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 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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea of a mental concept and mathematical calculation without significantly more. The claims recite the mental concept of a DNN with CCN layers, SAN layers, and a prediction layer; and the mathematical calculation of a risk score and Binary cross entropy loss to update weights. This judicial exception is not integrated into a practical application because receiving a batch of training data is mere data gathering which is insignificant extra solution activity. MPEP 2106.05(g). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the device and processing circuitry are generic computer parts which amount to mere instructions to apply an abstract idea to a computer. MPEP 2106.05(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 2 and 5 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant never describes how to calculate a risk, or even what a risk is. The prior art would usually calculate an error, so this risk not a term of art either.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “similar to a structure of layers from… a CNN” in claims 1 and 3 is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how the layers might be similar to a CNN and different from other neural networks.
The term “similar to a structure of… a SAN” in claims 1 and 3 is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how the layers might be similar to a SAN and different from other neural networks.
Claims 1 and 3 recite, “a feature extraction layer in which layers from an input layer to a predetermined intermediate layer have a structure similar to a structure of layers from an input layer to an intermediate layer of a CNN…” It is unclear whether the feature extraction layer in the DNN is on layer or several layers.
Claim 2 recites “calculate a risk…” This is never defined in the specification. Further, the phrase as a whole makes no sense, “a risk in correct answer label learning for a purpose of multi-label classification by use of the DNN model…” This is not a term of art.
Claims 1-5 don’t define the acronyms DNN, CNN, or SAN before using the acronyms. This makes the claims indefinite.
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.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(1) as being described by Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization by Kong et al.
Kong teaches claims 1 and 3. A learning device comprising:
processing circuitry configured to
acquire learning data including data and a correct answer label assigned to the data and learn a weight of a DNN model for predicting an acoustic event from the learning data, (Kong abs. “Sound event detection (SED) is a task to detect sound events in an audio recording. One challenge of the SED task is that many datasets such as the Detection and Classification of Acoustic Scenes and Events (DCASE) datasets are weakly labelled. That is, there are only audio tags for each audio clip without the onset and offset times of sound events. We compare segment-wise and clip-wise training for SED…” This shows labeled acoustic data. Kong abs. “for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN).” This shows a DNN (CNN-Transformer) used for prediction.)
the DNN model including: a feature extraction layer in which layers from an input layer to a predetermined intermediate layer have a structure similar to a structure of layers from an input layer to an intermediate layer of a CNN, and (Kong sec. III(B) “To build the CNN-Transformer system, we first apply a CNN described in Section II on the log mel spectrogram of an audio clip. Convolutional layers in the CNN are used to extract high level features of the input log mel spectrogram.”) layers from the predetermined intermediate layer to an output layer have a structure similar to a structure of layers from an intermediate layer to an output layer of a SAN; and (Kong sec. IV(B) “the aggregation can be a max, average or attention function over the prediction of all segments of each sound class.” Adding an attention layer over the output is making the structure similar to the self-attention network (SAN).)
a prediction layer that predicts an event from an output of the feature extraction layer. (Kong sec. IV(B) “the aggregation can be a max, average or attention function over the prediction of all segments of each sound class.”)
Kong teaches claim 2. The learning device according to claim 1, comprising:
processing circuitry configured to
extract a part of the learning data as a batch; (Kong sec. IV(A) “waveform of an audio clip as X. For an X lasting for several seconds, we split it into several segments … In training, a classifier f is trained on the segments.”)
acquire the batch and calculate a risk in correct answer label learning for a purpose of multi-label classification by use of the DNN model (Kong sec. IV(A) “a classifier f is trained on the segments. The loss function can be written…” Equation 3 is the loss function for segment wise training.) and a Binary Cross Entropy loss function; and (Kong sec. IV(B) “we calculate the categorical binary cross entropy loss between the clip-level prediction F(X) and the ground truth label of X…”)
acquire the risk and update the weight of the DNN model so as to minimize the risk. (The training based on the loss function is an update of weights for the classifier F.)
Kong teaches claims 4 and 5. A program for causing a computer to function as the learning device according to claim 1. (Kong sec. VII “the CNN-transfomer has the advantage of being computed in parallel.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Austin Hicks whose telephone number is (571)270-3377. The examiner can normally be reached Monday - Thursday 8-4 PST.
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/AUSTIN HICKS/ Primary Examiner, Art Unit 2142
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