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 .
Examiner’s Comments
individually and/or collectively, as recited in the claims, is to be read as any reasonable combination of digital processing modules and/or cores implementing the cited functions.
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 –
Claim(s) 1-4,6-13,15-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Simon et al (US 20260065895 A1).
As per claim 1, Simon discloses an electronic device (fig. 2) comprising:
at least one processor (the devices of fig 2 each require respective processors memory and software in order to function as shown) comprising processing circuitry; and
one or more memories storing instructions executable by at least one processor (required by the processors),
wherein, the at least one processor, individually and/or collectively, is configured to execute the instructions and to cause the electronic device to:
select speech data 404 (fig. 4), room impulse response data (per para 53: set of room impulse responses ), and noise data (402, fig. 4) from a training database,
determine a binaural input signal based on the selected speech data, the selected room impulse response data, and the selected noise data (the determination of the respective pairs of mixed signals 408 per para 54, for each of the left and right earpieces of the binaural hearing system of para 21 is a binaural input signal)
obtain a binaural output signal from a speech enhancement model based on a neural network with the binaural input signal as an input (the neural network, implemented as the machine learning algo per para 58),
determine a target binaural signal based on the selected speech data, the selected room impulse response data, and the selected noise data (any of the signals 408 that are used by the machine learning stage per para 58), and
update a parameter of the speech enhancement model, based on the obtained binaural output signal from the speech enhancement model and the determined target binaural signal (required as part of the learning of the machine learning).
As per claim 11, a method of operating an electronic device, the method comprising:
selecting speech data, room impulse response data, and noise data from a training database;
determining a binaural input signal based on the selected speech data, the selected room impulse response data, and the selected noise data;
obtaining a binaural output signal from a speech enhancement model based on a neural network with the binaural input signal as an input;
determining a target binaural signal based on the selected speech data, the selected room impulse response data, and the selected noise data; and
updating a parameter of the speech enhancement model, based on the obtained binaural output signal from the speech enhancement model and the determined target binaural signal. (per the claim 1 rejection).
As per claims 2,12, the electronic device of claim 1, wherein the at least one processor, individually and/or collectively, is configured to cause the electronic device to:
determine the target binaural signal based on the selected speech data, room impulse response data related to the selected speech data, the selected noise data, room impulse response data related to the selected noise data, and a noise suppression parameter related to a suppression degree of noise (as determined by the processor per the claim 1 rejection, in addition to the parameters used to reduce noise per para 13).
As per claims 3,13, the electronic device of claim 2, wherein the noise suppression parameter is randomly selected within a specified range of values (the noise reducing parameters cited above as implemented on the random access memory are randomly selected, where each digital value represents a range of values per the quantization inherent to digital signaling).
As per claims 4, the electronic device of claim 1, wherein the target binaural signal comprises a target binaural signal for a left channel and a target binaural signal for a right channel, wherein the target binaural signal for the left channel is determined based on room impulse response data for the left channel and noise data for the left channel, and the target binaural signal for the right channel is determined based on room impulse response data for the right channel and noise data for the right channel (the signals per the claim 1 rejection implemented per a pair of binaural hearing devices per para 21).
As per claims 6,15, the electronic device of claim 1, wherein the at least one processor, individually and/or collectively, is configured to cause the electronic device to:
determine a loss based on the obtained binaural output signal from the speech enhancement model and the determined target binaural signal (any of the parameters for the hearing loss profile per para 26) , and
update a parameter of the speech enhancement model, based on the loss (via the machine learning of the hearing loss profile used with the system described in the above claim rejections per para 26).
As per claims 7,16, the electronic device of claim 6, wherein the at least one processor, individually and/or collectively, is configured to cause the electronic device to:
update a parameter of the speech enhancement model (based on the machine learning), based on at least one loss of a speech-to-distortion ratio loss (the hearing system uses a hearing profile which can cause both a loss and gain to the ratio of the cited speech versus the cited noise) based on the obtained binaural output signal from the speech enhancement model and the determined target binaural signal (via the machine learning),
a loss based on an inter-channel level- difference (based on the binaural hearing system response of the left and right hearing devices), and
a loss based on an inter-channel time-difference (based on the binaural hearing system response and relationship of the left and right hearing device of the binaural hearing system).
As per claims 8,17, the electronic device of claim 1, wherein the speech enhancement model includes: a model configured to output the binaural output signal that reduces an intensity of a noise component while maintaining a directionality of the noise component included in the binaural input signal (per the noise reducing cited above, in combination with the background noise per para 12 and as cited above, which has a maintained directionality because it is background noise).
As per claims 9,18, the electronic device of claim 1, wherein the noise data comprises interference noise (the reduced noise cited above) data and diffuse noise (the background noise cited above) data.
As per claims 10,19, the electronic device of claim 9, wherein the room impulse response data comprises: room impulse response data from a sound image position of speech data to a binaural device and room impulse response data from the sound image position of the interference noise data to the binaural device (the speech and noise recordings from the impulse responses as cited above).
Allowable Subject Matter
Claims 5,14 are objected to as being dependent upon a rejected base claim, but would be allowable over the prior art of record if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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
July 23, 2026
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