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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-6, 8-9, 12-14, 16-17, and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Grgich et al. (US 20190187298 A1), hereinafter Grgich.
Regarding claims 1, 9, and 17, Grgich teaches a method and a computer system comprising:
at least one processor, and a non-transitory computer-readable storage medium comprising stored instructions executable by a processor (para. 67, “The instructions are preferably executed by computer-executable components preferably integrated with a system for outlier-reduced processing of satellite position data. The computer-readable medium can be stored on any suitable computer-readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices [CD or DVD], hard drives, floppy drives, or any suitable device. The computer-executable component is preferably a general or application specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions.”), the stored instructions executable to perform operations comprising:
receiving a set of satellite signals comprising a satellite signal from each of a plurality of satellites, wherein each satellite signal is captured by a location sensor of a client device (para. 32, “A method 200 for reduced-outlier satellite positioning includes receiving satellite positioning observations S210, generating a first receiver position estimate S220, and generating an outlier-reduced second receiver position estimate S230, as shown in FIG. 3.”; para. 34, “S210 includes receiving satellite positioning observations. S210 functions to receive [at a mobile receiver or at any other computer system] satellite data that can be used to calculate the position of the mobile receiver [potentially along with correction data]. […] Navigation satellite carrier signals received in Step S210 may include GPS signals, GLONASS signals, Galileo signals, SBAS signals and/or any other suitable navigation signal transmitted by a satellite.),
generating a plurality of subsets of the set of satellite signals, wherein each subset excludes at least one satellite signal of the set of satellite signals (para. 57, “Alternatively, in this technique, S230 may include calculating posterior residual variances for a number of set-reduced observations [i.e., different subsets of the whole set of observations] and choosing the reduced set with the lowest variance.”; para. 65, “Note that if the method 200 identifies data from one or more sources [e.g., satellites, base stations] as erroneous, the method 200 may include flagging or otherwise providing notification that said sources may be ‘unhealthy’. Further, the method 200 may disregard or weight differently observations from these sources.”),
computing a sensor location measurement based on each of the plurality of subsets of the set of satellite signals, computing an accuracy score for each of the sensor location measurements based on a corresponding subset of satellite signals, identifying a satellite signal of the set of satellite signals as inaccurate based on the accuracy scores for the sensor location measurements corresponding to subsets of satellite signals that include the identified satellite signal, and responsive to identifying the satellite signal as inaccurate, performing a remedial action with regards to the identified satellite signal (para. 14, “The systems and methods of the present disclosure are directed to the removal of inaccurate observation data in satellite positioning techniques, in turn increasing accuracy, efficiency, and/or any other metric of positioning performance [e.g., accuracy of corrections data].”; para. 54, “In a second implementation of an invention embodiment, S230 includes generating an outlier-reduced second receiver position estimate using the variance threshold technique described in this section. Note that the term ‘variance threshold technique’ is here coined to refer to exactly the technique described herein [any similarity in name to other techniques is purely coincidental].”; para. 57, “Alternatively, in this technique, S230 may include calculating posterior residual variances for a number of set-reduced observations [i.e., different subsets of the whole set of observations] and choosing the reduced set with the lowest variance.”).
Regarding claims 4, 12, and 20, Grgich teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the computer system of claim 17 respectively, wherein performing the remedial action comprises:
disregarding, for locating the client device, signals from the satellite associated with the identified satellite signal for a time period (para. 14, “The systems and methods of the present disclosure are directed to the removal of inaccurate observation data in satellite positioning techniques, in turn increasing accuracy, efficiency, and/or any other metric of positioning performance [e.g., accuracy of corrections data].”; para. 45, “The position estimate of S220 is preferably calculated by any number of prediction and update steps based on the observations received in S210. For example, S210 may include receiving observations at different times, and S220 may include generating a position estimate using all of those observations and a previous position estimate.”; para. 54, “In a second implementation of an invention embodiment, S230 includes generating an outlier-reduced second receiver position estimate using the variance threshold technique described in this section. Note that the term ‘variance threshold technique’ is here coined to refer to exactly the technique described herein [any similarity in name to other techniques is purely coincidental].”).
Regarding claims 5 and 13, Grgich teaches the method of claim 1 and the non-transitory computer-readable storage medium of claim 9 respectively, further comprising:
determining a location of the client device based on a subset of the plurality of satellite signals that excludes the identified satellite signal (para. 14, “The systems and methods of the present disclosure are directed to the removal of inaccurate observation data in satellite positioning techniques, in turn increasing accuracy, efficiency, and/or any other metric of positioning performance [e.g., accuracy of corrections data].”; para. 34, “S210 includes receiving satellite positioning observations. S210 functions to receive [at a mobile receiver or at any other computer system] satellite data that can be used to calculate the position of the mobile receiver [potentially along with correction data].”; para. 54, “In a second implementation of an invention embodiment, S230 includes generating an outlier-reduced second receiver position estimate using the variance threshold technique described in this section. Note that the term ‘variance threshold technique’ is here coined to refer to exactly the technique described herein [any similarity in name to other techniques is purely coincidental].”).
Regarding claims 6 and 14, Grgich teaches the method of claim 5 and the non-transitory computer-readable storage medium of claim 13 respectively, wherein performing the remedial action comprises:
disregarding, for locating the client device, signals from the satellite associated with the identified satellite signal until the client device is a threshold distance away from the determined location of the client device (para. 55, “In the variance threshold technique, the posterior residual, posterior residual covariance, and posterior residual variance are calculated as in the scaled residual technique. However, in this technique, the posterior residual variances are examined directly. If one or more posterior residual variances is outside of a threshold range, this is an indication that outliers may be present in the observation data.”).
Regarding claims 8 and 16, Grgich teaches the method of claim 1 and the non-transitory computer-readable storage medium of claim 9 respectively, wherein identifying the satellite signal based on the accuracy scores comprises:
identifying a sensor location measurement with an accuracy score indicating that the sensor location measurement is accurate (para. 14, “The systems and methods of the present disclosure are directed to the removal of inaccurate observation data in satellite positioning techniques, in turn increasing accuracy, efficiency, and/or any other metric of positioning performance [e.g., accuracy of corrections data].”), and
identifying the satellite signal excluded from the subset of satellite signals corresponding to the identified sensor location measurement (para. 47, “While techniques for removing or weighting measurement outliers exist in the prior art (as well as analysis of solution or measurement quality based on residuals), S230 includes specific techniques that may more efficiently mitigate the effect of outliers than existing techniques. For example, while techniques exist for mitigating for a single outlier at a time, the techniques of S230 may lend themselves to identifying and/or mitigating for multiple outliers in parallel.”).
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 2, 7, 10, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Grgich in view of Werner et al. (US 20240402279 A1), hereinafter Werner.
Regarding claims 2, 10, and 18, Grgich teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the computer system of claim 17 respectively, but fails to teach wherein computing the accuracy scores for the sensor location measurements comprises:
computing an accuracy score for a sensor location measurement based on a corresponding VIO location measurement.
However, Werner teaches
computing an accuracy score for a sensor location measurement based on a corresponding VIO location measurement (para. 265, “Combining GNSS signals with visual inertial odometry [VIO] measurements can enhance the accuracy and robustness of positioning and navigation in certain scenarios, particularly when GNSS signals are degraded or unavailable. This combination is often referred to as sensor fusion or sensor integration.”; para. 271, “ A fusion algorithm can assign weights or confidences to the GNSS and VIO measurements based on their reliability and accuracy. The weights can be dynamically adjusted based on the quality of the signals, the presence of signal obstructions, or the accuracy of the VIO system.”).
Grgich and Werner are considered to be analogous to the claimed invention because they are in the same field of geospatial positioning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Grgich with the teachings of Werner with the motivation of increasing positioning accuracy.
Regarding claims 7 and 15, Grgich teaches the method of claim 1 and the non-transitory computer-readable storage medium of claim 9 respectively, but fails to teach wherein performing the remedial action comprises:
capturing VIO data by the client device for locating the client device.
However, Werner teaches
capturing VIO data by the client device for locating the client device (para. 82, “By combining the visual information from the camera with the inertial measurements from the IMU, VIO can provide robust and accurate estimates of the camera and thus mobile device's motion, even in challenging conditions where only one sensor may not be sufficient. The integration of visual and inertial data enhances the system's ability to track objects, estimate their trajectory, and navigate in complex environments.”).
Grgich and Werner are considered to be analogous to the claimed invention because they are in the same field of geospatial positioning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Grgich with the teachings of Werner with the motivation of increasing positioning accuracy.
Claims 3, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Grgich in view of Ledvina et al. (US 20250164648 A1), hereinafter Ledvina.
Regarding claims 3, 11, and 19, Grgich teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the computer system of claim 17 respectively, but fails to teach wherein computing the accuracy scores for the sensor location measurements comprises:
applying a machine-learning model to the subset of satellite signals corresponding to a sensor location measurement,
wherein the machine-learning model is trained based on historical data to generate an accuracy score for a set of satellite signals based on the satellite signals.
However, Ledvina teaches
applying a machine-learning model to the subset of satellite signals corresponding to a sensor location measurement (para. 16, “The machine learning model is trained based on comparisons between the GNSS position estimates and reference positioning system estimates at respective times, together with parameter(s) indicating a position of the device relative to one or more GNSS satellites of the GNSS positioning system at the respective times that the measurements were captured.”),
wherein the machine-learning model is trained based on historical data to generate an accuracy score for a set of satellite signals based on the satellite signals (para. 37, “Another class of interactions may be the download of machine learning model(s), for specific areas. For example, these machine learning model(s) may be downloaded after the position of the electronic device 102 has been determined at a coarse level [e.g., a predefined level of accuracy].”; para. 53, “Residual error computation(s) 406 may be determined by comparing the GNSS receiver computations 402 with the reference device computations 404 [e.g., as provided by the reference device]. Moreover, these location errors may be stored as part of the training/testing data [e.g., within a database] that is used to generate the machine learning model 412 as discussed below with respect to FIG. 4B.”).
Grgich and Ledvina are considered to be analogous to the claimed invention because they are in the same field of geospatial positioning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Grgich with the teachings of Ledvina with the motivation of iteratively increasing positioning accuracy.
Conclusion
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/ERIC K HODAC/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648