Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. Claims 1 - 16 are pending. Claims 1, 5, 6 are independent. File date on 12-6-2023. This action is in response to application amendments filed on 8-12-2025.
Claim Rejections - 35 USC § 102
2. 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.
3. Claims 1, 3 - 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fujishima et al. (Patent JP 2019004419 A).
Regarding Claim 1, Fujishima discloses an estimation device comprising:
a) a memory storing instructions (Fujishima ¶ 080: a network monitoring apparatus according to an embodiment. 201 is a bus for communicating between devices. 202 is a memory for storing programs.), and processing circuitry (Fujishima ¶ 009: network monitoring device 104 and the devices 101 - 1 and 101 - 2 having the communication function to be monitored by the 104, and the communication required for the system operation between the devices 101 - 1 and 101 - 2 Has been done. It is the network switch 103 that mediates communication between devices. Each monitoring target device 101 and the network monitoring device 104 according to this embodiment are connected to input / output ports 102 - 1, 102 - 2, and 102 - 3 which are interfaces of the network switch 103; ¶ 011: the network monitoring apparatus … . In order to perform model creation and communication monitoring with the network monitoring apparatus 104, the whole apparatus has two modes, a model creation mode and a communication monitoring mode;) configured to:
b) acquiring communication data detected to be abnormal; (Fujishima ¶ 017: When the occurrence of the communication abnormality is detected (detecting abnormal action), the packet characteristic amount verification unit 115 outputs to the notification unit 116 that the abnormality has occurred.)
c) extract a fixed-length feature amount from a payload of the communication data detected to be abnormal; (Fujishima ¶ 044: In the vector type, a plurality of feature amounts are extracted for each packet (including detected abnormal packets). When evaluating the similarity between the byte sequences of the payload, a vector whose elements are the first byte, the second byte, and the like of the payload is extracted as a feature amount. A vector is generated for each packet. As a model, two representative vectors are used as models for all packet vectors in the packet queue.; ¶ 052: Similarly, the third row shows the result of extracting the byte sequence of the payload as the feature amount for the first packet queue. Here, as an example, the first 4 bytes of the payload are extracted as a feature vector, but the extracted byte position and length can be configured.; ¶ 032: The packet body is composed of a header and a payload, and the packet classification unit 112 extracts IP addresses, port numbers, and the like necessary for packet classification, that is, table creation in FIG. 6 from the header. Searches which index of the index in the table of FIG. 6 the information of the extracted IP address and port number corresponds, and stores the packet body in the packet queue corresponding to the index. When a combination of a new IP address and port number is detected at model creation, an index corresponding to the combination is newly issued, and a packet queue is additionally generated in accordance with index generation.)
d) calculate a similarity between feature amounts of abnormal communication data; (Fujishima ¶ 056: it is determined whether or not the feature quantity (see FIG. 11) extracted by the packet feature quantity extraction unit 113-1 for each of the packet classification index and the feature quantity item index has stationarity, and in the case of stationarity, Creates a model; (stationarity analogous to similarity value)) and
e) determine that events are of a same type when the calculated similarity is greater than a predetermined threshold. (Fujishima ¶ 062: The third row of the table is an example of the continuity evaluation on the payload feature amount of the first packet queue. According to FIG. 11, the values of the first three bytes are completely matched over the three feature amounts, and only the value of the fourth byte is different between the packets. Here, it is stationary when the number of bytes completely matched between packets is 3 or more, and it is judged that there is no stationarity when the number of bytes is 2 or less. In this example, since 3 bytes are perfectly matched, it is judged that there is stationarity. Here, the threshold value concerning the number of bytes that are completely matched is a configurable system parameter)
Regarding Claim 3, Fujishima discloses the estimation device according to claim 1, wherein the processing circuitry is further configured to detect the communication data detected to be abnormal by a model that determines whether communication data is normal or abnormal using the fixed-length feature amount extracted from the payload of the communication data. (Fujishima ¶ 017: When the occurrence of the communication abnormality is detected, the packet characteristic amount verification unit 115 outputs to the notification unit 116 that the abnormality has occurred.; ¶ 072: in the case of a list type model, if the value of the feature quantity does not exist in the model list, it is regarded as abnormal communication. In the case of a range type model, if the value of the feature value deviates from the normal value upper limit and the lower limit range of the model, it is regarded as abnormal communication. In the case of a vector type model (payload), [p 1, p 2, p 3, p 4], a mask vector [m 1, m 2, m 3, m 4], a representative vector of the payload as a model If one of m1 × o1 = p1, m2 × o2 = p2, m3 × o3 = p3, m4 × o4 = p4 is not satisfied, assuming that the vectors of the feature amounts are [o1, o2, o3, o4] It is regarded as abnormal communication.)
Regarding Claim 4, Fujishima discloses the estimation device according to claim 3, wherein the processing circuitry is further configured to learn the model by using the fixed-length feature amount extracted from the payload of the communication data. (Fujishima ¶ 056: it is determined whether or not the feature quantity (see FIG. 11) extracted by the packet feature quantity extraction unit 113-1 for each of the packet classification index and the feature quantity item index has stationarity, and in the case of stationarity, Creates a model)
Regarding Claim 5, Fujishima discloses an estimation method executed by an estimation device, the estimation method comprising:
a) acquiring communication data detected to be abnormal; (Fujishima ¶ 017: When the occurrence of the communication abnormality is detected (detecting abnormal action), the packet characteristic amount verification unit 115 outputs to the notification unit 116 that the abnormality has occurred.)
b) extracting a fixed-length feature amount from a payload of the communication data detected to be abnormal; (Fujishima ¶ 017: When the occurrence of the communication abnormality is detected (detecting abnormal action), the packet characteristic amount verification unit 115 outputs to the notification unit 116 that the abnormality has occurred.; ¶ 032: The packet body is composed of a header and a payload, and the packet classification unit 112 extracts IP addresses, port numbers, and the like necessary for packet classification, that is, table creation in FIG. 6 from the header. Searches which index of the index in the table of FIG. 6 the information of the extracted IP address and port number corresponds, and stores the packet body in the packet queue corresponding to the index. When a combination of a new IP address and port number is detected at model creation, an index corresponding to the combination is newly issued, and a packet queue is additionally generated in accordance with index generation.)
c) calculating a similarity between feature amounts of the communication data detected to be abnormal; (Fujishima ¶ 056: it is determined whether or not the feature quantity (see FIG. 11) extracted by the packet feature quantity extraction unit 113-1 for each of the packet classification index and the feature quantity item index has stationarity, and in the case of stationarity, Creates a model) and
d) determining that events are of a same type when the calculated similarity is greater than a predetermined threshold. (Fujishima ¶ 062: The third row of the table is an example of the continuity evaluation on the payload feature amount of the first packet queue. According to FIG. 11, the values of the first three bytes are completely matched over the three feature amounts, and only the value of the fourth byte is different between the packets. Here, it is stationary when the number of bytes completely matched between packets is 3 or more, and it is judged that there is no stationarity when the number of bytes is 2 or less. In this example, since 3 bytes are perfectly matched, it is judged that there is stationarity. Here, the threshold value concerning the number of bytes that are completely matched is a configurable system parameter)
Regarding Claim 6, Fujishima discloses a non-transitory computer-readable recording medium storing therein an estimation program that causes a computer to execute a process (Fujishima ¶ 080: a network monitoring apparatus according to an embodiment. 201 is a bus for communicating between devices. 202 is a memory for storing programs. 202 includes a packet classification unit 112, a packet feature amount extraction unit 113, a packet feature amount selection learning unit 114, and a packet feature amount verification unit 115. 203 is an arithmetic processing device such as a CPU for operating the program. Reference numeral 204 denotes a memory used by the program, which is used as a classification method storage unit 121, a monitor viewpoint selection storage unit 122, a model storage unit 123, and a work area of each program. 205 is a network interface device for capturing packets.) comprising:
a) acquiring communication data detected to be abnormal; (Fujishima ¶ 017: When the occurrence of the communication abnormality is detected (detecting abnormal action), the packet characteristic amount verification unit 115 outputs to the notification unit 116 that the abnormality has occurred.)
b) extracting a fixed-length feature amount from a payload of the communication data; (Fujishima ¶ 032: The packet body is composed of a header and a payload, and the packet classification unit 112 extracts IP addresses, port numbers, and the like necessary for packet classification, that is, table creation in FIG. 6 from the header. Searches which index of the index in the table of FIG. 6 the information of the extracted IP address and port number corresponds, and stores the packet body in the packet queue corresponding to the index. When a combination of a new IP address and port number is detected at model creation, an index corresponding to the combination is newly issued, and a packet queue is additionally generated in accordance with index generation.)
c) calculating a similarity between feature amounts of the communication data detected to be abnormal; (Fujishima ¶ 056: it is determined whether or not the feature quantity (see FIG. 11) extracted by the packet feature quantity extraction unit 113-1 for each of the packet classification index and the feature quantity item index has stationarity, and in the case of stationarity, Creates a model) and
d) determining that events are of a same type when the calculated similarity is greater than a predetermined threshold. (Fujishima ¶ 062: The third row of the table is an example of the continuity evaluation on the payload feature amount of the first packet queue. According to FIG. 11, the values of the first three bytes are completely matched over the three feature amounts, and only the value of the fourth byte is different between the packets. Here, it is stationary when the number of bytes completely matched between packets is 3 or more, and it is judged that there is no stationarity when the number of bytes is 2 or less. In this example, since 3 bytes are perfectly matched, it is judged that there is stationarity. Here, the threshold value concerning the number of bytes that are completely matched is a configurable system parameter)
Regarding Claim 7, Fujishima discloses the estimation device according to claim 1,
a) wherein the communication data detected to be abnormal includes a plurality of distinct instances of communication data detected to be abnormal, (Fujishima ¶ 019: each time a packet with a combination of an unknown IP address and port number is detected, a combination of the IP address and the port number Is accumulated in a form added. As described above, S1001 is responsible for packet classification and accumulation in the queue, management of the capture time, and updating of the classification claim method storage unit 121 as described above.; (multiple instances of abnormal objects)) and
b) the processing circuitry is configured to calculate the similarity between respective fixed-length feature amounts extracted from payloads of the plurality of distinct instances of communication data detected to be abnormal. (Fujishima ¶ 056: it is determined whether or not the feature quantity (see FIG. 11) extracted by the packet feature quantity extraction unit 113-1 for each of the packet classification index and the feature quantity item index has stationarity, and in the case of stationarity, Creates a model)
4. 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.
5. Claims 2, 10, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Zhou et al. (Patent CN 110554782A).
Regarding Claim 2, Fujishima discloses the estimation device according to claim 1, wherein the processing circuitry is further configured (Fujishima ¶ 009: network monitoring device 104 and the devices 101-1 and 101-2 having the communication function to be monitored by 104, and communication necessary for system operation between the devices 101-1 and 101-2. Has been made. The network switch 103 mediates communication between devices.; ¶ 011: network monitoring apparatus 104 according to an embodiment. Since the network monitoring device 104 performs model creation and communication monitoring, the entire device has two modes: a model creation mode and a communication monitoring mode.) to:
a) extract the fixed-length feature amount by converting the payload into a fixed-length feature vector, (Fujishima ¶ 044: In the vector type, a plurality of feature amounts are extracted for each packet. When evaluating the similarity between the byte sequences of the payload, a vector whose elements are the first byte, the second byte, and the like of the payload is extracted as a feature amount. A vector is generated for each packet. As a model, two representative vectors are used as models for all packet vectors in the packet queue.; ¶ 052: Similarly, the third row shows the result of extracting the byte sequence of the payload as the feature amount for the first packet queue. Here, as an example, the first 4 bytes of the payload are extracted as a feature vector, but the extracted byte position and length can be configured.)
b) calculate a distance between feature vectors as a similarity between the converted feature vectors of the communication data detected to be abnormal, (Fujishima ¶ 062: The third row of the table is an example of the continuity evaluation on the payload feature amount of the first packet queue. According to FIG. 11, the values of the first three bytes are completely matched over the three feature amounts, and only the value of the fourth byte is different between the packets. Here, it is stationary when the number of bytes completely matched between packets is 3 or more, and it is judged that there is no stationarity when the number of bytes is 2 or less. In this example, since 3 bytes are perfectly matched, it is judged that there is stationarity. Here, the threshold value concerning the number of bytes that are completely matched is a configurable system parameter) and
c) determine that the events are of the same type when the distance between the feature vectors is less than the predetermined threshold. (Fujishima ¶ 062: FIG. 11, the values of the first three bytes are completely matched over the three feature amounts, and only the value of the fourth byte is different between the packets. Here, it is stationary when the number of bytes completely matched between packets is 3 or more,; (determined same type when comparison match is determined))
Fujishima does not explicitly disclose utilizing Bidirectional Encoder Representations from Transformers (BERT).
However, Zhou discloses wherein utilizing Bidirectional Encoder Representations from Transformers (BERT). (Bert pages 13-14: using the algorithm model language character string inputted by the user to extract the feature vector, and the feature vector of the facial expression picture extracting and classifying good for accurate matching. wherein, precise matching of the character string with the expression picture, language represented by the BERT model. about BERT (Bidirectional Encoder Representation from Transformers) method,)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for utilizing Bidirectional Encoder Representations from Transformers (BERT) as taught by Zhou. One of ordinary skill in the art would have been motivated to employ the teachings of Zhou for the flexibility of a system utilizing multiple transformation techniques such as BERT. (Bert pages 13-14)
Regarding Claim 10, Fujishima-Zhou discloses the estimation device according to claim 2.
Fujishima does not explicitly disclose preprocess the payload by converting every eight bits of payload into a character (8 bit byte characters) of a two-digit hexadecimal number before converting the payload into the fixed-length feature vector by the BERT
wherein the processing circuitry is further configured to preprocess the payload by converting every eight bits of the payload into a character of a two-digit hexadecimal number before converting the payload into the fixed-length feature vector by the BERT. (Zhou pages 13-14: using the algorithm model language character string inputted by the user to extract the feature vector, and the feature vector of the facial expression picture extracting and classifying good for accurate matching. wherein, precise matching of the character string with the expression picture, language represented by the BERT model. about BERT (Bidirectional Encoder Representation from Transformers) method; (character string; each character, 8 bit byte))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for preprocess the payload by converting every eight bits of the payload into a character of a two-digit hexadecimal number before converting the payload into the fixed-length feature vector by the BERT as taught by Zhou. One of ordinary skill in the art would have been motivated to employ the teachings of Zhou for the flexibility of a system utilizing multiple transformation techniques such as BERT. (Zhou pages 13-14)
Regarding Claim 15, Fujishima-Zhou discloses the estimation device according to claim 2.
Fujishima does not explicitly disclose payload of communication data detected to be abnormal is input to the BERT as a sequence of characters representing byte values.
However, Zhou discloses wherein the payload of the communication data detected to be abnormal is input to the BERT as a sequence of characters representing byte values. (Zhou pages 13-14: using the algorithm model language character string inputted by the user to extract the feature vector, and the feature vector of the facial expression picture extracting and classifying good for accurate matching. wherein, precise matching of the character string with the expression picture, language represented by the BERT model. about BERT (Bidirectional Encoder Representation from Transformers) method; (character string; each character, 8 bit bytes))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for payload of communication data detected to be abnormal is input to the BERT as a sequence of characters representing byte values as taught by Zhou. One of ordinary skill in the art would have been motivated to employ the teachings of Zhou for the flexibility of a system utilizing multiple transformation techniques such as BERT. (Zhou pages 13-14)
6. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Mabuchi et al. (US PGPUB No. 20200242379).
Regarding Claim 8, Fujishima discloses the estimation device according to claim 1,
Fujishima does not explicitly disclose taking an average of feature components across a time index of the payload.
However, Mabuchi discloses wherein the processing circuitry is further configured to, prior to calculating the similarity, generate each of the fixed-length feature amounts by taking an average of feature components across a time index of the payload. (Mabuchi ¶ 034: the system may compare the feature graphs of a current image to those previously recorded. According to an aspect of the disclosure, each identified feature point may be compared to corresponding feature points of one or more previous image. ... The baseline feature graph 600 may be determined by averaging the locations of feature points 508 over a defined time period to establish an expected pose for the driver.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for taking an average of feature components across a time index of the payload as taught by Mabuchi. One of ordinary skill in the art would have been motivated to employ the teachings of Mabuchi for the flexibility of a system that enables the determination of an average value associated with the set of feature components processed to determine data abnormality. (Mabuchi ¶ 034)
7. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Natanzon et al. (US Patent 10,581,897).
Regarding Claim 9, Fujishima discloses the estimation device according to claim 1,
Fujishima does not explicitly disclose estimating a trend of a cyber security attack.
However, Natanzon discloses wherein determining that the events are of the same type is used to estimate a trend of a cyber security attack. (Natanzon col 11: track critical computing system and/or network characteristics exhibited by the set of AGIs restored in their assigned RIE. These critical characteristics may be tracked in order to detect anomalous events or trends that may be induced by the presence of an unknown cyber security threat or attack. Examples of these critical characteristics include, but are not limited to, network traffic volume, network bandwidth use, network protocol use, hard disk activity, CPU load, memory usage, changes in OS registries, system call use, etc.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for estimating a trend of a cyber security attack as taught by Natanzon. One of ordinary skill in the art would have been motivated to employ the teachings of Natanzon for the flexibility of a system that enables the determination of a trend in cyber security characteristics indicating a cybersecurity attack. (Natanzon col 11)
8. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Zhou and Yoshida et al. (Patent WO 2020195626 A1).
Regarding Claim 11, Fujishima discloses the estimation device according to claim 2.
Fujishima does not explicitly disclose distances calculated between feature vectors based on a cosine distance.
However, Yoshida discloses wherein the distance calculated between the feature vectors is based on a cosine distance. (Yoshida page 6: the comparison unit 22 calculates the degree of similarity between the anomaly detection feature vector and the registered feature vector of each knowledge by using the cosine distance between the feature vectors.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for distances calculated between feature vectors based on a cosine distance as taught by Yoshida. One of ordinary skill in the art would have been motivated to employ the teachings of Yoshida for the flexibility of a system that supports multiple methods for calculating for feature vector distances such as cosine distance. (Yoshida page 6)
9. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Zhou and Nomura et al. (Patent JP 2018045302 A).
Regarding Claim 12, Fujishima discloses the estimation device according to claim 2.
Fujishima does not explicitly disclose distance calculated between feature vectors is based on an L2 norm.
However, Nomura discloses wherein the distance calculated between feature vectors is based on an L2 norm. (Nomura page 6: the distance between cluster centers is calculated as the L2 norm of the feature vector difference that is the cluster center.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for distance calculated between feature vectors is based on an L2 norm as taught by Nomura. One of ordinary skill in the art would have been motivated to employ the teachings of Yoshida for the flexibility of a system that supports multiple methods for calculating for feature vector distances such as L2 norm. (Nomura page 6)
10. Claims 13, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Zhou and further in view of Batmaz et al. (Patent No. CN 112204578 A).
Regarding Claim 13, Fujishima-Zhou discloses the estimation device according to claim 2.
Fujishima does not explicitly disclose wherein configured to generate fixed-length feature vectors such that they relatively ignore portions of payload corresponding to a certain type of number.
However, Batmaz discloses wherein the BERT is configured to generate the fixed-length feature vectors such that they relatively ignore portions of the payload corresponding to a certain type of number. (Batmaz page 9: the content extractor 214 may reduce the processing power by ignoring portions of the payload that are independent of the anomaly detection; (ignore portions of payload that do not impact abnormality processing))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for configured to generate fixed-length feature vectors such that they relatively ignore portions of payload corresponding to a certain type of number as taught by Batmaz. One of ordinary skill in the art would have been motivated to employ the teachings of Batmaz for the flexibility of a system that processing required portions of the payloar and ignoring portions that do not impact detection of abnormal data. (Batmaz page 9)
Regarding Claim 14, Fujishima-Zhou-Batmaz discloses the estimation device according to claim 13.
Fujishima does not explicitly disclose a certain type of number is a serial number (does not impact abnormality processing).
However, Batmaz discloses wherein the certain type of number is a serial number. (Batmaz page 9: the content extractor 214 may reduce the processing power by ignoring portions of the payload that are independent of the anomaly detection; (ignore portions of payload that do not impact abnormality processing))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for configured to generate fixed-length feature vectors such that they relatively ignore portions of payload corresponding to a certain type of number as taught by Batmaz. One of ordinary skill in the art would have been motivated to employ the teachings of Batmaz for the flexibility of a system that processing required portions of the payloar and ignoring portions that do not impact detection of abnormal data. (Batmaz page 9)
11. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Fujishima in view of Cheong et al. (Patent KR 20200087299 A).
Regarding Claim 16, Fujishima discloses the estimation device according to claim 3, the model determines whether communication data is normal or abnormal. (Fujishima ¶ 017: When the occurrence of the communication abnormality is detected (detecting abnormal action), the packet characteristic amount verification unit 115 outputs to the notification unit 116 that the abnormality has occurred.)
Fujishima does not explicitly disclose determines whether communication data is based on a Variational Autoencoder.
However, Cheong discloses wherein determines whether communication data is based on a Variational Autoencoder. (Cheong page 4: a deep learning model to differentiate the traffic flow of mobile applications, and proposed a traffic identification model based on VEAN (Variational Autoencoder Network).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujishima for determines whether communication data is based on a Variational Autoencoder as taught by Cheong. One of ordinary skill in the art would have been motivated to employ the teachings of Cheong for the flexibility of a system that enables the utilization of multiple encoder such as the variational autoencoder processing abnormality data within a network environment. (Cheong page 4)
Response to Amendments
12. Applicant’s arguments have been fully considered but they were not persuasive.
A. The 101 Rejection for Claims 1 - 4 has been withdrawn due to claim amendments.
B. Applicant’s arguments are moot due to the new grounds of rejection.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kyung H Shin whose telephone number is (571)272-3920. The examiner can normally be reached M - F: 12pm - 8pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon H Hwang can be reached at 571-272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYUNG H SHIN/ 1-9-2026Primary Examiner, Art Unit 2447
Prosecution Insights