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 .
Response to Amendment
Applicant's arguments and remarks filed on 02/25/2026 have been fully considered.
Applicant’s statement that no new matter has been added has been considered.
Claims 1, 8, 10, 11, 12, 13, and 14 have been amended.
Claims 15, 16, 17, and 18 have been newly added.
Claims 1-18 are pending in the application.
Applicant's amendments overcome the objections to the claims.
Response to Arguments
Applicant's arguments with respect to amendments to independent claims 1-14 are moot based on the new grounds of rejection as necessitated by amendment. Applicant’s amendment to independent claims 1, 12, 13, and 14 added the new limitation requiring each element of the virtual array signal to include “a range-Doppler spectrum value of the range-Doppler bin for said one virtual antenna element.”
Applicant argues at pages 11-12 of the Remarks that “Park provides a trial-and-error approach where each possible phase compensation offset is tried out and the one giving the best result is selected, which fails to disclose the above claim 1 features.” Applicant contends that Park does not teach “identifying, jointly for the frequency spectra of said plurality of the subarrays, an amplitude-peak frequency” or “determining a residual phase shift between a pair of the subarrays within said plurality of subarrays by comparing, at the amplitude-peak frequency, the respective phases of the frequency spectra.” Applicant’s argument is UNPERSUASIVE under the new ground of rejection. Although Applicant correctly characterizes Park’s primary disclosed approach as a candidate-offset search ([0094]), Park is no longer being relied upon as the primary reference for these elements in isolation.
Applicant argues at pages 12-13 of the Remarks that the Office Action has “a fundamental misunderstanding of the claimed frequency spectrum, which is computed for each subarray, and the claimed amplitude-peak frequency, which is identified jointly for the frequency spectra of the plurality of subarrays.” Applicant contends that the Office Action incorrectly mapped Bechter’s range-Doppler spectrum found per virtual element (Bechter, p. 1164, col. 1) to the claimed frequency spectrum, when in fact “the claimed frequency spectra and the claimed amplitude-peak frequency are by necessity calculated after range-Doppler processing has been performed for each virtual antenna element, and after a specific range-Doppler bin has been selected.” Applicant’s argument regarding the prior mapping is acknowledged and rendered MOOT in view of the new ground of rejection. The prior Office Action’s reliance on Bechter for the claimed frequency spectrum and amplitude-peak frequency elements is no longer maintained.
Applicant argues at pages 13-14 of the Remarks that “while Bechter compares measured phases for given overlapping elements in the virtual array, the claimed invention compares phases of the frequency spectra of the subarrays at an amplitude-peak frequency identified jointly for the frequency spectra. This is apparently not the same thing.” Applicant further argues that the inventors devised the claimed method as an alternative to “using overlapping virtual antenna elements…a waste of hardware resources that do not contribute to a better angular resolution.” Applicant’s argument regarding the prior mapping of Bechter’s overlapping-element comparison is acknowledged and rendered MOOT under the new ground of rejection, because Bechter is not relied upon in the new rejection.
Applicant argues at page 14 of the Remarks that “Park and Bechter, whether alone or in combination, do not teach or suggest all of the elements of claim 1,” and that independent claims 12-14 are allowable for the same reasons as claim 1. Applicant’s argument is MOOT with respect to the prior Park and Bechter combination, because that combination is no longer being applied as the basis of the rejection.
Applicant argues at page 14 of the Remarks that “the addition of Chen does not cure the deficiencies of Park and Bechter noted above with respect to the independent claims from which these claims variously depend. Thus, Park, Bechter, and Chen, whether alone or in combination, do not teach or suggest all of the elements of dependent claims 8 and 10.” Applicant’s argument is MOOT because the prior Park, Bechter, and Chen combination is no longer being applied.
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 1-7, 9, 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 2018/0011170 A1) in view of Park et al. (US 2021/0333386 A1).
Regarding Claims 1, 12, 13, and 14, Rao et al. (‘483) in view of Park et al. (‘386) teaches:
Claims 1, 12, 13, and 14 are grouped because, apart from their respective preambles (method, signal processing device, non-transitory computer-readable storage medium), the operative claim limitations are substantively identical. Claim 12 includes an additional operative steps of “computing the angle of arrival on the basis of the processed virtual array signal”, which is addressed separately below.
Rao et al. (‘483) teaches: A method for resolving a phase ambiguity between subarrays in a virtual array of a time-division multiplexing (TDM), multiple-input multiple-output (MIMO), frequency modulated continuous-wave (FMCW), radar, ([0005]: “Multiple-Input Multiple Output (MIMO) Radar is a technique to improve the angle estimation capability of FMCW radar…In TDM-MIMO the signals from the different TX antennas occupy different time slots.”; [0049]: “In an aspect, the limitation of v.sub.max as stated above is ameliorated in TDM-MIMO radar. The following process is used. If |v| exceeds v.sub.max, then errors in the estimate of φ.sub.d also effect the Doppler correction (Step 2 hereinabove) that was done prior to angle estimation (Step 3 hereinabove). The errors thus introduced in the phase P.sub.c of the corrected virtual array signal S.sub.c result in unique signatures in its angle-FFT spectrum. These signatures are detected and used to correct for a condition where |v| has exceeded v.sub.max as further explained hereinbelow.”)
Rao et al. (‘483) teaches: wherein the TDM MIMO FMCW radar comprises an array of physical receivers including at least one row of physical receivers with a first spacing in a first direction, and further comprises a plurality of physical transmitters arranged with a second spacing in said first direction, (Figs. 4 and 5, [0029]: “Receivers 404-1-404-4 have uniformly spaced antennas having a spacing d.sub.ant.”; [0031]: “FIG. 5 is a schematic diagram of a TDM-MIMO system 500 having two transmitters 502-1 and 502-2.”; [0032]: “In FIG. 5, the spacing between transmitters 502-1 and 502-2 is chosen to be four times the spacing between adjacent receiver antennas..”)
Rao et al. (‘483) teaches: wherein each of the subarrays in the virtual array is generated by a combination of the array of physical receivers and one of the physical transmitters, ([0033-0034]: “First, transmitter 502-1 transmits and the phase seen at receivers 504-1-504-4 is [0 φ.sub.a 2φ.sub.a 3φ.sub.a], respectively. Subsequently, transmitter 502-2 transmits and the phase seen at receivers 504-1-504-4 is [4φ.sub.a 5φ.sub.a 6φ.sub.a 7φ.sub.a].”; [0059]: “the corrected virtual array signal S.sub.c, will have a single dominant eigenvalue. The corrected virtual array signal S.sub.c is an 8 element vector, with the elements 1-4 corresponding to the received signal at the four antennas from TX1 and the elements 5-8 corresponding to the received signals from TX2″ - explicitly identifying the elements from each TX as forming a subarray.)
Rao et al. (‘483) teaches: obtaining a virtual array signal of a range-Doppler bin relating to a scene with a moving object, each element of the virtual array signal corresponding to one virtual antenna element of the virtual array and including a range-Doppler spectrum value of the range-Doppler bin for said one virtual antenna element; ([0036]: “The virtual array signal S is then generated by picking a signal sample corresponding to a specific range-Doppler bin across all the generated 2D-FFT grids for every receiver transmitter pair”; [0036]: “A range-FFT is performed on the digitized samples corresponding to the IF signal…For each transmitter/receiver pair, a Doppler-FFT is then performed for each range-bin across chirps. This 2D-FFT (i.e., range-FFT followed by a Doppler-FFT) processing generates a two dimensional FFT grid and one such 2D-FFT grid is generated for each transmitter/receiver pair.”) This precisely matches the newly amended language requiring each element of the virtual array signal to include a range-Doppler spectrum value of the range-Doppler bin for the corresponding virtual antenna element.
Rao et al. (‘483) teaches: compensating a velocity-induced phase shift of the virtual array signal using a phase compensation method, which introduces a phase ambiguity between the subarrays if the moving object’s velocity exceeds a threshold, thereby obtaining a compensated virtual array signal; ([0044]: “The estimate of φ.sub.d in Step 1 is used to remove the dependence of the virtual array signal S on φ.sub.d by multiplying the last four elements of S by e^(-j(φd/2)). This operation creates a corrected virtual array signal S.sub.c, whose phase P.sub.c) is given by Equation 6”; [0050]: “If |v|>v.sub.max, then |φ.sub.d| will exceed π resulting in an erroneous estimate of φ.sub.d in Step 2 of the method described above. For example, if φ.sub.d exceeds π (i.e., φ.sub.d=π+Δ), the value of φ.sub.d estimated in Step 1 (φ.sub.d.sub._.sub.est) will be −π+Δ. Likewise; if the value of φ.sub.d is less than −π (i.e., φ.sub.d=−π−Δ), then φ.sub.d.sub._.sub.est=π−Δ. Thus, the estimation error φ.sub.d−φ.sub.d.sub._.sub.est=±2π. This estimation error results in an error in the phase of P.sub.c, the erroneous phase is given by Equation 7”) Rao explicitly teaches that a phase ambiguity (specifically, a discrete phase offset of π between subarrays for the 2 TX case) is introduced when the object’s velocity v exceeds the threshold v.sub.max.
Rao et al. (‘483) does not explicitly teach: for each of a plurality of the subarrays, computing a frequency spectrum of those elements of the compensated virtual array signal which correspond to consecutive virtual antenna elements generated by physical receivers belonging to the same row; Rao performs a single FFT over the entire combined corrected virtual array vector S.sub.c, rather than performing separate frequency spectrum computations for each subarray. However, Park et al. (‘386) teaches ([0092]: “coarse angle calculation 918 can be performed by DoA processing over each of the N.sub.TDM virtual arrays separately. DoA processing can be done by performing a 3.sup.rd FFT (angular FFT) across all antennas of a virtual array. Here, phase information of the detected peaks in the range-Doppler maps is used.”) Park explicitly teaches performing a separate per-subarray angular FFT for each TDM virtual subarray using the range-Doppler peak values from each virtual antenna element.
It would have been obvious to a POSITA before the effective filing date to apply Park’s per-subarray angular FFT processing approach to the corrected virtual array signal S.sub.c of Rao, computing a separate frequency spectrum for each subarray corresponding to consecutive virtual antenna elements from physical receivers in the same row, rather than computing a single FFT over the entire combined S.sub.c as Rao does. The motivation arises from Park’s own disclosure ([0092]: “Each coarse angular spectrum associated with range-Doppler bins from every TDM subset can be averaged or non-coherently integrated to get higher SNR”), which explicitly identifies per-subarray angular processing as a method to obtain coarse angular estimates independently for each subarray, thereby providing a basis for subsequent phase ambiguity analysis between subarrays. This motivation arises from Park itself. A POSITA would have had a reasonable expectation of success because computing an FFT over a subset of consecutive elements of S.sub.c — which correspond to one subarray — is mathematically a straightforward partition of the FFT operation that Rao already performs, and Park demonstrates that this per-subarray approach yields useful angular spectra.
Rao et al. (‘483) does not explicitly teach: identifying, jointly for the frequency spectra of said plurality of the subarrays, an amplitude-peak frequency; Rao identifies amplitude peaks in the combined S.sub.c spectrum (e.g., the two peaks separated by 3π/8 radians characteristic of the velocity excursion signature), but does not identify a common amplitude-peak frequency across separate per-subarray frequency spectra. However, Park et al. (‘386) teaches ([0092]: “coarse angle calculation 918 can be performed by DoA processing over each of the N.sub.TDM virtual arrays separately.”; [0093]: “The amplitude of angle spectrum at target angle is higher when it is compensated correctly.”) Park teaches that each subarray’s angular spectrum exhibits its dominant amplitude peak at the same spatial frequency corresponding to the target angle, providing the foundation for identifying a common amplitude-peak frequency across subarrays.
It would have been obvious to a POSITA to jointly identify the amplitude-peak frequency across the per-subarray frequency spectra produced by Park’s per-subarray angular FFTs, because both subarrays observe the same physical target and therefore both spectra exhibit their dominant peaks at the same spatial frequency corresponding to the target angle. The motivation arises from Park’s own teaching that the target angle produces the highest amplitude in each subarray’s angular spectrum ([0093]). A POSITA would have had a reasonable expectation of success because the joint identification of a common peak frequency across subarrays follows directly from the physical observation that all subarrays see the same target at the same angle — a fundamental property of the geometry and not an inventive insight.
Rao et al. (‘483) teaches: determining a residual phase shift between a pair of the subarrays within said plurality of subarrays by comparing, at the amplitude-peak frequency, the respective phases of the frequency spectra; Rao explicitly determines the residual phase shift between subarrays by examining phase discrepancies in the FFT spectrum and identifying the specific residual phase error vector that has produced the observed signature. ([0051]: “Thus, a vector of the error (“phase error vector”) for this radar architecture is φ.sub.error=[0 0 0 0 ππππ]”; [0042]: “any angle-FFT that displays the properties of 1) two peaks and 2) the peaks are separated by 3π/8 has a high likelihood of being indicative of a situation where |v| has exceeded v.sub.max.”; [0045]: “the FFT resulting from Check 2 has a single peak that is located midway and equidistant from the two peaks of the erroneous angle-FFT” — confirming the residual phase error between the TX1 subarray elements and the TX2 subarray elements is π in this architecture.) Rao therefore determines, by analyzing the FFT spectrum amplitude and phase properties at the target peak location, that the residual phase difference between the elements corresponding to subarray TX1 (elements 1-4 of S.sub.c) and the elements corresponding to subarray TX2 (elements 5-8 of S.sub.c) is π radians. This is a comparison of phases of the two subarrays at the amplitude-peak frequency. Park additionally teaches that the residual phase offset between subarrays may be determined through analysis of the per-subarray angular spectra at the target peak. ([0093]: “phase information of the detected peaks of the second virtual array may be rotated by 0° and 180° when combining it with the phase information of the first virtual array. The amplitude of angle spectrum at target angle is higher when it is compensated correctly.”; [0094]: “The phase offset candidate yielding the highest angular spectrum denotes correct compensation of phase ambiguity.”) Park explicitly identifies the residual phase offset as a discrete value selected from candidate offsets, and identifies the correct candidate by comparing the angular peak amplitudes. A POSITA would have understood, from these combined teachings, that the residual phase shift between subarrays manifests at the per-subarray angular spectra peaks and can be determined by phase comparison at that peak frequency. The motivation to perform this comparison at the amplitude-peak frequency rather than averaging over the entire spectrum is taught by Park’s own teaching that the target peak amplitude is the indicator of correct phase alignment ([0093-0094]). A POSITA would have had a reasonable expectation of success because the phase of a per-subarray DFT at its dominant peak is mathematically the phase of the dominant signal component at that subarray — a fundamental DFT property that was well-known in the art.
Rao et al. (‘483) teaches: applying an inverse of the residual phase shift to the compensated virtual array signal. ([0052]: “If check one is positive, negate the last 4 samples of S.sub.c and re-compute the angle-FFT. Since negating a sample is equivalent subtracting π from its phase, this restores the erroneous phase (equation [7]) to the ideal phase (equation [6]).”; Abstract: “the corrected virtual array vector S.sub.c to generate a corrected virtual array spectrum to detect a signature”) Negating the last four samples of S.sub.c is mathematically equivalent to applying an inverse phase rotation factor corresponding to the inverse of the residual phase shift of π between subarrays.
Regarding Claim 12, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1, and further:
Claim 12 recites all limitations of claim 1 (as amended) and additionally requires computing the angle of arrival on the basis of the processed virtual array signal.
Rao et al. (‘483) teaches: computing the angle of arrival on the basis of the processed virtual array signal. ([0045]: “Step 3 [0046] Angle Estimation [0047] From equation [6], the phase P.sub.c of the corrected virtual array signal S.sub.c has a linear progression in φ.sub.a. An FFT on P.sub.c will thus yield an estimate of φ.sub.a. This estimate of φ.sub.a is used in equation [4] to determine the angle of arrival θ.”) The remaining limitations are addressed in the analysis of claim 1 above, which is incorporated by reference.
Regarding Claim 2, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) does not explicitly teach wherein identifying the amplitude-peak frequency includes determining a frequency of a main amplitude peak in a sum of the two frequency spectra’s respective amplitude parts. However, Park et al. (‘386) teaches ([0092]: “Each coarse angular spectrum associated with range-Doppler bins from every TDM subset can be averaged or non-coherently integrated to get higher SNR.”) Non-coherent integration of per-subarray angular spectra is mathematically equivalent to summing the amplitude parts of those spectra, and the resulting main peak corresponds to the amplitude-peak frequency.
It would have been obvious to a POSITA before the effective filing date to combine Park’s non-coherent amplitude integration across subarrays with the phase ambiguity resolution method of Rao, such that the amplitude-peak frequency is identified as the main peak in the sum of the per-subarray amplitude spectra. The motivation to combine arises from Park’s express teaching ([0092]) that combining the angular spectra across TDM subsets via non-coherent integration improves SNR — a stated benefit that exists independently and that directly serves Rao’s broader goal of accurate angular estimation in the presence of velocity-induced phase ambiguity. A POSITA would have had a reasonable expectation of success because non-coherent integration of multiple amplitude spectra is a mathematically established SNR-enhancing technique whose performance characteristics are well-understood — the summed amplitude spectrum has reinforced peaks at the common target frequency and averaged-down noise, requiring no novel signal-processing insight to implement.
Regarding Claim 3, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
This element presents an “or” statement: identifying the amplitude-peak frequency includes determining a frequency which corresponds to a main or non-main amplitude peak in each of the respective frequency spectra’s amplitude parts.
Rao et al. (‘483) does not explicitly teach per-subarray amplitude peak identification, because Rao performs a single FFT over the combined S.sub.c rather than separate per-subarray FFTs., however Park et al. (‘386) teaches the first alternative of the “or” statement — identifying a main amplitude peak in each of the respective frequency spectra’s amplitude parts — through its per-subarray angular FFT processing. ([0092]: “Thus, coarse angle calculation 918 can be performed by DoA processing over each of the N.sub.TDM virtual arrays separately. DoA processing can be done by performing a 3.sup.rd FFT (angular FFT) across all antennas of a virtual array.”; [0093]: “The amplitude of angle spectrum at target angle is higher when it is compensated correctly.”) The dominant angular peak per subarray is the main amplitude peak in each respective frequency spectrum.
It would have been obvious to a POSITA to combine Park’s per-subarray identification of the main amplitude peak with Rao’s phase ambiguity resolution method. The motivation to combine arises from Park’s own disclosure ([0092]) which identifies per-subarray peak identification as the “coarse angle calculation” stage — a necessary preliminary step in Park’s own phase ambiguity resolution process. Identifying the main peak in each subarray’s spectrum is the natural prerequisite for any subsequent inter-subarray analysis, and Park provides this teaching. A POSITA would have had a reasonable expectation of success because identifying the maximum amplitude in a discrete spectrum is a trivial and deterministic operation, and Park demonstrates the practical implementation of per-subarray peak identification. Because the first alternative of the “or” statement is satisfied, the element is met.
Regarding Claim 4, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) teaches: determining the residual phase shift includes computing a difference between the respective phases of the frequency spectra at the amplitude-peak frequency. Rao computes the inter-subarray phase difference as the difference π between the phase of the TX1 subarray elements and the phase of the TX2 subarray elements at the target peak ([0051]: “Thus, a vector of the error (“phase error vector”) for this radar architecture is φ.sub.error=[0 0 0 0 ππππ].”; [0044]: “negate the last 4 samples of S.sub.c and re-compute the angle-FFT. Since negating a sample is equivalent subtracting π from its phase, this restores the erroneous phase (equation [7]) to the ideal phase (equation [6]).”). The π value is precisely the difference between the phases of the two subarrays at the target’s amplitude-peak frequency.
Regarding Claim 5, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 4.
Rao et al. (‘483) teaches: the ratio of the first and second spacings is such that the virtual antenna elements of the virtual array are equidistant in the first direction. ([0032]: “In FIG. 5, the spacing between transmitters 502-1 and 502-2 is chosen to be four times the spacing between adjacent receiver antennas”; [0035]: “The received signal obtained from consecutive transmission from transmitter 502-1 and transmitter 502-2 can be concatenated together to create a longer signal sequence whose phase has the following linear progression: P=[0 φa 2φa 3φa 4φa 5φa 6φa 7φa]” — showing linear phase progression characteristic of equidistant virtual array elements.)
Rao et al. (‘483) teaches: determining the residual phase shift further includes rounding the difference between the respective phases of the frequency spectra to a multiple of 2π/M, where M is the number of physical transmitters. Rao quantizes the residual phase shift to discrete values determined by the number of transmitters: for the 2 TX case, the residual phase shift is π = 2π/2 (a multiple of 2π/M where M=2) ([0051]: “φ.sub.error=[0 0 0 0 ππππ]”); for the 4 TX case shown in FIG. 10, Rao teaches discrete multiples of π/2 [0058-0059] - multiples of 2π/M where M=4. Rao expressly teaches that the residual phase shift candidates are quantized to multiples of 2π/M consistent with the geometry of the TDM-MIMO architecture.
Regarding Claim 6, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) does not explicitly teach wherein the respective phases of the frequency spectra are compared, at the amplitude-peak frequency, for a plurality of pairs of subarrays which are uniformly spaced in the first direction; and the residual phase shift is determined as a mean over all said pairs of the subarrays.
Park et al. (‘386) teaches the principle of averaging across multiple subarrays for improved estimation accuracy. ([0092]: “Each coarse angular spectrum associated with range-Doppler bins from every TDM subset can be averaged or non-coherently integrated to get higher SNR.”)
It would have been obvious to a POSITA to combine Park’s principle of cross-subarray averaging with Rao’s phase ambiguity resolution method, performing the phase comparison for each available pair of uniformly spaced subarrays and determining the residual phase shift as the mean over all pairs. The motivation to combine arises from Park’s own disclosure at [0092], which explicitly identifies averaging across subarrays as a method to improve SNR and estimation reliability. This motivation is grounded in Park itself. A POSITA would have had a reasonable expectation of success because averaging multiple independent estimates of a common quantity is a mathematically established and universally applied estimation-theoretic technique that reduces variance proportionally to the number of estimates — a predictable and deterministic benefit.
Regarding Claim 7, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) does not explicitly teach computing a frequency spectrum for a subarray via FFT, because Rao computes a single FFT over the combined corrected virtual array S.sub.c rather than per-subarray FFTs. However, Park et al. (‘386) teaches: computing the frequency spectrum for a subarray includes performing a Fast Fourier Transform, FFT. ([0092]: “DoA processing can be done by performing a 3.sup.rd FFT (angular FFT) across all antennas of a virtual array.”; [0003]: “A third FFT involving phase information of signals of different antenna elements of an (virtual) antenna array can yield additional spatial or angular information.”)
It would have been obvious to a POSITA to combine Park’s per-subarray FFT processing with Rao’s phase ambiguity resolution framework. The motivation to combine arises from Park’s express teaching that the angular FFT performed separately per virtual subarray ([0092]) is the computationally efficient and standard approach to obtain coarse angular estimates for each subarray independently — providing the per-subarray angular spectra needed to analyze inter-subarray phase relationships. Rao itself relies on FFT-based angular estimation ([0045]: “An FFT on Psub.c will thus yield an estimate of φ.sub.a”), so applying the same FFT computation on a per-subarray basis as taught by Park is a straightforward refinement of Rao’s existing FFT approach. A POSITA would have had a reasonable expectation of success because the FFT is the canonical and universally applied method for discrete spatial frequency analysis in antenna array signal processing, its computational properties are well-established, and partitioning Rao’s combined-array FFT into per-subarray FFTs is a routine mathematical decomposition with predictable results.
Regarding Claim 9, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) teaches: the physical transmitters are used sequentially according to a transmission schedule, and said pair of subarrays are consecutive with respect to the transmission schedule. ([0034]: “First, transmitter 502-1 transmits and the phase seen at receivers 504-1-504-4 is [0 φ.sub.a 2φ.sub.a 3φ.sub.a], respectively. [0034] b. Subsequently, transmitter 502-2 transmits and the phase seen at receivers 504-1-504-4 is [4φ.sub.a 5φ.sub.a 6φ.sub.a 7φ.sub.a]”; FIG. 3 description at [0021]: “FIG. 3 illustrates this point in frame 308. Graph 300 includes chirps 312-0-312-(N−1) that are transmitted by one transmitter (TX) and are interleaved in time with chirps 314-0-314-(N−1) from a second transmitter.”) Rao’s TDM-MIMO inherently uses transmitters sequentially in interleaved time slots, and the TX1 subarray and TX2 subarray are consecutive in the transmission schedule. No combination with Park is required for this element.
Regarding Claim 11, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) does not explicitly teach further comprising determining a residual phase shift for all remaining subarrays of the virtual array and applying the inverses thereof. While Rao acknowledges that its phase error compensation approach has wider applicability beyond the 2TX architecture ([0066]: “The example techniques described hereinabove are directed to the two transmitter (2TX) by four receiver (4RX) architecture of FIG. 5. However, the aspects of this application have wider applicability. In the aspects of the present application, exploiting the phase errors in the corrected virtual array signal may be used in many architectures.”), Rao applies a single combined phase error vector to the entire corrected virtual array signal Sc simultaneously rather than explicitly and individually determining a residual phase shift for each of the remaining subarrays and applying their respective inverses sequentially.
Park et al. (‘386) teaches: further comprising determining a residual phase shift for all remaining subarrays of the virtual array and applying the inverses thereof. ([0092]: “coarse angle calculation 918 can be performed by DoA processing over each of the NTDM virtual arrays separately. DoA processing can be done by performing a 3rd FFT (angular FFT) across all antennas of a virtual array.”; [0093]: “The amplitude of angle spectrum at target angle is higher when it is compensated correctly.”; [0094]: “by applying a number of different phase offset candidates to the second angular information…The phase offset candidate yielding the highest angular spectrum denotes correct compensation of phase ambiguity between the first and the second range-Doppler bins.”) Park explicitly processes all NTDM virtual subarrays, determining and applying a phase offset correction to each remaining subarray individually in order to achieve full-aperture virtual array synthesis.
It would have been obvious to a POSITA before the effective filing date to extend the pairwise residual phase shift determination of claim 1 to all remaining subarrays of the virtual array and apply the corresponding inverses, as taught by Park. The motivation to combine arises directly from Park’s own teaching at [0092-0094] that full-aperture DoA estimation - the stated objective of both Rao and Park - requires that all NTDM virtual subarrays be individually phase-corrected and combined, not merely a single pair. Leaving the remaining subarrays uncorrected would defeat the purpose of the phase ambiguity resolution method of claim 1 by producing a partially corrected virtual array that still contains inter-subarray phase errors, yielding a degraded angular spectrum. A POSITA would have recognized this directly from Rao’s own statement that its approach has wider applicability across architectures with more than two transmitters ([0066]), and from Park’s demonstration that all subarrays must be processed to achieve accurate full-aperture angle estimation. A POSITA would have had a reasonable expectation of success because the pairwise phase determination and inverse application of claim 1 is directly and predictably generalizable to all remaining subarrays - either by sequential pairwise application using each adjacent pair or by reference to a common reference subarray - a straightforward mathematical extension that requires no novel insight and is explicitly demonstrated by Park’s own processing of all NTDM virtual subarrays.
Regarding Claim 15, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) does not explicitly teach wherein the frequency spectrum is computed by applying a discrete Fourier transform to each row of the compensated virtual array signal, because Rao computes a single FFT over the entire combined virtual array vector S.sub.c rather than per-row DFT operations. However, Park et al. (‘386) teaches: the frequency spectrum is computed by applying a discrete Fourier transform to each row of the compensated virtual array signal. ([0092]: “DoA processing can be done by performing a 3.sup.rd FFT (angular FFT) across all antennas of a virtual array.”; [0003]: “A third FFT involving phase information of signals of different antenna elements of an (virtual) antenna array can yield additional spatial or angular information.”) An FFT is the computationally efficient form of a discrete Fourier transform, applied per row of the virtual array.
It would have been obvious to a POSITA to combine Park’s per-row (per-subarray) DFT processing with Rao’s phase ambiguity resolution framework. The motivation to combine arises from Park’s express teaching at [0092] that per-subarray angular spectra (each obtained via DFT over the virtual antenna elements of one subarray) provide the basis for coarse angular estimation and subsequent inter-subarray analysis. Park identifies the per-subarray DFT approach as a known and useful processing technique, providing motivation rooted in Park’s own disclosure. A POSITA would have had a reasonable expectation of success because the DFT is the canonical mathematical operation for spatial frequency analysis across array elements, and partitioning Rao’s combined-array DFT into row-wise DFT operations is a straightforward and mathematically equivalent decomposition with well-understood properties.
Regarding Claim 16, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) does not explicitly teach, but Park et al. (‘386) teaches: the amplitude-peak frequency is identified as a global maximum of a sum of the frequency spectra’s respective amplitude parts. ([0092]: “Each coarse angular spectrum associated with range-Doppler bins from every TDM subset can be averaged or non-coherently integrated to get higher SNR.”; [0093]: “The amplitude of angle spectrum at target angle is higher when it is compensated correctly.”) Non-coherent integration produces the sum of amplitude parts, and identifying the global maximum thereof produces the amplitude-peak frequency.
It would have been obvious to a POSITA to combine Park’s non-coherent integration of subarray amplitude spectra with Rao’s phase ambiguity resolution method, identifying the amplitude-peak frequency as the global maximum of the resulting sum. The motivation to combine arises from Park’s explicit teaching at [0092] that non-coherent integration across subarrays improves SNR - a benefit grounded in Park’s own disclosure. The global maximum of the summed amplitude spectrum is the natural choice for the amplitude-peak frequency because it represents the location where the target signal is most strongly reinforced across all subarrays. A POSITA would have had a reasonable expectation of success because non-coherent amplitude summation followed by maximum search is a standard signal-processing pipeline with well-established performance properties, and the global-maximum operation is computationally straight forward.
Regarding Claim 17, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) teaches: applying the inverse of the residual phase shift comprises multiplying elements of the compensated virtual array signal corresponding to a second subarray by an inverse phase rotation factor. ([0042-0049]: “b. Step 2 [0043] Doppler Correction [0044] The estimate of φ.sub.d in Step 1 is used to remove the dependence of the virtual array signal S on φ.sub.d by multiplying the last four elements of S by
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… This operation creates a corrected virtual array signal S.sub.c”.) This explicitly applies phase correction to the elements of the second subarray – the last four elements of S corresponding to TX2 – by multiplying each of those elements by the complex phase rotation factor of the equation. This is precisely an inverse phase rotation factor applied element-wise to the second subarray.
Regarding Claim 18, Rao et al. (‘483) in view of Park et al. (‘386) teaches: The method of claim 1.
Rao et al. (‘483) teaches: the residual phase shift is a folding-induced phase offset resulting from frequency folding when the moving object’s radial speed exceeds a maximum unambiguous detectable speed. ([0050]: “If |v|>v.sub.max, then |φ.sub.d| will exceed π resulting in an erroneous estimate of φ.sub.d in Step 2 of the method described above. For example, if φ.sub.d exceeds π (i.e., φ.sub.d=π+Δ), the value of φ.sub.d estimated in Step 1 (φ.sub.d.sub._.sub.est) will be −π+Δ. Likewise; if the value of φ.sub.d is less than −π (i.e., φ.sub.d=−π−Δ), then φ.sub.d.sub._.sub.est=π−Δ. Thus, the estimation error φ.sub.d−φ.sub.d.sub._.sub.est=±2π.”) This is precisely the frequency-folding mechanism: when the true Doppler phase exceeds the unambiguous range [−π, π], it folds back into that range, creating an estimation error of ±2π that propagates into the inter-subarray residual phase shift.
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Rao (US 2018/0011170 A1) in view of Park (US 2021/0333386 A1) and further in view of Chen et al. (US 2020/0233076 A1).
Regarding Claim 8, Rao et al. (‘483) in view of Park et al. (‘386) and further in view of Chen et al. (‘076) teaches: The method of claim 1.
Rao et al. (‘483) in view of Park et al. (‘386) does not explicitly teach wherein the array of physical receivers has a plurality of rows in the first direction. However, Chen et al. (‘076) teaches ([0037: “the transmitters and receivers may be arranged in any suitable manner including, for example, a one-dimensional array as shown in the illustrated example of FIG. 3 or in a two-dimensional array.”; [0073]: “A uniform rectangular MIMO array may be fully described by four parameters including column (azimuth) spacing (d.sub.x), the row (elevation) spacing (d.sub.z), the number of columns (M), and the number of rows (N). Based on these parameters, the position of an antenna element in the pth column and the qth row of an array is given by p.sub.i.j=[pd.sub.x,0,qd.sub.z].sup.T Eq. 36”)
Chen et al. (‘076) further teaches the steps of computing a frequency spectrum and identifying an amplitude-peak frequency are performed for all rows; and the residual phase shift is determined as a mean over all rows ([0072]: “the signals associated with each transmitter-receiver pair can be rearranged to model a virtual array corresponding to a different arrangement of array elements. In some examples, the signals associated with each transmitter-receiver pair are rearranged to correspond to a virtual uniform rectangular array for purposes of performing AOA estimation. An advantage of implementing AOA estimations using a virtual uniform rectangular array is that the analysis can be performed using FFT processing, which is much more efficient than conventional approaches involving computationally intensive discrete Fourier transforms (DFTs)”; [0074]: “the values corresponding to the received signals populating the virtual MIMO array matrix have already been modified to compensate for any phase offset due to range or Doppler effects as described above. Accordingly, the input signal model from the ith target follows the canonical model” of Eq. 37
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; “The values for the spatial frequency signals can be constructed by a 2D-FFT operation in the following manner” of Eq. 38
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; [0085]: “The example radar system 2100 of FIG. 21 includes the example virtual array generator 2124 to generate a virtual MIMO array matrix corresponding to a virtual uniform rectangular array. That is, in some examples, the virtual array generator 2124 generates a virtual array matrix with the values of the received signals associated with each transmitter-receiver pair arranged as if the transmitters 2102 and receivers 2104 were configured in a uniform rectangular array.”; [0086]: “The example radar system 2100 of FIG. 21 includes the example angle of arrival (AOA) analyzer 2126 to calculate the angle of arrive (e.g., the azimuth and elevation) of targets detected by the receivers. In some examples, the AOA analyzer 2126 calculates the AOA based on an FFT analysis of the virtual array matrix generated by the virtual array generator 2124.”; [0108]: “the example virtual array generator 2124 generates a MIMO array matrix based on the range-Doppler processed echo signals. In some examples, the array matrix is populated with the data to correspond to a virtual uniform rectangular array. At block 2522, the example AOA analyzer 2126 estimates the 2D angle of arrival of the targets. In some examples, the AOA estimation is based on the FFT analysis of the MIMO matrix array generated by the virtual array generator 2124 as described above in connection with Equations 36-41.”)
It would have been obvious to a POSITA to configure the TDM MIMO FMCW radar of Rao with a multi-row physical receiver array as taught by Chen, to perform the per-subarray frequency spectrum computation and amplitude-peak identification for each row of the rectangular array, and to determine the residual phase shift as a mean over all rows. The motivation arises directly from Chen’s explicit teaching that multi-row receiver arrays improve 2D angular estimation accuracy ([0072-0076]) that arranging physical receivers in a uniform rectangular array with multiple rows enables efficient 2D-FFT-based AOA estimation in both the row (elevation) and column(azimuth) dimensions – goals that are directly complementary to and consistent with Rao’s objective of accurate angular estimation after phase ambiguity resolution. A POSITA would have been motivated to exploit all available rows of the array to improve the robustness of the inter-subarray residual phase shift estimate, consistent with Chen’s teaching that multi-row arrays improve 2D angular resolution. A POSITA would have had a reasonable expectation of success because the per-subarray angular FFT and phase comparison operations taught by Rao in view of Park are equally applicable to each row of a rectangular array independently, and averaging the residual phase shift estimate across all rows reduces row-to-row estimation variance by a well-establish statistical principle -a predictable and mathematically guaranteed result requiring no inventive insight.
Regarding Claim 10, Rao et al. (‘483) in view of Park et al. (‘386) and further in view of Chen et al. (‘076) teaches: The method of claim 1.
Rao et al. (‘483) in view of Park et al. (‘386) teaches the method of claim 1, as set forth above.
Rao et al. (‘483) does not explicitly teach the second-direction extension recited in claim 10, however, Chen et al. (‘076) teaches: the array of physical receivers includes at least one column of physical receivers with a third spacing in a second direction and the physical transmitters are arranged with a fourth spacing in said second direction. ([0073]: “A uniform rectangular MIMO array may be fully described by four parameters including column (azimuth) spacing (d.sub.x), the row (elevation) spacing (d.sub.z), the number of columns (M), and the number of rows (N). Based on these parameters, the position of an antenna element in the pth column and the qth row of an array is given by [Eq. 36].”) The column spacing dx defines the third spacing in the second (azimuth/column) direction, and the physical transmitters are arranged with a corresponding fourth spacing in that direction such that the virtual array forms the uniform rectangular configuration.
Chen et al. (‘076) teaches: computing a frequency spectrum of those elements of the compensated virtual array signal which correspond to consecutive virtual antenna elements generated by physical receivers belonging to the same column, through its 2D-FFT processing applied column-wise across the virtual uniform rectangular array to extract angular information in the second (azimuth) direction. ([0072]: “the signals associated with each transmitter-receiver pair are rearranged to correspond to a virtual uniform rectangular array for purposes of performing AOA estimation. An advantage of implementing AOA estimations using a virtual uniform rectangular array is that the analysis can be performed using FFT processing, which is much more efficient than conventional approaches involving computationally intensive discrete Fourier transforms (DFTs).”; [0074]: “the values corresponding to the received signals populating the virtual MIMO array matrix have already been modified to compensate for any phase offset due to range or Doppler effects…The values for the spatial frequency signals can be constructed by a 2D-FFT operation in the following manner: [Eq. 38] where k and l are the discretized indices of the 2D spatial frequency (u, v).”) In the 2D-FFT of Equation 38, the index k corresponds to the column (azimuth) dimension and the FFT is performed across the column elements — i.e., across the consecutive virtual antenna elements belonging to the same column — directly teaching the claimed column-wise frequency spectrum computation.
Chen et al. (‘076) teaches: identifying, jointly for the frequency spectra of said second plurality of the subarrays, a second amplitude-peak frequency, through the 2D-FFT operation that identifies the dominant spatial frequency in the column dimension corresponding to the target’s azimuth angle. ([0074]: “The values for the spatial frequency signals can be constructed by a 2D-FFT operation in the following manner: [Eq. 38]… solving the following equation:
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”; [0086]: “the example angle of arrival (AOA) analyzer 2126 calculates the angle of arrival (e.g., the azimuth and elevation) of targets detected by the receivers. In some examples, the AOA analyzer 2126 calculates the AOA based on an FFT analysis of the virtual array matrix generated by the virtual array generator 2124.”) The peak in the column-dimension FFT at spatial frequency index k jointly identifies the second amplitude-peak frequency corresponding to the azimuth angle of the target across all subarrays, since all subarrays observe the same target at the same azimuth angle.
Chen et al. (‘076) does not explicitly teach in isolation the determination of the second residual phase shift by direct phase comparison at the second amplitude-peak frequency; however, as established in the analysis of claim 1, that step is taught by the combination of Rao and Park applied in the column dimension in exactly the same manner as in the row dimension. The mathematical operations for the column dimension are structurally identical to those for the row dimension, differing only in the axis of processing. ([0074]: “The values for the spatial frequency signals can be constructed by a 2D-FFT operation in the following manner: [Eq. 38] where k and l are the discretized indices of the 2D spatial frequency (u, v).”) Because the 2D-FFT produces independent spatial frequency spectra in both the row (index l) and column (index k) dimensions, the phase of each subarray’s column-dimension spectrum at the common amplitude-peak frequency k directly reflects the inter-subarray phase offset in the column direction, enabling the same direct phase comparison taught by Rao and Park in the row dimension to be applied equally in the column dimension.
It would have been obvious to a POSITA before the effective filing date to extend the phase ambiguity resolution method of claim 1 to the column (second) direction of a 2D virtual array as taught by Chen, in order to resolve inter-subarray phase ambiguities in both spatial dimensions and enable full 2D angle-of-arrival estimation. The motivation to combine arises from Chen’s express teaching at [0072] and [0073] that 2D spatial FFT processing in both the row (elevation) and column (azimuth) directions is necessary and beneficial for 2D angular estimation in a MIMO radar with a uniform rectangular virtual array - a motivation that is grounded in Chen’s own disclosure. A POSITA familiar with both the phase ambiguity resolution approach of Rao in view of Park (applied in the row dimension) and Chen’s teaching of 2D rectangular array processing would have been directly motivated to apply the same phase ambiguity resolution approach in the column dimension to complete the 2D estimation, because leaving the column dimension uncorrected would result in an inaccurate azimuth angle estimate that would undermine the very 2D AOA estimation that Chen’s system is designed to produce. A POSITA would have had a reasonable expectation of success because, as confirmed by Chen’s own 2D-FFT framework ([0074], Eq. 38), the mathematical operations for the column dimension are structurally identical to those for the row dimension - the same DFT is applied and the same peak identification is performed, additionally, the same phase comparison logic applies such that success in the column dimension follows directly and predictably from success already demonstrated in the row dimension.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/REMASH R GUYAH/Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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