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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/9/2024 was filed after and is being considered by the examiner.
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, 3, and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al (Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning) in view of Gidaris et al (Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning).
In regard to claims 1, 3, and 4, Zhu et al disclose a machine learning device that performs continual learning of a novel class with fewer samples than a base class, comprising:
a base class feature extraction unit that extracts feature vectors of samples in the base class using a pre-trained model;
a base class classification unit that uses feature vectors of the samples in the base class as input and classifies the samples in the base class using the classification weight of the base class;
a feature optimization unit that performs meta-learning of an optimization module that is based on the pre-trained model and optimizes feature vectors of samples in the novel class;
a novel class feature averaging unit that averages the feature vectors of the samples in the novel class for each class and calculates the classification weight of the novel class; and
an unknown class classification unit that uses, as input, feature vectors of samples in an unknown class extracted using the optimization module and classifies the samples in the unknown class using the reconstruction classification weight.
Zhu et al fail to disclose a graph neural network or a graph attention network that uses the classification weight of the base class and the classification weight of the novel class as input, performs meta-learning of the dependence relationship between the base class and the novel class, and outputs a reconstruction classification weight.
Gidaris et al teach a graph neural network or a graph attention network that uses the classification weight of the base class and the classification weight of the novel class as input, performs meta-learning of the dependence relationship between the base class and the novel class, and outputs a reconstruction classification weight.
It would have been obvious to one of ordinary skill in the art at the time of filing to implement Zhu’s inter-class dependency based prototype refinement using the GNN of Gidaris, because Gidaris teaches that a GNN model’s co-dependencies among base and novel classes and generates reconstructed classification weights therefrom, therby providing a known technique for accomplishing Zhu’s stated objective of refining classification prototypes based on dependencies among different classes.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al (Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning) in view of Gidaris et al (Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning), and further, in view of Saito et al (Maximum Classifier Discrepancy for Unsupervised Domain Adaptation).
In regard to claim 2, the combination of Zhu et al and Gidaris et al fail to disclose wherein parameters of the feature optimization unit are fixed at the time of learning of parameters of the graph neural network while the parameters of the graph neural network are fixed at the time of learning of the parameters of the feature optimization unit when the meta-learning is performed in units of episodes.
Saito et al teach parameters of the feature optimization unit are fixed at the time of learning of parameters of the graph neural network while the parameters of the graph neural network are fixed at the time of learning of the parameters of the feature optimization unit when the meta-learning is performed in units of episodes.
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the combined system of Zhu and Gidaris by alternately optimizing the feature optimization component and the GNN component while holding the parameters of the other component fixed, as taught by Saito, because Saito teaches alternating optimization of the interacting NN components in which classifier parameters are optimized while the feature generator is fixed and the feature generator is optimized while the classifier parameters are fixed. A PHOSITA would be motivated to apply this known training technique to the feature optimization unit and GNN of the combined Zhu and Gidaris system to separately optimize the interacting parameter sets and thereby facilitate stable training of the respective components.
Conclusion
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/CHRISTOPHER E DUNAY/Primary Examiner, Art Unit 2875
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