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 and Arguments
Applicant’s amendment filed on July 28, 2025 has been entered and made of record. Claims 1-20 are pending and are being examined in this application.
Applicant’s arguments with respect to the 103 rejections have been considered, but are moot in view of the new ground(s) of rejection provided below.
Allowable Subject Matter
Claims 2-7, 10-13, and 18-20 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.
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, 9, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US Pub. 20200104701) in view of Meissner et al. (US Pub. 20200341109).
Referring to claim 1, Lee discloses A method for suppressing noise in a signal, comprising the steps of:
receiving an input signal from a signal source [par. 43; a noise classifier of a neural processing device receives input data];
evaluating the input signal with a first stage noise pattern selection neural network, an automatic noise classification being generated from the evaluation of the input signal [pars. 53 and 68; the noise classifier is a neural network that pre-processes the input data to determine a noise characteristic, which may include a noise type and a noise degree];
retrieving, from a...weight table, a set of automatic...weight values stored in a...weight table that correlates noise classifications to sets of...weight values, correlated to the retrieved set of the automatic...weight values corresponding to the automatic noise classification as generated by the first stage noise pattern selection neural network [pars. 56, 68, 73, 74, 84, 86; weights are provided as input to an operator (i.e., a different neural network); the weights are stored in a weight address retrieved from a weight table and correspond to the noise characteristic stored in the weight table in association with the weight address; note that storing a pointer to the weights is functionally equivalent to storing the weights themselves]; and
selectively applying an automatic targeted...to the input signal with a second stage noise pattern...neural network trained and operating independently of the first stage noise pattern selection neural network, the automatic targeted...being based upon the retrieved set of automatic...weight values [pars. 54, 56, and 68; a network selector selects the operator from a plurality of neural networks (different from the noise classifier) based on the weights to perform operations using the weights and the input data].
Lee does not appear to explicitly disclose (though it is implied in pars. 4-6 of Lee) that the weight table is a noise suppression weight table; that the set of automatic weight values are automatic noise suppression weight values; that the sets of weight values are noise suppression weight values; that an automatic targeted noise suppression is applied; and that the second stage noise pattern neural network is a second stage noise pattern suppression neural network.
However, Meissner discloses that the weight table is a noise suppression weight table; that the set of automatic weight values are automatic noise suppression weight values; that the sets of weight values are noise suppression weight values; that an automatic targeted noise suppression is applied; and that the second stage noise pattern neural network is a second stage noise pattern suppression neural network [pars. 83 and 84; weight quantization is applied to a neural network that performs operations associated with interference and/or noise suppression].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural processing device taught by Lee so that the weights are noise suppression weights and the operations performed by the operator are associated with noise suppression as taught by Meissner, with a reasonable expectation of success. The motivation for doing so would have been to reduce memory and computation requirements [Lee, pars. 4-6; Meissner, pars. 83 and 84].
Referring to claim 9, see the rejection for claim 1, which incorporates all of the elements of claim 9.
Referring to claim 15, Lee discloses The multi-stage selectable neural network noise suppression system of Claim 9, further comprising: a data processor implementing the first stage noise pattern selection neural network and the second stage noise pattern suppression neural network [fig. 3; par. 52; the neural processing device includes the noise classifier, the network selector, a memory, and the operate; and a memory storing the noise suppression weight table [par. 74; the weight table is stored in the memory].
Referring to claim 17, see at least the rejection for claim 1. Lee further discloses An article of manufacture comprising a non-transitory program storage medium readable by a data processing apparatus, the medium tangibly embodying one or more programs of instructions executable by the data processing apparatus to perform a method for suppressing noise in a signal, the method comprising the claimed steps [par. 102; the neural processing device may be implemented using any suitable hardware, software, firmware, or a combination of hardware, software, and firmware; software may be machine code, firmware, embedded code, and application software].
Claims 8, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lee and Meissner in view of Gruhle et al. (US Pub. 20040131194).
Referring to claim 8, Lee and Meissner disclose The method of Claim 1, wherein the input signal is selected from a group consisting of: ...a radio frequency signal [Meissner: par. 30; note the RF signal], an electrical signal [Meissner: par. 30; note the RF signal], and an image stream [Lee: par. 40; note the image input].
Lee and Meissner do not appear to explicitly disclose an audio signal and an infrasound signal.
However, Gruhle discloses an audio signal and an infrasound signal [par. 14; a noise suppressor for suppressing audio, and in certain cases also ultra or infrasound, is employed to perform signal analysis].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural processing device taught by the combination of Lee Meissner so that the noise suppression is performed on audio and infrasound signals as taught by Gruhle, with a reasonable expectation of success. The motivation for doing so would have been to make possible simple analysis of signals received from audio/infrasound devices [Gruhle, par. 14].
Referring to claim 14, see the rejection for claim 8.
Referring to claim 16, Lee and Meissner do not appear to explicitly disclose The multi-stage selectable neural network noise suppression system of Claim 15, further comprising: an input audio transducer receptive to an audio wave, the audio wave being converted to the input signal.
However, Gruhle discloses The multi-stage selectable neural network noise suppression system of Claim 15, further comprising: an input audio transducer receptive to an audio wave, the audio wave being converted to the input signal [claim 14; par. 14; a noise suppressor for suppressing audio, and in certain cases also ultra or infrasound, is employed to perform signal analysis; an analog-digital converter (i.e., a transducer) receives and converts signals before providing them as input].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural processing device taught by the combination of Lee Meissner so that the noise suppression is performed on audio and infrasound signals (as provided by an analog-digital converter) as taught by Gruhle, with a reasonable expectation of success. The motivation for doing so would have been to make possible simple analysis of signals received from audio/infrasound devices [Gruhle, par. 14].
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Farhadi et al. (US Pub. 20190286953) discloses image classification using stacked neural networks.
EL-BAZ et al. (US Pub. 20210345970) discloses generating initial classifications in a first stage, assigning weights to the initial classifications, then generating final classifications in a second stage.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE PARK whose telephone number is (571)270-7727. The examiner can normally be reached M-F 8AM-5PM.
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/Grace Park/Primary Examiner, Art Unit 2144