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 .
Claim(s) 1-20 is/are pending and has/have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/25/2024, 08/09/2024, 04/30/2025, and 09/24/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 8-10 are objected to because of the following informalities:
Claim 8 recites “a signal feature at each layer” in the first limitation. The Examiner suggests amending the claim(s) to recite –the signal feature at each layer-- in order to maintain clear antecedent basis. Appropriate correction is required.
Claims 9 and 10 recite “a bitstream of the audio signal at each layer” in line 3 of each claim. The Examiner suggests amending the claim(s) to recite --the bitstream of the audio signal at each layer-- in order to maintain clear antecedent basis.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim(s) 1, 13, and 20, the limitation(s) of performing feature extraction, splicing, performing feature extraction, traversing, and coding, as drafted, are processes that, under broadest reasonable interpretation, are mathematical operations applied to audio data, where feature extraction, convolution, pooling, downsampling, subband decomposition, quantization, entropy coding, filtering, and coding bit rates involve mathematical relationships and algorithms. They do not merely describe a physical transformation of a tangible object; they describe data manipulation using mathematical techniques. The BRI also covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components. More specifically, the mental process of a human using pen and paper to perform a specific calculation on numerical data representing audio to identify a particular feature, combine the feature with a portion of the numerical data, and perform a specific calculation on the combined data to identify another particular feature, repeating the process a specific number of times, and writing out the particular features in a specific format for later use, which is a human using pen and paper to perform the mathematical techniques as discussed above for data manipulation. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts and the performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components, then it falls within the –Mathematical Concepts—and --Mental Processes-- groupings of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of an electronic device of claim 1, an electronic device, processors, and memory of claim 13, and a storage medium and processors of claim 10, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using [0053-60] in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea.
The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to perform feature extraction, splice, perform feature extraction, traverse, and code amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
With respect to claim(s) 2 and 14, the claim(s) recite(s) performing subband decomposition, performing feature extraction, and determining the signal features, which reads on a human performing a calculation on the numerical data to separate low and high frequency values into different datasets, performing a specific calculation on each dataset to identify a particular feature, and writing down the two features for the datasets as the particular feature for the first round of calculations. No additional limitations are present.
With respect to claim(s) 3 and 15, the claim(s) recite(s) sampling the audio signal, performing low-pass filtering, downsampling, performing high-pass filtering, and downsampling, which reads on a human taking certain data points from the numerical data a particular distance from each other to obtain a subset of the data, performing a calculation on the subset to obtain only low frequency values, performing a calculation on the low values to select an even smaller number of data points, performing a calculation on the subset to obtain only high frequency values, and performing a calculation on the high values to select an even smaller number of data points. No additional limitations are present.
With respect to claim(s) 4 and 16, the claim(s) recite(s) splicing the low-frequency subband signal, performing feature extraction, splicing the high-frequency subband signal, performing feature extractions, and determining the signal feature, which reads on a human performing a calculation on the numerical data to separate low and high frequency values into different datasets, performing a specific calculation on each dataset to identify a particular feature, combining the feature with a respective portion of the corresponding dataset, performing a specific calculation on each combined value to identify particular respective features, and writing down the two features for the datasets as the particular feature for the first round of calculations. No additional limitations are present.
With respect to claim(s) 5, 6, 7, 8, 17, 18, and 19, the claim(s) recite(s) a series of specific calculation steps, which reads on a human performing a specific series of calculations on a set of data to result in a particular output. No additional limitations are present.
With respect to claim(s) 9, the claim(s) recite(s) coding, coding, and determining, which reads on a human writing out the particular features for the different datasets in a specific format for later use. No additional limitations are present.
With respect to claim(s) 10, the claim(s) recite(s) coding, coding, coding, and determining, which reads on a human writing out the particular features for the different datasets in a specific format for later use, where the format includes segmenting the data into different size chunks. No additional limitations are present.
With respect to claim(s) 11 and 12, the claim(s) recite(s) configuring layer transmission priorities, which reads on a human determining which portion of the data is most important for another person to read first, where the importance is based on a specific piece of information. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, 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 Fu (US 2023/0090590, as found in the IDS) teaches extracting audio features for encoding, truncating the encoding feature to obtain feature fragments, obtaining historical feature abstraction information, and obtaining a recognition result using spliced historical feature information and a currently processed feature fragment. Fu, however, does not teach performing feature extraction on an audio signal at a first layer to obtain a signal feature, splicing the audio signal and the signal feature from the first layer, performing feature extraction on the spliced feature to determine a signal feature at the second layer, repeating the process for each layer, and coding the resulting signal features to obtain a bitstream of the audio signal at each layer.
Zhang (CN112420065B, as found in the IDS) teaches the extraction of feature data from different layers of a neural network with an encoder and decoder, where the output is coded. The input to the neural network is the audio signal, and the output of each layer is a feature that is the input of the next layer, and the features of each layer of the encoder are fused with the features of the corresponding layer of the decoder, where the final output of the neural network is spliced with the original input data is processed for coding. However, Zhang does not teach splicing the audio signal and the signal feature from the first layer, performing feature extraction on the spliced feature to determine a signal feature at the second layer, and repeating the process for each layer, where extracted features at each layer are spliced with the audio signal specifically as input to the next layer, and the extracted features at each layer are coded to obtain a bitstream.
Zeghidour (“SoundStream: An End-to-End Neural Audio Codec”, 2021) teaches a convolutional encoder that produces a latent representation of input audio samples, which is quantized using residual vector quantizers. The waveform is input to the encoder, and the output of each layer of the encoder is the input of the next layer of the encoder, where the final embeddings are output to the residual vector quantizer for compression to a target bitrate. However, Zeghidour does not teach specifically performing feature extraction on an audio signal at a first layer to obtain a signal feature, splicing the audio signal and the signal feature from the first layer, performing feature extraction on the spliced feature to determine a signal feature at the second layer, and repeating the process for each layer, where extracted features at each layer are spliced with the audio signal specifically as input to the next layer, and the extracted features at each layer are coded to obtain a bitstream.
None of Fu, Zhang, and Zeghidour, either alone or in combination, teaches or makes obvious performing feature extraction on an audio signal at a first layer to obtain a signal feature, splicing the audio signal and the signal feature from the first layer, performing feature extraction on the spliced feature to determine a signal feature at the second layer, and repeating the process for each layer, where extracted features at each layer are spliced with the audio signal specifically as input to the next layer, and the extracted features at each layer are coded to obtain a bitstream. Therefore, none of the cited prior art either alone or in combination, teaches or makes obvious the combination of limitations as recited in the independent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE A K SCHMIEDER whose telephone number is (571)270-1474. The examiner can normally be reached 8:00 - 5:00 M-F.
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/NICOLE A K SCHMIEDER/Primary Examiner, Art Unit 2659