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 .
Examiner’s Note
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 use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Claim Objections
Claim(s) 3 and 5 is/are objected to because of the following informalities:
Claim 3 recites “MCMC method” which is suggested to be amended to “[[MCMC]] Markov Chain Monte Carlo (MCMC) method”.
Claim 5 recites “MCMC method” which is suggested to be amended to “[[MCMC]] Markov Chain Monte Carlo (MCMC) method”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) are directed to a system and a method and recite(s) judicial exceptions as explained in the Step 2A, Prong 1 analysis below. The judicial exceptions are not integrated into a practical application as explained in the Step 2A, Prong 2 analysis below. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained in the Step 2B analysis below.
Independent claim(s) 1 and 15:
Claim 1:
A method of processing a plurality of GNSS measurements, comprising: obtaining the plurality of GNSS measurements; defining a state vector, wherein the state vector comprises state variables; 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, the one or more residual error models including at least one non-Gaussian model, wherein the posterior probability density has a number of dimensions, each dimension corresponding to one of the state variables; and inferring state information based on the posterior probability density, wherein the inferring comprises integrating the posterior probability density, wherein the integrating comprises, for one of the state variables: dividing a domain of the posterior probability density into a plurality of slices along the dimension associated with said one state variable; integrating separately each slice of the plurality of slices to produce a respective integration result for each slice; and combining the integration results for the slices.
Claim 15:
A GNSS receiver comprising: a signal processing unit, configured to produce a plurality of GNSS measurements; and at least one processor, configured to: obtain the plurality of GNSS measurements; define a state vector, wherein the state vector comprises state variables; obtain 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, the one or more residual error models including at least one non-Gaussian model, wherein the posterior probability density has a number of dimensions, each dimension corresponding to one of the state variables; and infer state information based on the posterior probability density, wherein the inferring comprises integrating the posterior probability density, wherein the integrating comprises, for one of the state variables: dividing a domain of the posterior probability density into a plurality of slices along the dimension associated with said one state variable; integrating separately each slice of the plurality of slices to produce a respective integration result for each slice; and combining the integration results for the slices.
Step
Analysis
1: Statutory Category?
Yes. Claim 1 recites a series of steps and therefore, is a process. Claim 15 recites a device, and therefore, is a machine/ manufacture. As such, the claim(s) are directed to one of the four categories of patent eligible subject matter, and are eligible for further analysis. Independent claim(s) 15 will not be evaluated separately because the claim(s) contain sufficiently the same limitations as those noted for claim 1 below.
2A - Prong 1: Judicial Exception Recited (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes)?
Yes.
The focus of the claim is on selecting certain information and analyzing it. These observations or evaluations are simply mathematical concepts (e.g., algorithms, spatial relationships, geometry). When given its broadest reasonable interpretation in light of the disclosure, the claim is simply selection and mathematical manipulation of data. Merely selecting information for collection and analysis does nothing significant to differentiate a process from an abstract idea.
Thus, the claim recites an abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim does not recite any additional elements that would integrate the judicial exception into a practical application.
The additional limitation(s) are recited at a high level of generality. The additional limitation(s) merely are used to perform the abstract idea, and are merely invoked as tools of performing generic functions. The further limitation(s) are considered insignificant extra-solution activities to the judicial exception. The limitation(s) represent no more than mere instructions to apply the judicial exception on generic devices, and can be viewed as nothing more than an attempt to link the use of the judicial exception to the technological environment. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 224-26 (2014). The additional limitation(s) represent no more than mere attempt to recite a field in which the device is intended to be applied.
Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application.
2B: Claim provides an Inventive Concept?
No.
Step 2 considers whether the claim provides limitations which amount to “significantly more” than the recited judicial exception. The claim as a whole does not provide any meaningful limitations which amount to significantly more than the mathematical concept of claim 1.
The limitation(s) are recited in a manner that is well understood, generic and conventional. The additional recitation(s) do not impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation(s) are at a high level of generality and are just a nominal or tangential addition to the claim. The limitation(s) are at best the equivalent of merely adding the words “apply it” to the judicial exception. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more.
Therefore, the claim as a whole does not provide meaningful limitations which amount to significantly more than the mathematical concept of claim 1 and does not state an inventive concept. The limitation(s) are just a nominal or tangential addition to the claim. Looking at the elements as a combination does not add anything more than the elements analyzed individually.
Applicant’s disclosure does not provide evidence that the additional element(s) recited in claim 1 (i.e., the claim element(s) in addition to the abstract idea) is sufficient to amount to significantly more than the abstract idea itself. This issue is explained by the Federal Circuit, as follows:
It has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention “significantly more” than that ineligible concept. In Alice, the Supreme Court held that claims directed to a computer-implemented scheme for mitigating settlement risks claimed a patent-ineligible abstract idea. 134 S.Ct. at 2352, 2355—56. Some of the claims at issue covered computer systems configured to mitigate risks through various financial transactions. Id. After determining that those claims were directed to the abstract idea of intermediated settlement, the Court considered whether the recitation of a generic computer added “significantly more” to the claims. Id. at 2357. Critically, the Court did not consider whether it was well-understood, routine, and conventional to execute the claimed intermediated settlement method on a generic computer. Instead, the Court only assessed whether the claim limitations other than the invention’s use of the ineligible concept to which it was directed were well-understood, routine and conventional. Id. at 2359-60. BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1290 (2018) (emphases added).
Therefore, independent claim(s) 1 and 15 are ineligible.
Claim(s) 2-14:
Step
Analysis
1: Statutory Category?
Yes. Claims 2-14 recite a method. As such, the claim(s) are directed to one of the four categories of patent eligible subject matter, and are eligible for further analysis. Claim(s) 3-14 will not be evaluated separately because the claim(s) contain the same or sufficiently similar defects as those noted for claim 2 below.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 1 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim(s) 2-14 are is/are ineligible.
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.
Claim(s) 1-3, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheret et al. (US 2021/0239845 A1 “SHERET”), in view of Hoitsma et al. (US 2011/0010140 A1 “HOITSMA”).
Regarding claim 1, SHERET discloses (Examiner’s note: What SHERET does not disclose is ) a method of processing a plurality of GNSS measurements, comprising:
obtaining the plurality of GNSS measurements (antenna 102 is configured to receive a GNSS signal. The GNSS signal may be received from a single satellite or a plurality of satellite signals respectively transmitted from a plurality of satellites [0024])
defining a state vector, wherein the state vector comprises state variables (the state x may be defined as the following state vector [0052])
obtaining a posterior probability density for the state vector (method 200 includes a step S250 of defining an unnormalized posterior probability density on the state x [0087]), 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, the one or more residual error models including at least one non-Gaussian model, wherein the posterior probability density has a number of dimensions, each dimension corresponding to one of the state variables (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 [0072]); (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 Eq. (27) [0072])
and inferring state information based on the posterior probability density, wherein the inferring comprises integrating the posterior probability density (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 [0034]),
In a same or similar field of endeavor, HOITSMA teaches that this discussion describes approximating the inverse function as a function of two variables (e.g., a surface approximation), however, one of the variables could be held fixed (e.g., a horizontal or vertical slice), for example, t could be held fixed, and a one-dimensional curve approximation of the inverse mapping could be calculated [0037]. Additionally, HOITSMA teaches that the process for evaluating equation (9) can be understood as computing a sequence of cdfs, where the cdfs are defined by the term FX, (x1(y,x2,t)) in the last expression in equation (9). Each cdf in the sequence is computed by fixing the value x2 to a particular value for a discrete set of x2 values [0060]. Furthermore, HOITSMA teaches that the last term is the double integral of the joint density function for the jointly distributed random variables X1 and X2 [0048]. The infinite integral can be approximated by a finite integral [0057].
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 system of SHERET to include the teachings of HOITSMA, because doing so would improve and conduct an accurate analysis of the complex system, as recognized by HOITSMA.
Regarding claim 2, SHERET/ HOISTMA discloses the method of claim 1, wherein the inferred state information comprises at least one of: a state estimate; and an error bound for the state estimate (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 [SHERET 0034], cited and incorporated in the rejection of claim 1).
Regarding claim 3, SHERET/ HOISTMA discloses the method of claim 1 wherein integrating each slice separately comprises, for every slice, at least one of: importance sampling; an MCMC method; and approximation of the posterior probability density with a mathematical model which can be integrated analytically (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 [SHERET 0021]).
Regarding claim 14, SHERET/ HOISTMA 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 (processor 106 may include one or more dedicated processing units [SHERET 0027]).
Regarding claim 15, SHERET discloses a GNSS receiver comprising: a signal processing unit (processor 106 may include one or more dedicated processing units [0027]), configured to produce a plurality of GNSS measurements (antenna 102 is configured to receive a GNSS signal. The GNSS signal may be received from a single satellite or a plurality of satellite signals respectively transmitted from a plurality of satellites [0024]); and at least one processor (processor 106 may include one or more dedicated processing units [0027]), configured to:
obtain the plurality of GNSS measurements (antenna 102 is configured to receive a GNSS signal. The GNSS signal may be received from a single satellite or a plurality of satellite signals respectively transmitted from a plurality of satellites [0024])
define a state vector, wherein the state vector comprises state variables (the state x may be defined as the following state vector [0052])
obtain a posterior probability density for the state vector (method 200 includes a step S250 of defining an unnormalized posterior probability density on the state x [0087]), 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, the one or more residual error models including at least one non-Gaussian model, wherein the posterior probability density has a number of dimensions, each dimension corresponding to one of the state variables (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 [0072]); (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 Eq. (27) [0072])
and infer state information based on the posterior probability density, wherein the inferring comprises integrating the posterior probability density (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 [0034]),
In a same or similar field of endeavor, HOITSMA teaches that this discussion describes approximating the inverse function as a function of two variables (e.g., a surface approximation), however, one of the variables could be held fixed (e.g., a horizontal or vertical slice), for example, t could be held fixed, and a one-dimensional curve approximation of the inverse mapping could be calculated [0037]. Additionally, HOITSMA teaches that the process for evaluating equation (9) can be understood as computing a sequence of cdfs, where the cdfs are defined by the term FX, (x1(y,x2,t)) in the last expression in equation (9). Each cdf in the sequence is computed by fixing the value x2 to a particular value for a discrete set of x2 values [0060]. Furthermore, HOITSMA teaches that the last term is the double integral of the joint density function for the jointly distributed random variables X1 and X2 [0048]. The infinite integral can be approximated by a finite integral [0057].
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 system of SHERET to include the teachings of HOITSMA, because doing so would improve and conduct an accurate analysis of the complex system, as recognized by HOITSMA.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over SHERET, in view of HOITSMA, and further in view of Cham et al. (US 6,226,409 B1 “CHAM”).
Regarding claim 4, SHERET/ HOISTMA discloses the method of claim 1,
In a same or similar field of endeavor, CHAM teaches that analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point [col. 2, lines 29-35]. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as ‘hypotheses’, as they are hypotheses for the states of the model [col. 2, lines 37-42].
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 system of SHERET to include the teachings of CHAM, because doing so would improve and refine the state of a model for tracking/ observations, as recognized by CHAM.
Regarding claim 5, SHERET/ HOISTMA/ CHAM discloses the method of claim 4, wherein integrating each slice separately comprises, for every slice, at least one of: importance sampling based on the identified set of modes; an MCMC method based on the identified set of modes; 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 (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 [SHERET 0021]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over SHERET, in view of HOITSMA, and CHAM, and further in view of Talbot et al. (US 2010/0214162 A1 “TALBOT”).
Regarding claim 6, SHERET/ HOISTMA/ CHAM discloses the method of claim 4,
In a same or similar field of endeavor, TALBOT teaches that a search is conducted to identify candidate sets of possibly correct fixed integer ambiguities 340 along with statistical information such as their respective probabilities of correctness [0009].
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 system of SHERET to include the teachings of TALBOT, because doing so would improve system detection accuracy, as recognized by TALBOT.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over SHERET, in view of HOITSMA, and CHAM, and further in view of Han et al. (US 2005/0114103 A1 “HAN”).
Regarding claim 7, SHERET/ HOISTMA/ CHAM discloses the method of claim 4,
In a same or similar field of endeavor, HAN teaches that a Gaussian mixture approximates the original density function by finding mode locations and estimating the curvature around each mode. For each mode, a Gaussian component is created, whose mean is given by the mode location. The covariance of each component is derived from a Hessian matrix estimated at the mode location. Variable-bandwidth mean shift is used to detect the modes. Density is represented as a weighted sum of Gaussians, whose number, weights, means and covariances are determined automatically at each time step, to include the new data into the model [0009].
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 system of SHERET to include the teachings of HAN, because doing so would further improve and refine system measurements and accuracy, as recognized by HAN.
Allowable Subject Matter
Claim(s) 8-13 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. It is further noted that, because an amendment to overcome the rejection(s) may potentially change the scope of the claim(s), a complete consideration would be given.
The following is a statement of reasons for the indication of allowable subject matter:
SHERET discloses a method for determining a protection level of a position estimate using a single epoch of GNSS measurements, the method includes: specifying a prior probability density P(x) of a state x; specifying a system model h(x) that relates the state x to observables z of the measurements; quantifying quality metrics q associated with the measurements; specifying a non-Gaussian residual error probability density model ƒ(r|θ, q) and fitting model parameters θ using a set of experimental data; and defining a posterior probability density P(x|z, q, θ); estimating the state x; and computing the protection level by integrating the posterior probability density P(x|z, q, θ) over the state x.
Furthermore, HOITSMA discloses a method includes: receiving a plurality of values of an input variable representative of a physical characteristic of a component or system, using a physics model to produce an estimate of an output for each of the input values, mapping the output estimates to the input values to produce an output probability density or cumulative distribution function for the physical characteristic at a future time, and outputting the probability density or cumulative distribution function.
Further still, CHAM discloses a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data.
However, Applicant’s claim also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Neither SHERET, HOISTMA, nor CHAM anticipates or renders fairly obvious, alone, or in combination, to teach all the additional limitations as cited in claim 9, within the context of Applicant' s claimed invention as a whole, that is, “wherein transforming 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” as recited in claim 8.
Similarly, Applicant’s claim also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Neither SHERET, HOISTMA, nor CHAM anticipates or renders fairly obvious, alone, or in combination, to teach all the additional limitations as cited in claim 9, within the context of Applicant' s claimed invention as a whole, that is, “wherein 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” as recited in claim 9.
Claim(s) 10-13 would be allowed by virtue of their dependence on claim 9.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAILEY R LE whose telephone number is (571)272-4910. The examiner can normally be reached 9:00 AM - 5:00 PM EST.
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/Hailey R Le/Examiner, Art Unit 3648 June 6, 2026
Prosecution Insights