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 Arguments
Applicant’s arguments, see pages 7 and 8, filed 01/09/2026, with respect to the rejection(s) of claims 1 to 15 under Green have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of TURUNEN; Seppo (US 20190339396 A1; hereinafter “Turunen”) and Healy; Liam M. (US 20150220488 A1; hereinafter “Healy”).
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 (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 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, 11, 12, 14, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Green in view of Turunen further in view of Healy.
As to claim 1, Green teaches a method of processing a plurality of GNSS measurements (see abstract, [0071-0072] teaching GNSS mechanics), comprising: obtaining the plurality of GNSS measurements, wherein the plurality of GNSS measurements includes a plurality of carrier phase measurements (esp. c.f. fig.1:117 and [0031] teaching the carrier phase measurements comprising the GNSS measurements); defining a state vector, the state vector comprising state variables; obtaining a posterior probability density for the state vector (esp. c.f. [0037, 0044] teaching the vector and [0055-0056] teaching the probability density), 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 (esp. c.f. [0057, 0066] teaching the residual error modelling),
Green fails to specifically fails to disclose the one or more residual error models including at least one non-Gaussian model; and performing a systematic search of the posterior probability density to identify a set of modes of the posterior probability density.
Turunen discloses the one or more residual error models including at least one non-Gaussian model (see Turunen, [0050] In step 106, the multivariate distribution model is determined from the range errors and the measurement quality indicator data. Estimation of the distribution may be done parametrically, by fitting a standard distribution to the measured data or it may be done empirically, without making assumptions about the shape of the distribution, for example, using multivariate kernel density estimators. Other examples of non-parametric representations include the Edgeworth series and the Gram-Charlier series. It is also possible to use a copula decomposition to represent the multivariate cumulative probability distribution function (CDF) in terms of univariate marginal distribution functions.);
Given that Green and Turunen are each directed towards minimizing error in wireless data processing, and given that all distributions are not normal, it would have been to of ordinary skill of the art before the effective filing date of the claimed invention to modify Green by the one or more residual error models including at least one non-Gaussian model, thereby allowing for efficient processing of data that doesn’t specifically follow a gaussian distribution.
The combination of Green and Turunen fail to specifically disclose performing a systematic search of the posterior probability density to identify a set of modes of the posterior probability density.
In the same field of endeavor, Healy discloses performing a systematic search of the posterior probability density to identify a set of modes of the posterior probability density (see Healy, [0086] Note that at each time, the identified modes correspond to a multimode posterior probability density function. For example, the modes in 9B at time t=30 circled at 970 might have a probability density function of FIG. 8D, for example--with two modes above the threshold. ….. [0102] … 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)…..and see 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”).
Given that Healy is directed towards analyzing probability densities of received wireless signals as are Green and Turunen, and further given that having several modes in a random data is well known, it would have been obvious before the effective filing date of the claimed invention to modify the combination of Green and Turunen by performing a systematic search of the posterior probability density to identify a set of modes of the posterior probability density as disclosed in Healy, thereby minimizing errors.
Regarding claims 14 and 15, the limitations have been addressed in the rejection of claim 1.
As to claim 11, the method of claim 1, Turunen discloses further comprising inferring state information based on the posterior probability density (see Turunen, [0057, 64], discloses position estimates based on posterior probability density), however, the combination of Green and Turunen fail to specifically disclose further comprising inferring state information based on the posterior probability density, wherein the inferring comprises integrating the posterior probability density, and wherein the integrating is based on the identified set of modes.
Healy discloses wherein the inferring comprises integrating the posterior probability density, and wherein the integrating is based on the identified set of modes (see Healy, [0039] eq 6 discloses integrating using found modes n1 and n2). See motivation to combine in the rejection of claim 1.
As to claim 12, the method of claim 11, Green fails to disclose to specifically disclose wherein the inferred state information comprises at least one of: a position estimate; an error bound for the position estimate. Turunen discloses the inferred state information comprises at least one of: a position estimate; an error bound for the position estimate (see Turunen, 0057, 64]). See motivation to combine in the rejection of claim 1.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Green in view of Turunen , Healy further in view of AAPA.
As to claim 13, the method of claim 11 the combination of Green, Turunen fail to specifically disclose wherein the integrating comprises at least one of: importance sampling based on the identified set of modes; an MCMC method based on the identified set of modes; or and approximation of the posterior probability density with a mathematical model which can be integrated analytically, wherein the mathematical model is based on the identified set of modes
However, according to AAPA, page 17 lines 1-5, discloses wherein the integrating comprises at least one of: importance sampling based on the identified set of modes; an MCMC method based on the identified set of modes; or and approximation of the posterior probability density with a mathematical model which can be integrated analytically, wherein the mathematical model is based on the identified set of modes.
Given that AAPA, Green, Turunen, and Healy each are directed towards using probability to minimize errors, it would have been obvious before the effective filing date of the claimed invention, to modify the combination of Green, Turunen and Healy with AAPA, thereby creating a more efficient system.
Allowable Subject Matter
Claims 2-10 are 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VLADIMIR MAGLOIRE whose telephone number is (571)270-5144. The examiner can normally be reached 9-5 PM M-F.
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/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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