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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 102a2 as being anticipated by Bastyr et al (US 20230335105 A1).
As per claim 1, Bastyr discloses an active noise control method performed by an active noise control apparatus, the active noise control method comprising:
detecting noise of a noise source by a reference microphone (sensors 104 can be microphones per para 190: a microphone may be used in place of a vibration sensor to output the noise signal X(n) indicative of noise generated from the interaction of the wheel 116 and the road surface 118.);
predicting the detected noise that propagates through a primary path from the noise source to a specific position and reaches the specific position by a noise prediction unit (para 19: a modeled transfer characteristic S′(z), which estimates the secondary path (i.e., the transfer function between an anti-noise speaker 110 and a physical microphone 108), by a secondary path filter 120);
generating a cancellation sound for noise control by a noise control filter using the predicted noise and a predetermined coefficient (para 21 anti noise signal, via the coefficients implementing stage 120);
emitting the generated cancellation sound by a secondary sound source (speaker 110);
estimating the predicted noise that propagates through a secondary path from the secondary sound source to the specific position and reaches the specific position by a secondary path model (the model implemented in stage 126,128);
detecting an interference sound between the noise of the noise source that propagates through the primary path and reaches the specific position and the emitted cancellation sound that propagates through the secondary path and reaches the specific position, by an error microphone disposed at the specific position (error microphone 108); and
updating the predetermined coefficient (via 128) used by the noise control filter, by a coefficient update unit using the estimated noise and the detected interference sound as an input (via inputs to 128).
As per claim 2, the active noise control method according to claim 1, wherein the noise prediction unit is configured by linear convolution (stages 126,128 comprise linear convolution as part of an updated lms algorithm via the feeback error microphone.
As per claim 3, the active noise control method according to claim 1, wherein the noise prediction unit is configured by a learned model trained by a neural network (the noise cancellation is configured based on sensors that detect occupant positions per para 24,28, where the occupant positions are based on a neural network per para 26).
As per claim 4, the active noise control method according to claim 3, wherein the learned model has been learned by a noise prediction learning method using a noise prediction learning apparatus, and the noise prediction learning method performed by the noise prediction learning apparatus includes:
outputting a stored noise prediction parameter by a parameter storage unit (any of the parameters used to create the output audio signal),
predicting a signal sequence received by propagating through the primary path and reaching a specific position, by a prediction model unit using the output noise prediction parameter (any of the parameters that are updated via the lms algorithm that are used to generate the output audio based on the modelling of S(z) and P(z) in order to update the parameters to create the speaker signal in order to drive the error signal lower),
calculating an objective function by an objective function calculation unit using the predicted signal sequence and a signal sequence being correct data, as an input (the modelling of the filters to model the paths S(z) P(z) comprise objective functions), and
by a parameter update unit, updating the noise prediction parameter stored in the parameter storage unit in a case where it is determined that the calculated objective function does not satisfy a predetermined condition (if the lms algorithm is not converged, the parameter used to create the output audio signal are updated by a stored prediction parameter/stepsize based on the error signal), and
outputting, as the learned model, the prediction model unit having a current noise prediction parameter in a case where it is determined that the calculated objective function satisfies the predetermined condition (once the error signal is below a certain level and the filter has converged, the filter parameters are not updated and are instead output as the learned/converged model as is part of the LMS algorithm).
As per claim 5, An active noise control method performed by an active noise control apparatus, the active noise control method comprising:
predicting noise that propagates through a primary path from a noise source to a specific position and reaches the specific position, from noise received as an input, by a noise prediction unit (128, via 120);
generating a cancellation sound for noise control by a noise control filter using the predicted noise and a predetermined coefficient (any of the parameters updated by the LMS used in order to produce the sound from speaker 110 in fig. 1)
emitting the generated cancellation sound by a secondary sound source (110);
estimating the predicted noise that propagates through a secondary path from the secondary sound source to the specific position and reaches the specific position by a first secondary path model (modelling of S(z) and P(z) );
detecting an interference sound between the noise that propagates through the primary path and reaches the specific position and the emitted cancellation sound that propagates through the secondary path and reaches the specific position by an error microphone disposed at the specific position *(per claim 1 rejection;
updating the predetermined coefficient used by the noise control filter, by a coefficient update unit using the estimated noise and the detected interference sound as an input ( per the claim 1 rejection);
estimating the generated cancellation sound that propagates through the secondary path and reaches the specific position by a second secondary path model (via the error signal and modelling of S(z) and P(z) ) ; and
generating the noise received as the input by the noise prediction unit, by a reference sound generation unit using the noise estimated by the second secondary path model and the interference sound detected by the error microphone as an input (via 120,126,128).
As per claim 6, an active noise control apparatus comprising:
a reference microphone that detects noise from a noise source (per claim 1 rejection);
a noise prediction circuitry that’s configured to predict the detected noise that propagates through a primary path from the noise source to a specific position and reaches the specific position (per claim 1 rejection);
a noise control filter that generates a cancellation sound for noise control by using the predicted noise and a predetermined coefficient (per claim 1 rejection);
a secondary sound source that emits the generated cancellation sound (per claim 1 rejection);
a secondary path model that estimates the predicted noise that propagates through a secondary path from the secondary sound source to the specific position and reaches the specific position (per claim 1 rejection);
an error microphone that is disposed at the specific position and detects an interference sound between the noise of the noise source, which propagates through the primary path and reaches the specific position, and the emitted cancellation sound that propagates through the secondary path and reaches the specific position (per claim 1 rejection); and
a coefficient update circuitry configured to update the predetermined coefficient used by the noise control filter, by using the estimated noise and the detected interference sound as an input (per claim 1 rejection).
As per claim 7, A non- transitory computer-readable recording medium on which a program for causing a computer to function as the active noise control method according to claim 1.(the system of the claim 1 rejection requires software on memory to be implemented).
As per claim 8, a non-transitory computer-readable recording medium on which a program for causing a computer to function as the active noise control method according to claim 2 .(the system of the claim 2 rejection requires software on memory to be implemented)..
As per claim 9, a non-transitory computer-readable recording medium on which a program for causing a computer to function as the active noise control method according to claim 3. .(the system of the claim 3 rejection requires software on memory to be implemented).
As per claim 10, a non-transitory computer-readable recording medium on which a program for causing a computer to function as the active noise control method according to claim 4 (the system of the claim 4 rejection requires software on memory to be implemented).
As per claim 11, a non-transitory computer-readable recording medium on which a program for causing a computer to function as the active noise control method according to claim 5 (the system of the claim 5 rejection requires software on memory to be implemented).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER KRZYSTAN whose telephone number is 571-272-7498, and whose email address is alexander.krzystan@uspto.gov
The examiner can usually be reached on m-f 7:30-4:00 est.
If attempts to reach the examiner by telephone or email are unsuccessful, the examiner’s supervisor, Carolyn Edwards can be reached on (571) 270-7136.
The fax phone numbers for the organization where this application or proceeding is assigned are 571-273-8300 for regular communications and 571-273-8300 for After Final communications.
/ALEXANDER KRZYSTAN/Primary Examiner, Art Unit 2653
Examiner Alexander Krzystan
August 27, 2026
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