DETAILED ACTION
Status of Claims
Claim 13 is amended.
Claims 1-13 are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application filed in EP 22184206.5 on 07/11/2022 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Turunen (US 20190339396).
Regarding Claim 1, 12-13, Turunen discloses the following limitations:
(Claim 1) A method of processing a plurality of GNSS measurements to infer state information, the method comprising: (Turunen - [0018] According to a first aspect of the present invention there is provided a method of determining a posterior error probability distribution for a parameter measured by a Global Navigation Satellite System (GNSS) receiver.)
(Claim 12) One or more tangible, non-transitory, computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: (Turunen - [Claim 41] A non-transitory computer-readable medium storing instructions that are executable by one or more processors of an apparatus to perform a method of obtaining a multivariate probability distribution model,)
(Claim 13) A GNSS receiver comprising: a signal processing unit, configured to produce a plurality of GNSS measurements; and at least one processor, configured to: (Turunen - [0001] The present invention relates to a method for obtaining the posterior error probability distribution for a measurement made by a Global Navigation Satellite System (GNSS) receiver and a method for obtaining a multivariate probability distribution model mapping measurement error of a GNSS receiver to one or more indicators of GNSS measurement quality.)
obtaining the plurality of GNSS measurements, for each GNSS measurement: obtaining a plurality of quality indicators associated with that GNSS measurement; (Turunen - [0032] a module for a navigation system comprising an interface sub-module for receiving a value for each of one or more measurement quality indicators associated with a Global Navigation Satellite System (GNSS) measurement of a parameter)
dividing a space of joint values of the plurality of quality indicators into at least a first region and a second region, (Turunen - [0016] modelling the error distribution for position measurements using a stationary Gaussian distribution, or the types of processes discussed above. However, as these approaches are typically concerned with addressing errors associated with the GNSS satellites, they may fail to adequately account for the effects of signal distortion or errors associated with the GNSS receiver. In particular, by assuming that only a single satellite is producing an erroneous measurement at a given time, many known approaches are ill-suited to situations in which signal distortion causes errors in multiple satellite signals simultaneously. As a consequence of these kinds of problems, the known approaches for calculating integrity risk may be less reliable when used for road vehicles. One approach for calculating integrity risk, which has been proposed for use with road vehicles, uses a Least Squares (LS) algorithm to estimate a position based on several position measurements and calculates a protection level using the LS residuals.)
wherein neither the first region nor the second region is box-shaped; (Turunen - [0016])
determining whether the plurality of quality indicators fall within the first region or the second region; and (Turunen – [0016], [0032])
responsive to the plurality of quality indicators falling within the first region, determining that the GNSS measurement should be included in the processing to infer the state information, and (Turunen – [0016], [0032])
calculating the state information based on those GNSS measurements that it was determined should be included. (Turunen - [0016])
Regarding Claim 2, Turunen further discloses:
wherein, for at least one of the GNSS measurements, the space consists of the first region and the second region. (Turunen - [0016])
Regarding Claim 3, Turunen further discloses:
wherein, for at least one of the GNSS measurements, the first region comprises a central region of the space of joint values, and/or the second region comprises a peripheral region of the space. (Turunen - [0016])
Regarding Claim 4, Turunen further discloses:
wherein, for at least one of the GNSS measurements, the second region surrounds the first region in the space of joint values. (Turunen - [0016])
Regarding Claim 5, Arkind further discloses:
further comprising, for at least one of the GNSS measurements, obtaining a probability density function defined over the space of joint values of the plurality of quality indicators, (Turunen - [0016])
wherein the first region is defined as the region where the probability density function exceeds a predefined threshold. (Turunen - [0016])
Regarding Claim 6, Turunen further discloses:
wherein the probability density function is represented by one of: (Turunen - [0016])
a non-parametric function; or (Turunen - [0016])
a parametric function, the parametric function optionally comprising at least one of: (Turunen - [0050] Estimation of the distribution may be done parametrically, by fitting a standard distribution to the measured data)
a Gaussian function; or a sum of Gaussian functions. (Turunen - [0016])
Regarding Claim 7, Turunen further discloses:
further comprising obtaining, for the plurality of GNSS measurements, one or more residual error models, describing a probability distribution of errors in the GNSS measurements, (Turunen - [0016])
wherein the probability distribution depends on the plurality of quality indicators, and (Turunen - [0016])
wherein the calculation of the state information is based on the one or more residual error models. (Turunen - [0016])
Regarding Claim 8, Turunen further discloses:
wherein the plurality of quality indicators comprises one or both of: (Turunen - [0032])
a carrier-to-noise density ratio of a GNSS signal on which the respective GNSS measurement was made; and (Turunen - [0020] the GNSS measurement quality indicator(s) may comprise one or more of: carrier-to-noise density, carrier-to-noise density variability, carrier phase variance, multipath deviation, loss-of-lock detection, code lock time and phase lock time, satellite elevation, and satellite azimuth. One or more of the measurement quality indicator(s) are determined from measurements made by one or more sensors.)
a window-based quality indicator, based on gathering similar GNSS measurements in a time window containing or near to an epoch of interest. (Turunen - [0020])
Regarding Claim 9, Turunen discloses the following limitations:
A method of estimating one or more residual error models describing a probability distribution of errors in a plurality of GNSS measurements, (Turunen - [0016], [0018])
wherein the probability distribution depends on a plurality of quality indicators, and (Turunen - [0016], [0032])
wherein the one or more residual error models are to be used for inferring state information based on GNSS measurements, the method comprising: (Turunen - [0016])
obtaining training data comprising a plurality of samples of the plurality of GNSS measurements, quality indicators associated with the samples, and residual errors associated with the plurality of samples; (Turunen - [0016], [0032])
estimating a local density of the training data over a space of joint values of the plurality of quality indicators, to produce a probability density function; and (Turunen - [0016], [0032])
estimating the one or more residual error models based on the training data. (Turunen - [0016], [0032])
Regarding Claim 10, Turunen further discloses:
comprising: before estimating the one or more residual error models, dividing the space of joint values of the plurality of quality indicators into at least a first region and a second region, (Turunen - [0016])
wherein the first region is a region where the training data is relatively dense and the second region is a region where the training data is relatively sparse; (Turunen - [0016])
identifying first samples that fall within the first region; identifying second samples that fall within the second region; and (Turunen - [0016])
estimating the one or more residual error models based on the first samples. (Turunen - [0016])
Regarding Claim 11, Turunen discloses the following limitations:
A method comprising: estimating a residual error model; and subsequently processing a plurality of GNSS measurements, (Turunen - [0016], [0018])
wherein estimating the residual error model includes: obtaining training data comprising a plurality of samples of a plurality of GNSS measurements, quality indicators associated with the samples, and residual errors associated with the samples; (Turunen - [0016], [0032])
estimating a local density of the training data over a space of joint values of the quality indicators to produce a probability density function; and (Turunen - [0016], [0032])
estimating one or more residual error models based on the training data, (Turunen - [0016])
wherein processing the plurality of GNSS measurements includes: for each GNSS measurement: dividing a space of joint values of the quality indicators into at least a first region and a second region, (Turunen - [0016], [0032])
wherein neither the first region nor the second region is box- shaped; (Turunen - [0016])
determining whether the quality indicators fall within the first region or the second region; and (Turunen - [0016], [0032])
responsive to the quality indicators falling within the first region, determining that the GNSS measurement should be included in the processing to infer the state information, and (Turunen - [0016], [0032])
calculating state information based on those GNSS measurements that it was determined should be included. (Turunen - [0016])
Response to Arguments
Applicant’s arguments, see Pages 7-8, filed 12/23/2025, with respect to the rejection under 35 U.S.C. § 112(b) have been fully considered and are persuasive. The rejection under 35 U.S.C. § 112(b) has been withdrawn.
Applicant’s arguments, see Pages 8-11, filed 12/23/2025, with respect to the rejection under 35 U.S.C. § 102(a)(1) have been fully considered and are not persuasive. Applicant argues that Turunen does not teach “dividing a space of joint values of the plurality of quality indicators into a first region and a second region”. The examiner disagrees, the BRI in light of the instant specification allows for quality indicators that are any GNSS quality measurement. The limitation is then clearly disclosed in Turunen - [0016] “modelling the error distribution for position measurements using a stationary Gaussian distribution” when considering how a Gaussian distribution is composed multiple region space of joint values that define a distribution. PHOSITA as well as the instant specification recognize that a Gaussian distribution is a well-known example of a probability density function and no region of a Gaussian distribution plot would be considered “box-shaped”. After considering the applicants clarification of these limitations, the examiner determined that the limitations are clear without amendment but still remain disclosed by Turunen. Applicant argues that Turunen does not teach “estimating a local density of the training data over a space of joint values of the plurality of quality indicators, to produce a probability density function”. The examiner disagrees, the term “local density” is described as “portions where the data is dense, and therefore the local density is high - may be treated as having a reliable, accurate residual error model. Portions of the space where there is very little training data - that is portions where the data is sparse, and therefore the local density is low” and only serves as alternative language for a first and second region of a probability density function. The term “training data” is described as “The residual error model(s) may be generated from empirical data, comprising GNSS measurements, quality indicators associated with those GNSS measurements, and residual errors associated with those GNSS measurements. Collectively, these may be referred to as empirical or training data.” and does not refer to training data in a typical sense (e.g. machine learning).
Applicant’s arguments, see Page 10, filed 12/23/2024, with respect to the rejection under 35 U.S.C. § 103 have been fully considered and are not persuasive. Applicant argues that the dependent claims are allowable due to the dependency on the independent claims. The examiner disagrees due to the above-mentioned rejections.
Applicant's remaining arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims is understandable and distinguishable from other inventions.
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure or directed to the state of art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Chansarkar (US 7881407) describes systems and methods for mitigating multipath signals in a receiver through processing generated pseudorange measurements to reduce its pseudorange residuals based on statistical modeling in order to mitigate multipath errors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON JAMES HENSON whose telephone number is (703)756-1841. The examiner can normally be reached Monday-Friday 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Hodge can be reached at 571-272-2097. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON JAMES HENSON/Examiner, Art Unit 3645
/ROBERT W HODGE/Supervisory Patent Examiner, Art Unit 3645