DETAILED ACTION
Claims 1-10 are examined and pending.
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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-9 recites a "apparatus" is not a process, machine, manufacture or composition of matter. The claimed element "module" is a non-structural limitation, and in light of the specification being silent regarding the “module”. Therefore, applying the broadest reasonable interpretation as understood by one of ordinary skill in the art, the claimed elements may be interpreted as software only. Thus the claimed subject matter as a whole fails to fall within the definition of a process, machine, manufacture or composition of matter, patentable eligible category subject matter.
The examiner encourages applicant to define with the claims the embodied features and limitations on hardware structural limitations by tying the process to the hardware structural limitation such as “comprising: at least one hardware processor; a non-transitory computer-readable storage medium storing instructions which, when executed by the at least one hardware processor, are configured to implement,” or any variants supported by the specification.
Therefore, the claimed subject matter as a whole fails to fall within the definition of a process, machine, manufacture or composition of matter, patentable eligible category subject matter.
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.
Claims 1-3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Lai et al. (CN 114157469A, hereinafter “Lai”) in view of Jan et al. (U.S. 2024/0232335 A1, hereinafter “Jan”).
As to claims 1 and 10, Lai discloses an adversarial attack apparatus, comprising:
a data generation module configured to collect a plurality of controller area network (CAN) messages, extract preset some piece of data (ID number and data field content) from the collected plurality of CAN messages, add type information (ID number) about the CAN message to the extracted some pieces of data to generate a CAN message packet, and aggregate the generated CAN message packet to configure a CAN message packet dataset (page 3, paragraph 1-3; discloses obtaining the flow data packet on the CAN bus from the real vehicle. Further, obtains ID number from the data packet from the vehicle. Paragraphs 4-5 discloses establishing the corresponding attack data packet for the ID on the CAN bus and its data segment content of the injection attack using the obtained ID number and injecting the created attack data packet into the real vehicle at a certain frequency);
message capable of avoiding an intrusion detection system (IDS) of a vehicle (page 5, paragraph 1; discloses CAN messages train the attack detection model to detect and avoid the attack)
However, Lai does not explicitly disclose the apparatus wherein
a preprocessing module configured to insert noise into some CAN message packets in the CAN message packet dataset, the noise being inserted based on the type information of each CAN message packet; an adversarial attack generation module configured to receive the CAN message packet into which the noise is inserted and generate an adversarial CAN message.
In an analogous art, Jan discloses the apparatus wherein preprocessing module configured to insert noise into some CAN message packets in the CAN message packet dataset, the noise being inserted based on the type information of each CAN message packet (para. [0024]; discloses generating a first noise and taking the noise and adding it to the training data message); and
an adversarial attack generation module configured to receive the CAN message packet into which the noise is inserted and generate an adversarial CAN message (para. [0025]; discloses adding the first noise into each of the training data to adjust the training data and generating the adversarial training data message).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lai by incorporating a function to generate a noise to the training message and generating the adversarial training message as 8taught by Jan in order to increase the ability of the system to defend against attacks.
As to claim 2, Lai-Jan discloses the adversarial attack apparatus of claim 1, wherein the preprocessing module includes: a data conversion unit configured to convert a data format of each CAN message packet of the CAN message packet dataset (Lai, page 6, paragraph 7; discloses converting the CAN message into characteristic matric representing the time sequence distribution); and a noise insertion unit configured to insert the noise based on the type information of each CAN message packet (Jan, para. [0051]; discloses adding the first noise into each of the validation data to adjust the validation data).
As to claim 3, Lai-Jan discloses the adversarial attack apparatus of claim 2, wherein the type information includes class information indicating whether the corresponding CAN message packet is a normal packet or an attack packet and subclass information indicating whether the corresponding CAN message packet is any type of attack among a flooding attack, a fuzzing attack, a relay attack, and a spoofing attack, when the corresponding CAN message packet is the attack packet (Jan, page 8, paragraph 2; discloses data frame that discloses that has different types of attack type in the format).
Allowable Subject Matter
Claims 4-9 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shin et al. (U.S. 2017/0286675 A1) discloses anomaly-based intrusion detection system is presented for use in vehicle networks. The intrusion detection system measures and exploits the intervals of periodic in-vehicle messages for fingerprinting electronic control units. Fingerprints are then used for constructing a baseline of clock behaviors, for example with a Recursive Least Squares algorithm. Based on the baseline, the intrusion detection system uses cumulative sum to detect any abnormal shifts in the identification errors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOE CHACKO whose telephone number is (571)270-3318. The examiner can normally be reached Monday-Friday 7am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ario Etienne can be reached at 5712724001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOE CHACKO/Primary Examiner, Art Unit 2457