DETAILED ACTION
This examination is in response to the communication filed on 03/09/2026. Claims 1-20 are currently pending, wherein claims 1-9, 15, 17, and 20 have been amended.
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 Amendments/Arguments
Applicant’s amendments/arguments, filed 03/09/2026, with respect to the objection of claims 1, 3-7, 15, and 17-20 have been fully considered and are persuasive. The claim objection has been withdrawn.
Applicant’s amendments/arguments with respect to the rejection of claims 1, 2, 9-10, and 15-16 under §101 have been fully considered and are persuasive. The rejection under §101 has been withdrawn.
Applicant's arguments with respect to the rejection of claims 1-20 have been fully considered but they are not persuasive. Applicant argues on page 10 to page 11 of the Response that independent claim 1, and for similar reasons independent claims 9 and 15, are allowable over Fejzo because Fejzo fails to disclose “obtaining first audio of speech and reference audio of a first environment, and using one or more neural networks to filter features generated from the first audio of speech based, at least in part, on the reference audio”. More specifically, Applicant argues that using a neural network to generate key parameters that are used to transform an input audio signal to output an audio signal that has target audio characteristics as taught by Fejzo is not equivalent to “obtaining” the first speech audio and reference audio and “filtering features” as recited in amended independent claims 1, 9, and 15.
The Examiner, respectfully disagrees. First, under a broadest reasonable interpretation “obtaining” including receiving. Therefore, Fejzo’s teaching of receiving the input speech signal and target signal is equivalent to obtaining the first and reference signals as claimed. Second, under a broadest reasonable interpretation “filtering features” includes determining feature desired features. This interpretation is supported by specification ¶[0054] which describes the filtering as determining which spectral portions are more or less important. Therefore, using a neural network to determine key parameters for transforming an input speech signal to match reference signal as taught by Fejzo is equivalent to using a neural network to filter features as recited independent claim 1, 9 and 15.
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)(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.
Claims 1-3, 5-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fejzo et al. (WO 2022/025922 A1; herein “Fejzo”).
Regarding claims 1, 9 and 15, Fejzo teaches one or more processors (Fig. 9, processor 916), comprising circuitry, a method (Fig. 8) and system comprising one or more processors (Fig. 9, processor 916) to:
obtain first audio of speech and reference audio of a first environment (Fig. 8, step 802 teaches the system “receive[s] input audio and target audio having target audio characteristics”; under a broadest reasonable interpretation, obtaining includes receiving and the input audio is interpreted as first audio of speech and the target audio is interpreted as a reference audio); and
use one or more neural networks (Fig. 1, Key Generator Model 102 and Audio Synthesis Model 104 ) to filter features (under a broadest reasonable interpretation “filter features” is interpreted as determining desired features; this interpretation is supported the specification in ¶[0054] which describes the filtering as determining which spectral portions are more or less important; Fig. 8 step 804 teaches using a first neural network (e.g., key generator ML model) trained to generate key parameters which are important) generated from the first audio of speech (Fig. 1, Output Signal) based, at least in part, on the reference audio (Fig. 1, target signal) to generate second audio of speech of a second environment (¶[0018] teaches “System 100 includes trained key generator ML model 102…and a trained audio synthesis ML model 104…key generator 102 receives key generation data that may include at least an input signal and/or a target or desired signal…generates a set of transform parameters KP…Audio synthesizer 104 performs a desired signal transformation of the input signal based on key parameters KP, to produce an output signal having an output signal characteristic similar to or that matches the desired/target signal characteristic of the target signal”).
Regarding claims 2, 10 and 16, Fejzo teaches all of the elements of claims 1, 9 and 15 (see detailed element mapping above). In addition, Fejzo further teaches the reference audio includes one or more speech signals (¶[0023] teaches “In the audio context, the target signal may be a speech or audio signal”).
Regarding claims 3, 11 and 17, Fejzo teaches all of the elements of claims 1, 9 and 15 (see detailed element mapping above). In addition, Fejzo further teaches the one or more circuits are further to use the one or more neural networks to generate one or more spectral features based, at least in part, on the second audio of speech (¶[0018] teaches “Key parameters KP parameterize or represent a desired/target signal characteristic of the target signal, such as a spectral/frequency-based characteristic…” ).
Regarding claims 5, 13 and 19, Fejzo teaches all of the elements of claims 1, 9 and 15 (see detailed element mapping above). In addition, Fejzo further teaches the one or more circuits are further to use the one or more neural networks to generate context information based, at least in part, on the reference audio of the second environment (Based on ¶[0079] of the Specification, context information is interpreted as information that “indicates desired characteristics of a high-quality audio”; ¶[0018] teaches “Key parameters KP parameterize or represent a desired/target signal characteristic of the target signal, such as a spectral/frequency-based characteristic…” and ¶[0015] teaches “given a low resolution/bandwidth representation and a high resolution/bandwidth representation of an audio signal, the key generator ML model generates a size-constrained set of metadata, e.g., key parameters, for guiding the audio synthesis ML model.” Thus the target/desired signal from which the key parameters are generated is a higher resolution/quality signal).
Regarding claims 6 and 20, Fejzo teaches all of the elements of claims 1 and 15 (see detailed element mapping above). In addition, Fejzo further teaches the one or more circuits are further to use the one or more neural networks to fuse one or more spectral features and one or more waveform features (¶[0039] teaches “Examples of target key parameters include a line spectral frequency, (LSF) key, a harmonic key, and a temporal envelope key, as described below…such that the key generator learns to generate key parameters KPT that approximate the target key parameters” Thus, Fejzo teaches fusing/combining both spectral (e.g., LSF) and waveform (e.g., temporal envelope) features).
Regarding claims 7 and 14, Fejzo teaches all of the elements of claims 1, 9 (see detailed element mapping above). In addition, Fejzo further teaches the one or more circuits are further to use the one or more neural networks to modify one or more different features generated from the second audio of speech based, at least in part, on the reference audio of a second environment (¶[0021] teaches “Thus, as the desired/target signal characteristics dynamically vary from frame-to-frame and the generator key parameters that represent the desired/target signal characteristics correspondingly vary from frame-to-frame, the key-guided signal transformation will correspondingly vary frame-by-frame to cause the output frames to have signal characteristics that track those of the target frames”).
Regarding claim 8, Fejzo teaches all of the elements of claim 1 (see detailed element mapping above). In addition, Fejzo further teaches the one or more circuits are further to determine a down sampling rate to perform down sampling of the second audio of speech (¶[0024] teaches “…the input signal and the target signal may be pre-processed to produce…a pre-processed target signal…Example pre-processing operations that may be performed…include one or more of: resampling (e.g., down-sampling or up-sampling)…”)
Regarding claim 12, Fejzo teaches all of the elements of claim 9 (see detailed element mapping above). In addition, Fejzo further teaches generating, using the one or more neural networks, one or more waveform features based, at least in part, on the second audio of speech (¶[0018] teaches “Key parameters KP parameterize or represent a desired/target signal characteristic of the target signal, such as … temporal/time-based characteristic of the target signal”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fejzo as applied to claims 1 and 15 above, and further in view of Buskies et al. (US 2015/0013528 A1; herein “Buskies”).
Regarding claims 4 and 18, Fejzo teaches all of the elements of claims 1 and 15 (see detailed element mapping above). In addition, Fejzo further teaches the one or more circuits are further to use the one or more neural networks to generate one or more waveform features based, at least in part, on the second audio of speech (¶[0018] teaches “Key parameters KP parameterize or represent a desired/target signal characteristic of the target signal, such as … temporal/time-based characteristic of the target signal”). In addition, ¶[0039] of Fejzo teaches fusing/combining spectral and waveform features. However, Fejzo fails to specifically disclose the one or more waveform features include phase information.
Buskies teaches a system and method for audio signal enhancement/processing that analyzes an audio signal waveform to determine basic accent patterns in the waveform. More specifically, Buskies teaches analyzing the waveform characteristics, including “audio peaks, patterns, frequency characteristics, phase characteristics, timing characteristics (e.g., tempo), and the like” (Buskies, ¶[0113]). In addition, Buskies teaches the one or more waveform features include phase information (¶[0111] teaches “The various types of audio waveforms and their pertinent characteristics (e.g., frequency, amplitude, phase etc.) would be understood by those of ordinary skill in the art”).
Fejzo differs for the claimed invention, as defined by claims 4 and 18, in that Fejzo fails to explicitly disclose that the temporal/time-based characteristic of the target signal includes phase information. Phase information is a well-known pertinent characteristic of audio waveform signals as taught in Buskies. Therefore, it would have been obvious to one having ordinary skill in the art to modify the temporal/time-based key parameters extracted for the target signal as taught by Fejzo to include phase information as suggested by Buskies as it merely constitutes the combination of known processes to achieve the predictable result to determining pertinent characteristics of the target audio signal for audio enhancement.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PENNY L CAUDLE whose telephone number is (703)756-1432. The examiner can normally be reached M-Th 8:00 am to 5:00 pm eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PENNY L CAUDLE/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657
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