DETAILED ACTION
Applicant’s submission on November 19, 2025 has been entered
Claims 1-2, 6, 8 and 10-15 are amended.
Claim 1-16 are pending this application.
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.
Claims 1-3 and 6-16 are rejected under 35 U.S.C. 103 as being unpatentable over Werner et al (US 2020/0049837 A1) in view of Zhao et al (CN110487271A).
Regarding Claim 1, Werner teaches a computer-implemented method of training a machine learnable model (MLM) to correct an output of a global satellite navigation receiver (GNSS-R), the method comprising [0048, 0053]:
obtaining geolocation data (PVT) which is generated by a global satellite navigation receiver, wherein an instance of the geolocation data represents a computed geolocation by the global satellite navigation receiver, wherein the computed geolocation is obtained by solving a set of navigation equations [0048-0051 for high precision location estimates with training data];
obtaining auxiliary data (RES, AZ, EL) which is generated by the global satellite navigation receiver in addition to the geolocation data, wherein an instance of the auxiliary data comprises, for a respective satellite [0049-0053 for using azimuth, elevation and residual error data]:
a residual (RES) associated with the satellite, which residual is an error term resulting from a computational solution to the set of navigation equations [0053-0055];
satellite direction information (AZ, EL) indicative of a direction of the satellite relative to the global satellite navigation receiver [0048, 0051];
obtaining reference data (TP) for the global satellite navigation receiver, wherein an instance of the reference data represents a reference geolocation of the global satellite navigation receiver [0055];
training the machine learnable model (MLM) by [0048-0051]:
for respective instances of the geolocation data and the reference data, determining a positioning error (CPE) as a difference between the computed geolocation and the reference geolocation [0018, 0057-0058];
in a training step, training the machine learnable model using the auxiliary data to predict the positioning error (PE) based on the residual and the satellite direction information [0055-0058];
outputting a data representation of a machine learned model [0055-0058 for facilitating device location (means to output data)].
Werner fails to explicitly teach - outputting a data representation of a machine learned model (TM) representing a trained version of the machine learnable model.
Zhao has an Elman neural network assisted tight combination navigation method when a GNSS signal is blocked (abstract) and teaches outputting a data representation of a machine learned model (TM) representing a trained version of the machine learnable model [0012, claim 5].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position correction techniques, as disclosed by Werner, further including the machine learning calculations as taught by Zhao for the purpose to obtain the updated value of the network weight until the error between the actual output of the network and the expected output is less than the set threshold (Zhao, 0012).
Regarding Claim 2, Werner teaches the residual (RES) is one of: a pseudorange residual; or an innovation residual obtained from a Kalman filtering performed by the global satellite navigation receiver [0018-0020, 0066-0067].
Regarding Claim 3, Werner teaches the satellite direction information comprises, for a respective satellite, an elevation (EL) and an azimuth (AZ) of the satellite in the sky at the computed geolocation [0049-0051].
Regarding Claim 6, Werner teaches the training is further based on at least one of: a carrier-to-noise ratio of a radio signal received from a satellite; a quality indicator associated with the radio signal; and a tracking indicator indicating presence and/or quality of signal tracking; a multipath indicator indicating multipath reception; an estimate of measurement noise; an environment type indicating a type of environment at the geolocation of the global satellite navigation receiver; and the geolocation, or a quantized version of the geolocation, of the global satellite navigation receiver [0051].
Regarding Claim 7, Werner teaches determining the positioning error as a 2D positioning error or as a 3D positioning error [0051].
Regarding Claim 8, Werner teaches a computer-implemented method of correcting an output of a global satellite navigation receiver, the method comprising [0048]:
obtaining an instance of geolocation data (PVT) which is generated by a global satellite navigation receiver (GNSS-R), wherein the instance of the geolocation data represents a computed geolocation by the global satellite navigation receiver, wherein the computed geolocation is obtained by solving a set of navigation equations [0048-0051 for high precision location estimates with training data];
obtaining an instance of auxiliary data (RES, AZ, EL) which is generated by the global satellite navigation receiver in addition to the instance of geolocation data, wherein the instance of the auxiliary data comprises, for a respective satellite [0049-0053 for using azimuth, elevation and residual error data]:
a residual (RES) associated with a satellite, which residual is an error term resulting from a computational solution to the set of navigation equations [0053-0055];
satellite direction information (AZ, EL) indicative of a direction of the satellite relative to the computed geolocation n [0048, 0051];
accessing a machine learned model (MLM) which is trained to predict a positioning error based on a residual and satellite direction information which are provided during training, wherein the positioning error is a difference between a computed geolocation and a reference geolocation provided during training [0055-0058 for facilitating device location];
using the machine learned model, predicting the positioning error (PE) for the computed geolocation based on the residual and the satellite direction information to obtain a predicted positioning error [0055-0058];
and correcting the computed geolocation [0021, 0048-0051].
Werner fails to explicitly teach correcting the computed geolocation to account for the predicted positioning error.
Zhao has an Elman neural network assisted tight combination navigation method when a GNSS signal is blocked (abstract) and correcting the computed geolocation to account for the predicted positioning error [0012, claim 5].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position correction techniques, as disclosed by Werner, further including the machine learning calculations as taught by Zhao for the purpose to obtain the updated value of the network weight until the error between the actual output of the network and the expected output is less than the set threshold (Zhao, 0012).
Regarding Claim 9, Werner teaches the method as a continuous learning step [0037-0039].
Regarding Claim 10, Werner teaches obtaining a reference geolocation for the continuous learning step by at least one of: enabling a user to manually enter a reference geolocation; and - sensing the reference geolocation in separation of the use of global satellite navigation, for example using a beacon [0105 for input device interface and laptop (means for manual entry)].
Regarding Claim 11, Werner teaches computer-readable medium comprising non-transitory data representing a computer program, the computer program comprising instructions for causing a processor system to perform the method [0105].
Regarding Claim 12, Werner teaches a computer-readable medium comprising non- transitory data (610) representing a machine learned model obtainable by the method [0034-0036].
Regarding Claim 13, Werner teaches a training system for training a machine learnable model to correct an output of a global satellite navigation receiver, the training system comprising [0048]:
an input interface subsystem for obtaining [0065 for user devices and input interfaces]:
geolocation data which is generated by a global satellite navigation receiver, wherein an instance of the geolocation data represents a computed geolocation by the global satellite navigation receiver, wherein the computed geolocation is obtained by solving a set of navigation equations [0048-0051 for high precision location estimates with training data]; and
auxiliary data which is generated by the global satellite navigation receiver in addition to the geolocation data, wherein an instance of the auxiliary data comprises, for a respective satellite [0049-0053 for using azimuth, elevation and residual error data]: and
a residual associated with a satellite, which residual is an error term resulting from a computational solution to the set of navigation equations [0053-0055];
satellite direction information indicative of a direction of the satellite relative to the global satellite navigation receiver [0048, 0051];
reference data for the global satellite navigation receiver, wherein an instance of the reference data represents a reference geolocation of the global satellite navigation receiver [0054-0055];
a processor subsystem configured to train the machine learnable model (310) by [0055-0058]:
for respective instances of the geolocation data and the reference data, determining a positioning error as a difference between a computed geolocation and a reference geolocation [0057-0059];
in a training step, training the machine learnable model using the auxiliary data to predict the positioning error based on the residual and the satellite direction information [0052 using training, testing, tracking position (auxiliary) data];
an output interface subsystem for outputting a data representation of a machine learned model [0055-0058 for facilitating device location (means to output data)].
Werner fails to explicitly teach an output interface subsystem (140) for outputting a data representation of a machine learned model representing a trained version of the machine learnable model.
Zhao has an Elman neural network assisted tight combination navigation method when a GNSS signal is blocked (abstract) and teaches an output interface subsystem for outputting a data representation of a machine learned model representing a trained version of the machine learnable model [0012, claim 5].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position correction techniques, as disclosed by Werner, further including the machine learning calculations as taught by Zhao for the purpose to obtain the updated value of the network weight until the error between the actual output of the network and the expected output is less than the set threshold (Zhao, 0012).
Regarding Claim 14, Werner teaches a correction system for correcting an output of a global satellite navigation receiver (GNSS-R), the correction system comprising [0048]:
an input interface subsystem for obtaining [0065 for inputting using user devices]:
an instance of geolocation data which is generated by a global satellite navigation receiver, wherein the instance of the geolocation data represents a computed geolocation by the global satellite navigation receiver, wherein the computed geolocation is obtained by solving a set of navigation equations [0048-0051 for high precision location estimates with training data];
an instance of auxiliary data which is generated by the global satellite navigation receiver in addition to the instance of geolocation data, wherein the instance of the auxiliary data comprises, for a respective satellite [0049-0053 for using azimuth, elevation and residual error data]: and
a residual associated with a satellite, which residual is an error term resulting from a computational solution to the set of navigation equations [0053-0055];
satellite direction information indicative of a direction of the satellite relative to the computed geolocation [0048, 0051];
a machine learned model which is trained to predict a positioning error based on a residual and satellite direction information which are provided during training, wherein the positioning error is a difference between a computed geolocation and a reference geolocation provided during training [0055-0058]; and
a processor subsystem configured to [0055-0058]:
using the machine learned model, predict the positioning error for the computed geolocation based on the residual and the satellite direction information to obtain a predicted positioning error [0053-0055];
correct the computed geolocation [0021, 0048-0051].
Werner fails to explicitly teach correct the computed geolocation to account for the predicted positioning error.
Zhao has an Elman neural network assisted tight combination navigation method when a GNSS signal is blocked (abstract) and correct the computed geolocation to account for the predicted positioning error [0012, claim 5].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position correction techniques, as disclosed by Werner, further including the machine learning calculations as taught by Zhao for the purpose to obtain the updated value of the network weight until the error between the actual output of the network and the expected output is less than the set threshold (Zhao, 0012).
Regarding Claim 15, Werner teaches device (UE) comprising a global satellite navigation receiver (GNSS-R) and the correction system to correct an output of the global satellite navigation receiver [0065-0066].
Regarding Claim 16, Werner teaches the training system as a continuous-learning subsystem [0037-0039].
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Werner et al (US 2020/0049837 A1) in view of Zhao et al (CN110487271A) as applied to claim 1 above, and further in view of Judd (US 10,884,132 B1).
Regarding Claim 4, Werner fails to explicitly teach the method comprises representing the elevation (EL), the azimuth (AZ) and the residual (RES) as a data tuple representing a spherical coordinate in a spherical coordinate system.
Judd has beacon-based Precision Navigation and Timing (abstract) and teaches representing the elevation (EL), the azimuth (AZ) and the residual (RES) as a data tuple representing a spherical coordinate in a spherical coordinate system [col 11, lines 10-25].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position correction techniques, as disclosed by Werner, further including the coordinate calculations as taught by Judd for the purpose to calculate its position accurately without having perfect ephemerides (Judd, col 11, lines 15-25).
Regarding Claim 5, Werner teaches converting the spherical coordinate to a cartesian coordinate in an earth-centered, earth-fixed coordinate system, wherein the cartesian coordinate is used in the training of the machine learnable model.
Judd has beacon-based Precision Navigation and Timing (abstract) and teaches the spherical coordinate to a cartesian coordinate in an earth-centered, earth-fixed coordinate system, wherein the cartesian coordinate is used in the training of the machine learnable mode [col 9, lines 20-35 and col 11, lines 10-25].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position correction techniques, as disclosed by Werner, further including the coordinate calculations as taught by Judd for the purpose to calculate its position accurately without having perfect ephemerides (Judd, col 11, lines 15-25).
Response to Arguments
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a
general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
On page 3, second paragraph of applicant’s arguments, the applicant states that Werner is missing the feature of using a GNSS internal residual error. The examiner respectfully disagrees, Werner defines pseudo range and range rate error terms (GNSS errors) [Werner 0017-0021], performs residual error computations from GNSS receiver solutions [Werner 0053], and uses the residuals to correct and improve GNSS positioning using a trained model [Werner, 0058, 0067-0069].
On page 4, first paragraph of applicant’s arguments, the applicant states that Werner’s residual error is from an external reference. The examiner respectfully disagrees: Werner’s error is GNSS internal because it is derived from pseudorange an range-rate error (internal) terms that explicitly arises from the GNSS receiver’s navigation equation solutions [Werner, 0071-0021] and then computes residual errors from those GNSS receiver’s computations before comparison to any reference data [Werner, 0053], confirming the residual originates from the internal navigation equations rather than an external reference.
On page 5, last paragraph of applicant’s arguments, the applicant states that Werner’s fails to disclose that the machine-learning model is trained to estimate a positioning error based on pseudorange errors. The examiner respectfully disagrees: Werner generates the model from residual errors between GNSS receiver computations (including pseudorange error) and reference positions, and uses the model to determine an amount of GNSS positioning error [Werner, 0015-0016, 0053, and 0058].
On page 6, last paragraph of applicant’s arguments, the applicant argues the combination of Werner and Zhao. The examiner respectfully disagrees: Both Werner and Zhao address the same GNSS positioning problem with machine-learning error modeling from GNSS measurements and satellite geometry. Zhao merely reinforces Werner’s predictable use of residual and geometry-based learning to improve positioning accuracy, yielding no technical incompatibility. Werner and Zhao are a straightforward results-oriented combination under KSR.
The examiner acknowledges that this is a broader interpretation than Applicant’s.
However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
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.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648