DETAILED ACTION
This action is in response to the initial filing filed on January 12, 2024 Claims 1-20 havebeen examined in this application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 1/12/2024 have been acknowledged.
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 § 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al (WO 2020/144679 A) in view of Underbrink et al (US 2005/0047493 A1).
Regarding Claim 1, Cohen teaches a system for detecting global navigation satellite system spoofing, the system comprising [page 8, lines 25-35 for spoofing a GNSS receiver]:
a peak suppression monitor coupled to a tracking channel, the peak suppression monitor includes memory storing instructions, which when executed by at least one data processer result in operations comprising [page 9, lines 3-9 for tracking PRN peaks and delays and page 13, lines 15-30 determining spoofed peaks and synchronizing the peaks]:
receiving, from the tracking channel over a time period, real-time correlation data derived from a global navigation satellite system (GNSS) signal, wherein the real-time correlation data comprises one or more peaks [page 8, lines 30-35 for using real times for spoofing signals, page 13, lines 15-25 for tracking correlations peaks];
determining predicted correlation data corresponding to the real-time correlation data based on historical correlation data [page 10 lines 5-15 for having two groups of navigation signals (real time signals) and page 20, lines 8-20 for knowing expected (historical) satellite signals];
identifying a presence of spoofing within the real-time correlation data based on one or more peaks of residual correlation data, the residual correlation data comprising a comparison between the real-time correlation data and the predicted correlation data [page 13, lines 10-20 for recording correlation peaks and analyzing to classify (compare) as legitimate or spoofed with page 14, lines 14-30 for decoding spoof correlations and identifying legitimate peaks to solve navigation];
generating spoofing detecting data based on the presence of spoofing and the residual correlation data [col 14, lines 1-7 for generating spoofing data based on correlation peaks].
Cohen fails to explicitly teach and providing at least one of (i) the generated spoofing detecting data to the tracking channel for further mitigation or (ii) a notification identifying the presence of spoofing to a user.
Underbrink has a receiver capable of receiving a spread spectrum signal and having a cross-correlator that enables a carrier wave (CW) jamming to be identified and tracked (abstract) and teaches and providing at least one of (i) the generated spoofing detecting data to the tracking channel for further mitigation or (ii) a notification identifying the presence of spoofing to a user [0062 for using cross correlation to cancel (mitigate) a jamming (spoofing) signal and tracking in the tracker].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS spoofing techniques, as disclosed by Cohen, further including the PRN cancelation calculations as taught by Underbrink for the purpose to removing CW jamming signals (Underbrink, 0062).
Regarding Claim 10, Cohen teaches a method for detecting global navigation satellite system spoofing, the method comprising [page 8, lines 25-35 for spoofing a GNSS receiver]:
receiving, from a tracking channel over a time period, real-time correlation data derived from a global navigation satellite system (GNSS) signal, wherein the real-time correlation data comprises one or more peaks [page 8, lines 30-35 for using real times for spoofing signals, page 13, lines 15-25 for tracking correlations peaks];
determining predicted correlation data corresponding to the real-time correlation data based on historical correlation data [page 10 lines 5-15 for having two groups of navigation signals (real time signals) and page 20, lines 8-20 for knowing expected (historical) satellite signals];
identifying a presence of spoofing within the real-time correlation data based on one or more peaks of residual correlation data, the residual correlation data comprising a comparison between the real-time correlation data and the predicted correlation data [page 13, lines 10-20 for recording correlation peaks and analyzing to classify (compare) as legitimate or spoofed];
generating spoofing detecting data based on the presence of spoofing and the residual correlation data [col 14, lines 1-7 for generating spoofing data based on correlation peaks].
Cohen fails to explicitly teach and providing at least one of (i) the generated spoofing detecting data to the tracking channel for further mitigation or (ii) a notification identifying the presence of spoofing to a user.
Underbrink has a receiver capable of receiving a spread spectrum signal and having a cross-correlator that enables a carrier wave (CW) jamming to be identified and tracked (abstract) and teaches and providing at least one of (i) the generated spoofing detecting data to the tracking channel for further mitigation or (ii) a notification identifying the presence of spoofing to a user [0062 for using cross correlation to cancel (mitigate) a jamming (spoofing) signal and tracking in the tracker].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS spoofing techniques, as disclosed by Cohen, further including the PRN cancelation calculations as taught by Underbrink for the purpose to removing CW jamming signals (Underbrink, 0062).
Regarding Claim 19, Cohen teaches a non-transitory computer program product for detecting global navigation satellite system spoofing, the non-transitory computer program product storing instructions [page 8, lines 25-35 for spoofing a GNSS receiver],
which when executed by at least one data processor forming part of at least one computing device, result in operations comprising [page 9, lines 3-9 for tracking PRN peaks and delays and page 13, lines 15-30 determining spoofed peaks and synchronizing the peaks]:
receiving, from a tracking channel over a time period, real-time correlation data derived from a global navigation satellite system (GNSS) signal, wherein the real-time correlation data comprises one or more peaks [page 8, lines 30-35 for using real times for spoofing signals, page 13, lines 15-25 for tracking correlations peaks];
determining predicted correlation data corresponding to the real-time correlation data based on historical correlation data [page 10 lines 5-15 for having two groups of navigation signals (real time signals) and page 20, lines 8-20 for knowing expected (historical) satellite signals];
identifying a presence of spoofing within the real-time correlation data based on one or more peaks of residual correlation data, the residual correlation data comprising a comparison between the real-time correlation data and the predicted correlation data [page 13, lines 10-20 for recording correlation peaks and analyzing to classify (compare) as legitimate or spoofed];
generating spoofing detecting data based on the presence of spoofing and the residual correlation data [col 14, lines 1-7 for generating spoofing data based on correlation peaks],
Cohen fails to explicitly teach and providing at least one of (i) the generated spoofing detecting data to the tracking channel for further mitigation or (ii) a notification identifying the presence of spoofing to a user.
Underbrink has a receiver capable of receiving a spread spectrum signal and having a cross-correlator that enables a carrier wave (CW) jamming to be identified and tracked (abstract) and teaches and providing at least one of (i) the generated spoofing detecting data to the tracking channel for further mitigation or (ii) a notification identifying the presence of spoofing to a user [0062 for using cross correlation to cancel (mitigate) a jamming (spoofing) signal and tracking in the tracker].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS spoofing techniques, as disclosed by Cohen, further including the PRN cancelation calculations as taught by Underbrink for the purpose to removing CW jamming signals (Underbrink, 0062).
Regarding Claim 2 and 11, Cohen teaches the real-time correlation data comprises (i) a first pseudorandom noise (PRN) code associated with an authentic GNSS signal and (ii) a second PRN code associated with a counterfeit GNSS signal [page 15, lines 13-20 for a spoofed attacked (counterfeit) PRN with page 20, lines 10-25 for two PRN codes group A and group B].
Regarding Claim 3 and 12, Cohen teaches the identifying the presence of spoofing comprises suppressing the first PRN code within the real-time correlation data [page 18, lines 15-25 for getting PRN for each satellite, detecting correlation peaks and mitigating spoofing].
Regarding Claim 4 and 13, Cohen teaches the operations further comprise: aligning the one or more peaks of the real-time correlation data with the one or more peaks of the predicted correlation data by delaying the predicted correlation data [page 10, lines 1-12 for two groups of correlations peaks and aligning arrival times];
and determining the residual correlation data subtracting the predicted correlation data from the real-time correlation data [page 9, lines 14-20 for knowing difference in times of navigation messages].
Regarding Claim 5 and 14, Cohen teaches the predicted correlation data is delayed to align a peak of the predicted correlation data with the real-time correlation data [page 10, lines 1-12 for two groups of correlations peaks and aligning arrival times].
Regarding Claim 6 and 15, Cohen teaches a peak of the predicted correlation data is determined based on the historical correlation data [page 15, liens 20-30 for storing information of correlations peaks in memory and PRN values to classify correlation peaks].
Regarding Claim 7 and 16, Cohen teaches the spoofing is identified based on (i) a presence of at least two peaks within the residual correlation data [figure 4 for presence of two peaks]
and (ii) one of the at least two peaks exceeds a predetermined spoofing threshold for at least a breach duration time period [page 8, lines 29 to page 9, line 15 for spoofer mimicking arrival times and determining the time delay difference].
Regarding Claim 8, 17 and 20, Cohen teaches the further characterization comprises: providing, by the peak suppression monitor, spoofing signal data comprising the residual correlation data to a tracking channel, wherein the tracking channel transmits the real-time correlation data to the peak suppression monitor [figure 9 element tracking, and page 1, lines 29-35 and page 4, lines 1-10 for correcting data].
Cohen fails to explicitly teach and mitigating the identified spoofing by (i) generating a corrected PRN code that is equal and opposite to generated spoofing detecting data and (ii) removing the corrected PRN code from the real-time correlation data.
Underbrink has a receiver capable of receiving a spread spectrum signal and having a cross-correlator that enables a carrier wave (CW) jamming to be identified and tracked (abstract) and teaches and mitigating the identified spoofing by (i) generating a corrected PRN code that is equal and opposite to generated spoofing detecting data and (ii) removing the corrected PRN code from the real-time correlation data [0062 for using cross correlation to cancel (mitigate) a jamming (spoofing) signal and tracking in the tracker based on PRN codes].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS spoofing techniques, as disclosed by Cohen, further including the PRN cancelation calculations as taught by Underbrink for the purpose to removing CW jamming signals (Underbrink, 0062).
Regarding Claim 9 and 18, Cohen teaches the operations further comprise periodically repeating the determining of the predicted correlation data over the time period [page 13, lines 10-20 for continuously seeking (repeating) correlation peaks for PRNs].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al (CN 110231633 A) has an LSTM-based GNSS spoofed interference identification and suppression method and system in the signal acquisition stage.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648