Detailed Action
Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/2/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Claim 1 recites a method comprising: applying machine learning model. Therefore, it is directed to the statutory category of Processes.
2A Prong 1:
comparing the at least one predicted activity map to corresponding ones of the plurality of activity maps; (mental process of evaluation – comparing activity maps does not require a computer component and can be performed in one’s mind)
in response to a result of the comparison indicating anomalous object behavior, initiating at least one
2A Prong 2:
A method, comprising: obtaining a plurality of activity maps comprising data characterizing one or more objects of at least one object type within a monitored physical environment; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
applying one or more of the plurality of activity maps to a machine learning model trained to generate … wherein the machine learning model is implemented using at least one hardware device; (mere instructions to apply an exception of comparing activity maps using a generic computer component. See MPEP 2106.05(f))
… initiating at least one automated action; (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B:
A method, comprising: obtaining a plurality of activity maps comprising data characterizing one or more objects of at least one object type within a monitored physical environment; (indicated as an insignificant extra-solution activity MPEP 2106.05(g) in Step 2A Prong 2. Therefore, it is re-evaluated in Step 2B as well understood, routine and conventional activity of gathering statistics. See MPEP 2106.05(d)(II)(iv))
applying one or more of the plurality of activity maps to a machine learning model trained to generate … wherein the machine learning model is implemented using at least one hardware device; (mere instructions to apply an exception of comparing activity maps using a generic computer component. See MPEP 2106.05(f))
… initiating at least one automated action; (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding claim 2,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion of the monitored physical environment. (a field of use and technological environment. See MPEP 2106.05(h))
2B: wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion of the monitored physical environment. (a field of use and technological environment. See MPEP 2106.05(h))
Regarding claim 3,
Step 1: Processes, as above.
2A Prong 1: The method of claim 2, wherein the given activity map of the plurality of activity maps corresponds to a particular object type and wherein the given cell of the given activity map comprises aggregated data characterizing one or more objects of the particular object type in the given cell. (mental process of observation – aggregating and structuring the data, which can be performed with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 4,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein a given activity map of the plurality of activity maps comprises a plurality of features obtained at least in part using sensor data from one or more sensors in the monitored physical environment. (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
2B: wherein a given activity map of the plurality of activity maps comprises a plurality of features obtained at least in part using sensor data from one or more sensors in the monitored physical environment. (indicated as an insignificant extra-solution activity MPEP 2106.05(g) in Step 2A Prong 2. Therefore, it is re-evaluated in Step 2B as well understood, routine and conventional activity of gathering statistics. See MPEP 2106.05(d)(II)(iv))
Regarding claim 5,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the at least one automated action comprises one or more of generating an alert and providing at least a portion of the data characterizing the one or more objects of the at least one object type within the monitored physical environment to at least one designated system associated with the monitored physical environment. (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
2B: wherein the at least one automated action comprises one or more of generating an alert and providing at least a portion of the data characterizing the one or more objects of the at least one object type within the monitored physical environment to at least one designated system associated with the monitored physical environment. (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
Regarding claim 6,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the machine learning model is trained to generate the at least one predicted activity map using a plurality of historical activity maps, wherein a first subset of the plurality of historical activity maps is used to generate at least one predicted training activity map and wherein one or more parameters of the machine learning model are adjusted based at least in part on a result of a comparison of a second subset of the plurality of historical activity maps to respective ones of the at least one predicted training activity map. (mere instructions to apply an exception of comparing activity maps using a generic computer component (machine learning model trained on a plurality of data). See MPEP 2106.05(f))
2B: wherein the machine learning model is trained to generate the at least one predicted activity map using a plurality of historical activity maps, wherein a first subset of the plurality of historical activity maps is used to generate at least one predicted training activity map and wherein one or more parameters of the machine learning model are adjusted based at least in part on a result of a comparison of a second subset of the plurality of historical activity maps to respective ones of the at least one predicted training activity map. (mere instructions to apply an exception of comparing activity maps using a generic computer component (machine learning model trained on a plurality of data). See MPEP 2106.05(f))
Regarding claim 7,
Step 1: Processes, as above.
2A Prong 1: The method of claim 1, wherein the comparing further comprises identifying one or more disparities between the at least one predicted activity map and the corresponding ones of the plurality of activity maps that represent anomalous object behavior. (mental process of evaluation – comparing activity maps does not require a computer component and can be performed in one’s mind)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 8,
Step 1: Claim 8 recites a non-transitory processor-readable storage medium having stored therein program code. Therefore, it is directed to the statutory category of a machine.
2A Prong 1: Claim 8 is a machine claim which recites the same feature as the method claim 1, and is rejected for at least the same reasons.
2A Prong 2: A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps: (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps: (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 9 is a machine claim which recites the same feature as the method claim 2, and is rejected for at least the same reasons.
Claim 10 is a machine claim which recites the same feature as the method claim 3, and is rejected for at least the same reasons.
Claim 11 is a machine claim which recites the same feature as the method claim 4, and is rejected for at least the same reasons.
Claim 12 is a machine claim which recites the same feature as the method claim 5, and is rejected for at least the same reasons.
Claim 13 is a machine claim which recites the same feature as the method claim 6, and is rejected for at least the same reasons.
Claim 14 is a machine claim which recites the same feature as the method claim 7, and is rejected for at least the same reasons.
Regarding claim 15,
Step 1: Claim 15 recites an apparatus comprising: at least one processing device comprising a processor. Therefore, it is directed to the statutory category of a machine.
2A Prong 1: Claim 15 is a machine claim which recites the same feature as the method claim 1, and is rejected for at least the same reasons.
2A Prong 2: An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured to implement the following steps: (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured to implement the following steps: (mere instructions to apply an exception using a generic computer component. See MPEP 2106.05(f))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding claim 16,
Step 1: A machine, as above.
2A Prong 1: The apparatus of claim 15, … wherein the given activity map of the plurality of activity maps corresponds to a particular object type and wherein the given cell of the given activity map comprises aggregated data characterizing one or more objects of the particular object type in the given cell. (mental process of observation – aggregating and structuring the data, which can be performed with the aid of pen and paper)
2A Prong 2: wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion of the monitored physical environment, (a field of use and technological environment. See MPEP 2106.05(h))
2B: wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion of the monitored physical environment, (a field of use and technological environment. See MPEP 2106.05(h))
Claim 17 is a machine claim which recites the same feature as the method claim 4, and is rejected for at least the same reasons.
Claim 18 is a machine claim which recites the same feature as the method claim 5, and is rejected for at least the same reasons.
Claim 19 is a machine claim which recites the same feature as the method claim 6, and is rejected for at least the same reasons.
Claim 20 is a machine claim which recites the same feature as the method claim 7, and is rejected for at least the same reasons.
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, 4-5, 7-8, 11-12, 14-15, 17-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaehler et al. (US 20230078625, hereinafter ‘Kaehler’).
Regarding claim 1, Kaehler teaches:
A method, comprising: obtaining a plurality of activity maps comprising data characterizing one or more objects of at least one object type within a monitored physical environment; ([0051] At 320, robot system 102 with computing system obtains sensor data of the robot, which includes image data generated by a camera. The object includes an indication of a position of one or more components (i.e., objects) of the robot such as a position of an arm, leg, body, wheel, tool, or other parts. [0022] supports that the embedding is a map, as the vector representation maps sensor data of the robot to vector space, and it corresponds to different tasks and movements)
applying one or more of the plurality of activity maps to a machine learning model trained to generate at least one predicted activity map, wherein the machine learning model is implemented using at least one hardware device; ([0052] The robot system generates a first embedding indicating a state of the robot after performing the action using the sensor data and an embedding model or [0041-0042] send the sensor data to the encoder model 203 which generates vector representation (e.g., an embedding) indicating the state of the robot (i.e., predicted activity map predicted using the embedding model or the encoder). The encoder model 203 may be trained via the encoder trainer 204)
comparing the at least one predicted activity map to corresponding ones of the plurality of activity maps; and ([0031] The anomaly detection model determine that an embedding corresponds to an anomaly using distance metrics calculated based on similarity metric between the first embedding (i.e., predicted map, which may be anomalous) disclosed in [0041] and [0052] with other embeddings (activity maps, which are non-anomalous). If the similarity score fails to exceed the threshold, the system determine that the first embedding corresponds to the anomaly)
in response to a result of the comparison indicating anomalous object behavior, initiating at least one automated action; ([0033] In response to determining that the first embedding corresponds to an anomaly, the computing system 100 may cause the robot to perform an action or prevent the robot from performing an action)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. ([0033] In response to determining that the first embedding corresponds to an anomaly, the computing system 100 may cause the robot to perform an action or prevent the robot from performing an action. [0049] The computing system has processors and memories)
Regarding claim 4, Kaehler teaches:
The method of claim 1, wherein a given activity map of the plurality of activity maps comprises a plurality of features obtained at least in part using sensor data from one or more sensors in the monitored physical environment. ([0019] discloses that the embeddings (vector representations) include information sensed by a sensor suit of the robot as it performs tasks. The embeddings include a plurality of features obtained by various sensors (multiple color 1080p cameras, touch sensors, motor sensors …) which corresponds to various features and monitored physical environments. [0051] further supports it)
Regarding claim 5, Kaehler teaches:
The method of claim 1, wherein the at least one automated action comprises one or more of generating an alert and providing at least a portion of the data characterizing the one or more objects of the at least one object type within the monitored physical environment to at least one designated system associated with the monitored physical environment. ([0033]-[0034] The robot system 102 may send an alert to the server 106 if the embedding is determined to be an anomaly. The alert may include the embedding that was classified as an anomaly and sensor information (data characterizing the object of the object type within the physical environment) from the robot)
Regarding claim 7, Kaehler teaches:
The method of claim 1, wherein the comparing further comprises identifying one or more disparities between the at least one predicted activity map and the corresponding ones of the plurality of activity maps that represent anomalous object behavior. ([0036] The server may determine that the anomalous embedding with the data from the anomalous instances (i.e., the plurality of activity maps that represent anomalous behavior) and determine that the anomalous embedding is a match with an embedding based on a threshold similarity score (i.e., disparities) calculated using a distance metric)
Regarding claim 8, Kaehler teaches:
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps: ([0033] In response to determining that the first embedding corresponds to an anomaly, the computing system 100 may cause the robot to perform an action or prevent the robot from performing an action. [0049] The computing system has processors and memories)
Claim 8 is a machine claim which recites the same feature as the method claim 1, and is rejected for at least the same reasons.
Claim 11 is a machine claim which recites the same feature as the method claim 4, and is rejected for at least the same reasons.
Claim 12 is a machine claim which recites the same feature as the method claim 5, and is rejected for at least the same reasons.
Claim 14 is a machine claim which recites the same feature as the method claim 7, and is rejected for at least the same reasons.
Regarding claim 15, Kaehler teaches:
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured to implement the following steps: ([0033] In response to determining that the first embedding corresponds to an anomaly, the computing system 100 may cause the robot to perform an action or prevent the robot from performing an action. [0049] The computing system has processors and memories)
Claim 15 is a machine claim which recites the same feature as the method claim 1, and is rejected for at least the same reasons.
Claim 17 is a machine claim which recites the same feature as the method claim 4, and is rejected for at least the same reasons.
Claim 18 is a machine claim which recites the same feature as the method claim 5, and is rejected for at least the same reasons.
Claim 20 is a machine claim which recites the same feature as the method claim 7, and is rejected for at least the same reasons.
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.
Claims 2-3, 9-10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kaehler in view of Pennisi et al., (“Online real-time crowd behavior detection in video sequences”, 2016, hereinafter ‘Pennisi’).
Regarding claim 2, Kaehler teaches:
The method of claim 1, wherein a given activity map of the plurality of activity maps [0022] shows that the embedding is a map, as the vector representation maps sensor data of the robot to vector space, and it corresponds to different tasks and movements)
However, Kaehler does not specifically disclose:
wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion.
Pennisi teaches:
wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion. ([Pennisi, page 169, Fig. 2. and 3], [page 170, left col, lines 1-18] and [page 170, right col, 3.3. Crowd behavior detection, lines 1-7] collectively disclose that the map is generated based on a grid that has the same size of the input image and it is divided into cells, wherein each cell is initialized with the value 0 and modified for detected feature and to cluster adjacent moving points)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would
have been obvious, having the teachings of Kaehler and Pennisi to use the method of utilizing an activity map comprising a plurality of cells which maps a corresponding portion of Pennisi to implement the anomaly detection machine learning model of the present invention. The suggestion and/or motivation for doing so is to improve the efficiency of the machine learning model by formatting the data captured from sensors and cameras to formats which are easier for a machine learning model to process [Pennisi, page 170, right col, 3.3. Crowd behavior detection, lines 18-27].
Regarding claim 3, Kaehler teaches:
The method of claim 2, wherein the given activity map of the plurality of activity maps corresponds to a particular object type ([Kaehler, 0022] shows that the embedding is a map, as the vector representation maps sensor data of the robot to vector space, and it corresponds to different tasks and movements)
However, Kaehler does not specifically disclose:
wherein the given cell of the given activity map comprises aggregated data characterizing one or more objects of the particular object type in the given cell.
Pennisi teaches:
wherein the given cell of the given activity map comprises aggregated data characterizing one or more objects of the particular object type in the given cell. ([Pennisi, page 169, Fig. 2. and 3] and [page 170, left col, lines 1-18] collectively disclose that the map is generated based on a grid that has the same size of the input image and it is divided into cells, wherein each cell is initialized with the value 0 and modified for detected feature (objects) and to cluster adjacent moving points)
Claim 9 is a machine claim which recites the same feature as the method claim 2, and is rejected for at least the same reasons.
Claim 10 is a machine claim which recites the same feature as the method claim 3, and is rejected for at least the same reasons.
Regarding claim 16, Kaehler teaches:
The apparatus of claim 15, wherein a given activity map of the plurality of activity maps [0022] shows that the embedding is a map, as the vector representation maps sensor data of the robot to vector space, and it corresponds to different tasks and movements)
However, Kaehler does not specifically disclose:
wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion of the monitored physical environment, and wherein the given activity map of the plurality of activity maps corresponds to a particular object type and wherein the given cell of the given activity map comprises aggregated data characterizing one or more objects of the particular object type in the given cell.
Pennisi teaches:
wherein a given activity map of the plurality of activity maps comprises a plurality of cells, wherein a given cell in the given activity map is mapped to a corresponding portion of the monitored physical environment, ([Pennisi, page 169, Fig. 2. and 3], [page 170, left col, lines 1-18] and [page 170, right col, 3.3. Crowd behavior detection, lines 1-7] collectively disclose that the map is generated based on a grid that has the same size of the input image and it is divided into cells, wherein each cell is initialized with the value 0 and modified for detected feature and to cluster adjacent moving points) and wherein the given activity map of the plurality of activity maps corresponds to a particular object type and wherein the given cell of the given activity map comprises aggregated data characterizing one or more objects of the particular object type in the given cell. ([Pennisi, page 169, Fig. 2. and 3] and [page 170, left col, lines 1-18] collectively disclose that the map is generated based on a grid that has the same size of the input image and it is divided into cells, wherein each cell is initialized with the value 0 and modified for detected feature (objects) and to cluster adjacent moving points)
Claims 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaehler in view of Volkovs et al., (US 20180196800, hereinafter ‘Volkovs’).
Regarding claim 6, Kaehler teaches:
The method of claim 1, wherein the machine learning model is trained to generate the at least one predicted activity map using a plurality of historical activity maps, [Kaehler, 0041-0042] send the sensor data to the encoder model 203 which generates vector representation (e.g., an embedding) indicating the state of the robot (i.e., predicted activity map predicted using the embedding model or the encoder). The encoder model 203 may be trained via the encoder trainer 204. [0047 and Fig. 2] indicates that the demonstrations including the sensor data and sequences of observations generated via the robot can be used to train the robot)
However, Kaehler does not specifically disclose:
wherein a first subset of the plurality of historical activity maps is used to generate at least one predicted training activity map and wherein one or more parameters of the machine learning model are adjusted based at least in part on a result of a comparison of a second subset of the plurality of historical activity maps to respective ones of the at least one predicted training activity map.
Volkovs teaches:
wherein a first subset of the plurality of historical[Volkovs, 0028] and [Fig. 4] collectively show that the parameters of the embedding model are adjusted based on the loss (i.e., comparison) between the embeddings (i.e., predicted training data) generated by applying a first subset of data in each training dataset, and the corresponding embeddings (i.e., a second subset of the plurality of historical data) for a second subset of data in the training dataset. Training the machine learning model using historical ‘activity maps’ is taught by Kaehler)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would
have been obvious, having the teachings of Kaehler and Volkovs to use the method of dividing the training data into two subsets, while using a subset to generate a training data and the other subset for comparison of Volkovs to implement the anomaly detection machine learning model of the present invention. The suggestion and/or motivation for doing so is to improve the accuracy of the embedding prediction model by allowing the model to learn from a collection of data that comes after another collection of data [Volkovs, 0028].
Claim 13 is a machine claim which recites the same feature as the method claim 6, and is rejected for at least the same reasons.
Claim 19 is a machine claim which recites the same feature as the method claim 6, and is rejected for at least the same reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 8:00AM – 5:00PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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