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 .
Status of Claims
The following is a non-final office action in response to prosecution being reopened in view of the Quick Path Information Disclosure Statement (QPIDS) submission filed on 06/17/2026 and Notice of Reopening of Prosecution mailed 07/14/2026. The IDS dated 06/17/2026 has been considered. Claims 1-20 are currently pending and have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Examiner interprets claim limitations of claims 16-19 under 35 U.S.C. 112(f). Supporting structure from the specification for these limitations is detailed below:
Regarding claim 16, structure for “means for determining a first carrier-to-noise (CNo delta…”, “means for detecting a time error…” and “means for performing an error mitigation operation…” can be found in paragraph [0005] of the instant specification: “An example global navigation satellite system (GNSS) receiver can include at least one memory and one or more processors communicatively coupled with the memory/memories. The processor(s) can be configured to determine a first carrier-to-noise (CNo) delta between a first CNo ratio of the first type of GNSS signal and a second CNo ratio of the second type of GNSS signal; detect a time error based on the first CNo delta and a first threshold CNo delta, the time error indicating a use of an erroneous time uncertainty (TUNC) by the GNSS receiver; and performing an error mitigation operation based on detecting the time error.”
Regarding claim 17, structure for “means for identifying a first level of susceptibility…”, “means for identifying a second level of susceptibility…” can be found in paragraph [0059] of the instant specification; “In one embodiment, performance characteristics of one or more GNSS signals, in this case, a susceptibility to errors of the GPS L1CA signal and the GPS L2C signal may be determined on an empirical basis. For example, empirical data may be obtained by performing operations described above with reference to the time error detection and mitigation block 330 of the GNSS receiver 300. In another embodiment, a susceptibility to errors of the GPS L1CA signal and the GPS L2C signal may be determined on the basis of simulation procedures. In yet another embodiment, a susceptibility to errors of the GPS L1CA signal and the GPS L2C signal may be determined on the basis of historical information.”
Further regarding claim 17, structure for “means for selecting the first type of GNSS signal based on…” can be found in paragraph [0087] of the instant specification: “At block 805, the functionality can include determining a first CNo delta between a first carrier-to-noise (CNo) ratio of a first type of GNSS signal and a second CNo ratio of a second type of GNSS signal. The first type of GNSS signal may be selected based on identifying a first level of susceptibility to errors associated with the first type of GNSS signal and a second level of susceptibility to errors associated with the second type of GNSS signal. The selection can further involve identifying that the first level of susceptibility to errors is lower than the second level of susceptibility to errors.” Further structure can be found in [0093]: “The GNSS receiver 980 can include various hardware and software components for performing various operations in accordance with the disclosure. These operations can include, for example, the functions shown in the flow charts illustrated in FIG. 7 and FIG. 8 and various other functions associated with the device 900 depending on the nature of the device 900. More particularly, the time error detection and mitigation block 330 shown in FIG. 3 can include components such as a processor(s) 910 and a memory 960 that are described below. The memory 960 can store computer-executable instructions that can be executed by the processor(s) 910 for performing the various functions shown in the flow charts illustrated in FIG. 7 and FIG. 8.”
Regarding claim 18, structure for “means for detecting a time error…” and “means for performing the error mitigation operation…” can be found in paragraph [0005] of the instant specification: “An example global navigation satellite system (GNSS) receiver can include at least one memory and one or more processors communicatively coupled with the memory/memories. The processor(s) can be configured to determine a first carrier-to-noise (CNo) delta between a first CNo ratio of the first type of GNSS signal and a second CNo ratio of the second type of GNSS signal; detect a time error based on the first CNo delta and a first threshold CNo delta, the time error indicating a use of an erroneous time uncertainty (TUNC) by the GNSS receiver; and performing an error mitigation operation based on detecting the time error.”
Further regarding claim 18, structure for “means for determining that the first CNo delta does not exceed…” can be found in paragraph [0088] of the instant specification: “In one implementation, determining the time error can include performing a first time error detection procedure and a second time error detection procedure. The first time error detection procedure may be performed in order to determine whether the first CNo delta between the first CNo ratio of the GPS L1 C/A signal and the second CNo ratio of the GPS L1C signal exceeds the first threshold CNo delta. A first test result may be obtained based on determining that the first CNo delta does not exceed the first threshold CNo delta over a time period that includes error detection blind spots corresponding to the 10 ms repetition rate. The first test result can provide a first indication of an absence of millisecond time errors over the time period. An alternative test result may be obtained upon performing the first time error detection procedure. The alternative test result may be based on determining that the first CNo delta exceeds the first threshold CNo delta over at least a portion of the time period.” Further structure can be found in [0093], quoted above regarding claim 17.
Further regarding claim 18, structure for “means for selecting a third type of GNSS signal” can be found in paragraph [0089] of the instant specification: “The second time error detection procedure may involve using a Galileo E1 signal that has a third PRN code having a 4 ms repetition rate. Selecting the Galileo E1 signal is generally based on extending the selection process of two GNSS signals described above to selecting a third GNSS signal based on a repetition rate of a third PRN code. More particularly, the third GNSS signal can be selected based on having a PRN code with a third repetition rate that is different than the first repetition rate and the second repetition rate of the PRN codes associated with the first two GNSS signals. In one embodiment, the third repetition rate is specifically selected so as to enable detecting time errors (if any present) in blind spots corresponding to the second repetition rate of the second GNSS signal.” Further structure can be found in [0093], quoted above regarding claim 17.
Further regarding claim 18, structure for “means for determining a second CNo delta…” and “means for determining that the second CNo delta exceeds a second threshold…” can be found in paragraph [0090] of the instant specification: “A second CNo delta between the first CNo ratio of the GPS L1 C/A signal and a third CNo ratio of the Galileo E1 signal may be determined. A second test result obtained can be based on determining that the second CNo delta does not exceed the first threshold CNo delta during at least the error detection blind spots corresponding to the 10 ms repetition rate. The second test result can indicate an absence of millisecond time errors during the error detection blind spots corresponding to the 10 ms repetition rate.” Further structure can be found in [0093], quoted above regarding claim 17.
Further regarding claim 18, structure for “means for performing the error mitigation operation…” can be found in paragraph [0047] of the instant specification: “The digitized output of the ADC 320 (or the output of the carrier NCO) is provided to a time error detection and mitigation block 330 that can be implemented in hardware, software, or a combination thereof. The hardware can include, for example, a digital signal processor (DSP), a processor, one or more memories containing instructions executable by the processor, and other components that can operate upon the digitized output of the ADC. Some of various operations performed by the time error detection and mitigation block 330 are shown in FIG. 3 in the form of functional blocks.”
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 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ollander et al. (OLLANDER S., et al., "The Dual-frequency Post-correlation Difference Feature for Detection of Multipath and non- Line-of-Sight Errors in Satellite Navigation", 2019 22th International Conference on Information Fusion (FUSION) ISIF International Society of Information Fusion, 2 July 2019, pp. 1-8, XP033725296; hereinafter Ollander ‘19) in view of Yoshida (US-20210286087-A1; hereinafter Yoshida).
Regarding claim 1, Ollander ‘19 teaches [note, what Ollander ‘19 fails to disclose is strike-through]:
A method for detecting and mitigating multipath and non-line-of-sight errors in a global navigation satellite system (GNSS) receiver (see at least Abs; “This article presents the new Dual-frequency Post-correlation Difference feature and compares its performance with two methods from the bibliography. This new feature outperforms the Code-Minus-Carrier and the signal-to-noise ratio difference features in multipath and non-line-of-sight detection.” See also Fig. 1, receiver.), comprising:
determining a first carrier-to-noise (CNo) delta between a first CNo ratio of a first type of GNSS signal and a second CNo ratio of a second type of GNSS signal (see at least Section IV part B; “The SNR difference feature [7] detects multipath by computing the difference in carrier-to-noise density ratio (C/N0, here designated by S, measured in dBHz) between two frequencies…”);
detecting a multipath or non-line-of-sight error based on the first CNo delta and a first threshold CNo delta (see again Section IV part B; “To compare the MP and NLOS detection performances of the features, we used filter feature selection methods. More specifically, we selected the area under receiver-operating characteristic curves (AUROC), and the absolute Fisher score.
The receiver-operating characteristic (ROC) [27] is a common tool to compare the performance of binary classification methods. A ROC curve for a feature is obtained by varying a threshold that decides the class. For each threshold two values are computed: a true positive rate (TPR, the number of correct positives divided by all positives) and the false positive rate (FPR, the number of false positives divided by the number of negatives). By plotting the TPR against the FPR in a diagram, the ROC curve is achieved.”),
performing an error mitigation operation based on detecting the (see at least Abs; “As a consequence, the method can be used to exclude satellites whose signals are affected by reflections, to provide a more accurate navigation solution.”).
However, Ollander ‘19 does not explicitly teach detecting and mitigating a time error, the time error indicating a use of an erroneous time uncertainty (TUNC) by the GNSS receiver.
Yoshida teaches that multipath errors cause time errors as well as positioning errors (see at least [0008]; “Examples of main factors that affect accuracy other than those described above in positioning and time synchronization using navigation satellite signals include a case in which reception of reflected waves and diffracted waves (so-called multipath signals) of navigation satellite signals that are generated by reflection and diffraction due to a structure or the ground around a reception position.”) and deteriorate the receiver’s accuracy (see at least [0020]; “In contrast, the method of calibrating an embedded clock by synchronizing a time accuracy measurement device with navigation satellite signals at a measurement location is advantageous in terms of facilities and running costs, but is liable to cause reflection and diffraction of navigation satellite signals due to a structure around the reception position, and causes a problem of deteriorating accuracy due to the influence of multipath signals of invisible satellites.”). When accuracy deteriorates, the system’s operational certainty is rendered incorrect or ‘erroneous’ by definition. Yoshida further teaches that excluding satellites with poor signal quality from computations improves the time precision (see at least [0014]; “As shown in FIG. 20, as a result of generating time by selecting GPS satellite signals having a carrier-to-noise ratio (CNR), which is one index of a signal-to-noise ratio (SNR), of 35 dB-Hz or more out of GPS satellite signals received in the measurement in the system of FIG. 19, it is confirmed that time errors are significantly improved to less than 100 ns.”).
Ollander ‘19 teaches methods for recognizing multipath GNSS signals and for improving navigational computations by excluding satellites whose signals are affected by reflections. Yoshida teaches that multipath errors affect position and time computations, and that time errors can be improved by excluding satellites with poor signal quality. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend the teaching of Ollander ‘19 to the application of time computations, in light of Yoshida’s teaching that multipath signals degrade the quality of both position and time computations, and the teaching by both Ollander ‘19 and Yoshida that computation results are improved by excluding satellites with degraded signals.
Regarding claim 16, Ollander ‘19 teaches [note, what Ollander ‘19 fails to disclose is strike-through]:
A global navigation satellite system (GNSS) receiver (see at least Fig. 1, receiver) comprising:
means for determining a first carrier-to-noise (CNo) delta between a first CNo ratio of a first type of GNSS signal and a second CNo ratio of a second type of GNSS signal (see at least Section IV part B; “The SNR difference feature [7] detects multipath by computing the difference in carrier-to-noise density ratio (C/N0, here designated by S, measured in dBHz) between two frequencies…”);
means for detecting a multipath or non-line-of-sight error based on the first CNo delta and a first threshold CNo delta (see again Section IV part B; “To compare the MP and NLOS detection performances of the features, we used filter feature selection methods. More specifically, we selected the area under receiver-operating characteristic curves (AUROC), and the absolute Fisher score.
The receiver-operating characteristic (ROC) [27] is a common tool to compare the performance of binary classification methods. A ROC curve for a feature is obtained by varying a threshold that decides the class. For each threshold two values are computed: a true positive rate (TPR, the number of correct positives divided by all positives) and the false positive rate (FPR, the number of false positives divided by the number of negatives). By plotting the TPR against the FPR in a diagram, the ROC curve is achieved.”),
means for performing an error mitigation operation based on the detected (see at least Abs; “As a consequence, the method can be used to exclude satellites whose signals are affected by reflections, to provide a more accurate navigation solution.”).
However, Ollander ‘19 does not explicitly teach detecting and mitigating a time error, the time error indicating a use of an erroneous time uncertainty (TUNC) by the GNSS receiver.
Yoshida teaches that multipath errors cause time errors as well as positioning errors (see at least [0008]; “Examples of main factors that affect accuracy other than those described above in positioning and time synchronization using navigation satellite signals include a case in which reception of reflected waves and diffracted waves (so-called multipath signals) of navigation satellite signals that are generated by reflection and diffraction due to a structure or the ground around a reception position.”) and deteriorate the receiver’s accuracy (see at least [0020]; “In contrast, the method of calibrating an embedded clock by synchronizing a time accuracy measurement device with navigation satellite signals at a measurement location is advantageous in terms of facilities and running costs, but is liable to cause reflection and diffraction of navigation satellite signals due to a structure around the reception position, and causes a problem of deteriorating accuracy due to the influence of multipath signals of invisible satellites.”). When accuracy deteriorates, the system’s operational certainty is rendered incorrect or ‘erroneous’ by definition. Yoshida further teaches that excluding satellites with poor signal quality from computations improves the time precision (see at least [0014]; “As shown in FIG. 20, as a result of generating time by selecting GPS satellite signals having a carrier-to-noise ratio (CNR), which is one index of a signal-to-noise ratio (SNR), of 35 dB-Hz or more out of GPS satellite signals received in the measurement in the system of FIG. 19, it is confirmed that time errors are significantly improved to less than 100 ns.”).
Ollander ‘19 teaches methods for recognizing multipath GNSS signals and for improving navigational computations by excluding satellites whose signals are affected by reflections. Yoshida teaches that multipath errors affect position and time computations, and that time errors can be improved by excluding satellites with poor signal quality. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend the teaching of Ollander ‘19 to the application of time computations, in light of Yoshida’s teaching that multipath signals degrade the quality of both position and time computations, and the teaching by both Ollander ‘19 and Yoshida that computation results are improved by excluding satellites with degraded signals.
Claims 2-7 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ollander ’19 in view of Yoshida, further in view of Ollander et al. (Ollander, Simon & Bode, Friedrich-Wilhelm & Baum, Marcus. (2018). Multi-Frequency GNSS Signal Fusion for Minimization of Multipath and Non-Line-of-Sight Errors: A Survey. 1-6. 10.1109/WPNC.2018.8555856; hereinafter Ollander ‘18).
Regarding claim 2, Ollander ‘19 in view of Yoshida teaches the method of claim 1. Ollander ’19 does not explicitly teach, but Ollander ’18 teaches:
further comprising: identifying a first level of susceptibility to errors associated with the first type of GNSS signal;
identifying a second level of susceptibility to errors associated with the second type of GNSS signal; and
selecting the first type of GNSS signal based on identifying that the first level of susceptibility to errors associated with the first type of GNSS signal is lower than the second level of susceptibility to errors associated with the second type of GNSS signal (see at least Section III, lines 6-12; “To minimize multipath and NLOS errors, the chip frequency is important, since a higher chip frequency will make it easier to separate the multipath components in the correlation sequence R. Therefore signals with higher chip frequency should be more prioritized for frequency fusion, and they should also be given higher weight, if such a fusion method is used”).
Ollander ’19 is directed to detection of multipath in satellite navigation. Ollander ’18 is directed to multi-frequency GNSS signal fusion for minimization of multipath. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select higher chip frequency signals in the method of Ollander ’19, in light of the teaching of Ollander ’18 that higher frequency signals make it easier to separate multipath components.
Regarding claim 3, Ollander ‘19 in view of Yoshida and Ollander ‘18 teaches the method of claim 2. Ollander ’19 further teaches:
wherein the first type of GNSS signal comprises a first pseudo-random noise (PRN) code having a first repetition rate, and wherein the second type of GNSS signal comprises a second PRN code having a second repetition rate that is different than the first repetition rate (see at least Section II.B paragraph 7; “We selected the frequency combination to be GPS L1C/A and GPS L2C, since it was the one most commonly available from real satellites at the time of the data acquisition (October 2018).”).
Regarding claim 4, Ollander ‘19 in view of Yoshida and Ollander ‘18 teaches the method of claim 3. Ollander ’19 further teaches:
(see at least Section II.B paragraph 7; “We selected the frequency combination to be GPS L1C/A and GPS L2C, since it was the one most commonly available from real satellites at the time of the data acquisition…”)
determining the first CNo delta between the first CNo ratio of the GPS L1 C/A signal and the second CNo ratio of the GPS (see at least Section IV part B; “The SNR difference feature [7] detects multipath by computing the difference in carrier-to-noise density ratio (C/N0, here designated by S, measured in dBHz) between two frequencies…”);
detecting the (see again Section IV part B; “To compare the MP and NLOS detection performances of the features, we used filter feature selection methods. More specifically, we selected the area under receiver-operating characteristic curves (AUROC), and the absolute Fisher score.
The receiver-operating characteristic (ROC) [27] is a common tool to compare the performance of binary classification methods. A ROC curve for a feature is obtained by varying a threshold that decides the class. For each threshold two values are computed: a true positive rate (TPR, the number of correct positives divided by all positives) and the false positive rate (FPR, the number of false positives divided by the number of negatives). By plotting the TPR against the FPR in a diagram, the ROC curve is achieved.”); and
performing the error mitigation operation based on detecting the (see at least Abs; “As a consequence, the method can be used to exclude satellites whose signals are affected by reflections, to provide a more accurate navigation solution.”).
Ollander ’19 does not explicitly teach, but Ollander ’18 teaches:
a GPS L1 C/A signal comprising the first PRN code, the second type of GNSS signal is a GPS L1C signal comprising the second PRN code, the first PRN code having a 1 millisecond repetition rate, the second PRN code having a 10 millisecond repetition rate (see at least Table 1 and entries for L1 C/A and L1C with associated code lengths).
Ollander ’19 selects GPS L1C/A and GPS L2C signals for analysis, stating that this is because they are the two most commonly available signals. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use other GNSS signals in the method of Ollander ’19, such as any of the signals listed as suitable for frequency fusion in Table 1 of Ollander ’18.
Ollander ’19 does not explicitly teach, but Yoshida teaches a time error (see again [0008], quoted above regarding claim 1) and a millisecond time error (see at least Fig. 4, where time error is expressed in nanoseconds. Nanosecond values may be equivalently expressed in units of milliseconds).
It would have been obvious to combine Ollander ’19 and Yoshida for the reasons given regarding claim 1.
Regarding claim 5, Ollander ‘19 in view of Yoshida and Ollander ‘18 teaches the method of claim 3. However, Ollander ’19 does not explicitly teach:
further comprising: determining that the first CNo delta does not exceed the first threshold CNo delta over a time period that includes error detection blind spots corresponding to the second repetition rate of the second PRN code of the second type of GNSS signal;
selecting a third type of GNSS signal comprising a third PRN code having a third repetition rate that is different than the first repetition rate and different than the second repetition rate;
determining a second CNo delta between the first CNo ratio of the first type of GNSS signal and a third CNo ratio of the third type of GNSS signal;
determining that the second CNo delta exceeds a second threshold CNo delta during at least one of the error detection blind spots corresponding to the second repetition rate; and
performing the error mitigation operation based on determining that the second CNo delta exceeds the second threshold CNo delta during the at least one of the error detection blind spots corresponding to the second repetition rate.
Ollander ’19 does not explicitly teach the time period over which the CNo delta is computed and compared to a threshold. However, in Figs. 5 and 8 Ollander ’19 estimates multipath over a time period of several minutes. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the same time-scale for other evaluation techniques taught by Ollander ’19, including the technique employing the CNo delta.
Ollander ’18 provides a motivation for applying the technique of Ollander ’19 to a third frequency in Section IV.V.1: “To detect multipath, the difference between the received SNR values on multiple frequencies can be computed, for dual [18], [19], [20], [36] or triple-frequency [8] combinations. Using three frequencies will reduce the probability that the interference is similar on all the frequencies, which is illustrated in Figure 3.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform the method of Ollander ’19 using an additional third frequency for the reason given in Ollander ’18 namely to reduce the probability of interference being on all evaluated signals.
Regarding claim 6, Ollander ‘19 in view of Yoshida and Ollander ‘18 teaches the method of claim 5. Ollander ’19 further teaches:
wherein the first type of GNSS signal is a GPS L1 C/A signal (see at least Section II.B paragraph 7; “We selected the frequency combination to be GPS L1C/A and GPS L2C, since it was the one most commonly available from real satellites at the time of the data acquisition…”)
Ollander ’19 does not explicitly teach, but Ollander ’18 teaches:
a GPS L1 C/A signal comprising the first PRN code, the second type of GNSS signal is a GPS L1C signal comprising the second PRN code, the third type of GNSS signal is a Galileo E1 signal comprising the third PRN code, the first PRN code having a 1 millisecond repetition rate, the second PRN code having a 10 millisecond repetition rate, and the third PRN code having a 4 millisecond repetition rate (see at least Table 1 and entries for L1 C/A, L1C and Galileo E1 with associated code lengths).
Ollander ’19 selects GPS L1C/A and GPS L2C signals for analysis, stating that this is because they are the two most commonly available signals. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use other GNSS signals in the method of Ollander ’19, such as any of the signals listed as suitable for frequency fusion in Table 1 of Ollander ’18.
Regarding claim 7, Ollander ‘19 in view of Yoshida and Ollander ‘18 teaches the method of claim 3. Ollander ’19 further teaches:
(see at least Section II.B paragraph 7; “We selected the frequency combination to be GPS L1C/A and GPS L2C, since it was the one most commonly available from real satellites at the time of the data acquisition…”)
determining the first CNo delta between the first CNo ratio of the GPS L1 C/A signal and the second CNo ratio of the (see at least Section IV part B; “The SNR difference feature [7] detects multipath by computing the difference in carrier-to-noise density ratio (C/N0, here designated by S, measured in dBHz) between two frequencies…”);
detecting the (see again Section IV part B; “To compare the MP and NLOS detection performances of the features, we used filter feature selection methods. More specifically, we selected the area under receiver-operating characteristic curves (AUROC), and the absolute Fisher score.
The receiver-operating characteristic (ROC) [27] is a common tool to compare the performance of binary classification methods. A ROC curve for a feature is obtained by varying a threshold that decides the class. For each threshold two values are computed: a true positive rate (TPR, the number of correct positives divided by all positives) and the false positive rate (FPR, the number of false positives divided by the number of negatives). By plotting the TPR against the FPR in a diagram, the ROC curve is achieved.”); and
performing the error mitigation operation based on detecting the (see at least Abs; “As a consequence, the method can be used to exclude satellites whose signals are affected by reflections, to provide a more accurate navigation solution.”).
Ollander ’19 does not explicitly teach, but Ollander ’18 teaches:
a GPS L1 C/A signal comprising the first PRN code, the second type of GNSS signal is a GPS L2CL signal comprising the second PRN code, the first PRN code having 1 millisecond repetition rate, the second PRN code having a 1500 millisecond repetition rate (see at least Table 1 and entries for L1 C/A and L2CL with associated code lengths).
Ollander ’19 selects GPS L1C/A and GPS L2C signals for analysis, stating that this is because they are the two most commonly available signals. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use other GNSS signals in the method of Ollander ’19, such as any of the signals listed as suitable for frequency fusion in Table 1 of Ollander ’18.
Ollander ’19 does not explicitly teach, but Yoshida teaches a time error (see again [0008], quoted above regarding claim 1) and a millisecond time error (see at least Fig. 4, where time error is expressed in nanoseconds. Nanosecond values may be equivalently expressed in units of milliseconds).
It would have been obvious to combine Ollander ’19 and Yoshida for the reasons given regarding claim 1.
Regarding claim 17, Ollander ‘19 in view of Yoshida teaches the GNSS receiver of claim 16. The remaining limitations of claim 17 are analogous to those of claims 2-3 and are rejected for similar reasons.
Regarding claim 18, Ollander ‘19 in view of Yoshida teaches the GNSS receiver of claim 17. The remaining limitations of claim 18 are analogous to those of claim 4 and are rejected for similar reasons.
Regarding claim 19, Ollander ‘19 in view of Yoshida teaches the GNSS receiver of claim 17. The remaining limitations of claim 19 are analogous to those of claim 5 and are rejected for similar reasons.
Regarding claim 20, Ollander ‘19 in view of Yoshida teaches the GNSS receiver of claim 19. The remaining limitations of claim 20 are analogous to those of claim 6 and are rejected for similar reasons.
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Ollander ’19 in view of Yoshida, further in view of Hoang et al. (US-20120033716-A1; hereinafter Hoang).
Regarding claim 8, Ollander ‘19 in view of Yoshida teaches the method of claim 1. Ollander ’19 does not explicitly teach, but Hoang teaches:
wherein the error mitigation operation comprises one of preforming a bit edge detection procedure, a frame synchronization procedure, a time decoding procedure (see at least [0084] – [0087]; “In accordance with certain further aspects, some example time-setting algorithms will now be described which may be useful and therefore selected if a time uncertainty exceeds a time uncertainty threshold value of approximately ±three seconds (e.g., in a GPS example), ±one second (e.g., in a GLONASS example), etc… As part of a decoding process, one or more of the plurality of strongest candidates may be used in an attempt to extract the time information. Here, for example, the decoding process may start with a candidate with strongest correlation and work down to a candidate with weaker correlation.”), or a cold start reset.
Ollander ’19 teaches detecting multipath conditions, which are taught by Yoshida to cause time errors. 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 mitigation techniques of Ollander ’19 to include the time error mitigation techniques of Hoang in order to mitigate these time errors.
Regarding claim 10, Ollander ‘19 teaches [note, what Ollander ‘19 fails to disclose is strike-through]:
A global navigation satellite system (GNSS) receiver (see at least Fig. 1, receiver) comprising:
determine a first carrier-to-noise (CNo) delta between a first CNo ratio of a first type of GNSS signal and a second CNo ratio of a second type of GNSS signal (see at least Section IV part B; “The SNR difference feature [7] detects multipath by computing the difference in carrier-to-noise density ratio (C/N0, here designated by S, measured in dBHz) between two frequencies…”);
detect a multipath or non-line-of-sight error based on the first CNo delta and a first threshold CNo delta(see again Section IV part B; “To compare the MP and NLOS detection performances of the features, we used filter feature selection methods. More specifically, we selected the area under receiver-operating characteristic curves (AUROC), and the absolute Fisher score.
The receiver-operating characteristic (ROC) [27] is a common tool to compare the performance of binary classification methods. A ROC curve for a feature is obtained by varying a threshold that decides the class. For each threshold two values are computed: a true positive rate (TPR, the number of correct positives divided by all positives) and the false positive rate (FPR, the number of false positives divided by the number of negatives). By plotting the TPR against the FPR in a diagram, the ROC curve is achieved.”),
perform an error mitigation operation based on the detected (see at least Abs; “As a consequence, the method can be used to exclude satellites whose signals are affected by reflections, to provide a more accurate navigation solution.”).
However, Ollander ‘19 does not explicitly teach detecting and mitigating a time error or the internal electronic components of the receiver.
Yoshida teaches that multipath errors cause time errors as well as positioning errors (see at least [0008]; “Examples of main factors that affect accuracy other than those described above in positioning and time synchronization using navigation satellite signals include a case in which reception of reflected waves and diffracted waves (so-called multipath signals) of navigation satellite signals that are generated by reflection and diffraction due to a structure or the ground around a reception position.”). Yoshida further teaches that excluding satellites with poor signal quality from computations improves the time precision (see at least [0014]; “As shown in FIG. 20, as a result of generating time by selecting GPS satellite signals having a carrier-to-noise ratio (CNR), which is one index of a signal-to-noise ratio (SNR), of 35 dB-Hz or more out of GPS satellite signals received in the measurement in the system of FIG. 19, it is confirmed that time errors are significantly improved to less than 100 ns.”).
Ollander ‘19 teaches methods for recognizing multipath GNSS signals and for improving navigational computations by excluding satellites whose signals are affected by reflections. Yoshida teaches that multipath errors affect position and time computations, and that time errors can be improved by excluding satellites with poor signal quality. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend the teaching of Ollander ‘19 to the application of time computations, in light of Yoshida’s teaching that multipath signals degrade the quality of both position and time computations, and the teaching by both Ollander ‘19 and Yoshida that computation results are improved by excluding satellites with degraded signals.
However, neither Ollander ’19 nor Yoshida explicitly teach the internal electronics of the receiver.
Hoang teaches:
A global navigation satellite system (GNSS) receiver (see at least Fig. 1, receiver) comprising:
at least one memory; and
one or more processors communicatively coupled with the at least one memory, the one or more processors configured (see at least [0038]; “Receiver 204 may comprise specific circuitry (e.g., analog and/or digital circuitry), and/or programmable logic such as one or more processing units 302 and/or memory 304.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a memory and processor in the receiver of Ollander ’19 to allow the receiver to carry out its functions.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ollander ’19 in view of Yoshida, further in view of Lennen (US-20210173091-A1; hereinafter Lennen).
Regarding claim 9, Ollander ‘19 in view of Yoshida teaches the method of claim 1. Ollander ’19 does not explicitly teach, but Hoang teaches:
wherein the error mitigation operation is selected based on an operating status of the GNSS receiver, the operating status comprising one of an acquisition status (see at least [0025]; “The present system and method provides identification of sidelobe tracking in the presence of multipath. The present system and method further determines that a sidelobe has been detected correctly based on a probability of false detection of a sidelobe being less than a threshold (e.g., <10.sup.−6) before processing the sidelobe. Otherwise, the present system performs multipath mitigation on the sidelobe and re-tests for ambiguity resolution.” Probability of a false detection is interpreted to be part of the acquisition status.) or a tracking status.
Ollander ’19 teaches methods to identify multipath reception and teaches that mitigation may be performed as a consequence. Yoshida teaches that multipath mitigation may be performed when there is a high probability of false detection, and it will not be performed otherwise. 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 Ollander ’19 to explicitly perform mitigation after acquisition shows a high probability of multipath, as suggested by Yoshida. Doing so would ensure that the mitigation is applied as needed.
Claims 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ollander ’19 in view of Yoshida and Hoang, further in view of Ollander ‘18.
Regarding claim 11, Ollander ‘19 in view of Yoshida and Hoang teaches the GNSS receiver of claim 10. The remaining limitations of claim 11 are analogous to those of claims 2-3 and are rejected for similar reasons.
Regarding claim 12, Ollander ‘19 in view of Yoshida, Hoang and Ollander ‘18 teaches the GNSS receiver of claim 11. The remaining limitations of claim 12 are analogous to those of claims 4 and are rejected for similar reasons.
Regarding claim 13, Ollander ‘19 in view of Yoshida, Hoang and Ollander ‘18 teaches the GNSS receiver of claim 11. The remaining limitations of claim 13 are analogous to those of claims 5 and are rejected for similar reasons.
Regarding claim 14, Ollander ‘19 in view of Yoshida, Hoang and Ollander ‘18 teaches the GNSS receiver of claim 13. The remaining limitations of claim 14 are analogous to those of claims 6 and are rejected for similar reasons.
Regarding claim 15, Ollander ‘19 in view of Yoshida, Hoang and Ollander ‘18 teaches the GNSS receiver of claim 11. The remaining limitations of claim 15 are analogous to those of claims 7 and are rejected for similar reasons.
Conclusion
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/ASHLEY BROWN RAYNAL/Examiner, Art Unit 3648
/OLUMIDE AJIBADE AKONAI/Primary Examiner, Art Unit 3648
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