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 Amendment
Claims 1, 3, 11, 16 and 18 are amended. Claims 1-20 are presented for examination.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 nonobviousness.
Claims 1-2, 5, 11-12 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Craven ( US 20130051579) and further in view of Caswell ( US 6009082) in view of Roy ( Source coding of audio signals with a generative model)
Regarding claim 1, Craven teaches an encoding method, comprising: determining a first whitened spectrum for to-be-encoded media data, the to-be- encoded media data is an audio signal (partially whiten signal, Para 0014) ; shaping the first whitened spectrum to obtain a second whitened spectrum ( feeding the partially whitened signal to a first adaptive whitening filter having coefficients that vary with time, the first adaptive whitening filter filtering the partially whitened signal with a time-varying response F.sub.1(z), Para 0016; adaptive whitening is a form of shaping; for a particular frequency regions, Para 0091, 0122) , wherein a spectral amplitude of the second whitened spectrum in a target frequency band is greater than or equal to a spectral amplitude of the first whitened spectrum in the target frequency band ( partial whitening gain is lesser than the full or additional whitening gain, Para 0088, frequency regions, Fig 3) , a dynamic range of statistical average energy distribution of the second whitened spectrum is smaller than a dynamic range of statistical average energy distribution of the first whitened spectrum (by definition whitening reduces the variation energy hence full or additional whiting will reduce that variation further) and processing the second whitened spectrum, encoding the whitened portion into a bitstream ( encoding the whitened signal, Para 0103, 0112, Fig 2-6)
Although its clear that partial whitening gain is less and further the whole point of whitening reduces variation in the distribution of the energy across frequency regions which obviously would mean a dynamic range of statistical average energy distribution of the second whitened spectrum is smaller than a dynamic range of statistical average energy distribution of the first whitened spectrum, but Craven does not explicitly mentions that
In the same field of endeavor Caswell teaches this idea that whitening will increase the amplitude ( increase in amplitude of high frequency component based on whitening – energy is analogous to amplitude ) and a dynamic range of statistical average energy distribution of the second whitened spectrum is smaller than a dynamic range of statistical average energy distribution of the first whitened spectrum ( The sub-blocks are first passed through a pre-emphasis stage which whitens the spectral content of the speech signal by balancing the extra energy in the low band with the reduced energy in the high band. The pre-emphasis essentially flattens the signal by reducing the dynamic range of the signal, Col 27, line 19-25)
It would have been obvious having the teachings of Craven to have the idea of Caswell, because that’s a definition and purpose of whitening By using pre-emphasis to flatten the dynamic range of the signal, less of a signal range is required for compression making the compression algorithm operate more efficiently ( Col 27, line 24-30, Caswell)
Craven does not explicitly teach processing the second whitened spectrum by using an encoding neural network model to obtain a first latent variable indicating a feature of the second whitened spectrum; and encoding the first latent variable into a bitstream
However, Roy teaches processing the whitened spectrum ( flatting, Under 3. SOURCE CODING WITH SAMPLERNN) by using an encoding neural network model ( generative encoder) to obtain a first latent variable ( f(.), Fig 2) indicating a feature of the second whitened spectrum ( On the encoder side, the MDCT coefficients are first spectrally flattened by F(·) according to the envelope E. The flattened MDCT lines are then quantized by a set of quantizers selected to fulfil a per-frame bitrate constraint, Under 3. SOURCE CODING WITH SAMPLERNN, Fig 2); and encoding the first latent variable into a bitstream ( bitstream is decoded at the decoder, 3. SOURCE CODING WITH SAMPLERNN)
It would have been obvious having the teachings of Fotopoulou to further modify with the concept of Roy before effective filing date since using SampleRNN as the generative model, it can be demonstrated that proposed coding structure provides performance competitive with state-of-the-art source coding tools for specific categories of audio signals ( Abstract, Roy)
Regarding claim 2, Craven as above in claim 1, teaches wherein the target frequency band is preset or determined based on the first whitened spectrum (frequency region, Para 0091)
Regarding claim 5, Craven as above in claim 1, teaches wherein the shaping the first whitened spectrum to obtain the second whitened spectrum comprises: shaping the first whitened spectrum based on a gain adjustment factor to obtain the second whitened spectrum (second whitening based on amplification measure, Para 0020)
Regarding claim 11, Craven teaches a decoding method, comprising: determining a reconstructed first whitened signal based on a bitstream of an audio signal ( Fig 2-6, decoder) ; processing the reconstructed first latent variable by using a decoding model to obtain a reconstructed second whitened spectrum ( The decoder similarly comprises a reconstruction filter whose coefficients are continually adjusted in dependence on the adaptive whitening filter within the decoder so that the reconstruction filter has a response that includes a factor F(z).sup.-n or F(z/.gamma.).sup.-n. The adjusting may be accomplished by using the same storage for the coefficients of the further filter or reconstruction filter as for the adaptive whitening filter, or alternatively by continually copying the coefficients of the adaptive whitening filter., Para 0046-0047) , a dynamic range of statistical average energy distribution of the reconstructed second whitened spectrum is smaller than a dynamic range of statistical average energy distribution of a first reconstructed whitened spectrum ( by definition the whitening reduces the dynamic range, Para 0009) ; adjusting the reconstructed second whitened spectrum to obtain a reconstructed first whitened spectrum, wherein a spectral amplitude of the reconstructed first whitened spectrum in a target frequency band is less than or equal to a spectral amplitude of the reconstructed second whitened spectrum in the target frequency band ( The present invention recognizes that the effect of the adaptive predictor in the decoder is to detect small deviations from of the spectrum of the received QIS 155b relative to a completely flat or `white` spectrum, and to amplify these deviations so that is the final decoded signal 152 has the same spectrum as the original signal 151, Para 0085, 0090-0091; increase in amplitude of high frequency component based on whitening – energy is analogous to amplitude) ; and determining reconstructed media data based on the reconstructed first whitened spectrum ( original signal, Para 0085)
Although its clear that partial whitening gain is less and further the whole point of whitening reduces variation in the distribution of the energy across frequency regions which obviously would mean a dynamic range of statistical average energy distribution of the second whitened spectrum is smaller than a dynamic range of statistical average energy distribution of the first whitened spectrum, but Craven does not explicitly mentions that
In the same field of endeavor Caswell teaches this idea that whitening will increase the amplitude ( increase in amplitude of high frequency component based on whitening – energy is analogous to amplitude ) and a dynamic range of statistical average energy distribution of the second whitened spectrum is smaller than a dynamic range of statistical average energy distribution of the first whitened spectrum ( The sub-blocks are first passed through a pre-emphasis stage which whitens the spectral content of the speech signal by balancing the extra energy in the low band with the reduced energy in the high band. The pre-emphasis essentially flattens the signal by reducing the dynamic range of the signal, Col 27, line 19-25)
It would have been obvious having the teachings of Craven to have the idea of Caswell, because that’s a definition and purpose of whitening By using pre-emphasis to flatten the dynamic range of the signal, less of a signal range is required for compression making the compression algorithm operate more efficiently ( Col 27, line 24-30, Caswell)
Craven does not teach determining a reconstructed first latent variable based on a bitstream and processing the reconstructed first latent variable by using a decoding neural network model to obtain a reconstructed second whitened spectrum
However, Roy teaches determining a reconstructed first latent variable based on a bitstream ( and processing the reconstructed first latent variable by using a decoding neural network model to obtain a reconstructed second whitened spectrum( On the decoder side, the MDCT lines are reconstructed in the flattened domain, and then the inverse spectral flattening F −1 (·) is applied. The inverse flattening is controlled by E, which is decoded from the bitstream along with quantized transform coefficients and the rate allocation parameter is offset, Under 3. SOURCE CODING WITH SAMPLERNN)
It would have been obvious having the teachings of Fotopoulou to further modify with the concept of Roy before effective filing date since using SampleRNN as the generative model, it can be demonstrated that proposed coding structure provides performance competitive with state-of-the-art source coding tools for specific categories of audio signals ( Abstract, Roy)
Regarding claim 12, Craven as above in claim 11, teaches wherein the adjusting the reconstructed second whitened spectrum to obtain the reconstructed first whitened spectrum comprises: adjusting the reconstructed second whitened spectrum based on a gain adjustment factor to obtain the reconstructed first whitened spectrum (second whitening based on amplification measure, Para 0020 ; and determining the original signal, Para 0085)
Regarding claim 16, arguments analogous to claim 1, are applicable.
Regarding claim 17, arguments analogous to claim 2, are applicable.
Regarding claim 18, arguments analogous to claim 1, are applicable.
Regarding claim 19, Craven as above in claim 1, teaches a non-transitory machine-readable storage medium, wherein the storage medium stores instructions, and when the instructions run on a computer, the computer is enabled to perform the steps of the method according to claim 1 ( fig 1)
Regarding claim 20, Craven as above in claim 1, teaches a non-transitory machine-readable storage medium, comprising the bitstream obtained in the encoding method according to claim 1 (fig 1)
Allowable Subject Matter
Claims 3-4, 6-10, 13-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Disch (US 20190198029) teaches – Applying the whitening operation. Whitening of a spectrum removes the coarse spectral envelope information and emphasizes the spectral fine structure which is of foremost interest for evaluating tile similarity. Therefore, a frequency tile on the one hand and/or the source signal on the other hand are whitened before calculating a cross correlation measure. When only the tile is whitened using a predefined procedure, a whitening flag is transmitted indicating to the decoder. For whitening the signal, first a spectral envelope estimate is calculated. Then, the MDCT spectrum is divided by the spectral envelope. The spectral envelope estimate can be estimated on the MDCT spectrum, the MDCT spectrum energies, the MDCT based complex power spectrum or power spectrum estimates. The signal on which the envelope is estimated will be called base signal from now on.
Philippe (US 20100250264) teaches – The whitening filter and shaping filter that shapes the whiten filter. It also teaches the gain the component. ( fig 2, Fig 0021-0030)
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