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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-2, 5-7 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 5-7 of U.S. Patent No. US 12,394,202 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because subject matter claimed in the instant application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter (See table below):
Instant Application
Patent No. US 12,394,202 B2
1. A method comprising: a) receiving, at a server, video content; b) extracting, by the server, individual frames from the video content; c) performing object detection on the individual frames to identify identified objects within the individual frames; d) tracking the identified objects across the individual frames to maintain object continuity to identify tracked objects; e) determining, by an object importance generative AI component, object importance for the tracked objects; f) analyzing, by a Long Short-Term Memory (LSTM) generative AI component, the tracked objects and the object importance to generate a contextual timeline of events; g) analyzing, by a scene understanding generative AI component, the contextual timeline of events to generate a contextual understanding of the scene; and h) generating, by a report generation generative AI component, a formatted report based on the contextual timeline of events, the contextual understanding of the scene, and a report template.
2. The method of claim 1, wherein the object detection is performed by an object detection generative AI component.
3. The method of claim 2, wherein the tracking of the identified objects is performed by an object tracking generative AI component.
1. A method for generating a report from a video, the method comprising: a) receiving, at a server, the video comprising audio data and multiple frames of image data; b) extracting, by the server, individual frames from the video; c) performing, by an object detection generative AI component, object detection on the individual frames to identify identified objects within the individual frames; d) tracking, by an object tracking generative AI component, the identified objects across the individual frames to maintain object continuity to identify tracked objects; e) analyzing, by a Long Short-Term Memory (LSTM) generative AI component, the tracked objects, the audio data, identified feature points, and geolocation-enhanced data to generate a contextual timeline of events; and f) detecting, by an event detection generative AI component, events within the contextual timeline generated by the LSTM generative AI component; g) analyzing, by a scene understanding generative AI component, the detected events to generate a contextual understanding of the scene; h) generating, based on the contextual timeline, the detected events, the contextual understanding, and a report template, a formatted report using a report generation generative AI component.
4. The method of claim 1, wherein the video content contains audio data, and the method further comprising: i) analyzing, by an audio analysis component, the audio data to create analyzed audio data, and ii) submitting the analyzed audio data as input to the LSTM generative AI component.
2. The method of claim 1, further comprising analyzing, by an audio analysis component, the audio data to create analyzed audio data, and submitting the analyzed audio data as input to the LSTM generative AI component.
5. The method of claim 4, wherein the analyzed audio data is textual, and includes an audio transcript of spoken words.
3. The method of claim 2, wherein the analyzed audio data is textual, and includes an audio transcript of spoken words.
6. The method of claim 5, wherein the analyzed audio data includes descriptions of non-spoken sounds.
4. The method of claim 3, wherein the analyzed audio data includes descriptions of non-spoken sounds.
7. The method of claim 1, wherein the step of generating, by a report generation generative AI component, a formatted report further comprises: i) generating, by a description generation generative AI component, a written description of the events based on the contextual timeline of events, and ii) applying, by the report generation generative AI component, the report template to the written description to produce the formatted report.
5. The method of claim 1, wherein step f) further comprises: i) generating, by a description generation generative AI component, a written description of the events based on the contextual timeline of events; and ii) applying, by the report generation generative AI component, the report template to the written description to produce the formatted report.
8. The method of claim 1, where the step of determining, by an object importance generative AI component, object importance further comprises: i) aggregating, by the server, metadata for the tracked objects identified across the individual frames to create a dataset of each object's behavior over time, and ii) assigning, by the object importance generative AI component, importance weights to the tracked objects based on the dataset of each object's behavior over time; wherein the importance weights are submitted to the LSTM generative AI component as the object importance.
6. The method of claim 1, further comprising: i) aggregating, by the server, metadata for the tracked objects identified across the individual frames to create a dataset of each object's behavior over time; and ii) assigning, by an object importance generative AI component, importance weights to the tracked objects based on the aggregated metadata, and iii) submitting the importance weights to the LSTM generative AI component for use in generating the contextual timeline of events.
9. The method of claim 1, further comprising: i) identifying, by a feature point identification component, identified feature points within the individual frames, and ii) submitting the identified feature points to the LSTM generative AI component for use in generating the contextual timeline of events.
7. The method of claim 1, further comprising: identifying, by a feature point identification component, the identified feature points within the individual frames [see claim 1 for “LSTM”].
10. The method of claim 1, further comprising: applying, by a geomapping component, geolocation data associated with the video content to the tracked objects to generate geolocation-enhanced data; and submitting the geolocation-enhanced data to the LSTM generative AI component to enhance location information in the contextual timeline of events.
8. The method of claim 1, further comprising: applying, by a geomapping component, geolocation data associated with the video to the tracked objects to generate the geolocation-enhanced data to enhance location information in the contextual timeline of events [see claim 1 for “LSTM”].
11. A method comprising: a) receiving, at a server, video content; b) extracting, by the server, individual frames from the video content; c) performing object detection on the individual frames to identify identified objects within the individual frames; d) applying, by a geomapping component, geolocation data associated with the video content to the identified objects to generate geolocation-enhanced data; e) tracking the identified objects across the individual frames to maintain object continuity to identify tracked objects; f) determining, by an object importance generative AI component, object importance for the tracked objects; g) analyzing, by an audio analysis component, audio data within the video content to create analyzed audio data; h) analyzing, by a Long Short-Term Memory (LSTM) generative AI component, the tracked objects, the analyzed audio data, and the geolocation-enhanced data to generate a contextual timeline of events; i) detecting, by an event detection generative AI component, detected events within the contextual timeline generated by the LSTM generative AI component; j) analyzing, by a scene understanding generative AI component, the detected events to generate a contextual understanding of the scene; and k) generating, by a report generation generative AI component, a formatted report based on the contextual timeline of events, the contextual understanding of the scene, and a report template.
12. The method of claim 11, further comprising identifying, by a feature point identification component, feature points within the individual frames.
13. The method of claim 12, further comprising using the feature points to identified tracked objects.
9. A method for generating a report from a video, the method comprising: a) receiving, at a server, the video comprising audio data and multiple frames of image data; b) extracting, by the server, individual frames from the video; c) performing, by an object detection generative AI component, object detection on the individual frames to identify identified objects within the individual frames; d) tracking, by an object tracking generative AI component, the identified objects across the individual frames to maintain object continuity to identify tracked objects; e) analyzing, by an audio analysis component, the audio data to create analyzed audio data; f) identifying, by a feature point identification component, feature points within the individual frames; g) applying, by a geomapping component, geolocation data associated with the video to the tracked objects to generate geolocation-enhanced data; h) analyzing, by an LSTM generative AI component, the tracked objects, the analyzed audio data, the identified feature points, and the geolocation-enhanced data to generate a contextual timeline of events; i) detecting, by an event detection generative AI component, events within the contextual timeline generated by the LSTM generative AI component; j) analyzing, by a scene understanding generative AI component, the detected events to generate a contextual understanding of the scene; k) generating, based on the contextual timeline, the detected events, the contextual understanding, and a report template, a formatted report using a report generation generative AI component.
14. The method of claim 11, wherein the step of generating, by a report generation generative AI component, a formatted report further comprises: i) generating, by a description generation generative AI component, a written description of the events based on the contextual understanding of the scene, and ii) applying, by the report generation generative AI component, the report template to the written description to produce the formatted report.
5. The method of claim 1, wherein step f) further comprises: i) generating, by a description generation generative AI component, a written description of the events based on the contextual timeline of events; and ii) applying, by the report generation generative AI component, the report template to the written description to produce the formatted report.
Claims 15-20 list all similar elements of claims 1, 5 and 7, but in device form rather than method form. Therefore, the supporting rationale of the rejection to claims 1, 5 and 7 applies equally as well to claims 15-20.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8-9, 15, 18-19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kellicker et al. US 2020/0321030 further in view of LI et al. US 2024/0062518.
In regarding to claim 1 Kellicker teaches:
1. A method comprising: a) receiving, at a server, video content;
[0019] The transcoder 110 is configured to generate multimedia content 112 based on the data 108. For example, the transcoder 110 may transcode the data 108 to comply with a particular file format to generate the multimedia content 112. In some implementations, the transcoder 110 is configured to generate segments (or “chunks”) 114. For example, each segment can include a particular duration of video and accompanying audio that is based on samples of sounds included in the data 108. As used herein, a “segment” includes video data, audio data, or both. A “video segment” includes at least video data, but not necessarily audio data, and an “audio segment” includes at least audio data, but not necessarily video data. It will be appreciated that when the transcoder 110 generates the multimedia content 112 on the basis of the data 108 as the capture device(s) 102 capture and transmit the data 108, the multimedia content 112 may correspond to a live stream.
Kellicker, 0019, emphasis added
b) extracting, by the server, individual frames from the video content;
[0021] In a particular example, the transcoder 110 is configured to provide segments 115 (which may or may not be identical to the segments 114) to the AI analyzer 120. In some implementations, the transcoder 110 is configured to selectively provide the segments 115, or portions thereof, or data generated from portions thereof, to the AI analyzer 120 (e.g., without other information included in the multimedia content 112) in order to reduce an amount of data transferred to the AI analyzer 120, to reduce processing overhead incurred by the AI analyzer 120, etc. In some examples, the AI analyzer 120 is configured to perform AI techniques on data, such as extracted features or feature vectors, that is derived from segments that are generated by the transcoder 110. In such examples, the features or feature vectors may be determined at the AI analyzer based on the received segments 115. Alternatively, the transcoder 110 may determine and provide such features or feature vectors to the AI analyzer 120 rather than providing the segments 115 themselves. As illustrative non-limiting examples, features or feature vectors may be generated by performing computer vision operations, including but not limited to image segmentation, color segmentation, image filtering, features from accelerated segment test (FAST), speeded up robust features (SURF), scale-invariant feature transform (SIFT), corner detection, edge detection, background subtraction, blob detection, other computer vision operations, etc.
Kellicker, 0021, emphasis added
c) performing object detection on the individual frames to identify identified objects within the individual frames;
[0004] In accordance with aspects of the disclosure, metadata is determined for multimedia content using content analysis techniques and in parallel with other processing of the multimedia content, with transport of the multimedia content, or both. The content analysis techniques may, in some examples, include artificial intelligence (AI)-based audio and/or video processing, as further described herein. Examples of such processing may include, but are not limited to, object detection, object tracking, facial detection, facial recognition, text recognition, text extraction, text-to-speech, speech-to-text, vehicle recognition, animal detection, person detection, clustering, anomaly detection, scene change detection, etc. In an illustrative example, after transcoding of data to generate the multimedia content, video segments of the multimedia content (or data generated therefrom) may be analyzed by an AI analyzer to generate the metadata in parallel with transport of the multimedia content to a multimedia player. Thus, the metadata for particular analyzed segment(s) of the multimedia content may be provided to the multimedia player “out of band” with respect to the segment(s).
Kellicker, 0004, emphasis added
d) tracking the identified objects across the individual frames to maintain object continuity to identify tracked objects;
[0004] In accordance with aspects of the disclosure, metadata is determined for multimedia content using content analysis techniques and in parallel with other processing of the multimedia content, with transport of the multimedia content, or both. The content analysis techniques may, in some examples, include artificial intelligence (AI)-based audio and/or video processing, as further described herein. Examples of such processing may include, but are not limited to, object detection, object tracking, facial detection, facial recognition, text recognition, text extraction, text-to-speech, speech-to-text, vehicle recognition, animal detection, person detection, clustering, anomaly detection, scene change detection, etc. In an illustrative example, after transcoding of data to generate the multimedia content, video segments of the multimedia content (or data generated therefrom) may be analyzed by an AI analyzer to generate the metadata in parallel with transport of the multimedia content to a multimedia player. Thus, the metadata for particular analyzed segment(s) of the multimedia content may be provided to the multimedia player “out of band” with respect to the segment(s).
Kellicker, 0004, 0029, emphasis added
e) determining, by an object importance generative AI component, object importance for the tracked objects;
[0004] In accordance with aspects of the disclosure, metadata is determined for multimedia content using content analysis techniques and in parallel with other processing of the multimedia content, with transport of the multimedia content, or both. The content analysis techniques may, in some examples, include artificial intelligence (AI)-based audio and/or video processing, as further described herein. Examples of such processing may include, but are not limited to, object detection, object tracking, facial detection, facial recognition, text recognition, text extraction, text-to-speech, speech-to-text, vehicle recognition, animal detection, person detection, clustering, anomaly detection, scene change detection, etc. In an illustrative example, after transcoding of data to generate the multimedia content, video segments of the multimedia content (or data generated therefrom) may be analyzed by an AI analyzer to generate the metadata in parallel with transport of the multimedia content to a multimedia player. Thus, the metadata for particular analyzed segment(s) of the multimedia content may be provided to the multimedia player “out of band” with respect to the segment(s).
Kellicker, 0004, 0029, emphasis added
however, Kellicker fails to explicitly teach, but Li teaches:
f) analyzing, by a Long Short-Term Memory (LSTM) generative AI component, the tracked objects and the object importance to generate a contextual timeline of events;
[0043] S150: performing classification using a Long Short-Term Memory (LSTM) network classification model, including inputting trajectory curve trend, object movement acceleration, intersection over union of previous and current frames of the object, object shape and pixel size changes as feature data into the LSTM, to obtain a classification result and determine whether a false positive occurs;
[0044] In the deep learning field, LSTM is a special type of RNN to solve the problem that RNN is not capable of handling long-term dependencies. FIG. 2 illustrates a schematic flow chart of the classification process of the Long Short-Term Memory (LSTM) network classification model according to an embodiment of the present application. The present embodiment uses Kalman filtering algorithm to obtain the trajectory curve trend, object movement acceleration, intersection over union of previous and current frames of the object, object shape and pixel size changes as the feature data that best represents the content of the moving objects Then input the feature data into the LSTM network for training and learning, and finally the classification result is output to determine whether the object is thrown from height.
Li, 0043-0048, emphasis added
g) analyzing, by a scene understanding generative AI component, the contextual timeline of events to generate a contextual understanding of the scene;
[0043] S150: performing classification using a Long Short-Term Memory (LSTM) network classification model, including inputting trajectory curve trend, object movement acceleration, intersection over union of previous and current frames of the object, object shape and pixel size changes as feature data into the LSTM, to obtain a classification result and determine whether a false positive occurs;
[0044] In the deep learning field, LSTM is a special type of RNN to solve the problem that RNN is not capable of handling long-term dependencies. FIG. 2 illustrates a schematic flow chart of the classification process of the Long Short-Term Memory (LSTM) network classification model according to an embodiment of the present application. The present embodiment uses Kalman filtering algorithm to obtain the trajectory curve trend, object movement acceleration, intersection over union of previous and current frames of the object, object shape and pixel size changes as the feature data that best represents the content of the moving objects Then input the feature data into the LSTM network for training and learning, and finally the classification result is output to determine whether the object is thrown from height.
Li, 0043-0048, emphasis added
and h) generating, by a report generation generative AI component, a formatted report based on the contextual timeline of events, the contextual understanding of the scene, and a report template.
[0044] In the deep learning field, LSTM is a special type of RNN to solve the problem that RNN is not capable of handling long-term dependencies. FIG. 2 illustrates a schematic flow chart of the classification process of the Long Short-Term Memory (LSTM) network classification model according to an embodiment of the present application. The present embodiment uses Kalman filtering algorithm to obtain the trajectory curve trend, object movement acceleration, intersection over union of previous and current frames of the object, object shape and pixel size changes as the feature data that best represents the content of the moving objects Then input the feature data into the LSTM network for training and learning, and finally the classification result is output to determine whether the object is thrown from height.
Li, 0043-0048, emphasis added
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the system of Kellicker in order e) analyzing, by a Long Short-Term Memory (LSTM) generative AI component, the tracked objects to generate a contextual timeline of events; and f) generating, based on the contextual timeline and a report template, a formatted report using a report generation generative AI component, as such, the method has strong anti-interference ability and can effectively filter out moving objects…--Abstract.
Note: The motivation that was applied to claim 1 above, applies equally as well to claims 2-3, 8-9, 15, 18-19, 13 and 20 as presented blow.
In regarding to claim 2 Kellicker and LI teaches:
2. The method of claim 1, furthermore, Kellicker teaches: wherein the object detection is performed by an object detection generative AI component.
Kellicker, 0004, 0029
In regarding to claim 3 Kellicker and LI teaches:
3. The method of claim 2, furthermore, Kellicker teaches: wherein the tracking of the identified objects is performed by an object tracking generative AI component.
Kellicker, 0004, 0029
In regarding to claim 6 Kellicker and LI teaches:
8. The method of claim 1, furthermore, Li teaches: where the step of determining, by an object importance generative AI component, object importance further comprises: i) aggregating, by the server, metadata for the tracked objects identified across the individual frames to create a dataset of each object's behavior over time,
Li, 0047-0048
and ii) assigning, by the object importance generative AI component, importance weights to the tracked objects based on the dataset of each object's behavior over time; wherein the importance weights are submitted to the LSTM generative AI component as the object importance.
Li, 0047-0048
In regarding to claim 6 Kellicker and LI teaches:
9. The method of claim 1, further comprising: furthermore, Li teaches: i) identifying, by a feature point identification component, identified feature points within the individual frames,
Li, 0047-0048
and ii) submitting the identified feature points to the LSTM generative AI component for use in generating the contextual timeline of events.
Li, 0047-0048
Claims 15, 18-19 and 20 list all similar elements of claims 1, 8-9 and 1, but in device form rather than method form. Therefore, the supporting rationale of the rejection to claims 1, 8-9 and 1 applies equally as well to claims 15, 18-19 and 20.
Claim Rejections - 35 USC § 103
Claims 4-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kellicker et al. US 2020/0321030 and LI et al. US 2024/0062518 further in view of Daugherty US 2022/0262406.
In regarding to claim 4 Kellicker and LI teaches:
4. The method of claim 1, however, Kellicker and LI fails to explicitly teach, but Daugherty teaches: wherein the video content contains audio data, and the method further comprising: i) analyzing, by an audio analysis component, the audio data to create analyzed audio data, and ii) submitting the analyzed audio data as input to the LSTM generative AI component.
[0110] Referring again to step 1013, wherein the generated transcript of words and corresponding timestamps are stored by audio analyzer 506, in a next step 1014, audio analyzer 506 may run a transcript clarity analysis. In an embodiment, audio analyzer 506 may run the transcript clarity analysis using a natural language processing (NLP) model, as depicted. In the embodiment, audio analyzer 506 may combine an array of words within the transcription of words, combine the array of words into singular text, and input the singular text to a neural network, e.g., a Long Short Term Memory neural network (LSTM). In an embodiment, the LSTM network may be trained by model creator 508 on a plurality of diarized text files of varying clarity levels, that have been tagged by model creator 508 for clarity via Amazon® Mechanical Turk and/or other crowdsourcing platforms.
Daugherty, 0110-0111, emphasis added
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Daugherty with the system of Kellicker and Li in order wherein the video content contains audio data, and the method further comprising: i) analyzing, by an audio analysis component, the audio data to create analyzed audio data, and ii) submitting the analyzed audio data as input to the LSTM generative AI component, as such, the system may then select a best audio track corresponding to each media object based on the audio scores and create a narrative sequence comprising of media object slots filled with media objects and corresponding best audio tracks for each media object …--Abstract.
Note: The motivation that was applied to claim 2 above, applies equally as well to claims 3-5, 12, 14 and 15 as presented blow.
In regarding to claim 5 Kellicker, Li and Daugherty teaches:
5. The method of claim 4, furthermore, Daugherty teaches: wherein the analyzed audio data is textual, and includes an audio transcript of spoken words.
[0110] Referring again to step 1013, wherein the generated transcript of words and corresponding timestamps are stored by audio analyzer 506, in a next step 1014, audio analyzer 506 may run a transcript clarity analysis. In an embodiment, audio analyzer 506 may run the transcript clarity analysis using a natural language processing (NLP) model, as depicted. In the embodiment, audio analyzer 506 may combine an array of words within the transcription of words, combine the array of words into singular text, and input the singular text to a neural network, e.g., a Long Short Term Memory neural network (LSTM). In an embodiment, the LSTM network may be trained by model creator 508 on a plurality of diarized text files of varying clarity levels, that have been tagged by model creator 508 for clarity via Amazon® Mechanical Turk and/or other crowdsourcing platforms.
Daugherty, 0110-0111, emphasis added
In regarding to claim 6 Kellicker, Li and Daugherty teaches:
6. The method of claim 5, furthermore, Daugherty teaches: wherein the analyzed audio data includes descriptions of non-spoken sounds.
Daugherty, 0110-0111
In regarding to claim 5 Kellicker, Li and Daugherty teaches:
7. The method of claim 1, furthermore, Daugherty teaches: wherein the step of generating, by a report generation generative AI component, a formatted report further comprises: i) generating, by a description generation generative AI component, a written description of the events based on the contextual timeline of events,
Daugherty, 0110-0111
and ii) applying, by the report generation generative AI component, the report template to the written description to produce the formatted report.
Daugherty, 0110-0111
Note: The motivation that was applied to claim 4 above, applies equally as well to claims 5-7 and 16-17 as presented blow.
Claims 16-17 list all similar elements of claims 4 and 7, but in device form rather than method form. Therefore, the supporting rationale of the rejection to claims 4 and 7 applies equally as well to claims 16-17.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T TEKLE whose telephone number is (571)270-1117. The examiner can normally be reached Monday-Friday 8:00-4:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Vaughn can be reached at 571-272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL T TEKLE/Primary Examiner, Art Unit 2481
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