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 .
Priority
Examiner acknowledges Applicant’s claim to priority benefits of EP23190360.0 filed 8/8/2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 8/7/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Claim Objections
Claim 2 is objected to because of the following informalities: claim 2 recites “transforming (242)”. The examiner suggests replacing “transforming (242)” with “transforming.” Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
For applicant’s benefit portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS. See MPEP 2141.02 VI.
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 1-2 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sheret et al. (US 2021/0239845 A1), in view of Healy et al. (US 2015/0220488 A1).
Regarding claim 1, Sheret et al. (‘845) discloses “a method of processing a plurality of GNSS measurements (paragraph 4: a method for determining a protection level of a position estimate using a single epoch of global navigation satellite system (GNSS) measurements), comprising:
obtaining the plurality of GNSS measurements (paragraph 6: a receiver configured to receive a GNSS signal and process the received signal to generate the measurements), wherein the plurality of GNSS measurements includes a plurality of carrier phase measurements (paragraph 19: Determining a protection level from a set of GNSS observables (e.g., a pseudorange or a carrier phase or a Doppler) is a statistical problem that depends primarily on the error probability distribution of the observables);
defining a state vector, the state vector comprising state variables (paragraph 52: he state x may be defined as the following state vector:
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where, in addition to the terms defined above, p is a position of a rover); obtaining a posterior probability density for the state vector, wherein the posterior probability density is based on one or more residual error models describing a probability distribution of errors in each of the GNSS measurements ( (paragraph 21: a posterior probability density is defined and a protection level associated with a position estimate is determined by integrating the posterior probability density, using Markov chain Monte Carlo (MCMC) or an importance sampling method. In an embodiment, bound propagation is achieved using delta phase error distribution or data obtained by a sensor), the one or more residual error models including at least one non-Gaussian model (paragraph 22: using a non-Gaussian error probability density model allows for proper consideration of the tail probability density, thereby ensuring accuracy of the process; paragraph 72: method 200 includes a step S240 of specifying a non-Gaussian error probability density model ƒ(r|θ, q) and fitting error probability density model parameters θ using experimental data. In an embodiment, specifying the non-Gaussian error probability density model and fitting the model parameters may be performed in advance of the measurements, for example, off-line a priori…the non-Gaussian model is a student-t distribution model, and the pseudorange errors are modelled as the student-t distribution, with a probability density expressed as:
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where r is a residual, ν is a degrees of freedom parameter, σ is a scaling parameter which defines a width of a core distribution, and r is the gamma function.”
Sheret et al. (‘845) does not explicitly disclose “performing a search to identify a set of modes of the posterior probability density, wherein the search comprises: transforming the posterior probability density into a mixture model comprising a plurality of mixture components, wherein each mixture component is a multivariate distribution; and identifying the set of modes using the mixture model, wherein each mode is associated with a respective one of the multivariate distributions; and inferring state information based on the posterior probability density, using the identified set of modes.”
Healy et al. (‘488) relates to radio frequency interferometry. Healy et al. (‘488) teaches “performing a search to identify a set of modes of the posterior probability density, wherein the search comprises: transforming the posterior probability density into a mixture model comprising a plurality of mixture components, wherein each mixture component is a multivariate distribution; and identifying the set of modes using the mixture model, wherein each mode is associated with a respective one of the multivariate distributions; and inferring state information based on the posterior probability density, using the identified set of modes (paragraph 32: the multimode posterior probability density function is computed based on the phase differences from the baseline (or baselines if there are more than one baseline), 530, at each observation…a threshold value of probability density is applied in order to separate the modes at each observation, 540…the probability of each individual mode is computed for each observation, 550…the probability of each sequence of modes over all the observations (the mode sequence probability) is found by multiplying the probabilities of the chosen modes at each observation, 560…for each of the sequences of modes, the net probability of each sequence of modes is computed 580 by multiplying the relative probability derived from the .chi..sup.2 with that of the mode sequence probability…the sequences are ordered by relative probability, 590…the top combinations are selected, and the parameter estimate derived from one of the top mode sequence combinations can be used; paragraph 86: at each time, the identified modes correspond to a multimode posterior probability density function…the modes in 9B at time t=30 circled at 970, might have a probability density function of Figure 8D, for example, with two modes above the threshold; paragraph 102: it will be recognized that the method and system described herein can also be used for applications in which these assumptions are not accurate (e.g., signal timing that is not independent, distributions of arrival time measurements that are non-Gaussian); Abstract “At each of a plurality of observation events, compute a posterior probability density function from the phase differences from the baseline, separate the modes with a threshold value of probability density).”
It would have been obvious to one of ordinary skill-in-the-art before the effective filing date of the claimed invention to modify the method of Sheret et al. (‘845) with the teaching of Healy et al. (‘488) for minimizing errors in identifying modes (Healy et al. (‘488 – paragraph 86). In addition, both of the prior art references, (Fleet et al. (‘259) and Healy et al. (‘488) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, analyzing probability densities of received wireless signals.
Regarding claim 2, which is dependent on independent claim 1, Sheret et al. (‘845)/Healy et al. (‘488) discloses the method of claim 1. Sheret et al. (‘845) further discloses “transforming (242) the posterior probability density comprises: defining a wrapped distribution for each carrier phase measurement; and transforming the wrapped distributions into the mixture model, wherein each mode is associated with a set of integers, a, each integer being associated with a respective one of the carrier phase measurements, wherein each integer indexes a number of cycles in the respective wrapped distribution (paragraph 45: a mathematical model for the carrier phase 4 can be expressed as follows:
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in addition to the terms defined as above,
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are receiver and satellite code instrumental delays, respectively,
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is an effect of multipath interference to the carrier phase, λ is a wavelength of a transmitted GNSS signal, n is an integer ambiguity, w is a circular polarization wind-up, and ∈.sub.L is an effect of receiver noise to the carrier phase. The carrier phase measurements can be traditional single frequency measurements or a combination of carrier phase measurements obtained from signals at different frequencies, such as a wide-lane combination; paragraph 49: the corrected phase observable can be expressed as follows:
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where, in addition to the terms defined above,
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is a difference in receiver code instrumental delays,
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is an effect of multipath on the carrier phase measured at the rover,
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is an effect of multipath on the carrier phase measured at the base station, n is an integer ambiguity, Δw is a difference in circular polarization wind-up. Both the rover and the base station receiver noises have been subsumed into
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).”
Regarding claim 10, which is dependent on independent claim 1, Sheret et al. (‘845)/Healy et al. (‘488) discloses the method of claim 1. Sheret et al. (‘845) further discloses “the state vector includes a phase bias per GNSS band per GNSS constellation (paragraph 67: Method 400 includes a step S440 of modeling the measurement data by a system that represents position and clock bias offsets relative to a first epoch of the window, along with fixed bias terms for phase and pseudorange in each band…the total number of free parameters is therefore
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; paragraph 17: a GNSS receiver receives satellite signals transmitted from one or several GNSS satellite constellations through an antenna and estimates its position using information contained in the satellite signals…many GNSS applications require a certain level of accuracy and reliability of the estimated position).”
Regarding claim 11, which is dependent on independent claim 1, Sheret et al. (‘845)/Healy et al. (‘488) discloses the method of claim 1. Sheret et al. (‘845) further discloses “the inferring comprises numerically integrating (252) the posterior probability density including the at least one non-Gaussian model, wherein the integrating is based on the identified set of modes (paragraph 4: quantifying, during operation, quality metrics q associated with the measurements…specifying, in advance, a non-Gaussian residual error probability density model ƒ(r|θ, q) and fitting model parameters θ determined off-line a priori…defining, during operation, a posterior state probability density P(x|z, q, θ)…estimating, during operation, the state x; and computing, during operation, a protection level by integrating the posterior probability density P(x|z, q, θ) over the state x; paragraph 21: a non-Gaussian measurement error probability density model is specified, based on fitting model parameters determined off-line a priori…a posterior probability density is defined and a protection level associated with a position estimate is determined by integrating the posterior probability density, using Markov chain Monte Carlo (MCMC) or an importance sampling method. In an embodiment, bound propagation is achieved using delta phase error distribution or data obtained by a sensor; paragraph 88: method 200 includes a step S260 of estimating state x and computing the protection level by integrating the posterior probability density over the state x…the state x may be estimated, for example, using the posterior probability density P(x|z, q, θ)…the integration may be done numerically, for example, using Markov chain Monte Carlo (MCMC) or importance sampling method. In an embodiment, estimating the state x and computing the protection level may be performed during operation).”
Regarding claim 12, which is dependent on claim 11, Sheret et al. (‘845)/Healy et al. (‘488) discloses the method of claim 11. Sheret et al. (‘845) further discloses “the numerically integrating comprises importance sampling based on the identified set of modes (paragraph 21: a posterior probability density is defined and a protection level associated with a position estimate is determined by integrating the posterior probability density, using Markov chain Monte Carlo (MCMC) or an importance sampling method; paragraph 88: the integration may be done numerically, for example, using Markov chain Monte Carlo (MCMC) or importance sampling method).”
Regarding claim 13, which is dependent on independent claim 1, Sheret et al. (‘845)/Healy et al. (‘488) discloses the method of claim 1. Sheret et al. (‘845) further discloses “the inferred state information comprises at least one of: a position estimate; and an error bound for the position estimate (paragraph 34: When applied to an inference problem given a set of continuous-domain measurements, Bayes formula can be translated into the following equation:
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where P(state|data) is the probability density of a particular state for a given set of observations, P(data|state) is the probability density of a particular set of observations for a given state, P(state) is a prior probability density of the state, and P(data) is the probability density of the set of observations…information about the state may be inferred from the observables (e.g., a pseudorange or a carrier phase), and the P(state|data) corresponds to a posterior probability density that needs to be determined in order to compute a protection level of a position estimate…P(data) may be treated as an unknown normalization factor, and can be inferred using the fact that an integral of the posterior probability density P(state|data) over all states equals one. P(data|state) is closely related to the measurement error probability distribution and may be specified by a mathematical model).”
Regarding claim 14, which is dependent on claim 11, Sheret et al. (‘845)/Healy et al. (‘488) discloses the method of claim 11. Sheret et al. (‘845) further discloses “a computer program comprising computer program code configured to cause one or more processors to perform all the steps of the method as claimed in claim 1 when said computer program is run on said one or more processors (paragraph 7: a non-transitory computer-readable medium having stored therein instructions that, when executed by a processor, perform a method for determining a protection level of a position estimate using a single epoch of GNSS measurements; paragraph 27: processor 106 may include one or more dedicated processing units, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or various other types of processors or processing units… processor 106 may receive, from receiver 104, the results of the pseudo range and carrier phase measurements, and the quality metrics associated with the measurements, and further process the information to estimate a current position of device 100 and a protection level of the position estimate…processor 106 may also provide measurement quality metrics of its own…processor 106 may include a navigation filter (e.g., a Kalman filter or a recursive LS filter) for determining a protection level; paragraph 29: Memory 306 may also store computer-readable program instructions, mathematical models, and algorithms that are used in signal processing in receiver 104 and computations in processor 106…memory 108 may further store computer-readable program instructions for execution by processor 106 to operate device 100).”
Regarding independent claim 15, which is a corresponding device claim of independent method claim 1, Sheret et al. (‘845)/Healy et al. (‘488) discloses all the claimed invention as shown above for claim 1. Sheret et al. (‘845) further discloses “a GNSS receiver comprising: a signal processing unit (paragraph 6: a receiver configured to receive a GNSS signal and process the received signal to generate the measurements)”, “at least one processor (a non-transitory computer-readable medium having stored therein instructions that, when executed by a processor, perform a method for determining a protection level of a position estimate using a single epoch of GNSS measurements; paragraph 27: processor 106 may include one or more dedicated processing units, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or various other types of processors or processing units… processor 106 may receive, from receiver 104, the results of the pseudo range and carrier phase measurements, and the quality metrics associated with the measurements, and further process the information to estimate a current position of device 100 and a protection level of the position estimate…processor 106 may also provide measurement quality metrics of its own…processor 106 may include a navigation filter (e.g., a Kalman filter or a recursive LS filter) for determining a protection level; paragraph 29: memory 306 may also store computer-readable program instructions, mathematical models, and algorithms that are used in signal processing in receiver 104 and computations in processor 106…memory 108 may further store computer-readable program instructions for execution by processor 106 to operate device 100).”
Allowable Subject Matter
Claim 3 is 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.
Allowable Subject Matter:
“identifying the set of modes comprises: defining a float cost function based on the mixture model, by relaxing the constraint that each value, a, indexing the number of cycles in the respective carrier phase measurement, is an integer; performing a first local search of the float cost function to find a float-valued state vector associated with a local minimum value of the cost function; approximating the float cost function in the region of the local minimum value by a multivariate Gaussian distribution; and identifying the set of modes based on the approximating multivariate Gaussian distribution.”
Claims 4-8 depends on claim 3, and therefore is also objected to be allowable.
Claim 9 is 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.
Allowable Subject Matter:
“the obtained posterior probability density is defined, prior to transforming it into the mixture model, as a product of: a plurality of first residual error models describing a probability distribution of errors in respective pseudorange measurements; a plurality of second residual error models describing a probability distribution of errors in respective first carrier phase measurements; and a plurality of third residual error models describing a probability distribution of errors in respective second carrier phase measurements, wherein the first carrier phase measurements comprise one carrier phase measurement for each GNSS frequency band for each visible GNSS constellation, the second carrier phase measurements comprise the remaining carrier phase measurements, the second residual error models do not include a phase bias term, and the third residual error models include a phase bias term.”
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
YUN ET AL: ("Carrier Phase-based RAIM using a Gaussian Sum Filter", GNSS 2009 - PROCEEDINGS OF THE 22ND INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS 2009), THE INSTITUTE OF NAVIGATION, 8551 RIXLEW LANE SUITE 360 MANASSAS, VA 20109, USA, 25 September 2009 (2009-09-25), pages 1666-167 4, XP05601 0612) describes Our simulation used a [Gaussian core-Laplacian tail] GL model as the receiver true [carrier phase] error distribution to simulate a heavy-tailed model, which will be applied to our Gaussian sum filter method…an arbitrary PDF can be expressed with a weighted sum of Gaussian distributions, which is called Gaussian mixture model (GMM) ((page 1667, right-hand column, first to last paragraphs); the carrier phase measurement error follows the Gaussian mixture model; we use several Kalman filters[ ... the Gaussian sum filter (GSF)] that deal with each Gaussian component to estimate the distribution of states more accurately (pages 1668-1669, section Gaussian Sum Filter); GSF can be formulated by implementing the Kalman filters for every Gaussian component [ ... ] a priori probability distribution can be expressed as in Eq. (12) (pages 1668-1669, section Gaussian Sum Filter); using these updated weights, states, and covariance, we can estimate the current states[ ... using equation (17)] (pages 1668-1669, section Gaussian Sum Filter; equation (17)); the carrier phase measurement [... ] to estimate the distribution of states more accurately (equations (10)-(13); pages 1668-1669, section Gaussian Sum Filter).
RUNNALLS ANDREW R ET AL: ("Terrain-referenced navigation using the IGMAP data fusion algorithm", PROCEEDINGS OF THE 61 ST ANNUAL MEETING OF THEINSTITUTE OF NAVIGATION, 27 June 2005 (2005-06-27), XP093117438) describes to approximate the multimodal functions such as the posterior probability function or the likelihood function…by a combination of multivariate Gaussian distributions, each one of which, by definition, approximates the function in the region of a local maximum (Figure 1; page 982, left-hand column).
Green (US 2018/0299562 A1) describes if the estimates are determined to be valid by the validator 112, the correction module 113 corrects the relative position estimate 107 between the receivers 120…the corrected relative position estimate 114 thus indicates a more precise position or range for the receivers …this higher precision position can be used to more accurately guide an aircraft, a vehicle or a person using global navigation satellite system (GNSS) satellites or global positioning system (GPS) satellites (paragraph 71); the signal sources 121 may be a set of global navigation satellite system (GNSS) satellites, and the receivers 120 may be a set of GNSS receivers. In other cases, the signal sources 121 may be GPS satellites, while the receivers 120 are GPS receivers (paragraph 72); the integer ambiguities via integer bootstrapping and then tests the fixed solution by applying bootstrapping to a scaled-up version of the ambiguity residual. In some embodiments, if the test returns the zero vector, then the fixed solution is selected…otherwise the float solution is selected. IAB is beneficial in that its probabilities have analytically computable values, which allows the decision threshold to be set analytically and, more generally, enables the strict performance requirements that safety-of-life applications demand to be provably satisfied (paragraph 37); the posterior distributions of the errors in one dimension of the fixed baseline are plotted for the three variations of GIAB in chart 400 of FIG. 4…this example shows results from a very large ambiguity residual so that the MMSE and MAP distributions (401 and 402, respectively) are visibly distinct…the intermediate distribution can now be computed by removing the conditioning upon the particular ambiguity residual…the probability density function implemented is the Gaussain density function, normalized for the support of the event (paragraphs 55); using fixing probabilities and a partially fixed covariance and an intermediate distribution, the final a priori distribution may be obtained…this distribution is a Gaussian mixture model…prior distributions of float, MAP, and MMSE GIAB, plotted in log scale. Float GIAB has a weak central mode with strong, but narrow secondary peaks…MAP and MMSE GIAB both have a strong central mode and secondary peaks that are wider, but weaker than those of float GIAB. MMSE GIAB has smoother and narrower secondary peaks than MAP GIAB (paragraph 57); the statistical model 119 may be stored in data store 115 or in another location…the statistical model may be a multivariate Gaussian distribution, and any statistical tests performed within the system may be performed using such a model…the validation threshold 111 established using a statistical model is used to set a baseline for ambiguity resolution. As mentioned above, range ambiguities (e.g. 118) are to be resolved in a manner that is fit for use in a safety-of-life application…fixed range ambiguity estimates are produced during integer bootstrapping…this process may include accessing an error model to determine which float range ambiguity estimate value 125 has the highest confidence level (paragraph 66).
Turunen (US 2019/033,9396 A1) describes a method of determining a posterior error probability distribution for a parameter measured by a Global Navigation Satellite System (GNSS) receiver…receiving a value for each of one or more GNSS measurement quality indicators associated with the GNSS measurement of the parameter…each received measurement quality indicator value is provided as an input into a multivariate probability distribution model to determine the posterior error probability distribution for the GNSS measurement, wherein the variates of the multivariate probability distribution model comprise error for said parameter, and the or each measurement quality indicator (paragraph 18).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NUZHAT PERVIN whose telephone number is (571)272-9795. The examiner can normally be reached M-F 9:00AM-5:00PM.
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/NUZHAT PERVIN/Primary Examiner, Art Unit 3648
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