Prosecution Insights
Last updated: April 19, 2026
Application No. 18/475,471

METHOD AND SYSTEM FOR RECOGNIZING TLS FINGERPRINTS BASED ON FINITE-STATE MACHINES

Final Rejection §102
Filed
Sep 27, 2023
Examiner
JAKOVAC, RYAN J
Art Unit
2445
Tech Center
2400 — Computer Networks
Assignee
Huazhong University Of Science And Technology
OA Round
2 (Final)
66%
Grant Probability
Favorable
3-4
OA Rounds
3y 9m
To Grant
83%
With Interview

Examiner Intelligence

Grants 66% — above average
66%
Career Allow Rate
402 granted / 613 resolved
+7.6% vs TC avg
Strong +17% interview lift
Without
With
+17.4%
Interview Lift
resolved cases with interview
Typical timeline
3y 9m
Avg Prosecution
32 currently pending
Career history
645
Total Applications
across all art units

Statute-Specific Performance

§101
7.5%
-32.5% vs TC avg
§103
50.5%
+10.5% vs TC avg
§102
20.7%
-19.3% vs TC avg
§112
17.6%
-22.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 613 resolved cases

Office Action

§102
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 . Response to Arguments Applicant’s arguments filed 11/27/2025 have been fully considered. Applicant argues the prior art to Kohout fails to teach: “wherein the fingerprint extracting module comprises a fingerprint updating unit which incrementally updates the multi-level fingerprints responsive to finding a new TLS state machine model fingerprint”. Applicant’s arguments are not persuasive as Kohout discloses incrementally updating the fingerprints responsive to finding new TLS state machine model fingerprint as illustrated in at least figure 6A which illustrates the incremental addition of fingerprints as they are encountered (see fig. 6A and at least ¶ 51-54, and 88. For example, ¶ 88 and fig. 6A illustrate and describe the incremental update via addition of discovered fingerprints). The term incremental describes an increase or addition, which corresponds to Kohout’s detection and addition of unique fingerprints. With regards to claim 5, applicant argues are not persuasive as in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the first-range fingerprints are paths exclusive to the first state machine A in contrast with the second state machine B; the second-range fingerprint are paths exclusive to the first state machine A in contrast with all the other known state machines) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Allowable Subject Matter Claims 7-10, 12, and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim Rejections - 35 USC § 102 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. Claim(s) 1-6, 11, 13-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210152526 to Kohout. Regarding claim 1, Kohout teaches a system for recognizing TLS fingerprints based on finite-state machines, the system at least comprising: a model inference module, for learning state machine models of target TLS implementations according to mapping information sent by a message mapping module (abstract, fig. 1, ¶ 33-37, model for learning state machine models for TLS; ¶ 37, Markov models); a fingerprint extracting module, for analyzing the state machine models and extracting multi-level fingerprints of the target TLS implementations (¶ 83, 87, 90-91, extraction of multi-level fingerprints); and a version recognizing module, for verifying the multi-level fingerprints for validity and/or recognizing version information of unknown TLS implementations (¶ 84, 87, verifying fingerprints; see also ¶ 51-52). wherein the fingerprint extracting module comprises a fingerprint updating unit which incrementally updates the multi-level fingerprints responsive to finding a new TLS state machine model fingerprint (fig. 6A and at least ¶ 51-54, and 88). Regarding claim 2, Kohout teaches: wherein the model inference module is connected to a state machine model library, and the model inference module verifies, on the basis of equivalence query algorithm in a model testing unit, whether the inferred state machine models represent complete behavior of the target TLS implementations (¶ 33-37, state machine models; ¶ 83-91, query algorithm). Regarding claim 3, 13, Kohout teaches: wherein if the inferred state machine models represent complete behavior of the target TLS implementations, the inferred state machine models are stored into the state machine model library(¶ 33-37); and if the inferred state machine models do not represent complete behavior of the target TLS implementations, counterexample information is fed back to a model learning unit to direct re-inference of the models until the verification is successful (¶ 33-37, 47, training, learning, optimization phases for models). Regarding claim 4, 14, Kohout teaches: wherein the model inference module at least comprises the model learning unit and the model testing unit, wherein the model learning unit is for learning the state machine models of the target TLS implementations according to a state machine learning algorithm (see learning features of models in at least ¶ 33-47); and the model testing unit is for determining whether the inferred state machine models represent the complete behavior of the target TLS implementations (see query comparison in at least ¶ 83-91). Regarding claim 5, 15, Kohout teaches: wherein the fingerprint extracting module at least comprises: a model analyzing unit, for extracting features of the state machine models and clustering the features (¶ 37-40, feature extraction/clustering; see fig. 6B); a model comparing unit, for performing analytic comparison among the state machine models of different types (¶ 33-37, models of different types), so as to obtain at least one fingerprints that is in a first range (¶ 52, obtaining first fingerprints); and a fingerprint extracting unit, for identifying intersections of comparison results between the state machines of individual types and the stored state machines, so as to obtain fingerprints in a second range (¶ 82-84, 87-91, extraction of fingerprints and comparison results, fingerprints of second range; see also fig. 6B). Regarding claim 6, 16, Kohout teaches: wherein when some of the state machine models does not have any fingerprints, the fingerprint extracting unit filters out the state machine models for which fingerprints have been found and feeds an instruction for re-comparison back to the model comparing unit, or the fingerprint extracting unit outputs all of the fingerprints (¶ 82-87, construction of model for new devices; ¶ 34-37, 47, feeding instructions); . Claim 11 is addressed by similar rationale as claim 1. 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 extension fee 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 RYAN J JAKOVAC whose telephone number is (571)270-5003. The examiner can normally be reached on 8-4 PM EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar A. Louie can be reached on 572-270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RYAN J JAKOVAC/Primary Examiner, Art Unit 2445
Read full office action

Prosecution Timeline

Sep 27, 2023
Application Filed
Sep 15, 2025
Non-Final Rejection — §102
Nov 27, 2025
Response Filed
Mar 16, 2026
Final Rejection — §102 (current)

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Prosecution Projections

3-4
Expected OA Rounds
66%
Grant Probability
83%
With Interview (+17.4%)
3y 9m
Median Time to Grant
Moderate
PTA Risk
Based on 613 resolved cases by this examiner. Grant probability derived from career allow rate.

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