Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. It would be of great assistance to the office if all incoming papers pertaining to a filed application carried the following items:
i. Application number (checked for accuracy, including series code and serial no.).
ii. Group art unit number (copied from most recent Office communication).
iii. Filing date.
iv. Name of the examiner who prepared the most recent Office action.
v. Title of invention.
vi. Confirmation number (See MPEP § 503).
3. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages, paragraph and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
4. Claim interpretation: When multiple limitations are connected with “OR”, one of the limitations doesn’t have any patentable weight since both of the limitations are optional.
Claim Rejection- 35 USC § 103
5. 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 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sedjelmaci (WO 2023/153990) and further in view of Baxley (Pub No. 2015/0350228).
Regarding claim 1, Sedjelmaci discloses a processor-implemented method, the method comprising: identifying an adversarial jammer is causing an impact on a wireless system (Page. 18: Jamming attack detection & Page. 24-25: Jamming attack and impact on a wireless device) & (Fig. 4-5: Attack detection on a device); generating a risk assessment caused by the adversarial jammer to a user (Page. 7: determining a measure of effectiveness for the first node that indicates how effective the first node 303 is at detecting attacks on the communications network. & Page 9: verify whether the suspected attack is a real attack[Wingdings font/0xE0] risk assessment & Page. 25); identifying an action to apply based on the risk assessment; and performing the identified action (Page. 12 & 21: Mitigation actions based on measurement of effectiveness & Fig. 5: Identify mitigate action 1 or 2 or 3 and apply mitigate action 1/ 2/ 3 based on the attack detection).
Sedjelmaci does not explicitly disclose generating a risk assessment of impact.
In a similar field of endeavor, Baxley et al discloses generating a risk assessment of impact caused by the adversarial jammer to a user (Para. 225: Risk detection and Risk levels associated with behaviors or characteristics determined by the device classification module 370) & (Para. 122: Classify jamming & 219 & Para. 197: threat level may be established for a known malicious entity).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the invention to use the threat detection and mitigation system of Baxley’s disclosure with the wireless device security system, as taught by Sedjelmaci. Doing so would have resulted in effectively determine the risk level of a jamming signal in a wireless communication and mitigate the jamming signals based on device’s requirements to produce risk free data transfer between multiple devices and generating higher security data communication.
Regarding claim 8, Claim 8 corresponds to claim 1 and is analyzed accordingly.
Regarding claim 15, Claim 15 corresponds to claim 1 and is analyzed accordingly.
Regarding claim 2 & 9 & 16, Sedjelmaci discloses generating a reinforcement learning training environment (Page. 12: Machine learning based attack detection); performing reinforcement learning training during an exploration phase; and performing reinforcement learning deployment during an exploitation phase (Page. 16 & 12: the second node 301 may request the first node to switch from a signature (e.g. rules)-based detection attack method to a machine learning detection method in order to detect more attacks).
Regarding claim 3 & 10 & 17, Sedjelmaci is silent regarding identifying the adversarial jammer further comprises; generating a preliminary characterization of the adversarial jammer based on a detected effect on the wireless system caused by the adversarial jammer.
Baxley et al discloses identifying the adversarial jammer further comprises; generating a preliminary characterization of the adversarial jammer based on a detected effect on the wireless system caused by the adversarial jammer (Para. 122: Classify jamming & Para. 163: preliminary characterization of the predetermined event).
At the time of filling, it would have been obvious to use classify jamming in the wireless system to determine the risk level of the device.
Regarding claim 4 & 11 & 18, Sedjelmaci is silent regarding the risk assessment considers an attack probability to a user utilizing the wireless system, adversarial jammer strength, task priority determined through machine learning of prior attacks, and task responsibility determined through machine learning of prior attacks.
Baxley et al discloses the risk assessment considers an attack probability to a user utilizing the wireless system, adversarial jammer strength, task priority determined through machine learning of prior attacks, and task responsibility determined through machine learning of prior attacks (Para. 122: Classify jamming & Para. 163: preliminary characterization of the predetermined event & Para. 219: Attack classification & Para. 194: machine-learning algorithms to monitored and/or localized over time and space).
At the time of filling, it would have been obvious to use classify jamming in the wireless system to determine the risk level of the device.
Regarding claim 5 & 12 & 19, Sedjelmaci discloses the action is identified through a rule-based system (Page. 11: Attack identified through a rule-based system).
Regarding claim 6 & 13 & 20, Sedjelmaci discloses the action is selected from a group consisting of delaying training, applying countermeasures, pre-emptively secure users, and continue as is (Page. 18-19: training & applying countermeasures).
Regarding claim 7 & 14, Sedjelmaci discloses storing data from the reinforcement learning training and reinforcement learning deployment in a repository, wherein the data is selected from a group consisting of rules, policies, metadata, and other historical data (Para. 21: Data store and attack detection & Fig. 4: Data monitoring & rules-based detection).
Conclusion
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/MD K TALUKDER/ Primary Examiner, Art Unit 2648