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The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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Not all references considered as they were not all received/translated. Please see annotated IDS.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 9-17, and 19-20 of U.S. Patent No. 12,146,962. Although the claims at issue are not identical, they are not patentably distinct from each other because the issued patent includes all of the limitations of the pending application with additional elements, thereby anticipating the claimed invention. Additionally, some dependencies vary, but they vary such that the reference claims would always anticipate the instant claims. See below table for corresponding elements, wherein identical subject matter is bolded, dependency differences are underlined, and some recited elements are underlined in the reference claims when they constitute the only difference between the subject matter.
Application (18/890,773)
Patent (US 12,146,962)
Claim 1. A method, comprising:
Claim 1. A method, comprising:
generating, in a light detection and ranging (LIDAR) system, a baseband signal;
generating, in a light detection and ranging (LIDAR) system, a baseband signal from a target return signal when the LIDAR system is in a target detection mode;
generating an estimate of system noise in the LIDAR system by measuring the baseband signal,
generating an estimate of system noise in the LIDAR system by measuring the baseband signal when the LIDAR system is in an anechoic calibration state, the baseband signal comprising frequencies corresponding to LIDAR target ranges;
the baseband signal associated with signal peaks; and
comparing signal peaks of the generated baseband signal to the estimate of the system noise in a frequency domain; and
determining a likelihood that a signal peak, from the signal peaks, indicates a detected target based on the estimate of the system noise.
determining a likelihood that a signal peak, from the signal peaks, indicates a detected target. (determination relies on above comparison and is therefore “based on the estimate of the system noise”)
Claim 2. The method of claim 1, wherein the LIDAR system is in an anechoic calibration state when the estimate of the system noise is generated,
Claim 1. “…generating an estimate of system noise in the LIDAR system by measuring the baseband signal when the LIDAR system is in an anechoic calibration stateClaim 2. The method of claim 1,
wherein the anechoic calibration state comprises one of: an anechoic factory calibration state, a low-power boot-up calibration state, the anechoic calibration state in an occluded field of view (FOV), or a target-absent calibration state, and wherein the signal peaks are based on one or more of signal energy across frequency bins of the baseband signal, autocorrelation of the baseband signal across the frequency bins, and cross-correlation between the baseband signal and the estimate of the system noise across the frequency bins.
wherein the anechoic calibration state comprises one of an anechoic factory calibration state, a low-power boot-up calibration state, the anechoic calibration state in an occluded field of view (FOV), or a target-absent calibration state, and wherein the signal peaks are based on one or more of signal energy across frequency bins of the baseband signal, autocorrelation of the baseband signal across the frequency bins, and cross-correlation between the baseband signal and the estimate of the system noise across the frequency bins.
Claim 3. The method of claim 2, wherein the estimate of the system noise further comprises a measurement of one or more of a noise energy, a mean of the noise energy, a variance of the noise energy, an asymmetry of the noise energy, and a kurtosis of the noise energy.
Claim 3. The method of claim 2, wherein the estimate of the system noise further comprises a measurement of one or more of a noise energy, a mean of the noise energy, a variance of the noise energy, an asymmetry of the noise energy, and a kurtosis of the noise energy.
Claim 4. The method of claim 2, wherein generating the estimate of the system noise when the LIDAR system is in the anechoic calibration state comprises absorbing an infrared (IR) optical beam of the LIDAR system with an external IR absorber that occludes the FOV of the LIDAR system.
Claim 4. The method of claim 3, wherein generating the estimate of the system noise when the LIDAR system is in the anechoic calibration state comprises absorbing an infrared (IR) optical beam of the LIDAR system with an external IR absorber that occludes the FOV of the LIDAR system.
Claim 5. The method of claim 2, wherein generating the estimate of the system noise when the LIDAR system is in the low-power boot-up calibration state comprises lowering an energy of an infrared (IR) optical beam of the LIDAR system to a level that prevents detection of a target in the FOV of the LIDAR system.
Claim 5. The method of claim 3, wherein generating the estimate of the system noise when the LIDAR system is in the low-power boot-up calibration state comprises lowering an energy of an infrared (IR) optical beam of the LIDAR system to a level that prevents detection of a target in the FOV of the LIDAR system.
Claim 6. The method of claim 2, wherein generating the estimate of the system noise when the LIDAR system is in the anechoic calibration state in the occluded FOV comprises directing an infrared (IR) optical beam of the LIDAR system to an IR absorber in the occluded FOV.
Claim 6. The method of claim 3, wherein generating the estimate of the system noise when the LIDAR system is in the anechoic calibration state in the occluded FOV comprises directing an infrared (IR) optical beam of the LIDAR system to an IR absorber in the occluded FOV.
Claim 7. The method of claim 2, wherein generating the estimate of the system noise when the LIDAR system is in the target-absent calibration state comprises directing an infrared (IR) optical beam of the LIDAR system to a location in the FOV of the LIDAR system without a reflecting target.
Claim 7. The method of claim 3, wherein generating the estimate of the system noise when the LIDAR system is in the target-absent calibration state comprises directing an infrared (IR) optical beam of the LIDAR system to a location in the FOV of the LIDAR system without a reflecting target.
Claim 8. The method of claim 1, wherein determining the likelihood that the signal peak indicates the detected target comprises selecting a highest signal that exceeds a signal-to-noise threshold based on the estimate of the system noise.
Claim 9. The method of claim 3, wherein determining the likelihood that the signal peak in the frequency domain indicates the detected target comprises selecting a highest signal that exceeds a signal-to-noise threshold based on the estimate of the system noise.
Claim 9. The method of claim 1, wherein determining the likelihood that the signal peak indicates the detected target comprises selecting a first signal peak with a highest signal-to-noise ratio based on the estimate of the system noise.
Claim 10. The method of claim 3, wherein determining the likelihood that the signal peak in the frequency domain indicates a detected target comprises selecting a first signal peak with a highest signal-to-noise ratio based on the estimate of the system noise.
Claim 10. The method of claim 1, determining the likelihood that the signal peak indicates the detected target comprises selecting a first signal peak with a highest non-negative signal minus noise to noise [(S-N)/N] ratio based on the estimate of the system noise.
Claim 11. The method of claim 3, wherein determining the likelihood that the signal peak in the frequency domain indicates the detected target comprises selecting a first signal peak with a highest non-negative signal minus noise to noise [(S-N)/N] ratio based on the estimate of the system noise.
Claim 11. The method of claim 1, wherein determining the likelihood that the signal peak indicates the detected target comprises at least one of: masking frequencies below a minimum threshold frequency in the baseband signal to mitigate internal reflections in the LIDAR system; masking frequencies above a maximum threshold frequency in the baseband signal to mitigate aliasing due to Doppler shifts; increasing a variance of the estimate of the system noise to compensate for non-stationary noise; or tracking impulse noise in a database and mask corresponding frequencies in the baseband signal.
Claim 12. The method of claim 3, wherein determining the likelihood that a signal peak in the frequency domain indicates a detected target comprises at least one of: masking frequencies below a minimum threshold frequency in the baseband signal to mitigate internal reflections in the LIDAR system; masking frequencies above a maximum threshold frequency in the baseband signal to mitigate aliasing due to Doppler shifts; increasing a variance of the estimate of the system noise to compensate for non-stationary noise; or tracking impulse noise in a database and mask corresponding frequencies in the baseband signal.
Claim 12. The method of claim 1, wherein the LIDAR system is a frequency-modulated continuous wave (FMCW) LIDAR system.
Claim 13. The method of claim 1, wherein the LIDAR system is a frequency-modulated continuous wave (FMCW) LIDAR system.
Claim 13. The method of claim 1, wherein generating the baseband signal comprises generating the baseband signal from a target return signal when the LIDAR system is in a target detection mode.
Claim 1. “…generating… a baseband signal from a target return signal when the LIDAR system is in a target detection mode…”
Claim 14. A non-transitory computer-readable medium containing instructions that, when executed by a processor in a light detection and ranging (LIDAR) system, cause the LIDAR system to:
Claim 14. A non-transitory computer-readable medium containing instructions that, when executed by a processor in a light detection and ranging (LIDAR) system, cause the LIDAR system to:
generate, in the LIDAR system, a baseband signal;
generate the baseband signal in the LIDAR system from a target return signal when the LIDAR system is in a target detection mode;
generate an estimate of system noise in the LIDAR system by measuring the baseband signal… (below) …wherein the estimate of the system noise comprises one or more of a noise energy and one or more moments of the noise energy; and
generate an estimate of system noise in the LIDAR system by measuring a baseband signal when the LIDAR system is in an anechoic calibration state, the baseband signal comprising frequencies corresponding to LIDAR target ranges, wherein the estimate of the system noise comprises one or more of a noise energy and one or more moments of the noise energy;
…(from above) the baseband signal associated with signal peaks…
compare signal peaks of the generated baseband signal to the estimate of the system noise in a frequency domain; and
determine a likelihood that a signal peak, from the signal peaks, indicates a detected target based on the estimate of the system noise.
determine a likelihood that a signal peak, from the signal peaks, indicates a detected target. (determination relies on above comparison and is therefore “based on the estimate of the system noise”)
Claim 15. The non-transitory computer-readable medium of claim 14, wherein the LIDAR system is a frequency-modulated continuous wave (FMCW) LIDAR system.
Claim 15. The non-transitory computer-readable medium of claim 14, wherein the LIDAR system is a frequency-modulated continuous wave (FMCW) LIDAR system.
Claim 16. The non-transitory computer-readable medium of claim 14, wherein the one or more moments of the noise energy comprises a first moment of the noise energy comprising a mean, a second moment of the noise energy comprising a variance, a third moment of the noise energy comprising an asymmetry, and a fourth moment of the noise energy comprising a kurtosis.
Claim 16. The non-transitory computer-readable medium of claim 15, wherein the one or more moments of the noise energy comprises a first moment of the noise energy comprising a mean, a second moment of the noise energy comprising a variance, a third moment of the noise energy comprising an asymmetry, and a fourth moment of the noise energy comprising a kurtosis.
Claim 17. The non-transitory computer-readable medium of claim 14, wherein the signal peaks are based on one or more of signal energy across frequency bins of the baseband signal, autocorrelation of the baseband signal across the frequency bins, and cross-correlation between the baseband signal and the estimate of the system noise across the frequency bins.
Claim 17. The non-transitory computer-readable medium of claim 15, wherein the signal peaks are based on one or more of signal energy across frequency bins of the baseband signal, autocorrelation of the baseband signal across the frequency bins, and cross-correlation between the baseband signal and the estimate of the system noise across the frequency bins.
Claim 18. The non-transitory computer-readable medium of claim 14, wherein the estimate of the system noise further comprises one or more of a mean of the noise energy, a variance of the noise energy, an asymmetry of the noise energy, or a kurtosis of the noise energy.
Claim 19. The non-transitory computer-readable medium of claim 14, wherein the estimate of the system noise further comprises one or more of a mean of the noise energy, a variance of the noise energy, an asymmetry of the noise energy, or a kurtosis of the noise energy.
Claim 19. The non-transitory computer-readable medium of claim 14, wherein to generate the estimate of the system noise, the instructions, when executed by the processor in the LIDAR system, cause the LIDAR system to absorb an infrared (IR) optical beam of the LIDAR system with an external IR absorber that occludes a field of view (FOV) of the LIDAR system.
Claim 20. The non-transitory computer-readable medium of claim 15, wherein to generate the estimate of the system noise when the LIDAR system is in the anechoic calibration state, the instructions, when executed by the processor in the LIDAR system, cause the LIDAR system to absorb an infrared (IR) optical beam of the system with an external IR absorber that occludes a field of view (FOV) of the LIDAR system.
Claim 20. The non-transitory computer-readable medium of claim 14, wherein to generate the baseband signal, the instructions, when executed by the processor in the LIDAR system, cause the LIDAR system to generate the baseband signal from a target return signal when the LIDAR system is in a target detection mode.
Claim 14. “…generate the baseband signal in the LIDAR system from a target return signal when the LIDAR system is in a target detection mode…”
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.
Claim(s) 1-3, 12-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1-2) as being anticipated by Wang et al. (US 2019/0339359).
Regarding Claim 1, Wang discloses A method, comprising:
generating, in a light detection and ranging (LIDAR) system, a baseband signal [0079, baseband FMCW waveform];
generating an estimate of system noise in the LIDAR system by measuring the baseband signal, the baseband signal associated with signal peaks [0090, Fig. 8, reference-based nonlinearity correction]; and
determining a likelihood that a signal peak, from the signal peaks, indicates a detected target based on the estimate of the system noise [0069, 0090].
This applies to Claim 14, mutatis mutandis.
Regarding Claim 2, Wang discloses The method of claim 1, wherein the LIDAR system is in an anechoic calibration state when the estimate of the system noise is generated,
wherein the anechoic calibration state comprises one of: an anechoic factory calibration state, a low-power boot-up calibration state, the anechoic calibration state in an occluded field of view (FOV), or a target-absent calibration state [0090, Fig. 8, occluded FOV], and
wherein the signal peaks are based on one or more of signal energy across frequency bins of the baseband signal, autocorrelation of the baseband signal across the frequency bins, and cross-correlation between the baseband signal and the estimate of the system noise across the frequency bins [0082, Fig. 4A, energy distribution/FFT].
Regarding Claims 3, 16, and 18, Wang discloses wherein the estimate of the system noise further comprises a measurement of one or more of a noise energy (zeroth moment), a mean (first moment) of the noise energy, a variance (second moment) of the noise energy, an asymmetry (skewness or third moment) of the noise energy, and a kurtosis (fourth moment) of the noise energy [0079, total and mean noise energy inherent; 0090, digitized reference-based nonlinearity correction can be distribution encoding all moments].
Regarding Claims 12 and 15, Wang discloses wherein the LIDAR system is a frequency-modulated continuous wave (FMCW) LIDAR system [Abstract].
Regarding Claims 13 and 20, Wang discloses wherein generating the baseband signal comprises generating the baseband signal from a target return signal when the LIDAR system is in a target detection mode [0090].
Regarding Claim 17, Wang discloses The non-transitory computer-readable medium of claim 14, wherein the signal peaks are based on one or more of signal energy across frequency bins of the baseband signal, autocorrelation of the baseband signal across the frequency bins, and cross-correlation between the baseband signal and the estimate of the system noise across the frequency bins [0082, Fig. 4A, energy distribution/FFT].
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4-11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2019/0339359) alone.
Regarding Claims 4-7 and 19, Wang does not explicitly teach
(Claims 4, 19) wherein generating the estimate of the system noise when the LIDAR system is in the anechoic calibration state comprises absorbing an infrared (IR) optical beam of the LIDAR system with an external IR absorber that occludes the FOV of the LIDAR system.
(Claim 5) wherein generating the estimate of the system noise when the LIDAR system is in the low-power boot-up calibration state comprises lowering an energy of an infrared (IR) optical beam of the LIDAR system to a level that prevents detection of a target in the FOV of the LIDAR system.
(Claim 6) wherein generating the estimate of the system noise when the LIDAR system is in the anechoic calibration state in the occluded FOV comprises directing an infrared (IR) optical beam of the LIDAR system to an IR absorber in the occluded FOV.
(Claim 7) wherein generating the estimate of the system noise when the LIDAR system is in the target-absent calibration state comprises directing an infrared (IR) optical beam of the LIDAR system to a location in the FOV of the LIDAR system without a reflecting target.
However, Wang does teach generating a reference-based nonlinearity correction using a reflector with known distance [0090]. The reflector is inherently not 100% reflective and is thereby an IR-absorber. Further, it is well known in both the art of lidar and the closely related art of radar to calibrate using the recited methods. Thus, it would have been obvious to one of ordinary skill in the lidar art, prior to the effective filing date of the claimed invention, to try the following:
(Claims 4, 6, 19) Substitute the reflector in Wang with something IR absorbing to test behavior with non-reflective targets.
(Claim 5) Adjust the power of the lidar in Wang to test behavior in a low-power mode.
(Claim 7) Remove the reflector in Wang to test for false positives.
Regarding Claims 8-11, Wang teaches a noise floor [0094]. It would have been obvious to one skilled in the lidar art, prior to the effective filing date of the claimed invention, to try determining, based on the specific applications or spurious signals in the noise, the likelihood that the signal peak indicates the detected target by
(Claim 8) selecting a highest signal that exceeds a signal-to-noise threshold based on the estimate of the system noise.
(Claim 9) selecting a first signal peak with a highest signal-to-noise ratio based on the estimate of the system noise.
(Claim 10) selecting a first signal peak with a highest non-negative signal minus noise to noise [(S-N)/N] ratio based on the estimate of the system noise.
(Claim 11) at least one of: masking frequencies below a minimum threshold frequency in the baseband signal to mitigate internal reflections in the LIDAR system; masking frequencies above a maximum threshold frequency in the baseband signal to mitigate aliasing due to Doppler shifts; increasing a variance of the estimate of the system noise to compensate for non-stationary noise; or tracking impulse noise in a database and mask corresponding frequencies in the baseband signal.
Relevant Prior Art
In addition to applicant-provided prior art and the prior art used above, the examiner identified the following relevant prior art:
Lashkari (US 2021/0339738 A1) discloses “instruction set being executable to determine a quantity of points with zero range or reflectivity values, mean, median, standard deviation and a minimum/maximum interval of range and reflectivity for nonzero measurements for the point cloud of the LiDAR sensor” [0008]
Harada (US 10,145,945 B2) discloses calibrating a lidar on a vehicle using another vehicle as a target.
Ferreira (US 2020/0284883 A1) discloses reference waveforms, which are derived and updated in various ways [4483, 4494], as well as FFTs [e.g. 4481], calibration [e.g. 4410], and demodulation of a pulse [4410].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ALEX DECARIA whose telephone number is (571)270-0565. The examiner can normally be reached Monday-Thursday, 6:45 a.m. - 5:15 p.m..
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/MAD/Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
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