DETAILED ACTION
The instant application having Application No. 18/8360,764 filed on 05/14/2023 is presented for examination by the Examiner.
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 .
Claim Objections
Claims 3-15, 17-20 and 22 are objected to because of the following informalities:
Claim 3 recites “..wherein determining the true match…” which should be changed to “..wherein the determining the true match…”
Claim 4 recites:
“..wherein determining the accuracy factor comprises:” which should be changed to “..wherein the determining the accuracy factor comprises:”
“..subset of the training domains:..” in lines 9-10, which should be changed to “..subset of the plurality of training domains:..”
Claim 5 recites:
“..wherein determining the accuracy factors further comprises…” which should be changed to “..wherein the determining the accuracy factors further comprises…”
“…of training domains..” which should be changed to “..the plurality of training domains:..”
Claim 6 recites “..wherein employing the machine..” which should be changed to “..wherein the employing the machine..”
Claim 7 recites “..identifying candidate domains..” which should be changed to “..identifying the plurality of candidate domains..”
Claim 8 recites:
“..wherein executing the plurality…” which should be changed to “..wherein the executing the plurality…”
“..candidate domain comprises:..” which should be changed to “..candidate domains comprises:..”
“..by each of the domain identification algorithms..” which should be changed to “..by each of the plurality of domain identification algorithms..”
Claim 9 is objected for the same rationale as claim 8 above.
Claim 10 recites:
“..wherein executing the .. “ which should be changed tov“..wherein the executing the .. “
“..plurality of candidate domain further…” which should be changed to “..plurality of candidate domains further…”
“..identified as candidate domains.” Which should be changed to “..identified as the candidate domains.”
Claim 11 recites “..wherein applying the filter..” which should be changed to “..wherein the applying the filter..”
Claim 12 recites:
“wherein generating a cyber risk mitigation action based on..” which should be changed to “wherein the generating a cyber risk mitigation ..”
“..based on the investigation of the entity..” which should be changed to “..based on ..”
Claim 13 is objected for the same rationale as claim 12 above.
Claim 14 recites “..cyber risk mitigation action based on..” in line 7, which should be changed to “..cyber risk mitigation ..”
Claim 15 recites “..at least one of the cyber assets…” in line 14, which should be changed to “..at least one of the two cyber assets…”
Claim 17 recites “..wherein determining the true match probability..” which should be changed to “..wherein the determining the true match probability..”
Claim 18 recites “..wherein determining the accuracy factor for each cyber asset identification algorithm comprises:” which should be changed to “..wherein the determining the accuracy factor for each cyber asset identification algorithm comprises:”
Claim 19 is objected for the same rationale as claim 18 above.
Claim 20 recites “..wherein employing the machine learning..” which should be changed to “..wherein the employing the machine learning..”
Claim 22 recites “..at least one of the cyber assets…” in line 18, which should be changed to “..at least one of the two cyber assets…” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 5, 10, 15, 17, 19, 22, 24 and 26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations:
“..true match probability is the probability…” in lines 7-8. There is a lack of antecedent basis for this limitation in the claim.
“..the candidate domain..” in line 8. It is unclear which one of the plurality of candidate domains this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of the plurality of candidate domains.
“classifying the candidate domains…” in line 11. It is unclear which candidates domains among the plurality of candidate domains this limitation refers to. To expedite prosecution, this limitation will be interpreted as any candidate domains of the plurality of candidate domains.
Claim 3 recites the limitations:
“..the candidate domain..” in lines 4 and 6. It is unclear which one of the plurality of accuracy factors this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of the plurality of accuracy factors.
“..the accuracy factor..” in line 8. It is unclear which one of the plurality of accuracy factors this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of the plurality of accuracy factors.
Claim 5 recites the limitation “..the subsets..” There is a lack of antecedent basis for this limitation in the claim.
Claim 10 recites the limitation “..the identified domains..” There is a lack of antecedent basis for this limitation in the claim.
Claim 15 recites the limitations:
“..the probability that ..” in line 8. There is a lack of antecedent basis for this limitation in the claim.
“..the two cyber assets..” in lines 8-9. It is unclear which two cyber assets in which candidate match pair this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of two cyber assets.
“..the candidate match pair..” in line 9. It is unclear which candidate match pair among a plurality of candidate match pairs this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of candidate match pair.
“..the match pair..” in line 16. There is a lack of antecedent basis for this limitation in the claim.
Claim 17 recites the limitations:
“..the match pair..” in lines 5 & 6. It is unclear which one of the plurality of candidate match pairs this limitation refers to.
Claim 19 recites the limitation “..the subsets…” There is a lack of antecedent basis for this limitation in the claim.
Claim 22 recites the limitations:
“..the probability that the two…” in line 12. There is a lack of antecedent basis for this limitation in the claim.
“..the candidate match pair..” in line 13. It is unclear which one of the plurality of candidate match pairs this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of the plurality of candidate match pairs.
Claim 24 recites the limitation:
“..the match pair..” in lines 6-7. It is unclear which one of the plurality of candidate match pairs this limitation refers to. To expedite prosecution, this limitation will be interpreted as any one of the plurality of candidate match pairs.
Claim 26 recites “..the subsets..” There is a lack of antecedent basis for this limitation in the claim.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 7-10, 12-13, 15-16, 21-23 and 28 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pon et al. (US 2022/0070194 A1-hereinafter Pon).
Regarding claim 1, Pon discloses a method for identifying cyber assets and implementing cyber risk mitigation actions, the method comprising:
selecting a subject entity for evaluation (at least figure 2, [0064], network data may be monitored for a resource based on one or more criteria with respect to an identity of an entity);
executing a plurality of domain identification algorithms to identify a plurality of candidate domains, wherein each candidate domain is identified by at least one of the domain identification algorithms as a potential asset of the subject entity (at least [0068], monitoring the network data includes analyzing the network data to discover one or more network events related to the criteria for which to monitor an identity of an entity. The criteria may include an internet domain. Network events may be discovered based on analyzing the network data for each distinct network asset during a time period. The network data may be accessed to determine whether it contains any threshold amount of information that matches the criteria);
determining a true match probability for each candidate domain, wherein the true match probability is the probability that the candidate domain is an asset of the subject entity (at least figure 2, [0085], i.e.: at block 214, based on determining that the measure of similarity satisfies the threshold, a set of terms may be generated by parsing the converted host name using a plurality of terms obtained from a data source) and wherein the true match probability is based on which of the domain identification algorithms identified the candidate domain (at least figure 2, [0068], the criteria that is used to identify/analyze network data );
classifying the candidate domains having a true match probability above a predetermined threshold as associated domains, wherein each associated domain is considered to be an asset of the subject entity (at least figure 2, [0068]);
generating an entity asset database for the subject entity based on the associated domains (at least [0047][0091], a database of threat classifications comprising a plurality of actual or potential threat scenarios is created); and
generating a cyber risk mitigation based on the entity asset database (at least figure 2, [0051][0063]).
Regarding claim 2, Pon discloses the method of Claim 1. wherein the true match probability is further based on a plurality of accuracy factors, wherein each accuracy factor corresponds to one of the domain identification algorithms (at least figure 2, [0085]).
Regarding claim 7, Pon discloses the method of Claim 1. Pon also discloses each of the plurality of domain identification algorithms employ a different method of identifying candidate domains (at least [0052]).
Regarding claim 8, Pon discloses the method of Claim 1. Pon also discloses wherein executing the plurality of domain identification algorithms to identify the plurality of candidate domain comprises:
identifying a seed domain of the subject entity (at least figure 2, [0051], i.e.: identifying and ranking identical strings in the domain name space for the tendency to be used in certain attacks); and
identifying, by each of the domain identification algorithms, domains that are potentially associated with the same entity as the seed domain (at least figure 2, [0051], identifying and ranking identical strings in the domain name space for the tendency to be used in certain attacks ).
Regarding claim 9, Pon discloses the method of Claim 1. Pon also discloses executing the plurality of domain identification algorithms to identify the plurality of candidate domain comprises:
identifying a seed domain of the subject entity (at least figure 2, [0051]); and
searching, by at least one of the domain identification algorithms, public data, proprietary data, or a combination thereof to identify domains having at least some of the same registration information as the seed domain (at least figure 2, [0051]).
Regarding claim 10, Pon discloses the method of Claim 9. wherein executing the plurality of domain algorithms to identify the plurality of candidate domain further comprises:
applying a filter, by the at least one of the domain identification algorithms, to exclude some of the identified domains having at least some of the same registration information as the seed domain from being identified as candidate domains (at least [0064]).
Regarding claim 12, Pon discloses the method of Claim 1. Pon also discloses investigating the entity asset database to identify associated domains linked to a device (at least figure 1, [0038]) comprising an insecure host configuration;
wherein generating a cyber risk mitigation action based on the entity asset database comprises:
automatically implementing a remediated host configuration when a device comprising an insecure host configuration is identified (at least figure 1, [0063]);
Regarding claim 13, Pon discloses the method of Claim 1. further comprising:
investigating the entity asset database to identify associated domains linked to a device communicating with a malicious actor (at least figure 1, [0038]);
wherein generating a cyber risk mitigation action based on the entity asset database comprises:
automatically implementing a remediated device communication configuration when communicating with a malicious actor is identified (at least figure 2, [0063]).
Regarding claim 15, Pon discloses a method for identifying cyber assets and implementing cyber risk mitigation actions, the method comprising:
executing, by cyber asset identification modules, a plurality of cyber asset identification algorithms to identify a plurality of candidate match pairs, wherein each candidate match pair comprises two cyber assets identified by at least one of the cyber asset identification algorithms as potential assets of the same entity (at least figure 2, [0068]);
determining, by a democratic matching module, a true match probability for each candidate match pair, wherein the true match probability is the probability that the two cyber assets in the candidate match pair are assets of the same entity (at least figure 2, [0085]), and wherein the true match probability is based on which of the cyber asset identification algorithms identified the candidate match pair (at least figure 2, [0068]);
determining, by the democratic matching module, for at least some of the candidate match pairs, that the true match probability is above a predetermined threshold (at least figure 2, [0068]);
adding, by a footprinting module, at least one of the cyber assets from each candidate match pair having a true match probability above the predetermined threshold to a cyber asset database corresponding to the same entity used to identify the match pair (at least [0091]); and
generating, by a risk mitigation module, a cyber risk mitigation based on the of cyber asset database (at least figure 2, [0063]).
Regarding claim 16, Pon discloses the method of Claim 15. Pon also discloses the true match probability is further based on an accuracy factor associated with each cyber asset identification algorithm (at least figure 2, [0085]).
Regarding claim 21, Pon discloses the method of Claim 15. Pon also discloses each of the plurality of cyber asset identification algorithms employ a different method of identifying candidate match pairs (at least [0052]).
Claim 22 is rejected for the same rationale as claim 15 above.
Claim 23 is rejected for the same rationale as claim 16 above.
Claim 28 is rejected for the same rationale as claim 21 above.
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 3- are rejected under 35 U.S.C. 103 as being unpatentable over Pon and in view of McNee et al. (US 2022/0150275 A1-hereinafter McNee).
Regarding claim 3, Pon discloses the method of Claim 2. Pon does not explicitly disclose assigning a binary value to each domain identification algorithm, wherein a one is assigned to each domain identification algorithm that identified the candidate domain, and wherein a zero is assigned to each domain identification algorithm that did not identify the candidate domain; and
calculating the true match probability based on the binary value assigned to each domain identification algorithm and the accuracy factor for each domain identification algorithm.
However, McNee discloses assigning a binary value to each domain identification algorithm, wherein a one is assigned to each domain identification algorithm that identified the candidate domain, and wherein a zero is assigned to each domain identification algorithm that did not identify the candidate domain (at least figure 6, [0048]); and
calculating the true match probability based on the binary value assigned to each domain identification algorithm and the accuracy factor for each domain identification algorithm (at least figure 8, [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Ponto to include the teachings of McNee to assign a binary value to each domain identification algorithm, where bits are assigned depending on identified domains, and calculating a match probability for each domain, such that the system can identify if the domain names are known to be malicious and assign a true or false bits signifying malicious actors (McNee [at least figure 6, [0047]-[0048]).
Regarding claim 4, Pon discloses the method of Claim 2. Pon also discloses selecting a known entity (at least [0064]);
identifying ground truth domains for the known entity, wherein the ground truth domains are domains that are known to be assets of the known entity (at least [0064]).
Pon does not explicitly disclose executing the plurality of domain identification algorithms to identify a plurality of training domains, wherein each training domain is identified by at least one of the domain identification algorithms as a potential asset of the known entity, and wherein each domain identification algorithm identifies a subset of the training domains; and comparing the subset of training domains identified by each domain identification algorithm to the ground truth domains.
However, McNee discloses executing the plurality of domain identification algorithms to identify a plurality of training domains (at least [0023]), wherein each training domain is identified by at least one of the domain identification algorithms as a potential asset of the known entity, and wherein each domain identification algorithm identifies a subset of the training domains (at least figure 1, [0024]); and
comparing the subset of training domains identified by each domain identification algorithm to the ground truth domains (at least [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pon to include the teachings of McNee to identify training domains and subsets of the training domains, such that the system can train the cybersecurity threat analysis system to improve reliability of predictions of malicious domains (at least [0022]-[0024]).
Regarding claim 5, Pon and McNee disclose the method of Claim 4. McNee also discloses the accuracy factors further comprises employing a machine learning technique to determine an accuracy factor for each domain identification algorithm based on comparing each of the subsets of training domains to the ground truth domains (at least figure 1, [0024]).
Regarding claim 6, Pon and McNee disclose the method of Claim 5. McNee also discloses employing the machine learning technique comprises employing a support vector machine (SVM) model (at least [0025], i.e.: support vector machine).
Regarding claim 17, Pon discloses the method of Claim 16. Pon does not explicitly disclose determining the true match probability for each match pair comprises:
assigning, by the democratic matching module, a binary value to each cyber asset identification algorithm, wherein a one is assigned to each cyber asset identification algorithm that identified the match pair, and wherein a zero is assigned to each cyber asset identification algorithm that did not identify the match pair; and calculating, by the democratic matching module, the true match probability based on the binary value assigned to each cyber asset identification algorithm and the accuracy factor for each cyber asset identification algorithm.
However, McNee also discloses assigning a binary value to each domain identification algorithm, wherein a one is assigned to each domain identification algorithm that identified the candidate domain, and wherein a zero is assigned to each domain identification algorithm that did not identity the candidate domain (at least figure 6, [0048]); and
Calculating the true match probability based on the binary value assigned to each domain identification algorithm and the accuracy factor for domain identification algorithm (at least figure 8, [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pon to include the teachings of McNee to assign a binary value to each domain identification algorithm, where bits are assigned depending on identified domains, and calculating a match probability for each domain, such that the method can identify if the domain names are known to be malicious and assign a true or false bits signifying malicious actors (at least figure 6, [0047]-[0048]).
Regarding claim 18, Pon discloses the method of Claim 16. Pon further comprising determining the accuracy factor for each cyber asset identification algorithm, wherein determining the accuracy factor for each cyber asset identification algorithm comprises:
selecting, by a training module, a known entity (at least [0064]);
identifying, by the training module, ground truth cyber assets for the known entity, wherein the ground truth cyber assets are cyber assets that are known to be assets of the known entity (at least [0064]);
Pon does not explicitly disclose executing, by the democratic matching modules, the plurality of cyber asset identification algorithms to identify a plurality of training match pairs, wherein each training match pair comprises two cyber assets identified by at least one of the cyber asset identification algorithms as a potential assets of the known entity, and wherein each cyber asset identification algorithm identifies a subset of the training match pairs; and comparing, by the training module, the subset of training match pairs identified by each cyber asset identification algorithm to the ground truth cyber assets.
However, McNee discloses executing, by the democratic matching modules, the plurality of cyber asset identification algorithms to identify a plurality of training match pairs, wherein each training match pair comprises two cyber assets identified by at least one of the cyber asset identification algorithms as a potential assets of the known entity (at least [0022]-[0024]), and wherein each cyber asset identification algorithm identifies a subset of the training match pairs (at least figure 1, [0024]); and comparing, by the training module, the subset of training match pairs identified by each cyber asset identification algorithm to the ground truth cyber assets (at least [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pon to include the teachings of McNee to identify training domains and subsets of the training domains, such that the system can train the cybersecurity threat analysis system to improve reliability and predictions of malicious domains (at least [0022)-[0024]
Regarding claim 19, Pon and McNee disclose the method of Claim 18. McNee also discloses determining the accuracy factor for each cyber asset identification algorithm further comprises employing a machine learning technique to determine the accuracy factor for each cyber asset identification algorithm based on comparing each of the subsets of training match pairs to the ground truth cyber assets (at least figure 1, [0024]).
Regarding claim 20, Pon and McNee disclose the method of Claim 19. McNee also discloses employing the machine learning technique comprises employing a support vector machine (SVM) model (at least [0025]).
Claim 24 is rejected for the same rationale as claim 17 above.
Claim 25 is rejected for the same rationale as claim 18 above.
Claim 26 is rejected for the same rationale as claim 19 above.
Claim 27 is rejected for the same rationale as claim 20 above.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Pon and in view of Usher et al. (US 2021/0014252 A1-hereinafter Usher).
Regarding claim 11, Pon discloses the method of Claim 10. Pon does not explicitly disclose applying the filter comprises excluding domains comprising redacted registration data.
However, Usher discloses applying the filter comprises excluding domains comprising redacted registration data (at least [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pon to include the teachings of Usher so that the method can identify domains that have been redacting personal information and update threat intelligence rating (at least [0026]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Pon and in view of Matkowsky (US Patent 11,310,120 B1-hereinafter Matkowsky).
Regarding claim 14, Pon discloses the method of Claim 1. Pon also discloses
investigating the entity asset database to identify associated domains comprising an email-related security threat (para [0054], however, threat actors use domain names that have a tendency to cause significant damage through deception attacks even where there is no content on the domain name to be analyzed in relation to the domain name, such as possibly a domain that is about to be used to deliver spear-phishing attacks by email, but has no related content hosted on the domain name because the payload is not hosted on the same domain);
wherein the email-related security threat comprises an email configuration lacking an email authentication method and/or an email configuration with a misconfigured email authentication method (para [0054], however, threat actors use domain names that have a tendency to cause significant damage through deception attacks even where there is no content on the domain name to be analyzed in relation to the domain name, such as possibly a domain that is about to be used to deliver spear-phishing attacks by email, but has no related content hosted on the domain name because the payload is not hosted on the same domain); and
wherein generating a cyber risk mitigation action based on the entity asset database comprises:
automatically implementing a remediated email authentication configuration when an associated domain comprising an email-related
security threat is identified (para [0078]);
generating a security alert when an associated domain comprising an email related security threat is identified (para [0040]), but does not explicitly state generating an automated label indicating that an email may not be authentic when received from an associated domain comprising an email-related security threat;
quarantining an email when received from an associated domain comprising an email-related security threat; or generating a cyber security risk report based on the investigation of the entity asset database; or a combination thereof.
However, Matkowsky discloses generating an automated label indicating that an email may not be authentic when received from an associated domain comprising an email-related security threat (para [0115] "Viewing the domain may include accessing the web data based on the DNS data for the domain. The web data may be displayed next to the records. In this example, viewing the domain may show that the domain redirects to a different domain. Based on determining that the domain redirects based on a Google mail server set up for the Facebooksecurity.info domain, a user may further determine that the domain may be malicious.");
quarantining an email when received from an associated domain comprising an email-related security threat (para [0116]);
generating a cyber security risk report based on the investigation of the entity asset database; or a combination thereof (para [0115] "Pivoting between multiple data sources may suggest a security threat based on improper use of Google mail servers and the Facebook domain. in a short period of time, a user has been able to reduce 22,000 records to a small set of records that are invested more heavily in mail servers with Google that are likely to be potentially used for broader spear phishing email campaigns. Such a threat can be verified by displaying the web data for a domain in the graphical interface displayed alongside the records.").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pon to include the teachings of Matkowsky to verify the authenticity of an email using associated domains comprising email-related security threats, and to quarantine the email that is a security threat, such that the method can identify if an email is coming from a known malicious actor, and act accordingly depending on the threat identified (at least [0115]-[0116]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHY ANH TRAN VU whose telephone number is (571)270-7317. The examiner can normally be reached Monday-Friday 7 am-1 pm.
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, Taghi T Arani can be reached at (571) 272-3787. 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.
/PHY ANH T VU/ Primary Examiner, Art Unit 2438