DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
The Applicant’s election with traverse of Group II, Claim(s) 4-6 and 8-20 is acknowledged. Upon reconsideration of the claimed subject matter, including the limitations incorporated through dependency and the claims combining model training with signal generation and analysis, the restriction requirement is withdrawn in its entirety. Claim(s) 1-3 and 7 are reinstated and Claim(s) 1-20 will be examined on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/16/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claim(s) 5-6 is/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 (e.g. Claim 5 AND Claim 4, into Claim 1 would be allowable. Claim 6 is objected to because of its dependency on claim 5 only.).
Claim(s) 8-20 is/are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 8-20 are considered allowable because the prior art considered and made of the record, does not establish the claimed combination of generating a training vector corresponding to a human-readable computing instruction, generating a synthetic side-channel signal responsive to that vector using a GAN generator, scoring an actual side-channel signal associated with the execution of the corresponding machine-executable instruction and the generated synthetic signal and updating the discriminator and generator based on the respective losses.
In addition, the various art of record mentioned below & the references mapped to Claim 1, do not disclose instruction-linked training relationships and actual-signal scoring within the claimed training process.
The reviewed references, either in combination or individually, do not provide a sufficient basis to establish obviousness of that complete arrangement without relying on the applicant’s disclosure to supply the missing relationship. Accordingly, the combination recited in independent Claim 8 and 17, together with the additional limitations of there respective dependent Claim(s) 9-16 and 18-20 distinguishes the claimed subject the claimed subject matter from the prior art considered.
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.
Claim(s) 1, 3, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over STRENSKI et al. (US 2023/0236813 A1) and further in view of Lin et al. (US 2021/0224452 A1).
Re Claim 1, STRENSKI teaches a side-channel analysis system, comprising:
one or more data storage devices including, stored thereon: (STRENSKI; FIG. 1-14; Background, ¶ [0101]-[0127]; Data storage devices.)
tokenizer data correlating different token values to different human-readable instructions of a human-readable computing language; and (STRENSKI; FIG. 1-14; Background, ¶ [0047]-[0051], [0092]-[0098]; Tokenizing circuit for converting tokens into a different human-readable instruction related to computing language.)
training data correlating different machine-learned configurations of a side-channel model to the different token values; and (STRENSKI; FIG. 1-14; Background, ¶ [0060]-[0070], [0090]-[0106]; Training data of various machine learned configuration related to modeling token values.)
one or more processors operably coupled to the one or more data storage devices, the one or more processors configured to: (STRENSKI; FIG. 1-14; Background, ¶ [0099]-[0127]; Processor operably coupled to one or more data storage devices, configured to.)
STRENSKI does not explicitly suggest generate a synthetic side-channel signal for a code for a software program using the side-channel model, the code comprising human-readable instructions of the human-readable computing language, the synthetic side-channel signal comprising an estimate of an actual side-channel signal that would be generated by a computer executing the code for the software program, analyze the synthetic side-channel signal to detect one or more of anomalies or vulnerabilities of the software program.
However, in analogous art, Lin teaches generate a synthetic side-channel signal for a code for a software program using the side-channel model, (Lin; FIG. 1-5; Background, Summary, ¶ [0021]-[0043]; The cited embodiment(s) detail simulates side channel leakage signatures, signals using a IC/package/board models.)
the code comprising human-readable instructions of the human-readable computing language, (Lin; FIG. 1-5; Background, Summary, ¶ [0021]-[0044]; Human readable programming language.)
the synthetic side-channel signal comprising an estimate of an actual side-channel signal that would be generated by a computer executing the code for the software program, (Lin; FIG. 1-5; Background, Summary, ¶ [0021]-[0044]; Estimate, analysis of the signal generated by the model executing by related computer code.)
the synthetic side-channel signal generated based, at least in part, on token values for the human-readable instructions of the code and the training data; and (STRENSKI; FIG. 1-14; Background, ¶ [0060]-[0070], [0090]-[0106]; Token values form human-readable instructions of the code and related training data.)
analyze the synthetic side-channel signal to detect one or more of anomalies or vulnerabilities of the software program. (Lin; FIG. 1-5; Background, Summary, ¶ [0021]-[0044]; Detecting issues related to analysis of the simulation of the physical side channel.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify STRENSKI in view of Lin to model side channel signals for the reasons of simulating the leakage of sensitive data in ICs. (Lin Abstract)
Re Claim 3, STRENSKI-Lin discloses the side-channel analysis system of claim 1, wherein the one or more of anomalies or vulnerabilities of the software program comprise a side-channel vulnerability. (STRENSKI; FIG. 1-14; Background, ¶ [0060]-[0070], [0090]-[0106]; Side channel related vulnerabilities.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify STRENSKI in view of Lin to model side channel signals for the reasons of simulating the leakage of sensitive data in ICs. (Lin Abstract)
Re Claim 7, STRENSKI-Lin discloses the side-channel analysis system of claim 1, wherein the synthetic side-channel signal is an estimate of one or more of an actual power consumption signal, (Lin; FIG. 1; Background/Summary; Power consumption.)
an actual acoustic signal,
an actual vibration signal,
an actual temperature signal,
an actual electro-optical radiation signal,
or
an actual electromagnetic radiation signal. (Lin; FIG. 1; Background/Summary; Electromagnetic emission.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify STRENSKI in view of Lin to model side channel signals for the reasons of simulating the leakage of sensitive data in ICs. (Lin Abstract)
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over STRENSKI et al. (US 2023/0236813 A1), in view of Lin et al. (US 2021/0224452 A1) and further in view of Fire et al. (US 2024/0045956 A1).
Re Claim 2, STRENSKI-Lin discloses the side-channel analysis system of claim 1, yet does not explicitly suggest wherein the one or more of anomalies or vulnerabilities of the software program comprise an injected code anomaly.
However, in analogous art, Fire teaches wherein the one or more of anomalies or vulnerabilities of the software program comprise an injected code anomaly. (Fire; FIG. 1-9; Background, ¶ [0026]-[0036]; The injecting of anomalous code.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify STRENSKI-Lin in view of Fire to inject anomalous code for the reasons of creating a method of malicious source code detection. (Fire Abstract)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over STRENSKI et al. (US 2023/0236813 A1), in view of Lin et al. (US 2021/0224452 A1) and further in view of Manevitz et al. (US 2024/0005651 A1).
Re Claim 4, STRENSKI-Lin discloses the side-channel analysis system of claim 1, yet does not explicitly suggest wherein the side-channel model includes a generative adversarial network (GAN) comprising a generator and a discriminator.
However, in analogous art, Manevitz teaches wherein the side-channel model includes a generative adversarial network (GAN) comprising a generator and a discriminator. (Manevitz; FIG. 1-8; Background/Summary, ¶ [0017]-[0030]; A GAN, generator and discriminator.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify STRENSKI-Lin in view of Manevitz to include a generative adversarial network for the reason training, using real data object to create a generative adversarial network. (Manevitz Abstract)
Conclusion
Additional exemplary references made of record:
Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks.
Data Augmentation Techniques using Generative Adversarial Neural Networks on Side Channel Analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER B ROBINSON whose telephone number is (571)270-0702. The examiner can normally be reached M-F 7:00-3:00 EST.
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, Nicholas R Taylor can be reached at 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER B ROBINSON/ Primary Examiner, Art Unit 2443
