Notice of Pre-AIA or AIA Status
1. 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
2. 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.
3. Claims 1-5, 9-11, 14, 16-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Biswas (US 2024/0055006).
Regarding Claim 1:
Biswas discloses audio device configured to act as a receiver device, the audio device comprising an interface, an audio speaker, and a microphone, the audio device comprising one or more processors and a memory , the one or more processors comprising a decoder and a first signal processor (Biswas: ¶38 ¶48, ¶167-168 discloses a system with a decoder for generating enhanced audio data from low bitrate coded audio data, where the decoder receives audio from the receiver, , ¶175 discloses processors and memory and a device capable of receiving signal input), wherein the first signal processor is configured to operate according to a generative model for generative-based signal processing (Biswas: ¶56-57 teaches that the audio enhancer may be a generator including a generator trained in a GAN setting and that decoded raw audio data is input into the generator for enhancing the raw audio data), wherein the audio device is configured to:
- obtain an audio input signal from a transmitter device, where the audio input signal is an encoded audio input signal encoded based on one or more encoder parameters (Biswas: ¶135 teaches original audio data are core encoded to obtain encoded audio data and ¶137 discloses the encoded audio data and enhancement metadata are output for transmission to a decoder);
- decode the audio input signal using the decoder and one or more decoder parameters for provision of a decoder output signal (Biswas: ¶167 discloses decoding encoded audio data to obtain core decoded raw audio data, the core decoded raw audio data is the claimed decoder output signal, explicitly stating core decoder is configured to core decode the encoded audio data to obtain core decoded raw audio data);
- obtain codec information indicative of the one or more encoder parameters and/or the one or more decoder parameters (Biswas: ¶54 expressly teaches encoder generated metadata including bitstream parameters, bitrate, scale factor values and global fain. ¶91 also teaches metadata detected during encoding and transmitted via the low bitrate bitstream to the decoder); and
- process, using the first signal processor and based on the codec information, the decoder output signal for provision of a first signal processor output signal (Biswas: ¶53 teaches inputting core decoded raw audio data into an audio enhancer/Generator and processing it based on enhancement metadata. ¶53 teaches the indication of encoding quality is used to guide enhancement of raw audio data in a generator and ¶54 teaches bitstream parameters are used to guide enhancement of raw audio data in a generator. The output enhanced audio is the claimed first signal processor output signal), wherein to process the decoder output signal using the first signal processor comprises to process the decoder output signal using the first signal processor by applying the generative model for codec information-based processing (Biswas: ¶51 teaches decoded raw audio data is enhanced by a generator reduce coding artifacts, stating the audio enhancement may be guided by encoder generated metadata including encoder parameters and bitstream parameters. ¶54 says bitrate, scale factors and global gain may guide enhancement in the generator. ¶57 and ¶60 disclose GAN generator training and operation).
Regarding Claim 2:
Biswas further discloses the audio device according to claim 1, wherein the one or more processors comprise an audio conditioner configured to process the decoder output signal for provision of one or more audio features (Biswas, ¶67-68, ¶74 and ¶76 discloses decoded raw audio data obtained from the decoded low bitrate coded bitstream is input into a generator. The generator includes an encoder stage and decoder stage. The encoder stage processed the raw audio data through layers/filters, increasing depth and narrowing time width and produces a vector representation/coded audio feature. This encoder bottleneck portion as it is called reasonably maps to the claimed audio conditioner and the coded audio feature space vector representation maps to the claimed one or more audio features),
and wherein to process the decoder output signal using the first signal processor comprises to process the decoder output signal based on the one or more audio features (Biswas, ¶70-76 teaches that decoded audio data is modified between the generators encoder stage and decoder stage based on the coded audio feature space and the generators decoder stage up samples the representation to output enhanced audio data. Therefore, the generator processes the decoded raw audio based on the audio features generated by its encoder bottleneck feature space).
Regarding Claim 3:
Biswas further discloses the audio device according to claim 1, wherein the one or more processors comprise a codec conditioner configured to determine codec information, and wherein to obtain codec information comprises to determine the codec information using the codec conditioner and to provide the codec information to the first signal processor (Biswas: ¶167 discloses that the decoder includes receiver 301 configured to receive encoded audio data and enhancement metadata via a low bitrate audio bitstream and that receiver 301 may demultiplex the received bitstream into encoded audio data and enhancement metadata. The receiver/demultiplexer functionality corresponds to the claimed codex conditioner because it determines or extracts codec related metadata from the received bitstream. ¶167 further discloses that the receiver provides the enhancement metadata to audio enhancer 303 and that the audio enhancer generator processes the core decoded raw audio data based on the enhancement metadata. ¶51, 54 and 74 further teaches that metadata may include bitstream parameters such as bitrate, scale factor values and Global Fain and that such parameters guide enhancement of raw audio data in the generator).
Regarding Claim 4:
Biswas further discloses the audio device according to claim 1, wherein to obtain codec information comprises to obtain the codec information from the transmitter device (Biswas: ¶126 discloses that the enhancement metadata is encoder generated metadata for guiding audio enhancement by a decoder and may be transmitted via a bitstream together with encoded audio data, ¶167 further discloses outputting the encoded audio data and enhancement metadata to a decoder and that receiver 301 of decoder 300 receives the encoded audio data and enhancement metadata. ¶170 discloses that the enhancement metadata is transmitted via a bitstream of encoded audio data from the encoder to the decoder).
Regarding Claim 5:
Biswas further discloses the audio device according to claim 1, wherein the codec information comprises one or more of: a codec type, a sampling rate, and a bit rate, and wherein to process the decoder output signal using the first signal processor comprises to process the decoder output signal based on one or more of the codec type, the sampling rate, and the bit rate (Biswas: ¶54 discloses the metadata codec information includes bitstream parameters, scale factor values and global gain, *Note the claim recites comprises one or more of*).
Regarding Claim 9:
Biswas further discloses the audio device according to claim 1, wherein the audio device is configured to determine a processing scheme for processing the decoder output signal (Biswas: ¶16-17 determines enhancement metadata and candidate enhancement metadata for controlling the type and amount of decoder side enhancement. The metadata determined based on the enhanced audio and may be modified/repeated, that is a processing scheme for how the decoder side audio enhancer will process the decoded raw audio), wherein the processing scheme comprises a number of iterations of processing and/or a noise reduction parameter associated with each iteration of processing(Biswas: ¶30 discloses the enhancement control data may further include information on an amount of audio enhancement, ¶151 discloses the enhancement control data may further include information on an amount of audio enhancement amount (amount of content cleanup)),
and wherein to process the decoder output signal using the first signal processor comprises to process the decoder output signal based on the processing scheme (Biswas: ¶35 teaches the decoder receives encoded audio and enhancement metadata, core decodes the encoded audio to obtain core decoded raw audio data and inputs the raw audio into an audio enhancer for processing based on the enhancement metadata, ¶36 further teaches processing may be performed by applying one or more audio enhancement modules in accordance with the enhancement metadata, therefore this discloses processing the decoder output signal using the first signal processor based on the processing scheme).
Regarding Claim 10:
Biswas further discloses the audio device according to claim 1, wherein the first signal processor comprises a neural network being a multiresolution network, wherein the first signal processor comprises a signal processing encoder and a signal processing decoder, wherein to process the decoder output signal comprises to expand a number of channels and to reduce a time resolution of the decoder output signal using the signal processing encoder, and to reduce a number of channels and to expand a time resolution of the decoder output signal using the signal processing decoder (Biswas: ¶56-57 discloses that the generator (first signal processor) is a neural network trained in a GAN setting and includes fully convolutional encoder and decoder stages. ¶68 further discloses that the encoder stage increases the number of filters from N = 16 to N = 32 and later to N = 512 and that the filters operate with stride 2 such that “the depth gets larger as the width (duration of signal in time gets narrower,” performing learnable down-sampling. Therefore, Biswas discloses expanding the number of channels and reducing expanding the number of channels and reducing time resolution using the signal processing encoder. ¶70-76 discloses that the decoder mirrors the encoder layers and performing upsampling/transposed convolution “to increase the width of the audio signal to the full duration” with the architecture summary showing the decoder reducing filters from 512 to 32 and finally 16. Therefore, Biswas discloses reducing the number of channels and expanding time resolution using the signal processing decoder).
Regarding Claim 11:
Claim 11 has been analyzed with regard to claim 1 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 14:
Claim 14 has been analyzed with regard to claim 2 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 16:
Claim 16 has been analyzed with regard to claim 3 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 17:
Claim 17 has been analyzed with regard to claim 5 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 20:
Claim 20 has been analyzed with regard to claim 9 (see rejection above) and is rejected for the same reasons of anticipation used above.
Claim Rejections - 35 USC § 103
4. 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 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 nonobviousness.
5. Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas (US 2021/0327445).
Regarding Claim 6:
Biswas does not explicitly disclose the audio device according to claim 1, wherein the one or more processors comprise a second signal processor configured to operate according to a non-generative model for non-generative-based signal processing, the second signal processor being configured to process a second signal processor input signal for provision of a second signal processor output signal, wherein the second signal processor input signal is based on the decoder output signal, and wherein the audio device is configured to process the decoder output signal using the second signal processor (Biswas: Biswas discloses this as a separate implementation, specifically ¶8-10 discloses that conventional non-generative audio enhancement techniques were for low bitrate coded audio including noise shaping, decoder side pre echo reduction using codec parameters, spectral subtraction, automatic noise removal using adaptive filtering and de-noising Kalman filter. ¶36 and ¶164 further discloses that processing the core decoded raw audio data based on enhancement metadata may be performed by applying one or more audio enhancement modules in accordance with the enhancement metadata. The conventional enhancement module such as the automatic noise removal or Kalman filter based module corresponds to the claimed second signal processor configured to operate according to a non-generative model).
Biswas teaches a decoder side generator for processing core decoded raw audio data to reduce coding artifacts introduced by low bitrate coding. Biswas recognizes that low bitrate coded audio suffers from known artifacts and noise/degradation, including pre-echo noise, quantization noise, spectral holes and other quality degradation. Therefore, at the time of the invention, there was a recognized problem or need in the art to improve the quality of decoded low bitrate audio by reducing such artifacts and noise. Biswas also identifies a finite number of known and predictable solutions to that recognized problem. In particular, Biswas discusses known non-generative enhancement techniques including short block switching, temporal noise shaping, decoder side pre echo reduction using codec parameters, spectral subtraction, automatic noise removal using adaptive filtering and a de-noising Kalman filter. Biswas further teaches that processing core decoded raw audio data based on enhancement metadata may be performed by applying one or more audio enhancement modules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recognize that the decoder side enhancement system of Biswas would use as an additional audio enhancement module, one of the known non-generative audio enhancement techniques identified by Biswas. One of ordinary skill in the art would have reasonable expectation of success in pursuing this known solution because Biswas teaches that such conventional non-generative enhancement techniques were already used to improve low bitrate coded audio and reduce coding artifacts/noise at the decoder side. Therefore, it would have been obvious to configure the decoder of Biswas to include a second signal processor operating according to a non-generative model.
Regarding Claim 18:
Claim 18 has been analyzed with regard to claim 6 (see rejection above) and is rejected for the same reasons of anticipation used above.
6. Claims 7-8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas in view of Borgstrom (US 2021/0074282).
Regarding Claim 7:
Biswas further discloses the audio device according to claim 1, wherein the audio device is configured to determine signal- to-noise ratio information based on the decoder output signal, and wherein to process the decoder output signal comprises to process the decoder output signal based on the signal-to-noise ratio information.
However, Borgstrom discloses wherein the audio device is configured to determine signal- to-noise ratio information based on the decoder output signal (Borgstrom: ¶16 discloses a signal to noise ratio estimator configured to receive the initial spectrum and noise variance estimate and calculate a posteriori SNR and also estimate a priori SNR), and wherein to process the decoder output signal comprises to process the decoder output signal based on the signal-to-noise ratio information (Borgstrom: ¶17 discloses the gain estimator calculates a gain mask based on respective SNR and the output processor modifies the initial spectrum based on the gain mask, therefore teaching processing an audio signal based on SNR information, in combination the signal being processed would be Biswas’ decoded raw audio / decoder output signal).
Biswas and Borgstrom are combinable because they both disclose audio enhancement systems. It would have been obvious to one of ordinary skill in the art to determine signal to noise ration information from the core decoded raw audio data and process the core decoded raw audio data based on the SNR. As taught by Borgstrom, both Biswas and Borgstrom are directed to improving degraded audio by reducing noise and distortion. The motivation for doing so is it: “can provide significant improvements in objective speech quality measures, relative to baseline systems” as disclosed in ¶7 of Borgstrom.
Regarding Claim 8:
Biswas further renders obvious the audio device according to claim 6, wherein the audio device comprises a mixer configured to combine the first signal processor output signal and the second signal processor output signal for provision of an audio output signal (Biswas ¶36 and ¶164 discloses that processing the core decoded raw audio data may be performed by applying one or more audio enhancement modules in accordance with enhancement metadata. Biswas repeats that processing the core decoded raw audio data may be performed by applying one or more audio enhancement modules, but does not expressly disclose a mixer combining two separate outputs),
In view of Biswas, it would have been obvious to disclose the second signal process mixing with the first. Biswas discloses processing core decoded raw audio data using a decoder -side generator and audio enhancement, and also recognizes known non-generative techniques for improving low bitrate coded audio, including short block switching, temporal noise shaping, spectral subtraction, automatic noise removal using adaptive filtering and a de noising Kalman filter. Biswas further teaches that processing the core decoded raw audio data may be performed by “applying one or more audio enhancement modules in accordance with the enhancement metadata” as disclosed by ¶36 of Biswas. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include as one of Biswas’ audio enhancement modules, a conventional non-generative enhancement processor to further reduce known noise and coding artifacts in the core decoded raw audio data, because Biswas identifies a finite number of known, predictable enhancement techniques for the same recognized problem of improving decoded low bitrate audio quality and a person of ordinary skill in the art would have had a reasonable expectation of success using such known techniques for their intended purposes, simply mixing them would combine the intended benefits of both.
Biswas does not explicitly disclose or render obvious wherein to combine the first signal processor output signal and the second signal processor output signal is based on the signal-to-noise ratio information.
However, Borgstrom discloses wherein to combine the first signal processor output signal and the second signal processor output signal is based on the signal-to-noise ratio information (Borgstrom ¶16-17 teaches a signal to noise ratio estimator that receives the initial spectrum and noise variance estimate and calculates SNR information, further teaching a gain estimator calculates a gain mask based on respective SNRs and an output processor that modifies the initial spectrum based on the gain masks. This teaches using SNR information to control audio output processing).
It would have been obvious to one of ordinary skill in the art before the effective filing date in the claimed invention to combine generative and non-generative process output based on signal-to-noise ratio information. As discussed above, Biswas teaches applying one or more audio enhancement modules to core decoded raw audio data and it would have been obvious to provide both a generative enhancement processor and a non-generative enhancement processor. Borgstrom teaches using SNR information to control audio enhancement, including a gain estimator that calculates gain masks based on signal-to-noise ratio information to control audio enhancement, including a gain estimator that calculates gain masks based on signal to noise ratio, where “a strength of each gain mask can correspond to the value of the corresponding SNR” (see ¶17 of Borgstrom). Further teaching that this framework provides “more control over the tradeoff between noise suppression and speech quality” (see ¶58). Therefore, it would have been obvious to use Borgstrom’s SNR based gain information to determine the relative contribution of the generative and non-generative enhancement outputs in Biswas when producing a single audio output signal, because doing so would predictably control the strength of enhancement and noise suppression according to the noise condition of the decoded audio signal.
Regarding Claim 19:
Claim 19 has been analyzed with regard to claim 7 (see rejection above) and is rejected for the same reasons of anticipation used above.
7. Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas in view of Quillen (US 2021/0241780).
Regarding Claim 12:
Biswas discloses the computer-implemented method for training a generative model for generative-based signal processing, wherein the method comprises:
- obtaining an audio dataset comprising one or more audio signals (Biswas: ¶60 teaches training using original audio data x);
- generating a codec distorted audio dataset by encoding the one or more audio signals encoded based on one or more encoder parameters for provision of one or more encoded audio signals, and decoding the one or more encoded audio signals using a decoder and one or more decoder parameters for provision of one or more codec distorted audio signals (Biswas: ¶60 discloses a raw audio data x obtained from coding at a low bitrate and subsequently decoding original audio data x, this is directly the codec-distorted dataset: original audio -> encoded -> decoded -> codec/distorted raw audio);
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Biswas does not explicitly disclose
- combining the one or more audio signals with one or more white noise signals for provision of a white noise audio dataset comprising one or more white noise audio signals;
- determining, by applying the generative model to the one or more white noise audio signal and the one or more codec distorted audio signals, one or more estimated white noise signals and one or more estimated audio signals; and
- training the generative model based on one or more of: the one or more audio signals, the one or more estimated audio signals, the one or more estimated white noise signals, and the one or white noise signals.
However, Quillen specifically discloses:
- combining the one or more audio signals with one or more white noise signals for provision of a white noise audio dataset comprising one or more white noise audio signals (Quillen: ¶28 noisy speech is created synthetically adding noises to the clean speech, ¶30 further discloses it does so by using a random sample x drawn from uniform random white noise);
- determining, by applying the generative model to the one or more white noise audio signal and the one or more codec distorted audio signals, one or more estimated white noise signals and one or more estimated audio signals (Quillen: ¶24-25 discloses the network maps speech to uncorrelated white noise, ¶29-30 discloses the neural network learns mapping clean data to uncorrelated noise and inverse mapping to estimated clean data, ¶35 discloses inverse mapping produces an estimate of denoised speech samples, the forward mapping produces and learns the white noise representation and the inverse mapping produces estimated clean audio, i.e., estimated white noise signals and estimated audio signals); and
- training the generative model based on one or more of: the one or more audio signals, the one or more estimated audio signals, the one or more estimated white noise signals, and the one or white noise signals (Quillen: ¶29 teaches training maps clean data to uncorrelated noise conditioned on noisy data, ¶33 discloses training minimizes spectral distance between clean speech and de-noised speech, ¶38 spectral distance is calculated between clean speech and de-noised) speech. Altogether this teaches training a generative model based on clean speech, noisy speech, white noise and estimated de-noised speech).
Biswas and Quillen are from the same field of endeavor of audio/speech enhancement using generative neural network based processing to recover cleaner or enhanced audio from degraded audio signals. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Biswas’s training of a generative audio enhancement model using codec distorted audio data to further use Quillen’s normalizing flow training framework, including mapping clean speech to white noise and inversely mapping white noise conditioned on degraded speech to estimate clean speech because Quillen teaches a known generative training approach for de noising audio enhancement. One of ordinary skill in the art would have had a reasonable expectation of success because both references train neural networks to improve degraded audio quality and Quillen expressly motivates the modification by explaining that “applications utilizing speech data can benefit from increased speech data quality” and that its embodiments provide “improved methods and systems for enhancing the quality of speech data” in ¶2 of the specification.
Regarding Claim 13:
The proposed combination of Biswas and Quillen further discloses the method according to claim 12, wherein the method comprises:- obtaining codec information indicative of the one or more encoder parameters and/or the one or more decoder parameters (Biswas: ¶54 teaches metadata including encoder parameters and bitstream parameters. ¶57 and ¶60 teach the generator may be trained using metadata and ¶74 teaches bitstream encoder parameters may be input in the coded audio feature space so generation is conditioned on metadata).
8. Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over Biswas in view of Shuang (US 2023/0267947).
Regarding Claim 15:
Biswas further discloses the method according to claim 14, except wherein the one or more audio features comprise one or more of: a Bark parameter and a Mel parameter, and wherein processing the decoder output signal using the first signal processor comprises processing the decoder output signal based on one or more of the Bark parameter and the Mel parameter.
However, wherein the one or more audio features comprise one or more of: a Bark parameter and a Mel parameter, and wherein processing the decoder output signal using the first signal processor comprises processing the decoder output signal based on one or more of the Bark parameter and the Mel parameter (Shaung: ¶22 and ¶24 discloses band features generated according to Mel scale or Bark scale).
Biswas and Shuang are combinable because they are both directed to audio enhancement using neural network based processing of audio signals. Biswas teaches processing decoded raw audio using a generator, including feature processing within the generator to improve the audio quality of low bitrate coded audio. Shuang teaches generating audio band features from transform features and using those band features as input to a neural network for noise reduction, where the band features may be generated according to the Mel scale or Bark scale and may include Mel band energy or Bark band energy, MFCCS, or BFCCs. It would have been obvious to one of ordinary skill in the art to disclose u Shuang’s Mel and Bark scale features as the audio features in Biswas’s decoder neural audio enhancer because Mel/Bark features were known perceptual audio features for neural network based noise reduction and would predictably provide compact and meaningful inputs for processing decoded audio. Shuang teaches that the band features “reduce the dimensionality of the data input into the neural network ” in ¶27, thereby providing an express reason to use such features in Biswas’s neural network decoder.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN SCOTT MCLEAN whose telephone number is (703)756-4599. The examiner can normally be reached "Monday - Friday 8:00-5:00 EST, off Every 2nd Friday".
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
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