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 .
Introduction
This office action is in response to RCE filed 03/04/2026. Claims 1-13 are pending and likewise have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/04/2026(Amended claims filed 02/18/2026) has been entered.
Response to Amendment
Amendment filed 03/04/2026(using amended claims filed 02/18/2026) has been fully considered by Examiner.
Response to Arguments
Applicant’s arguments, see Remarks, Pg 8-9, filed 02/18/2026, with respect to the rejection(s) of claim(s) 1 8 and 10, under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Purnhagen et al. (US 20160155448 A1), and further in view of Sikora et al. (WO 2017129546 A1).
Applicant’s arguments with respect to claim(s) 2 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.
Claim(s) 1, 8 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable Purnhagen et al. (US 20160155448 A1), and further in view of Sikora et al. (WO 2017129546 A1).
Regarding Claim 1:
Purnhagen teaches a method for coding an original multichannel sound signal, the method being implemented by a coding device and comprising(Abstract, Ln 1-7, An audio encoder (200) configured to encode a frame of a soundfield signal (110) comprising a plurality of audio signals is described):
coding at least one audio signal channel originating from the original multichannel sound signal(Abstract, Ln 1-7, An audio encoder (200) configured to encode a frame of a soundfield signal (110) comprising a plurality of audio signals is described);
dividing the original multichannel sound signal into frequency sub-bands(Para [0021], Ln 1-6, configured to convert a frame of a soundfield signal into a plurality of sub-bands……… individual sub-bands may comprise a plurality of frequency bins);
determining a covariance matrix per frequency sub-band, representative of a spatial image of the original multichannel sound signal(Para [0077], Ln 1-7, inter-channel covariance matrix may be estimated using a covariance estimation unit 203. The estimation may be performed in the sub-band domain. Para [0068], Ln 9-11, parametric coding unit 104 may provide a set of spatial parameters which may be used to reconstruct the signals E2 and E3 from the decoded signal E1 114);
Purnhagen does not teach decomposing the determined covariance matrices into eigenvalues; and coding by quantizing both the eigenvalues and eigenvectors obtained from the decomposition into eigenvalues of the determined covariance matrices.
In the same field of audio coding, Sikora teaches decomposing the determined covariance matrices into eigenvalues(Pg 14, Ln 25 – Pg 15, Ln 4, coding the covariance matrices is to perform a Eigen- decomposition on the covariance matrix for each kernel, an example of a transform operator j according to init 120), followed by quantization and coding of the Eigenvectors and Eigenvalues);
and coding by quantizing both the eigenvalues and eigenvectors obtained from the decomposition into eigenvalues of the determined covariance matrices(Pg 14, Ln 25 – Pg 15, Ln 4, coding the covariance matrices is to perform a Eigen- decomposition on the covariance matrix for each kernel, an example of a transform operator j according to init 120), followed by quantization and coding of the Eigenvectors and Eigenvalues. With such an approach it is possible to reduce the number of bits/kernel significantly).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Purnhagen with the quantization method of Sikora, as it can reduce the number of bits per kernel(Pg 14, Ln 25 – Pg 15, Ln 4).
Regarding Claim 8:
Purnhagen teaches a coding device comprising: a processing circuit configured to code an original multichannel sound signal by(Abstract, Ln 1-7, An audio encoder (200) configured to encode a frame of a soundfield signal (110) comprising a plurality of audio signals is described):
coding at least one audio signal channel originating from the original multichannel sound signal(Abstract, Ln 1-7, An audio encoder (200) configured to encode a frame of a soundfield signal (110) comprising a plurality of audio signals is described);
dividing the original multichannel sound signal into frequency sub-bands(Para [0021], Ln 1-6, configured to convert a frame of a soundfield signal into a plurality of sub-bands……… individual sub-bands may comprise a plurality of frequency bins);
determining a covariance matrix per frequency sub-band, representative of a spatial image of the original multichannel sound signal(Para [0077], Ln 1-7, inter-channel covariance matrix may be estimated using a covariance estimation unit 203. The estimation may be performed in the sub-band domain. Para [0068], Ln 9-11, parametric coding unit 104 may provide a set of spatial parameters which may be used to reconstruct the signals E2 and E3 from the decoded signal E1 114);
Purnhagen does not teach decomposing the determined covariance matrices into eigenvalues; and coding by quantizing both the eigenvalues and eigenvectors obtained from the decomposition into eigenvalues of the determined covariance matrices.
In the same field of audio coding, Sikora teaches decomposing the determined covariance matrices into eigenvalues(Pg 14, Ln 25 – Pg 15, Ln 4, coding the covariance matrices is to perform a Eigen- decomposition on the covariance matrix for each kernel, an example of a transform operator j according to init 120), followed by quantization and coding of the Eigenvectors and Eigenvalues);
and coding by quantizing both the eigenvalues and eigenvectors obtained from the decomposition into eigenvalues of the determined covariance matrices(Pg 14, Ln 25 – Pg 15, Ln 4, coding the covariance matrices is to perform a Eigen- decomposition on the covariance matrix for each kernel, an example of a transform operator j according to init 120), followed by quantization and coding of the Eigenvectors and Eigenvalues. With such an approach it is possible to reduce the number of bits/kernel significantly).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Purnhagen with the quantization method of Sikora, as it can reduce the number of bits per kernel(Pg 14, Ln 25 – Pg 15, Ln 4).
Regarding Claim 10:
Purnhagen teaches a non-transitory computer readable storage medium, storing a computer program comprising instructions for executing a method of coding an original multichannel sound signal when the instructions are executed by a processing circuit of a coding device, wherein the method comprises(Abstract, Ln 1-7, An audio encoder (200) configured to encode a frame of a soundfield signal (110) comprising a plurality of audio signals is described. Para [0045], Ln 1-5, According to another aspect, a storage medium is described. The storage medium may comprise a software program adapted for execution on a processor and for performing the method steps outlined in the present document when carried out on the processor):
coding at least one audio signal channel originating from the original multichannel sound signal(Abstract, Ln 1-7, An audio encoder (200) configured to encode a frame of a soundfield signal (110) comprising a plurality of audio signals is described);
dividing the original multichannel sound signal into frequency sub-bands(Para [0021], Ln 1-6, configured to convert a frame of a soundfield signal into a plurality of sub-bands……… individual sub-bands may comprise a plurality of frequency bins);
determining a covariance matrix per frequency sub-band, representative of a spatial image of the original multichannel sound signal(Para [0077], Ln 1-7, inter-channel covariance matrix may be estimated using a covariance estimation unit 203. The estimation may be performed in the sub-band domain. Para [0068], Ln 9-11, parametric coding unit 104 may provide a set of spatial parameters which may be used to reconstruct the signals E2 and E3 from the decoded signal E1 114);
Purnhagen does not teach decomposing the determined covariance matrices into eigenvalues; and coding by quantizing both the eigenvalues and eigenvectors obtained from the decomposition into eigenvalues of the determined covariance matrices.
In the same field of audio coding, Sikora teaches decomposing the determined covariance matrices into eigenvalues(Pg 14, Ln 25 – Pg 15, Ln 4, coding the covariance matrices is to perform a Eigen- decomposition on the covariance matrix for each kernel, an example of a transform operator j according to init 120), followed by quantization and coding of the Eigenvectors and Eigenvalues);
and coding by quantizing both the eigenvalues and eigenvectors obtained from the decomposition into eigenvalues of the determined covariance matrices(Pg 14, Ln 25 – Pg 15, Ln 4, coding the covariance matrices is to perform a Eigen- decomposition on the covariance matrix for each kernel, an example of a transform operator j according to init 120), followed by quantization and coding of the Eigenvectors and Eigenvalues. With such an approach it is possible to reduce the number of bits/kernel significantly).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Purnhagen with the quantization method of Sikora, as it can reduce the number of bits per kernel(Pg 14, Ln 25 – Pg 15, Ln 4).
Regarding Claim 11:
The combination of Purnhagen and Sikora teaches the coding device as claimed in claim 8, and Purnhagen teaches wherein the processing circuit comprises: a processor; and a non-transitory computer readable medium comprising instructions stored thereon which when executed by the processor configure the coding device to code the multichannel sound signal(Para [0045], Ln 1-5, According to another aspect, a storage medium is described. The storage medium may comprise a software program adapted for execution on a processor and for performing the method steps outlined in the present document when carried out on the processor).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Purnhagen and Sikora as applied to claim 1 above, and further in view of Kim et al. (KR-20110008966-A), and further in view of Swaminathan et al. (US 5751903 A).
Regarding Claim 2:
The combination of Purnhagen and Sikora teaches the method as claimed in claim 1, but does not teach wherein the eigenvalues are ordered before quantization and the quantizing is performed by a differential scalar quantization.
In the same field of audio coding Kim teaches wherein the eigenvalues are ordered before quantization(Pg 6, Para 1, Ln 1-2, Along the diagonal (diagonal) covariance matrix of the matrix ordered eigenvalues sub.i. It may be obtained by arranging the matrix. Pg 6, Para 6, Ln 1-3, Mass function estimator 200 is the covariance matrix via calculated eigenvalue matrix on the base by an encoder-side quantization unit 300).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Purnhagen and Sikora with the audio quantization method of Kim as it can help improve audio quality(Pg 12, Para 2, Ln 1-4, & Para 3, Ln 1-3).
The combination of Purnhagen, Sikora and Kim does not specifically teach and the quantizing is performed by a differential scalar quantization.
In the same field of Audio Coding, Swaminathan teaches and the quantizing is performed by a differential scalar quantization(Col 10, Ln 48-50, differential quantization is used for the scalar quantized LSFs in a Mode A signal frame. Col 3, Ln 50-55, illustrates the differential scalar quantization of the previously scalar quantized LSFs in a Mode A frame).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Purnhagen, Sikora and Kim with the quantization techniques of Swaminathan as it helps improve efficiency and quality of the speech coding(Col 3, Ln 15-22).
Allowable Subject Matter
Claims 3-6 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.
Claims 7, 9 and 12-13 are allowable.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 3:
Purnhagen teaches the method as claimed in claim 1, wherein the covariance matrix is decomposed into eigenvalues using the following steps: obtaining a matrix of eigenvectors Q such that C=Q Λ QT,( Para [0077], Ln 1-7, inter-channel covariance matrix may be estimated using a covariance estimation unit 203. The estimation may be performed in the sub-band domain. Para [0068], Ln 9-11, parametric coding unit 104 may provide a set of spatial parameters which may be used to reconstruct the signals E2 and E3 from the decoded signal E1 114)
where C is the covariance matrix and Λ = diag(λ1,..., λK) is a diagonal matrix of eigenvalues(Para [0078], Ln 1-6, decompose the inter-channel covariance matrix by means of an eigenvalue decomposition (EVD) yielding an orthonormal transformation V that diagonalizes the covariance matrix);
Purnhagen does not teach modifying the matrix of eigenvectors as a function of a determinant value of the matrix of eigenvectors Q; converting the matrix of eigenvectors Q into the domain of generalized Euler angles; the generalized Euler angles that are obtained forming part of the parameters to be quantized.
Briand et al. (KR 20080104065 A), does teach a calculation of Euler angles for each subband based on eigenvalues and the covariance(Pg 12, Para 13, Ln 1-4), but does not teach modifying the matrix of eigenvectors as a function of a determinant value of the matrix of eigenvectors Q and converting the matrix of eigenvectors Q into the domain of generalized Euler angles.
The prior art of record alone or in combination does not teach the above limitations.
Regarding Claim 4:
Claim 4 depends on Claim 3 therefore also contains allowable subject matter.
Regarding Claim 5:
Purnhagen teaches the method as claimed in claim1, wherein the covariance matrix is decomposed into eigenvalues using the following steps: obtaining a matrix of eigenvectors Q such that C=Q Λ QT,( Para [0077], Ln 1-7, inter-channel covariance matrix may be estimated using a covariance estimation unit 203. The estimation may be performed in the sub-band domain. Para [0068], Ln 9-11, parametric coding unit 104 may provide a set of spatial parameters which may be used to reconstruct the signals E2 and E3 from the decoded signal E1 114)
where C is the covariance matrix and Λ = diag(λ1,..., λK) is a diagonal matrix of eigenvalues(Para [0078], Ln 1-6, decompose the inter-channel covariance matrix by means of an eigenvalue decomposition (EVD) yielding an orthonormal transformation V that diagonalizes the covariance matrix);
Purnhagen does not teach modifying the matrix of eigenvectors as a function of a determinant value of the matrix of eigenvectors Q; converting the matrix of eigenvectors Q into the domain of quaternions; at least one quaternion that is obtained forming part of the parameters to be quantized.
Paris (US 20040101048 A1), Para [0301], Ln 1-4, does teach representing eigenvalues as quaternions, however, Paris does not teach all of the other above limitations.
The prior art of record alone or in combination does not teach the above limitations.
Regarding Claim 6:
Claim 6 depends on Claim 5 and therefore also contains allowable subject matter.
Regarding Claim 7:
Purnhagen teaches a method for decoding an original multichannel sound signal, the method being implemented by a decoding device and comprising(Abstract, Ln 1-5, multichannel audio encoding and decoding.):
decoding at least one coded channel of the original multichannel sound signal and obtaining a decoded multichannel signal(Para [0034], Ln 1-8, The audio decoder may further comprise a transform decoding unit which is configured to extract a set of transform parameters (e.g. the parameters d, φ, θ) indicative of an energy-compacting orthogonal transform V which has been determined by a corresponding audio encoder, based on a corresponding frame of a soundfield signal which is to be reconstructed);
determining a set of corrections to be made to the decoded signal based on the covariance matrices of the original multichannel sound signal…..and correcting the decoded multichannel signal using the determined set of corrections(Par [0111], Ln 1-8, further parameter γ may be determined and transmitted to describe the normalized correlation between the eigen-signals E2 and E3. This would allow the original covariance matrix of the two prediction errors to be reinstated in the decoder 250. As a consequence, the full covariance of the three-dimensional signal may be reinstated)
Purnhagen does not teach dividing the decoded multichannel signal into frequency sub-bands; decoding parameters resulting from a decomposition of covariance matrices of the original multichannel sound signal into eigenvalues; determining the covariance matrices of the original multichannel sound signal from the decoded parameters; determining a covariance matrix, per frequency sub-band, of the decoded multichannel signal; determining a set of corrections to be made to the decoded signal based on the covariance matrices of the original multichannel sound signal and the covariance matrices of the decoded multichannel signal.
While Purnhagen does teach similar limitations to some of the above limitations, as shown in the rejection of Claim 1, these citations are in the context of encoding the audio and not being done after the audio has been decoded as limited in the claim.
Villemoes et al. (US 20160261967 A1) teaches a similar method for correcting the decoded covariance matrix as in Purnhagen, using decorrelated signals, but likewise fails to teach the above limitations.
For these reasons the prior art of record alone or in combination does not teach the claimed limitations.
Claims 9 and 13 contain similar limitations as Claim 7 and therefore also contain allowable subject matter.
Claim 12 depends on Claim 9 and therefore also contains allowable subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. (US 6631347 B1)
Vector quantization with ordering of eigenvalues.
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/ALEXANDER G MARLOW/Assistant Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658