DETAILED ACTION
This action is in response to claims filed 14 June 2023 for application 18334697 filed 14 June 2023. Currently claims 1-20 are pending.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (Convolutional Autoencoder-Based Transfer Learning for Multi-Task Image Inferences) in view of Meng et al. (Research of stacked denoising sparse autoencoder).
Regarding claims 1, 8 and 15, Lu discloses: A computer-implemented method for training deep learning (DL) models (see §V.B System Analysis for computer device and memory used to train deep learning models), the method comprising:
training a[n] autoencoder (“The autoencoder is first trained using non-labeled data, as can be seen from Figure 2(b). The model parameters are updated based on how well the non-labeled data can be reconstructed. Once trained, the encoding part of the autoencoder, or autoencoder feature extraction, is kept constant and used for task-specific classifier training. During the classifier training phase, the autoencoder feature extraction is kept the same for all inference tasks, but the classifier is trained specifically for a given inference task.” P1049 §IV.A ¶2)
providing an artificial neural network (ANN) comprising multiple hidden layers, at least one hidden layer comprising at least a portion of an encoder of the DAE, the at least a portion of the encoder comprising parameters determined during training of the DAE (“The autoencoder is first trained using non-labeled data, as can be seen from Figure 2(b). The model parameters are updated based on how well the non-labeled data can be reconstructed. Once trained, the encoding part of the autoencoder, or autoencoder feature extraction, is kept constant and used for task-specific classifier training. During the classifier training phase, the autoencoder feature extraction is kept the same for all inference tasks, but the classifier is trained specifically for a given inference task.” P1049 §IV.A ¶2);
training the ANN using a training dataset (“The autoencoder is first trained using non-labeled data, as can be seen from Figure 2(b). The model parameters are updated based on how well the non-labeled data can be reconstructed. Once trained, the encoding part of the autoencoder, or autoencoder feature extraction, is kept constant and used for task-specific classifier training. During the classifier training phase, the autoencoder feature extraction is kept the same for all inference tasks, but the classifier is trained specifically for a given inference task.” P1049 §IV.A ¶2); and
providing a version of the ANN for inference (“The autoencoder is first trained using non-labeled data, as can be seen from Figure 2(b). The model parameters are updated based on how well the non-labeled data can be reconstructed. Once trained, the encoding part of the autoencoder, or autoencoder feature extraction, is kept constant and used for task-specific classifier training. During the classifier training phase, the autoencoder feature extraction is kept the same for all inference tasks, but the classifier is trained specifically for a given inference task.” P1049 §IV.A ¶2).
Lu does not explicitly disclose: training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set.
Meng teaches: training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set (p2085 ¶1 a stacked denoising autoencoder is trained, p2086 Algorithm 1 discloses the training method, formula 1 partially corrupts a dataset to have a noisy subset and normal subset of the original data).
Lu and Meng are in the same field of endeavor of autoencoders and are analogous. Lu discloses a system wherein an autoencoder is trained and then parts of the autoencoder are used to train classifiers for inference tasks. Meng discloses the training of a stacked denoising autoencoder (DAE). It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the autoencoder as disclosed by Lu with the known stacked DAE as taught by Meng to yield predictable results of a more robust and interesting feature set (Meng p2084 §2.1 ¶2).
Regarding claims 2, 9 and 16, Lu does not explicitly disclose, however Meng teaches: The method of claim 1, wherein training of the DAE comprises unsupervised training (“Use unsupervised greedy layer-wise training to initialize the parameters of the stacked model.” p2087 Algorithm 2).
Regarding claims 3, 10 and 17, Lu does not explicitly disclose, however Meng teaches: The method of claim 1, further comprising generating the noisy sub-set by selecting a pre-defined percentage of training samples of the training dataset and randomly adjusting data attributes of the training samples to provide noisy training samples and including the noisy training samples in the noisy sub-set (“The horizontal axes of (a) and (b) indicate the corrupted level from 0 to 100% (0% equal to not do corrupting operation).” P2089 §4.3 ¶4).
Regarding claims 4, 11 and 18, Lu discloses: The method of claim 1, wherein training of the ANN comprises supervised training (Fig 2 task-specific training data and training label indicates supervised learning).
Regarding claims 5, 12 and 19, Lu discloses: The method of claim 1, wherein the ANN comprises a classification model that is trained to predict a class in a set of classes (Fig 2 “Design flow of the proposed structure with the encoding layers of the autoencoder kept constant once trained and used for task-specific classifier training. Only the classifier is specifically trained for each task and changed during the inference phase depending on the desired inference task”).
Regarding claims 6, 13 and 20, Lu discloses: The method of claim 1, further comprising tuning the ANN to provide multiple versions of the ANN, the version of the ANN provided for inference determined to be a best performing version of the multiple versions (Fig 2 “Only the classifier is specifically trained for each task and changed during the inference phase depending on the desired inference task.”, multiple classifier ANNs can be trained, at least one for each task, and the best one for each task is selected for that task).
Regarding claims 7 and 14, Lu discloses: The method of claim 6, wherein the ANN is tuned based on one or more of activation function, learning rate, number of neurons, optimizer, batch size, and number of epochs (“Low-bit (quantized) neural networks. By operating on low-bit weights and activations, a network is able to work with low-bit kernels to reduce the computations required during a real-time inference operation [11]. The exact number of bits used for weights and activations of the network can vary based on the accuracy requirements.”, p1046 ¶4, note: the neural network has a number of neurons and an activation function, all of these elements are standard aspects of training a neural network and by BRI the training is based on these elements being present.).
Conclusion
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151