DETAILED ACTION
Response to Amendment
The amendment filed April 16, 2026 has been entered.
Claims 1, 11-12 and 18 are amended.
Claims 1-20 are pending this application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (Hindawi 2020) in view of Harnett et al (US 2021/0286067 A1).
Regarding Claim 1, Kim teaches a radar system, comprising: at least one transmitter and at least one receiver [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers],
wherein the at least one transmitter and the at least one receiver are configured to transmit and receive radar signals, wherein the at least one transmitter and the at least one receiver are coupled to a vehicle [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers];
and a radar processor, configured to: transmit, at a first time, a first radar signal, receive, using the at least one receiver, a received signal [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers],
process the received signal to generate a data frame, determine a first snapshot comprising a first plurality of values associated with a first bin of the data frame [page 3, right column and page 7, figure 4 for performing range extrapolation and right column, first two paragraphs],
process the first plurality of values in the first snapshot to generate an autoregressive model based upon the first plurality of values, use the autoregressive model to extrapolate a second snapshot [page 4, right column, 4th paragraph and last 2 paragraphs for using AR parameters with page 5 section 4],
wherein the second snapshot includes the first plurality of values and a second plurality of values generated using the autoregressive model, determine, using the second snapshot [page 4, right column, last two paragraph for using extrapolation for each chirps AR parameter, and page 6, last two paragraphs],
a covariance matrix, identify attributes of a plurality of objects using the covariance matrix [page 4, left column, first three paragraphs for covariance matrix],
and transmit the attributes of the plurality of objects to a vehicle controller [page 1 abstract for using TOA and DOA for automotive radar systems while vehicle in on the road].
Kim teaches data frames and covariance matrices, however Kim fails to explicitly teach Range-Doppler data and full rank covariance matrices.
Harnett has a method for facilitating the generation of additional training data and homogenization after pulse compression [abstract] and teaches Range-Doppler data [0062 for using range cell and Doppler bins to determine target of interest]
and full rank covariance matrices [0061 for combining covariance matrices to create a full covariance matrix].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the autoregressive modeling techniques, as disclosed by Kim, further including the range Doppler calculations as taught by Harnett for the purpose to test bins for the target of interest (Harnett, 0062).
Regarding Claim 2, Kim teaches the autoregressive model is configured to approximate the equality a =-Rz-rx, where a is a matrix containing prediction coefficients of the autoregressive model, rx is a cross-correlation vector, and Rz- is an inverse of the cross-correlation matrix [page 6, left col last paragraph and equation 20 for using a covariance matrix with AR parameters to estimate distance using linear equations].
Regarding Claim 4, Kim teaches the radar processor is configured to identify the attributes of the plurality of objects by [page 1 abstract for determining vehicles and positions on the road]:
determining a set of eigenvalues of the full rank covariance matrix [page 4, left column first three paragraphs];
and determining the attributes of the plurality of objects using the set of eigenvalues [page 4, equations 10 and left column, last paragraph].
Regarding Claim 5, Kim teaches the radar processor is configured to identify the attributes of the plurality of objects by by [page 1 abstract for determining vehicles and positions on the road]:
using at least one of a MUSIC spectral estimation algorithm and a CAPON spectral estimation algorithm to generate a pseudospectrum using the full rank covariance matrix [page 4, left column, first three paragraphs for covariance matrix using MUSIC];
and detecting a peak value in the pseudospectrum [page 4 left column last paragraph and equation 14 and page 4, right column first paragraph for using DOA spectrum functions with MUSIC].
Regarding Claim 6, Kim teaches the first snapshot includes a first number of values equal to a number of physical or virtual arrays in the radar system [page 3 figure 2 for physical receive antenna arrays, with page 4, equation 8].
Regarding Claim 7, Kim teaches the second snapshot includes a second number of values equal to two times the first number of values [page 6, right column last paragraph for having LE denote the number of extrapolated samples].
Regarding Claim 8, Kim teaches the full rank covariance matrix is a matrix having dimensions N x N, where the value of N is equal the second number of values divided by 1.5 [page 6, equation 20 for an N x N covariance matrix].
Regarding Claim 9, Kim teaches the first snapshot includes four values and the second snapshot includes eight values [page 6, right column last paragraph for having LE denote the number of extrapolated samples].
Regarding Claim 10, Kim teaches the full rank covariance matrix is a matrix having dimensions 5 by 5 [page 6, equation 20 for an N x N covariance matrix].
Regarding Claim 11, Kim teaches radar system, comprising [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers]:
at least one transmitter and at least one receiver [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers];
and a radar processor, configured to [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers]:
process a received signal to determine a first snapshot comprising a first plurality of values associated with a first bin of a data frame [page 3, right column and page 7, figure 4 for performing range extrapolation and right column, first two paragraphs],
determine a second snapshot, wherein the second snapshot includes the first plurality of values and a second plurality of values generated using an autoregressive model [page 4, right column, 4th paragraph and last 2 paragraphs for using AR parameters with page 5 section 4],
determine, using the second snapshot, a covariance matrix [page 4, right column, last two paragraph for using extrapolation for each chirps AR parameter, and page 6, last two paragraphs],
and identify attributes of a plurality of objects using the covariance matrix [page 4, left column, first three paragraphs for covariance matrix].
Kim teaches data frames and covariance matrices, however Kim fails to explicitly teach Range-Doppler data and full rank covariance matrices.
Harnett has a method for facilitating the generation of additional training data and homogenization after pulse compression [abstract] and teaches Range-Doppler data [0062 for using range cell and Doppler bins to determine target of interest]
and full rank covariance matrices [0061 for combining covariance matrices to create a full covariance matrix].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the autoregressive modeling techniques, as disclosed by Kim, further including the range Doppler calculations as taught by Harnett for the purpose to test bins for the target of interest (Harnett, 0062).
Regarding Claim 12, Kim teaches the radar processor, in determining the second snapshot, performs steps of: processing the first snapshot to generate an autoregressive model based upon the first plurality of values [page 4, right column, 4th paragraph and last 2 paragraphs for using AR parameters with page 5 section 4];
and use the autoregressive model to extrapolate the second snapshot, wherein the second snapshot includes the first plurality of values and the second plurality of values generated using the autoregressive model [page 4, right column, 4th paragraph and last 2 paragraphs for using AR parameters with page 5 section 4].
Regarding Claim 13, Kim teaches the autoregressive model is configured to approximate the equality a =-Rz-rx, where a is a matrix containing prediction coefficients of the autoregressive model,
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is a cross-correlation vector, and is an inverse of the cross- correlation matrix [page 6, left col last paragraph and equation 20 for using a covariance matrix with AR parameters to estimate distance using linear equations].
Regarding Claim 15, Kim teaches the first snapshot includes a first number of values equal to a number of physical or virtual arrays in the radar system [page 3 figure 2 for physical receive antenna arrays, with page 4, equation 8].
Regarding Claim 16, Kim teaches the second snapshot includes a second number of values equal to at least two times the first number of values [page 6, right column last paragraph for having LE denote the number of extrapolated samples].
Regarding Claim 17, Kim teaches the full rank covariance matrix is a matrix having dimensions N x N, where the value of N is equal to at least the second number of values divided by 1.5 [page 6, equation 20 for an N x N covariance matrix].
Regarding Claim 18, Kim teaches method, comprising [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers]:
processing a received signal to generate a data frame [page 2, figure 1 and right column 3rd paragraph for transmitter with page 4, figure 2 for multiple receivers];
determining a first snapshot comprising a first plurality of values associated with a first bin of the data frame [page 4, right column, 4th paragraph and last 2 paragraphs for using AR parameters with page 5 section 4];
processing the first plurality of values in the first snapshot to generate an autoregressive model based upon the first plurality of values [page 6, left column for using linear AR models for the signals and antennas];
using the autoregressive model to extrapolate a second snapshot, wherein the second snapshot includes the first plurality of values and a second plurality values generated using the autoregressive model [page 4, right column, last two paragraph for using extrapolation for each chirps AR parameter, and page 6, last two paragraphs];
determining, using the second snapshot, a covariance matrix [page 4, left column, first three paragraphs for covariance matrix];
and identifying attributes of a plurality of objects using the covariance matrix [page 6, left column, last paragraph and equation 20].
Kim teaches data frames and covariance matrices, however Kim fails to explicitly teach Range-Doppler data and full rank covariance matrices.
Harnett has a method for facilitating the generation of additional training data and homogenization after pulse compression [abstract] and teaches Range-Doppler data [0062 for using range cell and Doppler bins to determine target of interest]
and full rank covariance matrices [0061 for combining covariance matrices to create a full covariance matrix].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the autoregressive modeling techniques, as disclosed by Kim, further including the range Doppler calculations as taught by Harnett for the purpose to test bins for the target of interest (Harnett, 0062).
Regarding Claim 19, Kim teaches processing the first plurality of values in the first snapshot to generate the autoregressive model that approximates the equality a =-R-frx, where a is a matrix containing prediction coefficients of the autoregressive model, rx is a cross- correlation vector, and Rzz is an inverse of the cross-correlation matrix [page 6, left col last paragraph and equation 20 for using a covariance matrix with AR parameters to estimate distance using linear equations].
Claims 3, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (Hindawi 2020) in view of Harnett et al (US 2021/0286067 A1), as applied to Claim 1, 11, and 18 above, and further in view of Barbaresco (FR 2769373 A1).
Regarding Claim 3, 14, and 20, Kim teaches the radar processor is configured to process the first plurality of values in the first snapshot to generate the autoregressive model [page 6, left column last two paragraphs and equations 20-21].
Kim fails to explicitly teach using Burg's method, Yule Walker equations, or Levinson's method.
Barbaresco has a high-resolution spectral analysis of the echoes and validation via an auto regressive model [abstract] and teaches using Burg's method, Yule Walker equations, or Levinson's method [page 9, second paragraph for using Burg and Levinson].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the autoregressive modeling techniques, as disclosed by Kim, further including the model calculations as taught by Barbaresco for the estimation of forward and backward prediction errors (Barbaresco, page 9, second paragraph).
Response to Arguments
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a
general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In applicant’s arguments page 10, last paragraph of applicant’s arguments, the applicant states that Harnett fails to disclose the full rank covariance matrix is determined using an extrapolated snapshot generated using an autoregressive model. The examiner respectfully disagrees Kim teaches extrapolation using autoregression extrapolated signal which directly corresponds to generating the claimed “second plurality of values” via an autoregression model [Kim, page 4, right column, last two paragraphs]. Harnett further teaches generation of secondary training data to produce a plurality of datacubes demonstrating that original data is combined with additional generated data thereby rendering a dataset (snapshot) that inherently includes both the original and newly generated values [Harnett, 0100].
In applicant’s arguments page 11, first paragraph of applicant’s arguments, the applicant states that the combination of Kim and Harnett is not the same as the full rank covariance matrix of claim 1. The examiner respectfully disagrees Kim explicitly teaches constructing a covariance matrix from the stacked received data matrix [Kim page 4, Section 3, eq 9], and Harnett expressly teaches the augmenting radar data – even non independent mathematically derived data – improves covariance matrix estimation towards the rank sufficiency required for optimal object detection [Harnett, 0004]. The combination inherently produces a larger higher rank covariance matrix (full rank) with increased data strength.
In applicant’s arguments page 11, last paragraph of applicant’s arguments, the applicant states that the combination of Kim and Harnett is not the same as the full rank covariance matrix of claim 1. See paragraph 26 and 27 above.
In applicant’s arguments page 12, first paragraph of applicant’s arguments, the applicant states that the combination of Kim and Harnett is not the same as the full rank covariance matrix of claim 1. See paragraph 26 and 27 above.
The examiner acknowledges that this is a broader interpretation than Applicant’s. However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
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