DETAILED ACTION
Response to Amendment
Applicant's submission filed on January 28, 2026 has been entered.
Claims 1 and 9 are amended.
Claims 1-12 are pending this application.
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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Dai (WO 2008/150389 A1) in view of Guan (CN 112540393 A) and further in view of Madrid (US 2016/0109579).
Regarding Claim 1, Dai teaches a method, carried out by a navigation satellite system receiver, (NSS receiver) or [0026, 0031, and receiver element 130],
a processing entity capable of receiving data from the NSS receiver, for estimating parameters useful to determine a position, the NSS receiver observing a NSS signal from each of a plurality of NSS satellites over multiple epochs, the method comprising [0031-0033 and satellites element 110-1, 110-2, 110-3, and CPU processor element 200, 240]:
receiving input data comprising at least one of: NSS signals observed by the NSS receiver or information derived from said NSS signals [0032-0034];
operating an estimator that uses state variables and computes the values of its state variables at least based on the received input data [0032, 0038-0039];
obtaining precise observations residuals and less precise observations residuals from the estimator, each residual being associated with at least one NSS signal observed by the NSS receiver [0023, 0029-0032 for using carrier phase measurements],
wherein: the precise-observations residuals comprise at least one of: carrier phase observation residuals, delta carrier phase observation residuals, or Doppler observation residuals [0029-0032 for using carrier phase measurements];
and the less-precise-observations residuals comprise at least one of: code observation residuals or delta code observation residuals [0023, 0030-0032 for using carrier phase and code observations];
and estimating an expected accuracy of at least one of a position or a velocity [0028, 0038 for using baseline or expected measurements],
wherein estimating the expected accuracy is based on at least some of the obtained less-precise-observations residuals [0023, 0030-0032 for using carrier phase and code observations]
and providing the determined geospatial position to an automatic control system or triggering an alert based on whether the estimated expected accuracy satisfies a threshold [Dai, 0042 for estimating and updating (automatically) the position using Kalman filter].
Dai fails to explicitly teach less precise residuals that have been modified using at least some of the precise-observations residuals, wherein the modification comprises a smoothing process that uses a smoothing coefficient that is incremented at at least one epoch.
Guan has method and system for checking the quality of marine precision single-point positioning protection level (abstract) and teaches less precise residuals that have been modified using at least some of the precise-observations residuals [0081-0085 for position deviation level (precise vs. less precise) for positioning],
wherein the modification comprises a smoothing process that uses a smoothing coefficient that is incremented at least one epoch [0078-0080],
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 satellite position techniques, as disclosed by Dai, further including the residual calculations as taught by Guan for the purpose to eliminate the impact of isolated noise points on positioning accuracy (Guan, 0078).
Dai fails to explicitly teach a smoothing process that uses a smoothing coefficient that is incremented at each epoch to recursively weight the residuals and determining a geospatial position of the NSS receiver based at least in part on the values of the state variables of the estimator.
Madrid has a method for computing a bound up to a given confidence level of an error in a state vector estimation KSV of a state vector TSV of a physical system (abstract) and teaches a smoothing process that uses a smoothing coefficient that is incremented at each epoch to recursively weight the residuals [0041 for temporal correlation factor (smoothing), 0063].
determining a geospatial position of the NSS receiver based at least in part on the values of the state variables of the estimator [0076-0077 for getting position coordinates for Doppler or carrier-phase observations].
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 satellite position techniques, as disclosed by Dai, further including the recursive calculations as taught by Madrid for the purpose to compute bounds for the Kalman solution errors (Madrid, 0074).
Regarding Claim 2, Dai teaches estimating the expected accuracy comprises: applying a non-linear mapping function to a linear combination of at least some of the obtained precise-observations residuals and at least some of the obtained less-precise-observations residuals, the mapping function being a function of the number of received residuals [0037].
Regarding Claim 3, Dai teaches the estimator comprises at least one of a Kalman filter and a least squares estimator [0029-0030].
Regarding Claim 4, Dai teaches after obtaining the precise-observations residuals and the less-precise-observations residuals and before estimating the expected accuracy [0023, 0030-0032 for using carrier phase and code observations]:
for each of at least some of the obtained residuals, scaling the residual based on a stochastic model, the stochastic model applying to the at least one NSS signal with which the residual is associated [0022];
wherein estimating the expected accuracy then comprises estimating the expected accuracy based on at least one of: the scaled residuals and information derived from the scaled residuals [0022-0023].
Regarding Claim 5, Dai teaches at least one of: after scaling the obtained residuals and before estimating the expected accuracy, smoothing the scaled residuals in time; or after obtaining the residuals and before scaling the residuals, smoothing the residuals in time [0028-0030].
Regarding Claim 6, Dai teaches the expected accuracy comprises at least one of: an expected horizontal error on or near the Earth surface; an expected vertical error on or near the Earth surface; or an expected three-dimensional error on or near the Earth surface [0019 and figure 3A element 344].
Regarding Claim 7, Dai teaches computing a scale factor based on at least one of: the residuals and information derived from the residuals [0037].
Regarding Claim 8, Dai teaches the estimator outputs, at each epoch, a covariance matrix, and the method further comprising: determining whether to change the covariance matrix of the estimator using a decision procedure taking the estimated expected accuracy and the computed scale factor as input [0036-0037].
Regarding Claim 9, Dai teaches a system comprising a navigation satellite system receiver (NSS receiver) [0026, 0031, and receiver element 130],
or a processing entity capable of receiving data from the NSS receiver [0031-0033 and satellites element 110-1, 110-2, 110-3, and CPU processor element 200, 240],
the system being for estimating parameters useful to determine a position [0032-0034],
the NSS receiver being configured for observing a NSS signal from each of a plurality of NSS satellites over multiple epochs, and the system being configured for [0031-0033 and satellites element 110-1, 110-2, 110-3, and CPU processor element 200, 240]:
receiving input data comprising at least one of: NSS signals observed by the NSS receiver, or information derived from said NSS signals [0032-0034];
operating an estimator that uses state variables and computes the values of its state variables at least based on the received input data [0032, 0038-0039];
obtaining first precise-observation residuals and less-precise-observations residuals, from the estimator, each residual being associated with at least one NSS signal observed by the NSS receiver [0023, 0029-0032 for using carrier phase measurements],
wherein: the precise-observations residuals comprise at least one of: carrier phase observation residuals, delta carrier phase observation residuals, or Doppler observation residuals [0029-0032 for using carrier phase measurements];
and the less-precise-observations residuals comprise at least one of: code observation residuals or delta code observation residuals [0023, 0030-0032 for using carrier phase and code observations];
and estimating an expected accuracy of at least one of a position or a velocity [0028, 0038 for using baseline or expected measurements],
wherein estimating the expected accuracy is based on at least some of the obtained less-precise-observations residuals [0023, 0030-0032 for using carrier phase and code observations],
and providing the determined geospatial position to an automatic control system or triggering an alert based on whether the estimated expected accuracy satisfies a threshold [Dai, 0042 for estimating and updating (automatically) the position using Kalman filter].
Dai fails to explicitly teach less precise residuals that have been modified using at least some of the precise-observations residuals, wherein the modification comprises a smoothing process that uses a smoothing coefficient that is incremented at at least one epoch.
Guan has method and system for checking the quality of marine precision single-point positioning protection level (abstract) and teaches less precise residuals that have been modified using at least some of the precise-observations residuals [0081-0085 for position deviation level (precise vs. less precise) for positioning],
wherein the modification comprises a smoothing process that uses a smoothing coefficient that is incremented at least one epoch [0078-0080],
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 satellite position techniques, as disclosed by Dai, further including the residual calculations as taught by Guan for the purpose to eliminate the impact of isolated noise points on positioning accuracy (Guan, 0078).
Dai fails to explicitly teach a smoothing process that uses a smoothing coefficient that is incremented at each epoch to recursively weight the residuals and determining a geospatial position of the NSS receiver based at least in part on the values of the state variables of the estimator.
Madrid has a method for computing a bound up to a given confidence level of an error in a state vector estimation KSV of a state vector TSV of a physical system (abstract) and teaches a smoothing process that uses a smoothing coefficient that is incremented at each epoch to recursively weight the residuals [0041 for temporal correlation factor (smoothing), 0063].
determining a geospatial position of the NSS receiver based at least in part on the values of the state variables of the estimator [0076-0077 for getting position coordinates for Doppler or carrier-phase observations].
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 satellite position techniques, as disclosed by Dai, further including the recursive calculations as taught by Madrid for the purpose to compute bounds for the Kalman solution errors (Madrid, 0074).
Regarding Claim 10, Dai teaches the vehicle preferably being at least one of: a motor vehicle, an agricultural tractor, a combine harvester, a crop sprayer, a construction equipment, a truck, a bus, a train, a motorcycle, an autonomous vehicle, a self-driving vehicle, a driverless vehicle, a robotic vehicle, a highly automated vehicle, an aircraft, and an unmanned aerial vehicle [0026].
Regarding Claim 11, Dai teaches a computer program or set of computer programs comprising computer-readable instructions configured, when executed on a computer or set of computers, to cause the computer or set of computers to carry out the method [0043].
Regarding Claim 12, Dai teaches a computer program product or storage mediums comprising a computer program or set of computer programs [0043].
Response to Arguments
Applicant’s arguments with respect to claims 1-12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
On page 6, last paragraph of applicant’s argument, applicant addresses the 101 rejection. The examiner appreciates the clarification to the claims.
On page 8, third paragraph of applicant’s argument, applicant states that Tominga’s and Guan do not teach smoothing coefficients that is incremented at each epoch. The examiner respectfully disagrees, new reference Madrid teaches a smoothing coefficient that is incremented at each epoch, [Madrid, 0138 for updating correlations at each epoch and equation 4].
On page 9, first paragraph of applicant’s argument, applicant states that the applied references do not teach determining geospatial position to an automatic control system. The examiner respectfully disagrees, Dai teaches determining geospatial position to an automatic control system [Dai, 0042 for estimated position is updated in accordance with the ambiguity state values in the Kalman filter state].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
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