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 .
Drawings
Figures 4-7 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 15 is objected to because of the following informalities: claim 15 should be dependent to claim 6 since claim 5 does not provide antecedent basis for “the first block”. Appropriate correction is required.
Claim Interpretation
Nonfunctional Descriptive Material
Claim 20 recites “A non-transitory computer readable medium storing bitstream generated by a method”. There is no recitation of instruction stored on the CRM and when the instructions are executed by processor perform the method—merely data content (bitstream of video data generated by a recited method). Under MPEP 2111.05/(III), these claims are merely machine- readable media. The Examiner finds that there is no disclosed or claimed functional relationship between the stored data and medium. Instead, the medium is merely a support or carrier for the data being stored. Therefore, the data stored and the way such data is generated should not be given patentable weight. See MPEP 2111.05 applying n re Lowry, 32 F.3d 1579, 1583-84, 32 USPQ2d 1031, 1035 (Fed. Cir. 1994) and In re Ngai, 367 F.3d 1336, 70 USPQ2d 1862 (Fed. Cir. 2004). As such, claim 20 is subject to a prior art rejection based on any non- transitory computer readable medium known before the earliest effective filing date of the present application.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-9 and 13-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al (US 20210297667).
As to claim 1, Zhou discloses a method for visual data processing (FIG. 1), comprising:
obtaining, for a conversion between visual data and a bitstream of the visual data with a neural network (NN)-based model (FIG. 1, image data x and bit stream 100; see [0056]: deep neural network), a first representation of the visual data (see [0057]: generate a quantized latent variable {circumflex over (z)} _enc);
adjusting a plurality of sets of first samples of the first representation with different parameters (FIG. 1, quantization step adjuster 15; see [0086]-[0089]: The non-uniform quantizing processing may include: taking the latent variable z to which a probability distribution peak value (or center value) of the latent variable z corresponds as a zero point, and making the latent variable z in a first range containing the zero point correspond to the first quantized latent variable {circumflex over (z)}_enc; for other quantized latent variables {circumflex over (z)}_enc than the first quantized latent variable {circumflex over (z)}_enc, each quantized latent variable {circumflex over (z)}_enc corresponds to the latent variable z in a second range … the length of the first range is 2*(1−offset)*Q. A length of the second range is equal to the quantization step Q; see [0093]-[0094], z.sub.high and z.sub.low); and
performing the conversion based on the plurality of sets of adjusted first samples (see [0091]).
As to claim 2, Zhou further discloses wherein the first representation is associated with a second representation of the visual data (FIG. 1, latent variable z), and adjusting the plurality of sets of first samples comprises: determining the plurality of sets of first samples from the first representation based on at least one of the following: indices of samples of the first representation, or at least one reference statistical value associated with the second representation (see [0086]-[0089], probability distribution).
As to claim 3, Zhou further discloses wherein the first representation is obtained by quantizing the second representation (FIG. 1 and [0057]: The quantizer 12 may perform quantizing processing according to a quantization step Q on the latent variable z outputted by the image encoder 11 to generate a quantized latent variable {circumflex over (z)}_enc), and the second representation is generated based on applying a first neural network in the NN-based model to the visual data (see [0056], The image encoder 11 encodes the inputted image data x to obtain a latent variable z. The image encoder 11 may perform encoding processing based on a deep neural network).
As to claim 4, Zhou further discloses wherein determining the plurality of sets of first samples from the first representation comprises dividing a single first sample into one of the plurality of sets of first samples based on at least one of the following: at least one sample of the at least one reference statistical value, the at least one sample corresponding to the single first sample; a probability distribution determined based on the at least one sample; a probability value determined based on the at least one sample; a threshold; a function of the at least one sample; or an index of the single first sample (see [0086]: The non-uniform quantizing processing may include: taking the latent variable z to which a probability distribution peak value (or center value) of the latent variable z corresponds as a zero point, and making the latent variable z in a first range containing the zero point correspond to the first quantized latent variable {circumflex over (z)}_enc; for other quantized latent variables {circumflex over (z)}_enc than the first quantized latent variable {circumflex over (z)}_enc, each quantized latent variable {circumflex over (z)}_enc corresponds to the latent variable z in a second range, the second range being not greater than the first range), or
wherein determining the plurality of sets of first samples from the first representation comprises dividing first samples in a first block of the first representation into one of the plurality of sets of first samples based on at least one of the following: a set of samples of the at least one reference statistical value, the set of samples corresponding to the first samples in the first block; probability distributions determined based on the set of samples; probability values determined based on the set of samples; a threshold; a function of the set of samples; indices of the first samples in the first block; or a metric determined based on one of: the set of samples, the probability distributions, the probability values or the indices.
As to claim 5, Zhou further discloses wherein determining the plurality of sets of first samples from the first representation comprises: generating at least one target statistical value based on the at least one reference statistical value; and determining the plurality of sets of first samples from the first representation based on the at least one target statistical value (see [0086]: The non-uniform quantizing processing may include: taking the latent variable z to which a probability distribution peak value (or center value) of the latent variable z corresponds as a zero point, and making the latent variable z in a first range containing the zero point correspond to the first quantized latent variable {circumflex over (z)}_enc; for other quantized latent variables {circumflex over (z)}_enc than the first quantized latent variable {circumflex over (z)}_enc, each quantized latent variable {circumflex over (z)}_enc corresponds to the latent variable z in a second range, the second range being not greater than the first range).
As to claim 6, Zhou further discloses wherein determining the plurality of sets of first samples from the first representation based on the at least one target statistical value comprises dividing a single first sample into one of the plurality of sets of first samples based on at least one of the following: at least one sample of the at least one target statistical value, the at least one sample corresponding to the single first sample; a probability distribution determined based on the at least one sample; a probability value determined based on the at least one sample; or a function of the at least one sample (see [0086]: The non-uniform quantizing processing may include: taking the latent variable z to which a probability distribution peak value (or center value) of the latent variable z corresponds as a zero point, and making the latent variable z in a first range containing the zero point correspond to the first quantized latent variable {circumflex over (z)}_enc; for other quantized latent variables {circumflex over (z)}_enc than the first quantized latent variable {circumflex over (z)}_enc, each quantized latent variable {circumflex over (z)}_enc corresponds to the latent variable z in a second range, the second range being not greater than the first range), or
wherein determining the plurality of sets of first samples from the first representation based on the at least one target statistical value comprises dividing first samples in a first block of the first representation into one of the plurality of sets of first samples based on at least one of the following: a set of samples of the at least one target statistical value, the set of samples corresponding to the first samples in the first block; probability distributions determined based on the set of samples; probability values determined based on the set of samples; a function of the set of samples; or a metric determined based on one of: the set of samples, the probability distributions, or the probability values.
As to claim 7, Zhou further discloses wherein adjusting the plurality of sets of first samples comprises:
adjusting a first set of first samples among the plurality of sets of first samples by scaling the first set of first samples with a first parameter (see [0093]; see FIG. 3); and
adjusting a second set of first samples among the plurality of sets of first samples by scaling the second set of first samples with a second parameter different from the first parameter (see [0093]).
As to claim 8, Zhou further discloses wherein the first representation is obtained by performing an entropy decoding process on the bitstream based on the at least one reference statistical value or the at least one target statistical value (see [0092]-[0093]).
As to claim 9, Zhou further discloses wherein the second representation comprises a latent representation of the visual data or a residual latent presentation of the visual data (FIG. 1, latent variable z), or
wherein the second representation is a latent representation of the visual data, and performing the conversion comprises: reconstructing the visual data by performing a synthesis transform on the plurality of sets of adjusted first samples, or
wherein the second representation is a residual latent representation of the visual data, and performing the conversion comprises: generating a quantized latent representation of the visual data based on the plurality of sets of adjusted first samples and a first reference statistical value of the at least one reference statistical value; and reconstructing the visual data by performing a synthesis transform on the quantized latent representation.
As to claim 13, Zhou further discloses wherein the metric is an average a minimum or a maximum, or wherein an index of a sample indicates one of the following: a channel number of the sample, a feature map identifier of the sample, or a spatial coordinate of the sample (Not part of the Bri based on the rejection of claim 4).
As to claim 14, Zhou further discloses wherein the least one reference statistical value is generated by using a second neural network in the NN-based model, and the second neural network comprises a hyper scale decoder subnetwork for generating a variance, or
wherein the at least one reference statistical value comprises at least one of a mean or a variance of a probability distribution (see [0086]).
As to claim 15, Zhou further discloses wherein a size of the first block is predetermined or indicated in the bitstream (Not part of the Bri based on the rejection of claim 6).
As to claim 16, Zhou further discloses wherein at least one of the following is indicated the bitstream:
information on whether to apply the method, or information on how to apply the method, or
wherein at least one of the following is dependent on a color format and/or a color component of the visual data: information on whether to apply the method, or information on how to apply the method, or
wherein a value included in the bitstream is coded at one of the following: a sequence level, a picture level, a slice level, or a block level, or
wherein a value included in the bitstream is binarized before being coded, or
wherein a value included in the bitstream is coded with at least one arithmetic coding context, or
wherein the visual data comprise a picture of a video or an image (see [0054], image data x).
As to claim 17, Zhou further discloses wherein the conversion includes encoding the visual data into the bitstream, or wherein the conversion includes decoding the visual data from the bitstream (FIG. 1).
As to claim 18, Zhou discloses an apparatus for visual data processing comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to perform acts comprising (FIG. 8 and [0139]-[0141]):
obtaining, for a conversion between visual data and a bitstream of the visual data with a neural network (NN)-based model (FIG. 1, image data x and bit stream 100; see [0056]: deep neural network), a first representation of the visual data (see [0057]: generate a quantized latent variable {circumflex over (z)} _enc);
adjusting a plurality of sets of first samples of the first representation with different parameters (FIG. 1, quantization step adjuster 15; see [0086]-[0089]: The non-uniform quantizing processing may include: taking the latent variable z to which a probability distribution peak value (or center value) of the latent variable z corresponds as a zero point, and making the latent variable z in a first range containing the zero point correspond to the first quantized latent variable {circumflex over (z)}_enc; for other quantized latent variables {circumflex over (z)}_enc than the first quantized latent variable {circumflex over (z)}_enc, each quantized latent variable {circumflex over (z)}_enc corresponds to the latent variable z in a second range … the length of the first range is 2*(1−offset)*Q. A length of the second range is equal to the quantization step Q; see [0093]-[0094], z.sub.high and z.sub.low); and
performing the conversion based on the plurality of sets of adjusted first samples (see [0091]).
As to claim 19, Zhou discloses a non-transitory computer-readable storage medium storing instructions that cause a processor to perform acts comprising (see [0139]):
obtaining, for a conversion between visual data and a bitstream of the visual data with a neural network (NN)-based model (FIG. 1, image data x and bit stream 100; see [0056]: deep neural network), a first representation of the visual data (see [0057]: generate a quantized latent variable {circumflex over (z)} _enc);
adjusting a plurality of sets of first samples of the first representation with different parameters (FIG. 1, quantization step adjuster 15; see [0086]-[0089]: The non-uniform quantizing processing may include: taking the latent variable z to which a probability distribution peak value (or center value) of the latent variable z corresponds as a zero point, and making the latent variable z in a first range containing the zero point correspond to the first quantized latent variable {circumflex over (z)}_enc; for other quantized latent variables {circumflex over (z)}_enc than the first quantized latent variable {circumflex over (z)}_enc, each quantized latent variable {circumflex over (z)}_enc corresponds to the latent variable z in a second range … the length of the first range is 2*(1−offset)*Q. A length of the second range is equal to the quantization step Q; see [0093]-[0094], z.sub.high and z.sub.low); and
performing the conversion based on the plurality of sets of adjusted first samples (see [0091]).
As to claim 20, a bit stream generated by a method, the method comprising… is a product by process claim limitation where the product is the bit stream and the process is the method steps to generate the bitstream. MPEP §2113 recites “Product-by-Process claims are not limited to the manipulations of the recited steps, only the structure implied by the steps”. Thus, the scope of the claim is the storage medium storing the bitstream (with the structure implied by the method steps). The structure includes the information and samples manipulated by the steps. “To be given patentable weight, the printed matter and associated product must be in a functional relationship. A functional relationship can be found where the printed matter performs some function with respect to the product to which it is associated”. MPEP §2111.05(I)(A). When a claimed “computer-readable medium merely serves as a support for information or data, no functional relationship exists. MPEP §2111.05(III). The storage medium storing the claimed bitstream in claim 20 merely services as a support for the storage of the bitstream and provides no fictional relationship between the stored bitstream and storage medium. Therefor the structure bitstream, which scope is implied by the method steps, is non-functional descriptive material and given no patentable weight. MPEP §2111.05(III). Thus, the claim scope is just a storage medium storing data and is anticipated by Zhou which recites a storage medium storing a bitstream (see [0054] and [0141]).
Allowable Subject Matter
Claims 10-12 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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