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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 20 APR 2026 has been entered.
Response to Amendment
Applicant's amendment filed on 20 APR 2026 has been entered. Claims 1, 4-8, 11-14, 17 and 19-20 have been amended. Claims 2, 9, and 15 have been cancelled. Claims 1, 3-8, 10-14, and 16-20 are still pending in this application, with claims 1, 8, and 14 being independent.
Response to Arguments
Applicant’s arguments, see p. 8, filed 20 APR 2026, with respect to the rejection(s) of claim(s) 1, 8, and 14 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Shima and (US 10,879,946 B1) and Farmer et al. (US 6,366,236).
Claim Objections
Claims 5-6, 12-13 and 18 are objected to because of the following informalities:
In line 3 of claims 5 and 12, “noise” should be “the noise”
In line 3 of claims 6 and 13, “noise” should be “the noise”
In line 4 of claims 6 and 13, “a cleansed version” should be “the cleansed version”
In line 4 of claim 18, “noise” should be “then noise”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 1, 7-8, 14, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shima (US 10,879,946 B1, previously cited by the examiner) in view of Farmer et al. (US 6,366,236, previously relied upon by the examiner).
Regarding claim 1 (Currently Amended), Shima discloses:
A method of estimating pulse parameters (Shima , the method comprising:
receiving a digitized radio frequency (RF) signal as a digital input signal (Shima “The received energy 404 can be collected by an antenna or other sensor 304 and provided to receiver circuitry308 for conversion to a time-varying voltage 406.” – Col. 6, lines 1-3) including a series of values each including both an in-phase (I) component and a quadrature (Q) component (Shima “the time-frequency gram 410 output of the transform block 408 can include real and imaginary values” – Col. 6, lines 17-19);
feeding the digital input signal into a plurality of input nodes of a trained Pulse Parameter Estimation Neural Network (PPENN) without prior extraction of pulse snippets from the digital input signal (Shima “the real values are input into an input layer 412 of a neural network 416 as a first channel of information, and the imaginary values are input to the input layer 412 as a second channel of information.” – Col. 6, lines 21-24), the PPENN having been trained using machine learning (Shima “The neural network 416 is trained by providing inputs in the form of noise, and inputs in the form of desired signals.” – Col. 6, lines 45-47),
operating the trained PPENN to estimate a plurality of pulse parameters of a set of pulses embedded within a waveform of the digital input signal (Shima “the de-noising network 600 can be tasked with identifying signal 120 parameters from a time series signal 604. These signal 120 parameters can include SNR, start frequency, chirp rate, pulse width, repetition interval (PRI), and bandwidth.” – Col. 8, lines 14-18) including pulses embedded within noise in the waveform (Shima “the received energy 404 includes one or more desired signals 124 and one or more interfering signals 128 or other noise.” – Col. 5, lines 60-62) and to output the plurality of pulse parameters from the trained PPENN (Shima “The output is a de-noising or output signal mask 624…” – Col. 7, lines 57-58;“all of the signal parameters can be extracted directly from the mask 624.” – Col. 8, lines 30-31); and
processing the plurality of pulse parameters to further quantify the set of pulses (Shima “The output 432 can be applied by receiver components included as part of the system 300, and can be provided to other systems or nodes.” – Col. 6, lines 58-60).
Farmer et al. discloses:
each component of each value of the series of values of the digital input signal provided to a separate and different input node of plurality of input nodes (Farmer et al. “Responsive to a different sample of the in-phase modulated IF radar signal being fed to one of each pair of input nodes, and to a respective different sample of the quadrature-phase modulated IF radar signal being fed to the other of each pair of input nodes…” – Col. 9, lines 46-50; “The first plurality of nodes 126 comprise first 128 and second 130 subsets of nodes. An input from each of the first subset of nodes 128 is operatively connected to a sample of a first time series 132 of radar data, and each node 108 of the first subset 128 is operatively connected to a different time sample. An input from each of the second subset of nodes 130 is operatively connected to a sample of a second time series 134 of radar data and the second time series 134 is of quadrature phase to the first time series. Each node 108 of the second subset 130 is operatively connected to a different time sample, and the first 128 and second 130 subsets of the first plurality of nodes 126 correspond in time.” – Col. 12, lines 38-50)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Farmer et al. into the invention of Shima to yield the invention of claim 1 above. Both Shima and Farmer et al. are considered analogous arts to the claimed invention as they both disclose the use of neural networks for processing radar signals. Shima discloses the limitations of claim 1 outlined above. However, Shima fails to explicitly disclose each component of each value of the series of values of the digital input signal provided to a separate and different input node of plurality of input nodes. This feature is disclosed by Farmer et al. where “Responsive to a different sample of the in-phase modulated IF radar signal being fed to one of each pair of input nodes, and to a respective different sample of the quadrature-phase modulated IF radar signal being fed to the other of each pair of input nodes…” (Farmer et al. Col. 9, lines 46-50). The combination of Shima and Farmer et al. would be obvious with a reasonable expectation of success to implement a neural network processor chip for reduced cost and improved reliability (Farmer et al. Col. 5, lines 10-11).
Regarding claim 7 (Currently Amended), Shima as modified above discloses:
The method of claim 1 wherein operating the trained PPENN to output the plurality of pulse parameters from the trained neural network includes outputting (Shima “all of the signal parameters can be extracted directly from the mask 624…” – Col. 8, lines 30-31) at least two of:
pulse detection;
pulse-on-pulse detection;
intrapulse modulation;
start time;
stop time;
signal power;
signal-to-noise ratio (Shima SNR, Col. 8, line 15);
pulse width (Shima pulse width, Col. 8, line 16); and
center frequency.
Regarding claim 8 (Currently Amended), Shima discloses:
A system comprising:
a radio frequency (RF) antenna (Shima antenna 304, Fig. 3) configured to receive an RF signal (Shima “The antenna 304 is configured to receive signals 120 within at least a selected range of frequencies.” – Col. 4, lines 65-67);
an analog-to-digital converter configured to digitize the received RF signal to yield a digital input signal (Shima “the receiver circuitry 308 can output a raw or noisy time series signal or collection of signals 120 as a voltage that varies over time at the original or (modulated ) frequency.” – Col. 5, lines 16-19), the digital input signal including a series of values each including both an in-phase (I) component and a quadrature (Q) component (Shima “the time-frequency gram 410 output of the transform block 408 can include real and imaginary values” – Col. 6, lines 17-19); and
processing circuitry (Shima processor 312, Fig. 3) configured to:
feed the digital input signal into a plurality of input nodes of a trained Pulse Parameter Estimation Neural Network (PPENN) without prior extraction of pulse snippets from the digital input signal (Shima “the real values are input into an input layer 412 of a neural network 416 as a first channel of information, and the imaginary values are input to the input layer 412 as a second channel of information.” – Col. 6, lines 21-24), the PPENN having been trained using machine learning (Shima “The neural network 416 is trained by providing inputs in the form of noise, and inputs in the form of desired signals.” – Col. 6, lines 45-47),
operate the trained PPENN to estimate a plurality of pulse parameters of a set of pulses embedded within a waveform of the digital input signal (Shima “the de-noising network 600 can be tasked with identifying signal 120 parameters from a time series signal 604. These signal 120 parameters can include SNR, start frequency, chirp rate, pulse width, repetition interval (PRI), and bandwidth.” – Col. 8, lines 14-18) including pulses embedded within noise in the waveform (Shima “the received energy 404 includes one or more desired signals 124 and one or more interfering signals 128 or other noise.” – Col. 5, lines 60-62) and to output the plurality of pulse parameters from the trained PPENN (Shima “The output is a de-noising or output signal mask 624…” – Col. 7, lines 57-58;“all of the signal parameters can be extracted directly from the mask 624.” – Col. 8, lines 30-31); and
process the plurality of pulse parameters to further quantify the set of pulses (Shima “The output 432 can be applied by receiver components included as part of the system 300, and can be provided to other systems or nodes.” – Col. 6, lines 58-60).
Farmer et al. discloses:
each component of each value of the series of values of the digital input signal provided to a separate and different input node of plurality of input nodes (Farmer et al. “Responsive to a different sample of the in-phase modulated IF radar signal being fed to one of each pair of input nodes, and to a respective different sample of the quadrature-phase modulated IF radar signal being fed to the other of each pair of input nodes…” – Col. 9, lines 46-50; “The first plurality of nodes 126 comprise first 128 and second 130 subsets of nodes. An input from each of the first subset of nodes 128 is operatively connected to a sample of a first time series 132 of radar data, and each node 108 of the first subset 128 is operatively connected to a different time sample. An input from each of the second subset of nodes 130 is operatively connected to a sample of a second time series 134 of radar data and the second time series 134 is of quadrature phase to the first time series. Each node 108 of the second subset 130 is operatively connected to a different time sample, and the first 128 and second 130 subsets of the first plurality of nodes 126 correspond in time.” – Col. 12, lines 38-50)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Farmer et al. into the invention of Shima to yield the invention of claim 8 above. Both Shima and Farmer et al. are considered analogous arts to the claimed invention as they both disclose the use of neural networks for processing radar signals. Shima discloses the limitations of claim 8 outlined above. However, Shima fails to explicitly disclose each component of each value of the series of values of the digital input signal provided to a separate and different input node of plurality of input nodes. This feature is disclosed by Farmer et al. where “Responsive to a different sample of the in-phase modulated IF radar signal being fed to one of each pair of input nodes, and to a respective different sample of the quadrature-phase modulated IF radar signal being fed to the other of each pair of input nodes…” (Farmer et al. Col. 9, lines 46-50). The combination of Shima and Farmer et al. would be obvious with a reasonable expectation of success to implement a neural network processor chip for reduced cost and improved reliability (Farmer et al. Col. 5, lines 10-11).
Regarding claim 14 (Currently Amended), Shima discloses:
A computer program product comprising a non-transitory computer-readable storage medium storing a set of instructions, which, when performed by a computing device (Shima “The processor 312 generally operates to execute programming code or instructions, such as instructions 328 stored in the memory 316 and/or data storage 320, that implements a signal processing network in accordance with embodiments of the present disclosure.” – Col. 5, lines 27-31), causes the computing device to:
receive a digitized radio frequency (RF) signal as a digital input signal (Shima “The received energy 404 can be collected by an antenna or other sensor 304 and provided to receiver circuitry308 for conversion to a time-varying voltage 406.” – Col. 6, lines 1-3), the digital input signal including a series of values each including both an in-phase (I) component and a quadrature (Q) component (Shima “the time-frequency gram 410 output of the transform block 408 can include real and imaginary values” – Col. 6, lines 17-19);
feed the digital input signal into a plurality of input nodes of a trained Pulse Parameter Estimation Neural Network (PPENN) without prior extraction of pulse snippets from the digital input signal (Shima “the real values are input into an input layer 412 of a neural network 416 as a first channel of information, and the imaginary values are input to the input layer 412 as a second channel of information.” – Col. 6, lines 21-24), the PPENN having been trained using machine learning (Shima “The neural network 416 is trained by providing inputs in the form of noise, and inputs in the form of desired signals.” – Col. 6, lines 45-47),
operate the trained PPENN to estimate a plurality of pulse parameters of a set of pulses embedded within a waveform of the digital input signal (Shima “the de-noising network 600 can be tasked with identifying signal 120 parameters from a time series signal 604. These signal 120 parameters can include SNR, start frequency, chirp rate, pulse width, repetition interval (PRI), and bandwidth.” – Col. 8, lines 14-18) including pulses embedded within noise in the waveform (Shima “the received energy 404 includes one or more desired signals 124 and one or more interfering signals 128 or other noise.” – Col. 5, lines 60-62) and to output the plurality of pulse parameters from the trained PPENN (Shima “The output is a de-noising or output signal mask 624…” – Col. 7, lines 57-58;“all of the signal parameters can be extracted directly from the mask 624.” – Col. 8, lines 30-31); and
process the plurality of pulse parameters to further quantify the set of pulses (Shima “The output 432 can be applied by receiver components included as part of the system 300, and can be provided to other systems or nodes.” – Col. 6, lines 58-60).
Farmer et al. discloses:
each component of each value of the series of values of the digital input signal provided to a separate and different input node of plurality of input nodes (Farmer et al. “Responsive to a different sample of the in-phase modulated IF radar signal being fed to one of each pair of input nodes, and to a respective different sample of the quadrature-phase modulated IF radar signal being fed to the other of each pair of input nodes…” – Col. 9, lines 46-50; “The first plurality of nodes 126 comprise first 128 and second 130 subsets of nodes. An input from each of the first subset of nodes 128 is operatively connected to a sample of a first time series 132 of radar data, and each node 108 of the first subset 128 is operatively connected to a different time sample. An input from each of the second subset of nodes 130 is operatively connected to a sample of a second time series 134 of radar data and the second time series 134 is of quadrature phase to the first time series. Each node 108 of the second subset 130 is operatively connected to a different time sample, and the first 128 and second 130 subsets of the first plurality of nodes 126 correspond in time.” – Col. 12, lines 38-50)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Farmer et al. into the invention of Shima to yield the invention of claim 14 above. Both Shima and Farmer et al. are considered analogous arts to the claimed invention as they both disclose the use of neural networks for processing radar signals. Shima discloses the limitations of claim 14 outlined above. However, Shima fails to explicitly disclose each component of each value of the series of values of the digital input signal provided to a separate and different input node of plurality of input nodes. This feature is disclosed by Farmer et al. where “Responsive to a different sample of the in-phase modulated IF radar signal being fed to one of each pair of input nodes, and to a respective different sample of the quadrature-phase modulated IF radar signal being fed to the other of each pair of input nodes…” (Farmer et al. Col. 9, lines 46-50). The combination of Shima and Farmer et al. would be obvious with a reasonable expectation of success to implement a neural network processor chip for reduced cost and improved reliability (Farmer et al. Col. 5, lines 10-11).
Regarding claim 18 (Original), Shima as modified above discloses:
The computer program product of claim 14 wherein the set of instructions, when performed by the computing device, further causes the computing device to, prior to feeding the digital input signal into the plurality of input nodes of the PPENN, perform a preliminary cleansing operation on the digital input signal to remove noise or channel effects (Shima “the receiver circuitry 308 can include filters or other processors that remove some unwanted signals 128” – Col. 5, lines 19-21).
Regarding claim 20 (Currently Amended), the same cited section and rationale as corresponding method claim 7 is applied.
Claim(s) 3, 10 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shima (US 10,879,946 B1, previously cited by the examiner) in view of Farmer et al. (US 6,366,236, previously relied upon by the examiner) as applied to claims 1, 8 and 14 above, and further in view of Driggs et al. (US 2009/0135052 A1, cited in IDS dated 2 JAN 2024 and previously relied upon by the examiner).
Regarding claim 3 (Previously Presented), Shima as modified above discloses:
The method of claim 1 wherein:
the RF signal is a radar signal (Shima “the received energy 404 can include a communication signal, a radar return, or any other RF signal.” – Col. 5, lines 63-64); and
Driggs et al. discloses:
processing the plurality of pulse parameters to further quantify the set of pulses includes performing pulse deinterleaving (Driggs et al. “To deinterleave an incoming signal, it is necessary to compare one or more parameters associated with incoming signal portions with corresponding parameters stored in memory, for example, characteristics from previous pulses or expected signal characteristics.” - ¶ [0011]).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Driggs et al. into the invention of Shima to yield the invention of claim 1 above. Both Shima, Farmer et al. and Driggs et al. are considered analogous arts to the claimed invention as they both disclose the use of neural networks for processing radar signals. Shima discloses the limitations of claim 1 outlined above. However, Shima fails to explicitly disclose processing the plurality of pulse parameters to further quantify the set of pulses includes performing pulse deinterleaving. This feature is disclosed by Driggs et al. where “To deinterleave an incoming signal, it is necessary to compare one or more parameters associated with incoming signal portions with corresponding parameters stored in memory, for example, characteristics from previous pulses or expected signal characteristics.” (Driggs et al. ¶ [0011]). The combination of Shima and Farmer et al. would be obvious with a reasonable expectation of success to implement a neural network processor chip for reduced cost and improved reliability (Farmer et al. Col. 5, lines 10-11) and sort incoming pulses in order to determine the location of a signal source (Driggs et al. ¶ [0002]).
Regarding claim 10 (Previously Presented), the same cited section and rationale as corresponding method claim 3 is applied.
Regarding claim 16 (Previously Presented), the same cited section and rationale as corresponding method claim 3 is applied.
Allowable Subject Matter
Claims 4-6, 11-13, 17 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding dependent claim 4, the prior art of record fails to explicitly teach or render obvious, either alone or in combination, a signal-to-noise ratio (SNR) of the RF signal is less than a predetermined threshold value; and operating the trained PPENN to estimate the plurality of pulse parameters is performed without thresholding.
Regarding dependent claim 5, the prior art of record fails to explicitly teach or render obvious, either alone or in combination, the preliminary cleansing operation is performed by a trained neural network that outputs a cleansed version of the digital input signal and wherein the cleansed version of the digital input signal is fed into the plurality of input nodes of the PPENN.
Dependent claim 6 is objected to as depending from objected claim 5.
Dependent claim 11 is objected to for similar reasons as claim 4.
Dependent claim 12 are objected to for similar reasons as claim 5.
Dependent claims 13 is objected to as depending from objected claim 12.
Dependent claim 17 is objected to for similar reasons as claim 4.
Dependent claim 19 is objected to for similar reasons as claim 5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAOMI M WOLFORD whose telephone number is (571)272-3929. The examiner can normally be reached Monday - Friday, 8:30 am - 4:30 pm EST.
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./ Examiner, Art Unit 3648
16 JUN 2026
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3648
Prosecution Insights