; 4.1.1; “dataset is comprised of 140 high-quality full HD thermal infrared video sequences”);
training the network model based on the training data to detect the one or more targeted objects from among the plurality of potential objects (2; 3.3; “proposing a MG-VTOD object detector that is illustrated in Fig. 2”);
processing a first set of image data based on a first image frame (3.3; “the original video frames are preprocessed to improve the visual quality”) and a second set of image data based on the first image frame and a second image frame (3.1; “implement the motion-processing pathway computation model based on the adjacent frames that have gone by. These adjacent frames can be reshaped into a sequential frame cuboid”; 3.3; “The motion computational module outputs the visual motion strength, yielding a one-channel grayscale map, in which the motion areas have large intensity (e.g., 1), while the static backgrounds have small intensity values (e.g., 0);
inputting the first set of image data and the second set of image data into the network model, the network model configured to detect the one or more targeted objects in the first image frame (FIG. 3, 3.3, 4.1; appearance and motion; “utilize the motion strength to guide the object detection process… Following the working mechanism of the YOLOv5 method, all the input channels are sliced and sent to the convolutional layers. Subsequently, the convolutional responses of the motion strength maps and the preprocessed video frames are concatenated together.”), the one or more potential objects comprising less than 1/100th pixels of a total number of pixels in the image frame (3.3; “multi-scale prediction scheme and pre-defined anchor boxes are used to improve the detection ability for targets with different sizes; 4.1.1; “the target sizes in the Anti UAV-2021 dataset are mostly less than 2500 pixels, according to the statistical data. Compared to the field-of-view of the frame (327680 pixels), most of the targets only occupy a small region”… 1/100 of 327680 is 3,277 pixels… most targets are 2500 pixels, which is < 3,277 pixels);
processing at least a portion of the first image frame including at least one of the one or more targeted objects with the network model to detect the one or more targeted objects (Sections 3, 4);
inputting(2, 4; ground truth annotation, missed detections);
determining at least one loss function associated with each of the one or more targeted objects detected (2.1; “the YOLOv4 method employs the complete intersection-over-union loss to compute the cost function, which can better describe the difference between the detection result and the ground truth”); and
Yang fails to explicitly disclose: inputting, via a user interface, a ground truth for any of the one or more targeted objects not detected by the network model;
re-training the network model based on the at least one loss function.
In related art, Yao discloses inputting, via a user interface, a ground truth for any of the one or more targeted objects not detected by the network model (Yao Incremental Hough Forests, Interactive Annotation; “The user then only needs to correct wrong hypotheses, i.e., missed detections (FN) and false positives (FP)”);
re-training the network model based on the at least one loss function (Yao Incremental Hough Forests; “These newly sampled patches Pincr are used to continue training the trees.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the user corrected missed detections through an interactive interface of Yao into the object detection method of Yang so that missed detections are manually annotated and returned to Yang’s loss-based training process, therein predictably reducing subsequent false negatives (Yang 4, Yao 3).
In related art, Lee further supports manually annotating the ground truth, and retraining the network model (Lee 1, 4.1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the manual annotation of Lee into the object detection of Yang, as modified by Yao, to further improve model accuracy (Yang 4; Lee 4.1).
Claims 2-3, 5, 10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yang as applied to claims 1, 6, 9, 11, 17, 20, and 21 above, and further in view of Singh (‘Real-time object detection and tracking using color feature and motion”).
Consider claim 2, while disclosing the first set of image data, and wherein the second set of image data comprises motion data (Yang 3.1-3.3), Yang fails to explicitly disclose the claimed invention wherein the first set of image data comprises color data.
In related art, Sing discloses wherein the first set of image data comprises color data (V. Implementation; VI Proposed Algorithm).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the color data of Singh into the object detection system of Yang to more efficiently detect and track objects (Yang 2-3, 5; Singh V, VI).
Consider claim 3, Yang, as modified by Singh, discloses the claimed invention wherein execution of the instructions causes the one or more processors to determine the motion data based on a difference between the first image frame and the second image frame (Yang 3.1-3.3; Singh V, VI).
Consider claim 5, Yang, as modified by Singh, discloses the claimed invention wherein at least one of the motion data is determined based on a comparison of the difference between the first image frame and the second image frame with a differential threshold value (Yang 3.3; Singh V, VI), or
the color data comprises at least one threshold value determined based on a comparison of at least one value for each pixel of the first image frame and a threshold value (Yang 3.3; Singh V, VI).
Consider claim 10, Yang fails to explicitly disclose the claimed invention wherein the features further comprise at least one of an expected velocity of the one or more targeted objects, a predicted position of the one or more targeted objects, or a direction of travel of the one or more targeted objects.
In related art, Singh discloses wherein the features further comprise at least one of an expected velocity of the one or more targeted objects, a predicted position of the one or more targeted objects, or a direction of travel of the one or more targeted objects (Singh VI).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the features of Singh into the object detection method of Yang to accurately track objects (Yang 5; Singh VI).
Consider claim 12, Yang fails to explicitly disclose the claimed invention wherein execution of the instructions causes the one or more processors to track the one or more targeted objects from the first image frame to the second image frame.
In related art, Singh discloses wherein execution of the instructions causes the one or more processors to track the one or more targeted objects from the first image frame to the second image frame.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the frame difference tracking of Singh into the object detection method of Yang to accurately track objects (Yang 5; Singh III, VI).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Yang as applied to claims 1, 6, 9, 11, 17, 20, and 21 above, and further in view of Shuigen (‘Motion Detection Based on Temporal Difference Method and Optical Flow field’).
Consider claim 4, while disclosing frame difference (Yang 3, 4), Yang fails to explicitly disclose the claimed invention wherein the motion data is determined according to: D(x, y) = |It(x, y) − It + 1(x, y)|, wherein It(x, y) comprises a value at each pixel in the first image frame and It + 1(x, y) comprises a value for each pixel in the second image frame.
In related art, Singh discloses wherein the motion data is determined according to: D(x, y) = |It(x, y) − It + 1(x, y)|, wherein It(x, y) comprises a value at each pixel in the first image frame and It + 1(x, y) comprises a value for each pixel in the second image frame (Shuigen Abstract; IV, V).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the motion detection method of Shuigen into the object detection method of Yang to detect/track objects in motion (Yang 4, 5; Shuigen Abstract, IV, V). See equations 1-13 of Shuigen.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Yang as applied to claims 1, 6, 9, 11, 17, 20, and 21 above, and further in view of Lai (“Detection of a Moving UAV based on Deep Learning-Based Distance Estimation”).
Consider claim 14, Yang discloses the claimed invention wherein the image capture system comprises at least one of:
one or more cameras configured for thermal detection, (Yang 4.1.1)
In related art, Lai discloses wherein the image capture system comprises at least one of:
a multi-modality image capture system,
one or more radars, or
one or more cameras configured to be synchronized together to capture images at a constant rate (Lai Introduction; LIDAR, cooperative, noncooperative; 2 monocular camera; 4.2 CMOS).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the capture components of Lai into the object detection system of Yang to predictably include at least one given image capture system component (Lai 1, 2, 4; Yang 4) in the object detection system.
Claims 13, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yang as applied to claims 1, 6, 9, 11, 17, 20, and 21 above, and further in view of Abramov (US 2020/0041234 A1).
Consider claim 13, Yang discloses the claimed invention wherein the plurality of potential objects comprises (Yang 3, 4; UAV video target detection).
In related art, Abramov discloses wherein the plurality of potential objects comprises animals, unmanned vehicles, and manned vehicles, and wherein the one or more targeted objects comprise the unmanned vehicles (Abramov ¶45, 148).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the potential objects of Abramov into the object detection system of Yang to lessen the occurrence of false positives in detections (Yang 3, 4).
Consider claim 18, Yang fails to explicitly disclose the claimed invention wherein execution of the instructions causes the one or more processors to:
detect that the one or more targeted objects is carrying a payload, and
alert a user on a location of the one or more targeted objects carrying the payload.
In related art, Abramov discloses detect that the one or more targeted objects is carrying a payload (Abramov ¶93, 95, 118), and
alert a user on a location of the one or more targeted objects carrying the payload (Abramov ¶8, 10, 92, 114-115, 118).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the payload identification of Abramov into the object detection system of Yang to identify and neutralize UAV threats (Abramov ¶8-10, 92-93, 114-118).
Consider claim 19, Yang, as modified by Abramov, discloses the claimed invention further comprising: a neutralization system configured to neutralize the one or more targeted objects carrying the payload (Abramov ¶119-1).
Allowable Subject Matter
Claims 6 and 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.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2022/0073204 A1discloses a method for transportation using UAVs.
US 2022/0301188 A1 discloses a method for target object tracking.
US 2025/0054265 A1 discloses systems and methods for object detection of unmanned aerial vehicles.
CN111460968A discloses a video-based UAV identification and tracking method.
Zarei ("Fast-Yolo-rec: Incorporating Yolo-base detection and recurrent-base prediction networks for fast vehicle detection in consecutive images.")
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY HYTREK whose telephone number is (703)756-4562. The examiner can normally be reached M-F 9:00-5:00.
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/ASHLEY HYTREK/ Examiner, Art Unit 2665
/Stephen R Koziol/ Supervisory Patent Examiner, Art Unit 2665
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