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 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.
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.
Claim(s) 1, 3-6, 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu (CN117611901B) in view of Li (U.S. PG-PUB NO. 2021/0374553).
-Regarding claim 1, Liu discloses a data classification method, the data classification method being performed by a data classification apparatus (abstract), the data classification method comprising extracting features from input data through a learning network model (extracting image features by using the trained model in a testing stage, and finally classifying by using a K nearest neighbor algorithm, paragraph 8) and outputting prediction results based on the features (selecting the nearest category as a final prediction category, paragraph 43); wherein the learning network model compares local features derived through an individual layer other than a final layer of the learning network model (Local contrast learning takes the features before ResNet-12 last pooling layer, adds up to local features with the size of 5x 640, and performs supervised contrast learning on each 640-dimensional local feature vector, paragraph 56).
Liu is silent to teaching that with label embedding vectors corresponding to a classification label. However, the claimed limitation is well known in the art as evidenced by Li.
In the same field of endeavor, Li teaches with label embedding vectors corresponding to a classification label (embedding 135, paragraph 29).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Liu with the teaching of Li in order to apply a known prototype-contrast technique to a similar contrastive classifier for predictable per-label discriminability (KSR: known technique applied to similar device).
-Regarding claim 3, the combination further discloses the learning network model directly compares the label embedding vectors and the local features by using a label embedding dictionary which is connected to at least one layer of the learning network model and in which the label embedding vectors are mapped (Li, The normalized embeddings are output from the autoencoder 260 a to the prototype computation module 266, which computes a class prototype as the normalized mean embedding, paragraph 37; Liu, Using KNN to conduct prediction classification, calculating cosine distances between features and each category prototype of all samples in the query set, paragraph 83).
-Regarding claim 4, the combination further discloses the label embedding vectors of the label embedding dictionary are adaptively and dynamically updated based on the error signals of the local features (Li, the class prototypes are calculated at the beginning of each epoch, paragraph 37).
-Regarding claim 5, the combination further discloses at least one layer of the learning network model receives the error signals of the local features from a loss function set based on dictionary contrastive learning (Li, the prototypical contrastive loss, the consistency contrastive loss, the cross-entropy loss and the reconstruction loss may be combined to jointly train the neural network, paragraph 44).
-Regarding claim 6, the combination further discloses parameters of the learning network model are updated in order to maximize similarity between label embedding vectors corresponding to the local features in the label embedding dictionary and the local features while minimizing similarity between label embedding vectors not corresponding to the local features in the label embedding dictionary and the local features (Li, push images that belong to the same class to the class prototype in the low-dimensional subspace, while pulling images that belong to different classes away from the class prototype, paragraph 29).
-Regarding claim 9, the combination further discloses a non-transitory computer-readable storage medium having stored thereon a program that, when executed by a processor, causes the processor to execute the method set forth in claim 1 (Li, computing device 300, memory 320, FIG. 3; paragraph 50).
-Regarding claim 10, the combination further discloses a computer program that is executed by a data classification apparatus and stored in a non-transitory computer-readable storage medium to perform the method set forth in claim 1 (Li, computing device 300, memory 320, FIG. 3; paragraph 50).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. PG-PUB NO. 2021/0374553) in view of Liu (CN117611901B).
-Regarding claim 8, Li discloses a data classification apparatus (computing device 300, FIG. 3), comprising: memory configured to store a learning network model having a plurality of layers (memory 320, FIG. 3; paragraph 50); and a controller configured to extract features from input data through the learning network model and output prediction results based on the features (a classifier 255 (e.g., a fully-connected layer followed by softmax) that receives vi as input and outputs class predictions, paragraph 33); with label embedding vectors corresponding to a classification label (embedding 135, paragraph 29).
Li is silent to teaching that the learning network model compares local features derived through an individual layer other than a final layer of the learning network model. However, the claimed limitation is well known in the art as evidenced by Liu.
In the same field of endeavor, Liu teaches the learning network model compares local features derived through an individual layer other than a final layer of the learning network model (Local contrast learning takes the features before ResNet-12 last pooling layer, adds up to local features with the size of 5x 640, and performs supervised contrast learning on each 640-dimensional local feature vector, paragraph 56).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of Liu in order to improve the generalization performance of the model.
Allowable Subject Matter
Claims 2 and 7 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 PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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