DETAILED ACTION
This is responsive to the application filed 08 January 2025.
Claims 1-20 are pending and considered below.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the received audio signals" in line 14. There is insufficient antecedent basis for this limitation in the claim. Further, it is unclear what Applicant is trying to cover with “excluding the first audio signal among the received audio signals”. According to the specification ([0069]), the Examiner suggests amending the limitation to ‘excluding the first audio signal from the received audio signal’.
Independent claims 9 and 17 suffer from the same deficiency and are likewise rejected. The dependent claims are rejected for depending upon a rejected claim without providing a remedy.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art of record, Chen et al. (US 2023/0095526) discloses a method comprising: based on an audio signal (a mixture of speech) being received, acquiring a first audio signal (speech from the target speaker) by inputting information on a characteristic of a first user (the target speaker's voiceprint) set as a target speaker among a plurality of users (multiple speakers) and the received audio signal to an artificial intelligence model (a ML model) configured to acquire a voice signal of a user from an audio signal (“Target speaker separation model 178 provides system functionality for target speaker separation, which comprises separating speech from the target speaker from a mixture of speech from multiple speakers in an audio recording. In an embodiment, the input to the target speaker separation model 178 comprises an audio recording containing speech from multiple speakers, and the output comprises an audio recording with just the speech from the target speaker. In one embodiment, the target speaker separation model 178 uses the target speaker's voiceprint for target speaker separation. In some embodiments, the target speaker separation model 178 receives as input an audio frame. Target speaker separation model 178 may process a plurality of audio frames to generate a plurality of audio frames containing only the target speaker's speech. In an embodiment, target speaker separation model 178 may comprise a ML model, such as one or more neural networks, CNNs, DNNs, or other ML models. Target speaker separation model 178 may include one or more parameters, such as internal weights of a neural network, that may determine the operation of target speaker separation model 178”, [0051]).
Chen, individually or in combination with the prior art of record, does not disclose based on voice recognition based on the first audio signal failing, identifying a similarity between information on a characteristic of a second audio signal excluding the first audio signal among the received audio signals and information on characteristics of remaining users excluding the first user among the plurality of users; and changing the target speaker to a second user among the plurality of users.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wasserblat et al. (US 2006/0111904) discloses a method and apparatus for spotting a target speaker within a call interaction by generating speaker models based on one or more speaker's speech; and by searching for speaker models associated with one or more target speaker speech files.
Wang et al. (US 2020/0219517) discloses a method which includes receiving an utterance of speech and segmenting the utterance of speech into a plurality of segments. For each segment of the utterance of speech, the method also includes extracting a speaker-discriminative embedding from the segment and predicting a probability distribution over possible speakers for the segment using a probabilistic generative model configured to receive the extracted speaker-discriminative embedding as a feature input. The probabilistic generative model trained on a corpus of training speech utterances each segmented into a plurality of training segments. Each training segment including a corresponding speaker-discriminative embedding and a corresponding speaker label. The method also includes assigning a speaker label to each segment of the utterance of speech based on the probability distribution over possible speakers for the corresponding segment.
Picco et al. (US 2022/0179903) discloses a system where the voice of a target speaker can be separated from multi-speaker signals by making use of a reference signal from the target speaker for training two separate neural networks. The first network is a speaker recognition network that produces speaker-discriminative embeddings and the second network is a spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. The system may include two separately trained components: a speaker encoder and the voice filter which uses the output of the speaker encoder as an additional input. The purpose of the speaker encoder is to produce a speaker embedding from an audio sample of the target speaker. The voice filter system is a neural network that takes two inputs: a d-vector of the target speaker, and a magnitude spectrogram computed from a noisy audio.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
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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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/SAMUEL G NEWAY/Primary Examiner, Art Unit 2657
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