DETAILED ACTION
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 .
Response to Amendment
Claim 10 previously canceled.
Claims 1, 11, and 13-14 have been amended.
Claims 1-9, and 11-16 are pending.
Applicant's arguments and remarks filed on 5/15/25 have been fully considered.
Applicant's amendments overcome the objections to the claims.
Applicant's amendments/arguments traverse the previous 35 U.S.C. 112(f) interpretation.
Applicant's amendments overcome the previous 35 U.S.C. 112(b) rejection.
Applicant's amendments overcome the previous 35 U.S.C. 101 rejection.
Contingent limitations. Claims 1 and 11 recite “if the ionospheric disturbance level exceeds the threshold, … performing at least one of: adapting an ionospheric noise model … or adapting an observation noise model”. The immediately antecedent step - “determining that the ionospheric disturbance information indicates an ionospheric disturbance level exceeding a threshold” - is a positive, non-contingent step that, by its terms, establishes that the level does exceed the threshold. The condition precedent of the contingent step is therefore necessarily satisfied whenever the method is performed as claimed, so the adapting step is given patentable weight and is addressed on the merits for the method claims (Claims 1–9, 13–15). For the system and product claims (Claims 11, 12, 16, and the storage medium of Claims 13–14), the recited structure configured to perform the function must be taught regardless of whether the condition occurs (MPEP § 2111.04(II)); the combination is shown in the 35 U.S.C. 103 rejection below to teach that capability.
Response to Arguments
Applicant’s arguments with respect to claims 1–9 and 11–16 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.
Applicant traverses the interpretation of “processing entity” under 35 U.S.C. § 112(f), arguing under MPEP § 2181(I)(C) that the term is recited with structural context - “a processing entity that is capable of receiving data from an NSS receiver” - and is a type of structural device with a generally understood meaning in the art, as evidenced by Glocker (US 10,670,734). The argument is persuasive. The recited “processing entity capable of receiving data from the NSS receiver” conveys, to a person of ordinary skill in the satellite-navigation art, a class of structural data-processing devices having a reasonably well-understood meaning, and the term is therefore not a non-structural generic placeholder invoking § 112(f). The § 112(f) interpretation is withdrawn, and the limitation is given its plain meaning.
Applicant amended Claim 1 to recite “estimating the position of the NSS receiver based on the NSS signals observed by the NSS receiver and at least one of the adapted ionospheric noise model or the adapted observation noise model,” and argues that the claims require a particular machine (an NSS receiver) and could not be performed without it, citing SiRF Tech., Inc. v. Int’l Trade Comm’n (Fed. Cir. 2010). The argument is persuasive. As amended, the independent claims tie any recited mathematical operations to the operation of an NSS receiver and direct them to an improvement in the technological process of satellite-navigation positioning under ionospheric disturbance - adapting an ionospheric or observation noise model upon detection of a threshold-exceeding disturbance and estimating position using the adapted model. This integrates any recited judicial exception into a practical application under Step 2A, Prong Two.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 11, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Averin et al. (US 2015/0226855 A1) in view of Susi et al. (Susi, M., Andreotti, M., Aquino, M. Tuning a Kalman filter carrier tracking algorithm in the presence of ionospheric scintillation. GPS Solut 21, 1149 1160 (2017). https://doi.org/10.1007/s10291-016-0597-y (Year: 2017)).
Regarding Claims 1, 11, and 13, Claim 1 is a method, Claim 11 is a system comprising the NSS receiver and/or processing entity configured to perform steps that are substantively identical to the steps of Claim 1, and Claim 13 is a non-transitory computer-readable storage medium storing instructions to carry out the method of Claim 1. The three claims are grouped, and the full analysis is presented for Claim 1; Claims 11 and 13 are rejected for the same reasons, with the system structure of Claim 11 and the storage medium of Claim 13 addressed under the contingent-limitation interpretation. Averin et al. (‘855) in view of Susi et al. (2017) teaches:
Averin et al. (‘855) teaches: Method, carried out by at least one of a navigation satellite system receiver, hereinafter abbreviated as “NSS receiver”, and a processing entity capable of receiving data from the NSS receiver, for estimating a position of the NSS receiver, the NSS receiver observing NSS signals from NSS satellites, the method comprising: (Figs. 1A-C, 4A, [0149]: “Target parameters (such as position, velocity, and time) can be calculated from IFC parameters”) Averin et al. (‘855) is directed to a GNSS receiver (and associated control and computing system) that receives GNSS signals from navigation satellites and computes target parameters including the receiver position, thereby estimating parameters useful to determine a position of the NSS receiver.
Averin et al. (‘855) does not explicitly teach, but Susi et al. (2017) teaches: operating at least one estimation process, each estimation process being hereinafter referred to as “NSS estimator” and the at least one NSS estimator being hereinafter referred to as “NSS estimator set”, wherein each NSS estimator uses state variables and computes values of its state variables based on at least one of: NSS signals observed by the NSS receiver, and information derived from the NSS signals; (Pg. 1149: Kalman filter background: “self-tune their dynamic models by exploiting the knowledge about scintillation”) Susi et al. (2017) discloses a Kalman-filter (KF) based estimator operated on the received GNSS signal, in which a KF state vector of signal parameters (e.g., carrier phase error, Doppler frequency, and Doppler frequency rate) is predicted and updated from the GNSS signal at each epoch (See 1151–1152, Kalman filter background and Eqs. (6)-(10)). The KF state vector and its epoch-by-epoch update from the observed GNSS signal constitute an NSS estimator that uses state variables and computes values of its state variables based on NSS signals observed by the NSS receiver.
Averin et al. (‘855) teaches: obtaining ionospheric disturbance information comprising at least one of: ionospheric scintillation information and ionospheric gradient information, wherein obtaining the ionospheric disturbance information comprises at least one of: receiving, from a reference station or reference station system, the ionospheric disturbance information; or obtaining, within the NSS receiver or processing entity capable of receiving data from the NSS receiver, the ionospheric disturbance information after generating the ionospheric disturbance information on the NSS receiver or the processing entity capable of receiving data from the NSS receiver, the ionospheric disturbance information generated based on NSS observations, or information representing the NSS observations, received from a reference station or reference station system; ([0132]: “the dispersion of the GFC parameter is used for detecting scintillations”) Averin et al. (‘855) computes, within the receiver/control-and-computing system, a geometry-free combination (GFC) parameter and its dispersion from input GNSS measurements over a moving time window ([0122-0132]); the resulting scintillation detection is ionospheric scintillation information generated within the receiver based on NSS observations. The first alternative of the obtaining step (information generated within the receiver from NSS observations) is relied upon.
Averin et al. (‘855) teaches: determining that the ionospheric disturbance information indicates an ionospheric disturbance level exceeding a threshold; ([0132]: “If the value of the dispersion exceeds a specified threshold value, then a scintillation is detected”) Averin et al. (‘855) compares the dispersion (e.g., standard deviation) of the GFC parameter—an ionospheric scintillation disturbance level - to a specified threshold and determines, as an operative step of its algorithm, that a scintillation is present when the threshold is exceeded. This is the recited determination that the ionospheric disturbance information indicates an ionospheric disturbance level exceeding a threshold.
Averin et al. (‘855) does not explicitly teach, but Susi et al. (2017) teaches: if the ionospheric disturbance level exceeds the threshold, for at least one NSS estimator of the NSS estimator set, performing at least one of: adapting an ionospheric noise model of the NSS estimator based on the ionospheric disturbance information; or adapting an observation noise model of the NSS estimator based on the ionospheric disturbance information; and (Pgs. 1153–1154, “Measurement noise matrix tuning” and “Covariance matrix tuning under scintillation”: “self-tune their dynamic models by exploiting the knowledge about scintillation”) Averin et al. (‘855), upon detecting a threshold-exceeding scintillation, mitigates by forming ionosphere-free combinations rather than by adapting a noise model of a state-variable estimator. Susi et al. (2017) supplies the missing adaptation: Susi et al. (2017) self-tunes the Kalman filter’s measurement-noise covariance matrix R according to the detected scintillation level (see 1153–1154, “Measurement noise matrix tuning”), and likewise incorporates the continuously estimated scintillation noise contribution into the process-noise covariance Q (See 1153, “Covariance matrix tuning under scintillation”). Tuning the measurement-noise covariance based on the scintillation information teaches adapting an observation noise model of the NSS estimator based on the ionospheric disturbance information (the relied-upon alternative); Susi et al. (2017)’s tuning of the process-noise covariance additionally teaches adapting an ionospheric noise model.
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to operate Averin et al. (‘855)’s scintillation-detecting GNSS positioning system using the self-tuning Kalman-filter estimator of Susi et al. (2017) and to adapt that estimator’s noise covariance upon Averin et al. (‘855)’s threshold-based detection of scintillation. One would have been motivated to do so because Averin et al. (‘855) and Susi et al. (2017) address the same problem—ionospheric scintillation degrading the quality of GNSS measurements and the resulting position solution—and Averin et al. (‘855) already produces the very scintillation-level determination that Susi et al. (2017)’s tuning consumes: Averin et al. (‘855) determines when the scintillation disturbance exceeds a threshold ([0132]), while Susi et al. (2017) teaches that, under such conditions, the a priori fixed noise model is no longer valid and the scintillation noise contribution must be estimated and incorporated into the filter’s covariance matrices to preserve estimation accuracy and lock (Susi et al. (2017) Pg. 1153). Using Averin et al. (‘855)’s threshold determination to trigger Susi et al. (2017)’s covariance adaptation would predictably improve the accuracy and integrity of the position solution during scintillation. There is a reasonable expectation of success because Susi et al. (2017) demonstrates that scintillation-driven covariance tuning maintains signal lock and provides reliable estimation under strong scintillation (See 1149, 1153–1154), and Averin et al. (‘855) supplies the threshold-based scintillation determination on which that tuning depends, so the combination uses each reference for its established and compatible function within a Kalman-filter GNSS estimator.
Averin et al. (‘855) teaches: estimating the position of the NSS receiver based on the NSS signals observed by the NSS receiver and at least one of the adapted ionospheric noise model or the adapted observation noise model. ([0150]: “calculating target parameters from IFC parameters yields overall better accuracy”) Averin et al. (‘855) estimates the receiver position (a target parameter) from the GNSS measurements. In the combination, the position is estimated using the estimator whose observation noise model has been adapted per Susi et al. (2017); the combination as a whole therefore meets estimating the position of the NSS receiver based on the NSS signals observed and at least one of the adapted ionospheric noise model or the adapted observation noise model.
Claims 11 and 13 are rejected for the same reasons as Claim 1. The system of Claim 11 recites the NSS receiver and/or processing entity “configured to perform steps comprising” the steps of Claim 1; Averin et al. (‘855) (a GNSS receiver with a control and computing system) in view of Susi et al. (2017) (a KF estimator) teaches the recited structure configured to perform those steps, including the configured capability to adapt the noise model upon a threshold-exceeding determination. Claim 13 recites a non-transitory computer-readable storage medium storing instructions to carry out the method of Claim 1; Averin et al. (‘855)’s control and computing system stores the program instructions on a persistent, non-transitory, tangible computer-readable medium (data storage device 308) ([0072], [0077]), and the combined method is the method of Claim 1.
Regarding Claim 2, Averin et al. (‘855) in view of Susi et al. (2017) teaches the method according to Claim 1.
Averin et al. (‘855) teaches: wherein the ionospheric disturbance information comprises at least one of: ionospheric disturbance information applicable to a point on or near the surface of the Earth, said ionospheric disturbance information being hereinafter referred to as “station-specific ionospheric disturbance information”; and ionospheric disturbance information applicable to a line of sight between a point on or near the surface of the Earth and a satellite, said ionospheric disturbance information being hereinafter referred to as “satellite-specific ionospheric disturbance information”. ([0132]: “Typically, input GNSS measurements are calculated at discrete time instants, referred to as epochs. In an embodiment of the invention, the dispersion of the GFC parameter is used for detecting scintillations. Here "dispersion" refers to a generic statistical metric that characterizes the variation of the GFC parameter over a specified time interval.”) Averin et al. (‘855) computes the GFC parameter and detects scintillation separately for each navigation satellite (the GFC parameter corresponds to measurements from the same navigation satellite at different carrier frequencies; abstract; [0122]). The per-satellite scintillation detection is satellite-specific ionospheric disturbance information applicable to the line of sight between the receiver and a satellite. Under the “at least one of” recitation, this alternative is relied upon.
Regarding Claim 3, Averin et al. (‘855) in view of Susi et al. (2017) teaches the method according to Claim 1.
Averin et al. (‘855) does not explicitly teach, but Susi et al. (2017) teaches: wherein the ionospheric disturbance information comprises ionospheric scintillation information comprising at least one of: ionospheric amplitude scintillation information, and ionospheric phase scintillation information. (Susi et al. (2017) Pgs. 1152–1153, “Scintillation”: “first detects the level of phase scintillation”) Susi et al. (2017) characterizes the scintillation by a phase scintillation index (Phi60, derived from the phase-scintillation power spectral density) and detects the level of phase scintillation to select the estimator’s dynamic model (See 1152–1153). This teaches ionospheric phase scintillation information; under the “at least one of” recitation, this alternative is relied upon.
It would have been obvious to a PHOSITA before the effective filing date to characterize the scintillation disturbance of the Averin et al. (‘855)/Susi et al. (2017) combination by a phase scintillation index as taught by Susi et al. (2017). One would have been motivated to do so because Susi et al. (2017) teaches that the phase scintillation level governs the correct tuning of the estimator’s covariance during scintillation (See 1153–1154), so quantifying phase scintillation directly informs the adaptation already adopted in the combination. There is a reasonable expectation of success because Susi et al. (2017) demonstrates the phase scintillation index is computed from the same received GNSS signal used by the estimator (See 1152–1153).
Regarding Claim 4, Averin et al. (‘855) in view of Susi et al. (2017) teaches the method according to Claim 1.
Averin et al. (‘855) does not explicitly teach, but Susi et al. (2017) teaches: wherein adapting an ionospheric noise model comprises applying at least one of a scale factor and an additive value to the ionospheric noise model. (Susi et al. (2017) Pgs. 1153-1154, “Measurement noise matrix tuning”: “a weighting factor is applied”). Susi et al. (2017) applies a weighting factor to the noise estimation when tuning the filter’s noise covariance according to the scintillation conditions (See 1154). Applying a multiplicative weighting factor to the noise model teaches applying a scale factor to the noise model.
It would have been obvious to a PHOSITA before the effective filing date to implement the noise-model adaptation of the Averin et al. (‘855)/Susi et al. (2017) combination by applying Susi et al. (2017)’s scintillation-dependent weighting (scale) factor. One would have been motivated to do so because Susi et al. (2017) teaches the weighting factor enhances the robustness of the noise estimation under scintillation (See 1154), directly serving the combination’s purpose of preserving estimation accuracy during a threshold-exceeding disturbance. There is a reasonable expectation of success because Susi et al. (2017) demonstrates the weighting-factor adjustment operating within the same Kalman-filter covariance framework adopted in the combination (See 1153–1154).
Regarding Claim 14, Averin et al. (‘855) in view of Susi et al. (2017) teaches the non-transitory computer-readable storage medium according to Claim 13.
Claim 14 recites the medium as “at least one of” an enumerated list of storage media; under the broadest reasonable interpretation, the art need teach only one of the listed alternatives.
Averin et al. (‘855) teaches: wherein the non-transitory computer-readable storage medium includes at least one of: a magnetic tape, an optical memory disk, a magnetic disk, a magneto-optical disk, a solid-state disk (SSD), a CD-ROM, a DVD, a CD, or a flash memory unit. ([0072]: “non-volatile semiconductor memory, a magnetic hard drive, or a compact disc read only memory”) Averin et al. (‘855) discloses that the data storage device 308 includes at least one persistent, non-transitory, tangible computer-readable medium, and expressly identifies a “compact disc read only memory,” which is a CD-ROM. Averin et al. (‘855)’s “compact disc read only memory” meets the recited “a CD-ROM” alternative verbatim; Averin et al. (‘855)’s “magnetic hard drive” likewise meets “a magnetic disk” and its “non-volatile semiconductor memory” meets “a flash memory unit.” Under the “at least one of” recitation, the “a CD-ROM” alternative is relied upon.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Averin et al. (‘855) (US 2015/0226855 A1) in view of Susi et al. (2017) (Susi et al. (2017) Pgs. 1149–1160). Claim 15 depends from Claim 1 (method) and Claim 16 depends from Claim 11 (system); they recite the same additional limitations and are grouped, with the full analysis presented for Claim 15. Claim 16 is rejected for the same reasons, with the system structure of Claim 11 addressed under the contingent-limitation interpretation above.
Regarding Claim 15, Averin et al. (‘855) in view of Susi et al. (2017) teaches the method according to Claim 1, wherein, for the at least one NSS estimator of the NSS estimator set:
Averin et al. (‘855) does not explicitly teach, but Susi et al. (2017) teaches: adapting an ionospheric noise model of the NSS estimator based on the ionospheric disturbance information is not performed; adapting an observation noise model of the NSS estimator based on the ionospheric disturbance information is performed; and (Susi et al. (2017) Pgs. 1153–1154, “Measurement noise matrix tuning”: Pg. 1149: “self-tune their dynamic models by exploiting the knowledge about scintillation”) Susi et al. (2017) performs adaptation of the measurement-noise covariance (the observation noise model) of the Kalman estimator in response to the scintillation conditions (See 1153–1154). Relying on the observation-noise-model adaptation alone satisfies “adapting an observation noise model … is performed” while “adapting an ionospheric noise model … is not performed,” as the two are recited as mutually exclusive in this claim. The motivation to combine and reasonable expectation of success are as stated for Claim 1 and are incorporated here.
Averin et al. (‘855) teaches: switching the NSS estimator to ionospheric free observations. ([0146], [0150]: “Exclusion of ionospheric impact with dual frequency measurements is based on forming the so-called ionosphere-free combination”) Averin et al. (‘855), upon detecting a threshold-exceeding scintillation, switches to computing target parameters from ionosphere-free combination (IFC) parameters, which exclude the ionospheric impact ([0146]); Averin et al. (‘855) teaches this switch is favorable when scintillations occur ([0150]). Switching the estimator to ionosphere-free combination measurements teaches switching the NSS estimator to ionospheric free observations. It would have been obvious to a PHOSITA before the effective filing date to switch the estimator of the Averin et al. (‘855)/Susi et al. (2017) combination to ionosphere-free observations upon the threshold determination, as Averin et al. (‘855) teaches; one would have been motivated to do so because Averin et al. (‘855) teaches that, when scintillations cause the ionospheric biases to exceed the noise, computing from IFC parameters yields overall better accuracy ([0150]). There is a reasonable expectation of success because Averin et al. (‘855) demonstrates the ionosphere-free switching operating in the same scintillation-detecting GNSS positioning system ([0146], [0150]).
Regarding Claim 16, the claim is substantially the same as claim 15 and thus, the same cited sections and rationale as corresponding apparatus claim 15 is applied.
Claims 5-9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Averin et al. (US 2015/0226855 A1) in view of Susi et al. (Susi, M., Andreotti, M., Aquino, M. Tuning a Kalman filter carrier tracking algorithm in the presence of ionospheric scintillation. GPS Solut 21, 1149 1160 (2017). https://doi.org/10.1007/s10291-016-0597-y (Year: 2017)), and further in view of Dai et al. (US 2011/0316735 A1).
Regarding Claim 5, Averin et al. (‘855) in view of Susi et al. (2017) and further in view of Dai et al. (‘735) teaches the method according to Claim 1.
Averin et al. (‘855) does not explicitly teach, but Dai et al. (‘735) teaches: wherein the ionospheric noise model is or comprises an ionospheric delay state noise model. ([0010]: “residual ionospheric delays are modeled as states in a Kalman filter”) Dai et al. (‘735) models residual ionospheric delays as states in a Kalman filter, with a process-noise (dynamic) model governing the evolution of those ionospheric-delay states ([0010], [0037-0039], Eq. (14)). This is an ionospheric delay state noise model.
It would have been obvious to a PHOSITA before the effective filing date to implement the “ionospheric noise model” of the Averin et al. (‘855)/Susi et al. (2017) combination as Dai et al. (‘735)’s ionospheric-delay state noise model. One would have been motivated to do so because Dai et al. (‘735) provides an established, physically grounded stochastic model of residual ionospheric delay for a GNSS Kalman estimator ([0032-0039]), which is the same class of state-variable estimator used in the combination, and modeling the ionospheric delay as a filter state provides a defined noise model that Susi et al. (2017)’s scintillation-driven tuning can adapt. There is a reasonable expectation of success because Dai et al. (‘735) demonstrates the ionospheric-delay state noise model operating in a conventional GNSS Kalman filter ([0010], [0037-0039]).
Regarding Claim 6, Averin et al. (‘855) in view of Susi et al. (2017) and further in view of Dai et al. (‘735) teaches the method according to Claim 1.
Averin et al. (‘855) does not explicitly teach, but Dai et al. (‘735) teaches: wherein the ionospheric noise model is or comprises an ionospheric noise model specific to a NSS satellite. ([0010]: “a plurality of residual ionospheric delays are modeled as States in a Kalman filter”) Dai et al. (‘735) models a plurality of residual ionospheric delays as separate states in the Kalman filter, one corresponding to each satellite/line of sight ([0010], [0024-0028]). The per-satellite residual-ionospheric-delay state model is an ionospheric noise model specific to a NSS satellite. The motivation to combine and reasonable expectation of success are as stated for Claim 5 and are incorporated here.
Regarding Claim 7, Averin et al. (‘855) in view of Susi et al. (2017) and further in view of Dai et al. (‘735) teaches the method according to Claim 1.
Averin et al. (‘855) does not explicitly teach, but Dai et al. (‘735) teaches: wherein the ionospheric noise model is or comprises a Gauss-Markov noise model. ([0022], [0039]: “the correlation time of the differential ionosphere bias, typically between 30 and 300 seconds”) Dai et al. (‘735) models the residual (differential) ionosphere bias as a first-order Gauss-Markov process, characterized by a correlation time and a driving process-noise variance (Eq. (14); the residual tropospheric counterpart is expressly described as “a first-order Gauss-Markov process” at [0022], and the ionospheric state is modeled in the same Gauss-Markov form with a correlation time at [0039]). This teaches a Gauss-Markov ionospheric noise model. The motivation to combine and reasonable expectation of success are as stated for Claim 5 and are incorporated here.
Regarding Claim 8, Averin et al. (‘855) in view of Susi et al. (2017) and further in view of Dai et al. (‘735) teaches the method according to Claim 7.
Averin et al. (‘855) does not explicitly teach, but Dai et al. (‘735) teaches: wherein the Gauss-Markov noise model is parametrized by a correlated noise and a correlation time, and adapting the ionospheric noise model comprises modifying at least one of: the correlated noise and the correlation time. ([0039]: “the correlation time of the differential ionosphere bias, typically between 30 and 300 seconds”) Dai et al. (‘735) parametrizes the Gauss-Markov ionospheric model by a correlation time (1/βtop) and by the variance of the differential ionosphere bias (the correlated noise) (Eq. (14)-(15); [0039]). Susi et al. (2017), as combined, adapts that noise model by modifying its noise covariance contribution according to the detected scintillation (Susi et al. (2017) Pg. 1153). The combination thus teaches a Gauss-Markov model parametrized by a correlated noise and a correlation time, in which adapting the model comprises modifying at least the correlated noise.
It would have been obvious to a PHOSITA before the effective filing date to modify the correlated-noise (variance) parameter of Dai et al. (‘735)’s Gauss-Markov ionospheric model in response to Averin et al. (‘855)’s threshold determination, in the manner Susi et al. (2017) tunes the estimator covariance. One would have been motivated to do so because Susi et al. (2017) teaches that, under scintillation, the previously fixed ionospheric noise contribution is no longer valid and must be increased to reflect the actual disturbance (Susi et al. (2017) Pg. 1153), and Dai et al. (‘735)’s Gauss-Markov variance is the natural parameter by which that contribution is represented. There is a reasonable expectation of success because adjusting the driving variance of a first-order Gauss-Markov state is a defined, conventional operation within the Kalman filters disclosed by both Dai et al. (‘735) and Susi et al. (2017).
Regarding Claim 9, Averin et al. (‘855) in view of Susi et al. (2017) and further in view of Dai et al. (‘735) teaches the method according to Claim 8.
Averin et al. (‘855) does not explicitly teach, but Susi et al. (2017) teaches: wherein adapting the ionospheric noise model comprises at least one of: increasing the correlated noise; and decreasing the correlation time. (Susi et al. (2017) Pg. 1153, “Covariance matrix tuning under scintillation”: “the scintillation noise contribution is continuously estimated and included”) Susi et al. (2017) increases the noise contribution of the estimator’s model under scintillation - the scintillation noise contribution is continuously estimated and added to the filter’s noise covariance, increasing it relative to the quiescent (a priori fixed) value (See 1153). Applied to Dai et al. (‘735)’s Gauss-Markov ionospheric model, this teaches increasing the correlated noise; under the “at least one of” recitation, this alternative is relied upon. The motivation to combine and reasonable expectation of success are as stated for Claim 8 and are incorporated here.
Regarding Claim 12, Averin et al. (‘855) in view of Susi et al. (2017) teaches the system according to Claim 11 and further in view of Dai et al. (‘735) teaches the recited vehicle is qualified by “preferably,” so the enumerated vehicle types (motor vehicle, tractor, aircraft, unmanned aerial vehicle, etc.) are non-limiting; the limitation is a vehicle comprising the system of Claim 11.
Averin et al. (‘855) does not explicitly teach, but Dai et al. (‘735) teaches: Vehicle comprising a system according to claim 11, 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. ([0025]: “a vehicle-mounted or otherwise mobile positioning and/or navigation system”) Dai et al. (‘735) teaches that the GNSS receiver and computing system are integrated into a vehicle-mounted positioning and/or navigation system ([0025]). This teaches a vehicle comprising the recited system.
It would have been obvious to a PHOSITA before the effective filing date to deploy the Averin et al. (‘855)/Susi et al. (2017) GNSS positioning system as a vehicle-mounted system as taught by Dai et al. (‘735). One would have been motivated to do so because Dai et al. (‘735) teaches that such GNSS positioning systems are conventionally embodied as vehicle-mounted mobile navigation systems ([0025]), and vehicle navigation is a principal application benefiting from the accurate, scintillation-robust positioning provided by the combination. There is a reasonable expectation of success because Dai et al. (‘735) demonstrates the same class of GNSS receiver/computing system operating as a vehicle-mounted navigation system ([0025]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REMASH R GUYAH/Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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