DETAILED ACTION
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 19 May 2023 by the applicant has been considered and is included in the file.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
"608" as shown in Fig. 6
"902" as shown in Fig. 9
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The disclosure is objected to because of the following informalities:
In reference to Fig. 2C, Paragraphs [0081] - [0083] refer to method 250 as being completed by system 200, however the reference numbers to parts (on left side, for example emitter (102) or transmitter (104)) are in reference to system 100B. While the two systems (200 and 100B) have similar components, some components in Fig. 2 do not have equivalents within Fig. 1B as written this may lend to confusion on what specific component is being referenced to complete a given step within Fig. 2C’s method.
Appropriate correction is required.
Claim Objections
Claims 7 and 17 are objected to because of the following informalities:
The limitation which reads "for causal estimation of a current phase error " should appropriately be 'the', as current phase errors are already introduced in claim 3 (15). Use of "a" current phase unwrapping number is considered to be correct antecedent basis as previously only a general phase unwrapping number was introduced, not a current phase unwrapping number.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitations "the autocorrelation function of the emitter phase noise and the autocorrelation function of the receiver additive noise" in lines 2-3 (emphasis added by examiner). There is insufficient antecedent basis for this limitation in the claim. These limitations are not previously introduced. For examination purposes, this claim will be interpreted to further limit the phase noise statistics introduced in claim 1, where the phase noise statistics will include an autocorrelation function for both an emitter phase noise value and a receiver additive noise value, respectively.
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.
Claim(s) 1-2, 10-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michaels et al. (hereinafter Michaels, US 20220075076 A1) and in view of Cassidy (“Compensating For Point Response Degradation In A High Resolution FMCW Imaging Radar”, International Radar Symposium IRS 2019, June 26-28, 2019).
Regarding claim 1, Michaels teaches a frequency modulation continuous wave (FMCW) device, comprising:
an emitter configured to transmit at least one wave of radiation to a scene, wherein the transmitted wave is modulated in frequency domain using linear modulation that is subject to impairments causing a non-linearity of the transmitted wave in the frequency domain ([0091], [0099]; Fig. 1 where laser source (101) is frequency-modulated by laser controller (205));
a receiver configured to receive a reflection of the transmitted wave from the scene ([0100]; Fig. 2, N-channel receiver (212));
a mixer operatively connected to the emitter and the receiver and configured to interfere a copy of the transmitted wave with the received reflection of the transmitted wave ([0097]; Fig. 1, optical hybrid (107) and (112) mix signals) to generate a beat signal ([0094]);
an analog-to-digital converter (ADC) operatively connected to the mixer and configured to generate a sequence of samples of the beat signal with wrapped phases in time domain ([0100], [0107]; Fig. 2, 3B R-channel ADC (213) converts signals from optical hybrids and where reference measurement unit (302) includes phase unwrap block (313));
and a processor configured to provide phase measurements where calculations are iterated ([0100], [0122]; Fig. 2, microcomputer (203))
Michaels is silent on the processor determining beat frequencies based on phase unwrapping with respect to phase error, and linear regression fitting.
Cassidy teaches an FMCW RADAR system, where the processor (Pg. 3, Fig. 1, control module includes imbedded computer) is configured to estimate, a frequency of the beat signal in the time domain based on 1) phase unwrapping of the samples of the beat signal subject to correlated phase error derived from phase noise statistics of the emitter and 2) a linear regression fitting the frequency of the beat signal into unwrapped phases of the beat signal (Pg. 4, where for each sample and after unwrapping the phase, a linear fit is performed to obtain a phase error and phase progression of the beat frequency).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Michaels to incorporate the teachings of Cassidy to determine beat frequencies based on phase unwrapping with respect to phase error, and linear regression fitting for a current iteration with a reasonable expectation of success. As Michaels teaches determining beat frequencies in an FMCW system, and iterating processes to reduce errors or noise, it is an obvious incorporation of Cassidy’s use of linear regression fitting and phase error. Further, this would have a predictable result of compensating for chirp non-linearity (Cassidy, Introduction) as well as increasing the computational efficiency of a detection system, such as a LIDAR as taught by Michaels or a RADAR as taught by Cassidy.
Regarding claim 2, Michaels as modified above teaches the FMCW device of claim 1, wherein to perform a current iteration of the estimation, the processor is configured to
determine, for each sample of the sequence of samples of the beat signal, a current phase error and a phase unwrapping number fitting a previous frequency of the beat signal and a previous phase offset of the beat signal determined during a previous iteration, wherein the current phase error for a current sample in the sequence of the beat signal is correlated with a previous phase error for a previous sample in the sequence of the beat signal by predetermined phase noise statistics;
and update a current frequency of the beat signal and a current phase offset of the beat signal for the current iteration based on the determined current phase error and the determined phase unwrapping number ([0101] - [0109], where phase cancellation unit (303) cancels phase noise from each signal, including phase unwrapping and a value representative for the difference in phase from a delayed signal and a collected light signal to return a "clean" beat signal which may be passed to range calculation unit (304)).
Regarding claim 10, Michaels as modified above teaches the FMCW device of claim 1, wherein
the termination condition is a number of iterations ([0129], where system may repeat removal of phase noise by completing iterations until a number of calculations equal to the number of peak pairs is completed).
Regarding claim 11, Michaels as modified above a Lidar including the FMCW device of claim 1 ([0003]).
Regarding claim 12, Michaels as modified above teaches the FMCW device of claim 1, wherein
the processor is further configured to estimate a distance to an object in the scene, based on the estimated frequency of the beat signal ([0045]).
Regarding claim 13, a Lidar including the FMCW device of claim 12, wherein
the emitter includes a laser source ([0091]; Fig. 1, emitter is a laser source (101)) with a coherence length shorter than the distance to the object ([0078], where emitter is a laser operating in the visible or near-infrared range, and where an unambiguous range for the system would be in the several to 100 m range, and object detection ranges exceed this range for systems such as an autonomous vehicle in a driving environment).
Claims 14 and 20 are similarly rejected to claim 1.
Claim 15 is similarly rejected to claim 2.
Claim(s) 3-7 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michaels et al. (hereinafter Michaels, US 20220075076 A1), in view of Cassidy (“Compensating For Point Response Degradation In A High Resolution FMCW Imaging Radar”, International Radar Symposium IRS 2019, June 26-28, 2019) and further in view of Ryan (US 20230308324 A1).
Regarding claim 3, Michaels as modified above teaches the FMCW device of claim 2, wherein the processor is configured to estimate values iteratively, which may include the beat frequency ([0112], where calibration and calculation is completed iteratively).
Michaels does not teach use of Viterbi algorithm as a means for optimization.
Ryan teaches a system for demodulating frequency-modulated signals, which includes determining a frequency of the beat signal in the time domain, using an alternative optimization that includes a Viterbi algorithm determining the current phase error and the phase unwrapping number probabilistically to maximize a likelihood of their fitting in the entire sequence of samples of the beat signal ([0013] - [0021]; Fig. 3, where a cost function which is a function of the received data and parameterized by a frequency-offset parameter and by a modulation-index parameter, may be a Viterbi decoder acting as decision logic).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Michaels and Cassidy to incorporate the teachings of Ryan to utilize a Viterbi algorithm to determine offset, error or unwrapping values for a current iteration with a reasonable expectation of success. Algorithms for decision logic such as a Viterbi algorithm are useful in demodulation within a decision unit, and may increase accuracy within demodulation, as taught by Ryan ([0021], [0077]).
Regarding claim 4, Michaels as modified above teaches the FMCW device of claim 3.
Michaels does not teach use of linear regression algorithms.
Ryan teaches a system for demodulating frequency-modulated signals, which includes an alternative optimization updates the frequency of the beat signal and the phase offset of the beat signal using a generalized least squares (GLS) regression ([0029] - [0030], the cost function may be a least-squares function, where a generalized LSR is similar to an LSR but encompasses non-constant variances, and where both are known in LSR algorithms).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Michaels and Cassidy to incorporate the teachings of Ryan to utilize GLS regression in optimizing beat frequency signals and phase offset values with a reasonable expectation of success. Algorithms for minimizing errors, or optimizing signals, such as a least-squares or generalized least squares are useful in demodulation within a decision unit, and are known within the art of sensor data optimization such as in LIDAR.
Claim 5 is similarly rejected to claim 4.
Regarding claim 6, Michaels as modified above teaches the FMCW device of claim 3.
Michaels is silent on the use of a threshold with the Viterbi algorithm as a termination condition.
Ryan teaches a system for demodulating frequency-modulated signals, where a termination condition compares a likelihood given by the Viterbi algorithm with a threshold ([0035]; where a cost function may be determined based on a result such as a lowest minimum cost value).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Michaels and Cassidy to incorporate the teachings of Ryan to utilize a Viterbi algorithm to determine offset, error or unwrapping values for a current iteration, and to terminate iterations when a threshold is met, with a reasonable expectation of success. Algorithms for decision logic such as a Viterbi algorithm are useful in demodulation within a decision unit, and may increase accuracy within demodulation, as taught by Ryan ([0021], [0077]). Ending operation when a function meets a minimum or maximum value has a known and predictable result in data analysis which saves computational resources once there is determined to no longer be a need for further calculations.
Regarding claim 7, Michaels as modified above teaches the FMCW device of claim 3.
Michaels is silent on the use a Viterbi algorithm and incorporation of prior values within that calculation.
Ryan teaches a system for demodulating frequency-modulated signals, where for each current sample of the sequence of samples of the beat signal, the Viterbi algorithm uses the previous phase errors and the previous phase unwrapping number determined for the previous samples for causal estimation of a current phase error and a current phase unwrapping number ([0089] - [0097], Fig. 3, where a current bit's phase offset, such as in steps (313-315), is dependent on an accumulation of offsets, including modulation indices, up to and including the immediately preceding bit to the current bit).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Michaels and Cassidy to incorporate the teachings of Ryan to utilize a Viterbi algorithm to determine offset, error or unwrapping values for a current iteration, and to use prior values within a current calculation with a reasonable expectation of success. Algorithms for decision logic such as a Viterbi algorithm are useful in demodulation within a decision unit, and may increase accuracy within demodulation, as taught by Ryan ([0021], [0077]). As Ryan further discusses, having an accurate estimate of the modulation index used by a transmitter can also improve the sensitivity of a receiver by preventing phase errors accumulating over time ([0020]).
Claim 16 is similarly rejected to claim 3.
Claim 17 is similarly rejected to claim 7.
Claim 18 is similarly rejected to claim 4.
Claim 19 is similarly rejected to claim 5.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michaels et al. (hereinafter Michaels, US 20220075076 A1), in view of Cassidy (“Compensating For Point Response Degradation In A High Resolution FMCW Imaging Radar”, International Radar Symposium IRS 2019, June 26-28, 2019) and Ryan (US 20230308324 A1), and further in view of Lai et al. (hereinafter Lai, US 20190327124 A1).
Regarding claim 8, Michaels as modified above teaches the FMCW device of claim 7.
Michaels is silent on the use of linear minimum mean squared error estimation for causal estimation.
Lai teaches a system for object tracking, where images may be collected via LIDAR, and where the causal estimation of the current phase error and the current phase unwrapping number are determined via a linear minimum mean squared error estimation ([0230] optional functions for causal estimation are varied and error functions may comprise mean functions, square functions, linear functions, etc.).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Michaels and Cassidy to incorporate the teachings of Lai to utilize various error functions in the analysis of causal estimations, such as linear functions and mean squared functions, of a system with a reasonable expectation of success. As iterative algorithms are known in the art of data analysis within systems such as LIDAR and object detection, the use of a specific linear mean squared error function would be an obvious choice to one of ordinary skill in the art.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michaels et al. (hereinafter Michaels, US 20220075076 A1), in view of Cassidy (“Compensating For Point Response Degradation In A High Resolution FMCW Imaging Radar”, International Radar Symposium IRS 2019, June 26-28, 2019), and further in view of Lai et al. (hereinafter Lai, US 20190327124 A1).
Regarding claim 9, Michaels as modified above teaches the FMCW device of claim 1.
Michaels is silent on the use of autocorrelation functions for noise phase statistics.
Lai teaches a system for object tracking, where images may be collected via LIDAR, and where noise statistics include an autocorrelation function of the emitter phase noise and the autocorrelation function of the receiver additive noise ([0338], [0556], where channel information may pre-process, process, or post-process to integrate imperfections such as transmitter/receiver noise, phase errors, etc. via an autocorrelation function).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Michaels and Cassidy to incorporate the teachings of Lai to utilize autocorrelation functions in the analysis of noise statistics of a system with a reasonable expectation of success. As noise reduction is a common goal in object ranging and tracking such as LIDAR, incorporation of specific functions such as autocorrelation functions would have predictable results of controlling a system’s ability to reduce or compensate for noise from multiple sources, which increases signal-to-noise ratios.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ott (US 20210239810 A1) teaches an optical range calculation apparatus, where a photonic mixer cell detects phase wrapping of a phase angle associated with an FMCW LIDAR signal in order to calculate correction values for calculated measurements.
Herzog et al. (US 20240053452 A1) teaches a method and apparatus for determining a Doppler signal from a LIDAR system, where phase or frequency fluctuations in the laser are known to produce additional uncertainties and residual phase errors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kara Richter whose telephone number is (571)272-2763. The examiner can normally be reached Monday - Thursday, 8A-5P EST, Fridays are variable.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.M.R./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645