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 1, 3, 12, 14, and 23 are objected to because of the following informalities:
Claim 1, line 2 recites “Device under Monitoring, DuM,,” which should be changed to --Device under Monitoring (DuM),--.
Claim 1, line 8 recites “of that removes” which should be changed to --that removes--.
Claim 3, line 3 recites “known operational states 410” which should be changed to --known operational states--.
Claim 12, page 2, line 3 recites “the DuM” which should be changed to --the Device under Monitoring (DuM)--.
Claim 14, line 4 recites “known operational states 410” which should be changed to --known operational states--.
Claim 23, line 1 recites “a DuM” which should be changed to --a Device under Monitoring--.
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 23-25 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 23 recites the limitation “processing … one or more of payload data and payload instructions”, however this limitation is indefinite because the one of “one or more” implies that a singular instance of data can exist, but the “payload data and payload instructions” (emphasis added) implies a plurality. Thus, the recitations of “one or more” and “of payload data and payload instructions” contradict each other, making the overall limitation indefinite. Further, it’s also unclear if the “one or more” pertains to “payload data and payload instructions” together or individually (i.e., only one or more of “payload data” or “payload instructions”.
Claim 23 further recites “transmitting first enumeration information about the payload data and instructions”, however this limitation lacks proper antecedent basis.
Claim 24 recites “processing … one or more of payload data and payload instructions” under the clauses “at a third period of time” and “at a fourth period of time”, however the limitation “one or more of payload data and payload instructions is found indefinite for the same rationale stated for claim 23 above. Furthermore, both of these recitations lack proper antecedent basis as it’s unclear whether these recitations of “one or more of payload data and payload instructions” is intended to further refer to the same “one or more of payload data and payload instructions” introduced within claim 23, or other sets of “payload data and payload instructions” collected at different points in time.
Claim 24 further recites “transmitting third enumeration information comprising information about the payload data, payload instructions …”, however the recitations of “the payload data, payload instructions” lacks proper antecedent basis.
Claim Rejections - 35 USC § 103
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, 10-14, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Prvulovic” (US 10810310)in view “Guilley” (US 2017/0270307).
Regarding Claim 1:
Prvulovic teaches:
A method implemented in an authorized monitoring device (Fig. 3, element 300) for monitoring side-channel emissions of a Device under Monitoring, DuM (Fig. 3, element 350),, the method comprising:
receiving a combined side-channel emission from the DuM (Col. 24, lines 6-22, “Referring to FIG. 7, trace-event alignment is performed 705, for example, by the analyzer processor 432 to account for recognized events (e.g., interrupts and cache misses) that effectively splice-and-blend relatively strong and long-duration event-related signals into the trace… the HW/SW interaction model 436 may be used to identify which event signals are likely to have been spliced into the observed signal, and composing the corresponding reference traces whose length matches the observed signal … In addition, the analyzer processor 432 may, as needed, perform 705 feature extraction on both training and observed traces”), …
filtering the combined side-channel emission to determine the payload side-channel emission based at least in part on a known filtering characteristic (Col. 24, lines 23-26, “The analyzer processor 432 can select 710 likely traces for comparison with an observed trace. This reduces the search space from every recorded trace to the most likely traces for comparison”; Col. 24, lines 46-48, “The probabilities of each match are combined with probabilities from the models 434 and 436 and used for multi-hypothesis matching 725”) … and
determining an operational state of the DuM based on the payload side-channel emission (Col. 24, lines 55-57, “Based on the multi-hypothesis matching 725, the analyzer processor 432 creates 730 a confidence threshold and decides whether an anomaly has occurred”).
Prvulovic does not disclose:
receiving a combined side-channel emission from the DuM, wherein the combined side-channel emission comprises a payload side-channel emission and a masking side-channel emission;
filtering the combined side-channel emission to determine the payload side-channel emission based at least in part on a known filtering characteristic of that removes the masking side-channel emission from the combined side-channel emission; and
Guilley teaches:
receiving a combined side-channel emission from the DuM, wherein the combined side-channel emission comprises a payload side-channel emission and a masking side-channel emission (Fig. 1 details receiving a side-channel measurement that comprises both an applied key and mask to a plaintext input);
filtering the combined side-channel emission to determine the payload side-channel emission based at least in part on a known filtering characteristic of that removes the masking side-channel emission from the combined side-channel emission (Fig. 1 details applying an optimal distinguisher to the side-channel measurement in order to remove a mask and obtain only the applied key; ¶0032, “Application of a leakage model to the sidechannel measurements allows the calculation of an “optimal distinguisher”, which provides an estimate of the unknown cryptographic key”); and
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Prvulovic’s side channel analysis system by enhancing Prvulovic’s system to filter received side channel emissions in order to remove a mask, as taught by Guilley, in order to more accurately determine the security of a cryptographic device being monitored.
The motivation is to provide means to recover secret data in order to properly evaluate a cryptographic device by way of removing a mask applied to the secret data utilizing leakage models (Guilley, ¶0007; ¶0008; ¶0009).
Regarding Claim 2:
The method of claim 1, wherein Prvulovic in view of Guilley further teaches the determining the operational state of the DuM is further based on a known status model (Prvulovic, Abstract, “… signal processing, based on a software model and a hardware-software (HW/SW) interaction model of the monitored device …”).
Regarding Claim 3:
The method of claim 2, Prvulovic in view of Guilley further comprising:
training the known status model based on received payload side-channel emissions during a payload training stage that correspond to known operational states 410 of the DuM (Prvulovic, Col. 2, lines 39-42, “Machine learning may be applied to learn “normal” behavior. After training, new signal traces are obtained, subjected to similar signal processing, and compared to learned “normal” program behavior”).
Regarding Claim 10:
The method of claim 1, Prvulovic in view of Guilley further comprising:
receiving additional combined side-channel emissions and information regarding corresponding operational states of the DuM (Prvulovic, Col. 11, lines 65-67 & Col. 12, lines 1-4, “The spectral monitor 426 also may, for example, identify potential anomalies, report the potential anomalies to the anomaly reporter 450, and trigger deeper analysis by the analyzer 430. The monitor 420 also may include a buffer 428 that buffers the signals. The buffer 428 may buffer the signals continuously and in real-time to enable the deeper analysis of the signals”); and
based on the additional combined side-channel emissions and the information regarding corresponding operation states of the DuM, updating the known filtering characteristic (Prvulovic, Col. 12, lines 42-48, “As shown in FIG. 4, in some embodiments, the analyzer 430 may include an analyzer processor (or feature extractor) 432 that performs deeper analysis model-driven feature extraction of the program and hardware characteristics (obtained, for example, during training) to continuously maintain a software model 434 of the software state and hardware/software (HW/SW) interactions model 436”).
Regarding Claim 11:
The method of claim 1, wherein Prvulovic in view of Guilley further teaches the combined side-channel emission comprises at least one of power consumption of the DuM, timing of the DuM, a thermal emission, an electromagnetic emission (Prvulovic, Col. 4, lines 60-62, “The system may further include at least one electro-magnetic sensor configured to detect the one or more signals emanated from the monitored device”), an electromagnetic field, or an audio emission.
Regarding Claims 12-14, 21, and 22:
Authorized monitoring device claims 12-14, 21, and 22 correspond to respective method claims 1-3, 10, and 11, and contain no further limitations. Therefore claims 12-14, 21, and 22 are each rejected by applying the same rationale used to reject claims 1-3, 10, and 11 above, respectively.
Claim(s) 4 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Prvulovic” (US 10810310)in view “Guilley” (US 2017/0270307) in further view of “Ben Ami” (US 2025/0168183).
Regarding Claim 4:
Prvulovic in view of Guilley teaches:
The method of claim 1, …
Prvulovic in view of Guilley does not disclose:
… further comprising:
training the filtering characteristic based on received masking side-channel emissions during a filter training state that correspond to known masking inputs.
Ben Ami teaches:
… further comprising:
training the filtering characteristic based on received masking side-channel emissions during a filter training state that correspond to known masking inputs (¶0028, “The trained machine learning model generates a distribution of probabilities for the candidate (i.e. potential) values of the masked value (which may also be referred to as the ‘masked attribute’ as it is a value that corresponds to an attribute of the event) and then makes a determination as to whether the event is an anomalous event (e.g. whether an anomalous event has occurred) using the known value of the masked value (i.e. the actual value of the attribute before it was masked)”; ¶0029, “This is repeated for a plurality of masked logs (e.g. for each of the masked logs in the training data). The training therefore enables the trained machine learning model to generate more refined probability distributions after each iteration of training”).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Prvulovic in view of Guilley’s side channel analysis system by enhancing Prvulovic in view of Guilley’s leakage model to be repeatedly trained for a plurality of masks, as taught by Ben Ami, in order to create a model comprising an accurate distribution of masks utilized by a cryptographic device.
The motivation is to provide a greater distribution of probabilities for possible values of a masked value by training a model based on a plurality of different masks (Ben Ami, ¶0005), thus allowing for more efficient separation of a masked value from a secret payload.
Regarding Claim 15:
Authorized monitoring device claim 15 corresponds to method claim 4, and contains no further limitations. Therefore claim 15 is rejected by applying the same rationale used to reject claim 4 above.
Claim(s) 9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Prvulovic” (US 10810310)in view “Guilley” (US 2017/0270307) in further view of “Yu” (US 2025/0173240).
Regarding Claim 9:
Prvulovic in view of Guilley teaches:
The method of claim 1, …
Prvulovic in view of Guilley does not disclose:
… further comprising:
converting the received combined side-channel emission from a time domain signal to a frequency domain signal; and
filtering the frequency domain signal based on the known filtering characteristic.
Yu teaches:
… further comprising:
converting the received combined side-channel emission from a time domain signal to a frequency domain signal (¶0028, “For example, data values of the current data window may be firstly transformed from the time domain to the frequency domain through Fourier transform”); and
filtering the frequency domain signal based on the known filtering characteristic (¶0028, “Subsequently, an abnormal data value may be detected from a frequency domain curve through a trained deep learning model”).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Prvulovic in view of Guilley’s side channel analysis system by enhancing Prvulovic in view of Guilley’s system to convert received emissions from a time domain to a frequency domain, as taught by Yu, in order to represent the signals on a frequency basis.
The motivation is to represent signal emissions via their spectral content by converting receiving signal emissions from a time domain to a frequency domain. Such conversion makes filtering of the signal emissions more efficient as well as revealing characteristics that may not be visible in the time domain.
Regarding Claim 20:
Authorized monitoring device claim 20 corresponds to method claim 9, and contains no further limitations. Therefore claim 20 is rejected by applying the same rationale used to reject claim 9 above.
Allowable Subject Matter
Claims 5-8 and 16-19 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.
The following is a statement of reasons for the indication of allowable subject matter: The cited prior art of record does not fairly teach or suggest, either alone or in combination, the subject matter recited within claims 5-8 and 16-19. Thus these claims, when considered in view of their respective base independent claims and all intervening claims, are deemed to be allowable over the prior art of record.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL B POTRATZ whose telephone number is (571)270-5329. The examiner can normally be reached on M-F 10 A.M. - 6 P.M. CST.
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/DANIEL B POTRATZ/Primary Examiner, Art Unit 2491
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