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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/11/2025, 12/19/2025, and 05/28/2026 is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Deng et al (US Pub No. 2023/0282225, hereinafter Deng).
Regarding claim 1, Deng teaches an electronic device (Fig 10, computer system 1000) comprising: memory storing instructions (Fig 10, memory 1006); and at least one processor (Fig 10, processor 1004); wherein the instructions, when executed by the at least one processor, cause the electronic device to: identify a first audio signal (Fig 4, input audio 402) comprising a plurality of audio sources (Fig 2, noise scenarios NS1-5); identify a first audio source, corresponding to at least one type, from the first audio signal (Figs 2 & 3, NS1); based on at least one configuration value corresponding to each of the at least one type, perform signal processing on the first audio source (Fig 3 & ¶ [0022], NR module 302 processes incoming audio based on configured characteristics); generate a second audio signal based on at least a portion of a remaining signal of the first audio signal excluding the first audio source, and the signal-processed first audio source (¶ [0023], NR module 302 including noise removal modules to suppress portions of the audio); and output the second audio signal (Fig 3, output).
Regarding claim 2, Deng teaches the electronic device of claim 1, wherein, to identify the first audio source, the instructions, when executed by the at least one processor, further cause the electronic device to: based on the first audio signal being input into a first artificial intelligence model (¶ [0023], artificial intelligence model), identify at least one second audio source included in the first audio signal; identify a type corresponding to each of the at least one second audio source; and identify the first audio source corresponding to the at least one type, wherein the first artificial intelligence model is trained such that a sum of a first output value of the remaining signal and a second output value of the at least one second audio source is equal to a third output value of the first audio signal (Figs 2 & 3, varied output based on incoming noise scenarios (NS)).
Regarding claim 3, Deng teaches the electronic device of claim 1, wherein, to perform signal processing on the first audio source, the instructions, when executed by the at least one processor, further cause the electronic device to: obtain a signal-processed audio source by performing the signal processing on the first audio source based on each of a filter and a first weight corresponding to the at least one type, and wherein the second audio signal is generated by mixing the at least a portion of the remaining signal and the signal-processed audio source (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 4, Deng teaches the electronic device of claim 3, wherein, to perform signal processing on the first audio source, the instructions, when executed by the at least one processor, further cause the electronic device to: based on identifying a first type corresponding to a first audio source from among the at least one second audio source, obtain a first filtered audio source by filtering the first audio source via a first filter corresponding to the first type; and perform the signal processing on the first filtered audio source based on a first weight corresponding to the first type and the first filtered audio source (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 5, Deng teaches the electronic device of claim 2, wherein the instructions, when executed by the at least one processor, further cause the electronic device to: based on identifying that a fourth output value of a third audio source from among the at least one second audio source is greater than or equal to the first value, identify a fourth type corresponding to the third audio source; and based on identifying that the fourth output value is less than the first value, identify the third audio source as the remaining signal (Fig 3 & ¶ [0022], NR module 302 processes incoming audio based on configured characteristics).
Regarding claim 6, Deng teaches the electronic device of claim 2, wherein the instructions, when executed by the at least one processor, further cause the electronic device to: based on the remaining signal being input into a second artificial intelligence model, identify a third audio source, from the remaining signal, corresponding to at least one noise type (Fig 4 & ¶ [0033], NVR module 304 detector AI model).
Regarding claim 7, Deng teaches the electronic device of claim 6, wherein the electronic device further comprises a display (Fig 10, display 1012), and wherein the instructions, when executed by the at least one processor, further cause the electronic device to: output, via the display, a user interface (UI) including information about the first audio source and information about the third audio source (¶ [0040], generated gain table of input audio 402).
Regarding claim 8, Deng teaches the electronic device of claim 1, wherein the instructions, when executed by the at least one processor, further cause the electronic device to: based on an output value of the first audio source from among the plurality of audio sources and an output value of the remaining signal, identify a first weight corresponding to a first type corresponding to the first audio source; and perform the signal processing on the first audio source based on the first weight (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 9, Deng teaches the electronic device of claim 1, wherein the instructions, when executed by the at least one processor, further cause the electronic device to: identify context information corresponding to video content, wherein the video content corresponds to the first audio signal (¶ [0024] audio signals NS1-NS5 captured in the context of online audio/video conferencing); and based on the context information, identify a first weight corresponding to the at least one type (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 10, Deng teaches the electronic device of claim 9, wherein the instructions, when executed by the at least one processor, further cause the electronic device to: identify a second type related to the video content based on the context information; and increase a second weight corresponding to the second type, and decrease a third weight corresponding to a third type different from the second type (¶ [0036], recorded audio clips used to generate a second group of audio recordings whose energy levels are modified used to simulate varying energy levels).
Regarding claim 11, Deng teaches the electronic device of claim 3, wherein the instructions, when executed by the at least one processor, further cause the electronic device to: update a plurality of weights corresponding to the at least one type respectively, based on a user input being received for changing at least one of the at least one configuration value corresponding to the at least one type; and perform the signal processing on the first audio sources , based on the updated plurality of weights (Fig 3 & ¶ [0020], configured characteristics and parameters including signal energy level).
Regarding claim 12, Deng teaches an electronic device (Fig 10, computer system 1000), the method comprising: identifying a first audio signal (Fig 4, input audio 402) comprising a plurality of audio sources (Fig 2, noise scenarios NS1-5); identifying a first audio source corresponding to at least one type from the first audio signal (Figs 2 & 3, NS1); based on at least one configuration value corresponding to each of the at least one type, signal-processing on the first audio source (Fig 3 & ¶ [0022], NR module 302 processes incoming audio based on configured characteristics); generating a second audio signal based on at least a portion of a remaining signal of the first audio signal, excluding the first audio source, and the signal-processed first audio sources (¶ [0023], NR module 302 including noise removal modules to suppress portions of the audio); and outputting the second audio signal (Fig 3, output).
Regarding claim 13, Deng teaches the method of claim 12, wherein the identifying the first audio source comprises: based on the first audio signal being input into a first artificial intelligence model (¶ [0023], artificial intelligence model), identifying at least one second audio source included in the first audio signal; and identifying a type corresponding to each of the at least one second audio source, and identifying the first audio source corresponding to the at least one type, wherein the first artificial intelligence model is trained such that a sum of a first output value of the remaining signal and a second output value of the at least one second audio source is equal to a third output value of the first audio signal (Figs 2 & 3, varied output based on incoming noise scenarios (NS)).
Regarding claim 14, Deng teaches the method of claim 12, wherein the performing signal processing comprises, each of a filter and the first weight, obtaining a signal-processed audio source by performing the signal-processing on the first audio source based on each of a filter and a first weight corresponding to the at least one type; and wherein the second audio signal is generated by mixing at least a portion of the remaining signal and the signal-processed audio source (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 15, Deng teaches the method of claim 14, wherein the performing the signal processing comprises: based on identifying a first type corresponding to a first audio source from among the at least one second audio source, obtaining a first filtered audio source by filtering the first audio source via a first filter corresponding to the first type; and processing the first filtered audio source, based on a first weight corresponding to the first type and the first filtered audio source (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 16, Deng teaches the method of claim 13, wherein the identifying the at least one second type comprises: based on identifying that a fourth output value of a third audio source from among the at least one second audio source is greater than or equal to a first value, identifying a fourth type corresponding to the third audio source; and based on identifying that the fourth output value is less than the first value, identifying the third audio source as the remaining signal (Fig 3 & ¶ [0022], NR module 302 processes incoming audio based on configured characteristics).
Regarding claim 17, Deng teaches the method of claim 13, further comprising, based on the remaining signal being input into a second artificial intelligence model, identifying a third audio source, from the remaining signal, corresponding to at least one noise type (¶ [0031], NVR module 304 AI-based detector).
Regarding claim 18, Deng teaches the method of claim 17, further comprising outputting, via a display of the electronic device, a user interface (UI) comprising information about the first audio source and information about the third audio source (¶ [0040], generated gain table of input audio 402).
Regarding claim 19, Deng teaches the method of claim 12, wherein performing the signal processing comprises: based on an output value of the first audio source from among the plurality of audio sources and an output value of the remaining signal, identifying a first weight corresponding to a first type corresponding to the first audio source; and processing the first audio source based on the first weight (¶ [0023], energy level thresholds used to filter low energy (weight) audio signals).
Regarding claim 20, Deng teaches a non-transitory computer readable medium storing instructions (Fig 10, memory 1006) that, when executed by at least one processor of an electronic device (Fig 10, processor 1004), cause the electronic device to: identify a first audio signal (Fig 4, input audio 402) comprising a plurality of audio sources (Fig 2, noise scenarios NS1-5); identify a first audio source, corresponding to at least one type, from the first audio signal (Figs 2 & 3, NS1); based on at least one configuration value corresponding to each of the at least one type, perform signal processing on the first audio source (Fig 3 & ¶ [0022], NR module 302 processes incoming audio based on configured characteristics); generate a second audio signal based on at least a portion of a remaining signal of the first audio signal excluding the first audio source, and the signal-processed first audio source (¶ [0023], NR module 302 including noise removal modules to suppress portions of the audio); and output the second audio signal (Fig 3, output).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wichern et al (US Pub No. 2024/0194213) teaches audio source separation using hyperbolic embeddings.
Mosayyebpour Kaskari (US Pub No. 2024/0371386) teaches audio source separation based on voice activity.
Johnson et al (US Pub No. 2025/0037705) teaches a beamformer capturing audio from different sources for analysis, categorization and output generation.
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/T.M.L./Examiner, Art Unit 2694
/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692
