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 allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). 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, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on August 7, 2026 has been entered.
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.
Claim(s) 1-12, 14-17, 19 and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fitz et al (US 20230328463 A1) in view of Kof et al (US 20220200540 A1).
With respect to claim 1, Fitz discloses an apparatus, comprising: a memory storing processor-readable code; and one or more processors coupled to the memory (Par.[0029]), the one or more processors configured to:
receive an input audio signal (fig.2 #205 “Xn”), wherein the input audio signal includes a desired audio component and a feedback component (Par.[0026] microphone #204 receives input audio signals #205, which includes both feedback signals #210 from speaker #206 and desired ambient audio signals available to the user of hearing aid #100); and
determine an output audio signal (fig.2 #207) by applying a machine learning model to the input audio signal, in which the machine learning model is configured to reduce the feedback component (Par.[0026-0027] a machine learning model encompassed by neural network #250 may be trained to control acoustic feedback cancellation on the input signal).
Fitz discloses wherein the apparatus comprises processing electronics #208 (“amplification circuit”) for processing and amplifying the compensated signal #212 to generate the output signal #207; see Par.[0026]; however does not disclose expressly wherein the machine learning model is configured to generate a cancellation signal to cancel nonlinearities created by the amplification circuit.
Kof discloses the use of a machine-learning model (fig.5 #208) to generate a cancellation signal to cancel nonlinearities created by an amplification circuit (fig.5 #117)(Par.[0028][0077] the pre-distortion signal generated by DPD ANN #201b is a cancellation signal to cancel nonlinearities generated by power amplifier #117).
It would have been obvious before the effective filing date of the present invention to a person of ordinary skill in the art to use the machine-learning model and pre-distorter of Kof in the apparatus of Fitz. The motivation for doing so would have been to compensate for signal distortions caused by the amplification circuit of Fitz.
With respect to claim 2, Fitz discloses the apparatus of claim 1, wherein the machine learning model is configured to preserve a desired component (Par.[0026] “In various embodiments, the adaptive feedback cancellation filter 225 mirrors the feedback path 209 transfer function and signal yn 210 to produce a feedback cancellation signal ŷn 211. When ŷn 211 is subtracted from the input signal xn 205, the resulting compensated input signal en 212 contains minimal, if any, feedback signal yn 210 components”; Therefore the feedback signal components #210 are reduced while desired signal components are preserved).
With respect to claim 3, Fitz discloses the apparatus of claim 1, wherein the amplification circuit (fig.2 #208) coupled to the one or more processors and configured to drive a transducer (fig.2 #206) from the output audio signal, wherein:
the one or more processors are configured to reduce the feedback component by causing the combination of the input audio signal with a cancellation signal (fig.2 #211) generated by the machine learning model to determine the output audio signal (Par.[0026] the input audio signal and the cancellation signal are combined via a subtraction).
With respect to claim 4, Fitz discloses the apparatus of claim 1, wherein the one or more processors are further configured to:
determine a feedback cancellation signal (fig.2 #211) to reduce linear components of the feedback component of the input audio signal; and combine the feedback cancellation signal with the input audio signal prior to determining the output audio signal by applying the machine learning model (Par.[0026] the input audio signal and the cancellation signal are combined via a subtraction), and wherein the apparatus further comprises: an amplification circuit (fig.2 #208) coupled to the one or more processors and configured to amplify the output audio signal to drive a transducer from the output audio signal (Par.[0026]), and the machine learning model is configured to reduce the feedback component by reducing nonlinearities of the amplification circuit (See: 35 USC 103 Rejections under Kof Above).
With respect to claim 5, Fitz discloses the apparatus of claim 4, wherein the apparatus further comprises: an additional amplification circuit coupled to the one or more processors and configured to amplify the input audio signal after combining the feedback cancellation signal with the input audio signal and before reducing the feedback component by applying the machine learning model, and wherein the machine learning model is configured to reduce the feedback component by reducing nonlinearities of the additional amplification circuit (As shown in figure 2, amplification #208 is performed after combination of the input signal and feedback cancellation signal via subtraction, and before application of the machine learning model via OPM #230).
With respect to claim 6, Fitz discloses the apparatus of claim 4, wherein the machine learning model is configured to reduce the feedback component based on parameters relating to the feedback cancellation signal (Par.[0027]).
With respect to claim 7, Fitz discloses the apparatus of claim 6, wherein the one or more processors comprise: a digital signal processor (fig.2 #203) configured to determine the feedback cancellation signal and to output the parameters relating to the feedback cancellation signal (Par.[0029]); and a neural signal processor (fig.2 #250) configured to execute the machine learning model based on the parameters relating to the feedback cancellation signal ( ).
With respect to claim 8, Fitz discloses the apparatus of claim 4, wherein the machine learning model is configured to reduce the feedback component based on input parameters corresponding to input from a sensor (#204) uncorrelated with the feedback component. The present claim language does not provide sufficient detail on what comprises a sensor that is uncorrelated with the feedback component such that the microphone of Fitz does not anticipate such a limitation.
With respect to claim 9, Fitz discloses the apparatus of claim 8 in view of Kof, wherein the machine learning model is configured to reduce one or more artifacts resulting from the amplification circuit without reducing other howling in the input audio signal (See Kof: [0028][0077]).
With respect to claim 10, Fitz discloses the apparatus of claim 9, wherein the one or more processors are configured to reduce the feedback component by applying the machine learning model comprises applying a time-domain filter to the input audio signal after the amplifying of the input audio signal, the time-domain filter configured based on the machine learning model (Par.[0029] processing may be performed in the time domain via time-domain filtering).
With respect to claim 11, Fitz discloses the apparatus of claim 1, further comprising: a first microphone (fig.2 #204) coupled to the one or more processors, wherein the input audio signal is received from the first microphone; and a transducer (fig.2 #206) coupled to the one or more processors, wherein the transducer is configured to reproduce the output audio signal.
With respect to claim 12, Fitz discloses a method, comprising:
receiving an input audio signal, wherein the input audio signal includes a desired audio component and a feedback component (Par.[0026] microphone #204 receives input audio signals #205, which includes both feedback signals #210 from speaker #206 and desired ambient audio signals available to the user of hearing aid #100); and
reducing the feedback component by applying a machine learning model to the input audio signal to determine an output audio signal (Par.[0026-0027] a machine learning model encompassed by neural network #250 may be trained to control acoustic feedback cancellation on the input signal).
Fitz discloses wherein the apparatus comprises processing electronics #208 (“amplification circuit”) for processing and amplifying the compensated signal #212 to generate the output signal #207; see Par.[0026]; however does not disclose expressly wherein the machine learning model is configured to generate a cancellation signal to cancel nonlinearities created by the amplification circuit.
Kof discloses the use of a machine-learning model (fig.5 #208) to generate a cancellation signal to cancel nonlinearities created by an amplification circuit (fig.5 #117)(Par.[0028][0077] the pre-distortion signal generated by DPD ANN #201b is a cancellation signal to cancel nonlinearities generated by power amplifier #117).
It would have been obvious before the effective filing date of the present invention to a person of ordinary skill in the art to use the machine-learning model and pre-distorter of Kof in the apparatus of Fitz. The motivation for doing so would have been to compensate for signal distortions caused by the amplification circuit of Fitz.
With respect to claim 14, Fitz discloses the method of claim 12, further comprising:
amplifying (fig.2 #208) the output audio signal for output to a transducer (fig.2 #206), wherein reducing the feedback component comprises combining the input audio signal with a cancellation signal (fig.2 #211) generated by the machine learning model to determine the output audio signal prior to amplifying the output audio signal, (Par.[0026] the input audio signal and the cancellation signal are combined via a subtraction) and the machine learning model is configured to reduce the feedback component by reducing nonlinearities of the amplification circuit (See: 35 USC 103 Rejections under Kof Above).
With respect to claim 15, Fitz discloses the method of claim 12, further comprising:
determining a feedback cancellation signal (fig.2 #211) to reduce linear components of the feedback component of the input audio signal; combining the feedback cancellation signal with the input audio signal prior to reducing the feedback component by applying the machine learning model (Par.[0026] the input audio signal and the cancellation signal are combined via a subtraction); and amplifying the output audio signal to drive a transducer from the output audio signal (fig.2 #208; Par.[0026]), however does not disclose expressly wherein the machine learning model is configured to reduce the feedback component by reducing nonlinearities of the amplification circuit.
Kof discloses the use of a machine-learning model (fig.5 #208) to generate a cancellation signal to cancel nonlinearities created by an amplification circuit (fig.5 #117)(Par.[0028][0077] the pre-distortion signal generated by DPD ANN #201b is a cancellation signal to cancel nonlinearities generated by power amplifier #117).
It would have been obvious before the effective filing date of the present invention to a person of ordinary skill in the art to use the machine-learning model and pre-distorter of Kof in the apparatus of Fitz. The motivation for doing so would have been to compensate for signal distortions caused by the amplification circuit of Fitz.
With respect to claim 16, Fitz discloses the method of claim 15, wherein the method further comprises: amplifying the input audio signal after combining the feedback cancellation signal with the input audio signal and before reducing the feedback component by applying the machine learning model, wherein the machine learning model is configured to reduce the feedback component by reducing nonlinearities of amplifying the input audio signal (As shown in figure 2, amplification #208 is performed after combination of the input signal and feedback cancellation signal via subtraction, and before application of the machine learning model via OPM #230).
With respect to claim 17, Fitz discloses the method of claim 15, wherein the machine learning model is configured to reduce the feedback component based on input parameters relating to the feedback cancellation signal (Par.[0027]).
With respect to claim 19, Fitz discloses the method of claim 15 in view of Kof, wherein: the amplifying results in one or more artifacts resulting from the feedback component in the input audio signal, the one or more artifacts comprising howling, and the machine learning model is configured to reduce the one or more artifacts resulting from the amplifying without reducing other howling in the input audio signal (See Kof: [0028][0077]).
With respect to claim 21, Fitz discloses the apparatus of claim 1, wherein the machine learning model is configured to generate a feedback cancellation signal (fig.2 #211) separate from the input audio signal (fig.2 #212), and wherein the one or more processors are configured to combine the feedback cancellation signal with the input audio signal to reduce the feedback component (Par.[0026]).
With respect to claim 22, Fitz discloses the apparatus of claim 21 wherein the one or more processors are further configured to determine a second feedback cancellation signal using an adaptive filter to reduce linear components of the feedback component (See Fitz: fig.2 #202; Par.[0026]), and wherein the machine learning model is configured to reduce nonlinear components of the feedback component (See Kof: [0028][0077]).
With respect to claim 23, Fitz discloses the apparatus of claim 21 wherein the feedback cancellation signal generated by the machine learning model corresponds to predicted nonlinear distortion introduced by the amplification circuit (See Kof: [0028][0077]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON R KURR whose telephone number is (571)270-5981. The examiner can normally be reached M-F: 9-5.
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JASON R. KURR
Primary Examiner
Art Unit 2695
/JASON R KURR/ Primary Examiner, Art Unit 2695
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