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 .
Applicant’s election without traverse of Species I (claims 1-9 and 41-42) in the reply filed on 05/04/2026 is acknowledged. Non-elected Claims 16-22 and 31-33 have been withdrawn from consideration, and claims 10-15 and 23-30 have been canceled.
Claims 1-9 and 41-42 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/29/2024 and 10/04/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are considered by the examiner.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 and 41-42 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.
Claim 1 recites the limitation "the local samples of instances" in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the local majority class label" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation " the local minority class label " in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the autoencoder" in lines 6 and 10. It is unclear whether “the autoencoder” refers to “a decentralized autoencoder” in line 2 of claim 1.
Claim 4 recites the limitation "the autoencoder" in lines 4 and 7-9. It is unclear whether “the autoencoder” refers to “a decentralized autoencoder” in line 2 of claim 1.
Claim 41 recites the limitation "the autoencoder" in lines 7 and 11. It is unclear whether “the autoencoder” refers to “a decentralized autoencoder” in line 1 of claim 8.
Claim 42 recites the limitation "the autoencoder" in lines 7 and 10-12. It is unclear whether “the autoencoder” refers to “a decentralized autoencoder” in line 1 of claim 8.
Allowable Subject Matter
Claims 1-9 and 41-42 would be allowable if rewritten or amended to overcome the 35 U.S.C. 112(b) rejection above.
The closest prior art, SINGH et al. (US 2022/0188568 A1) discloses method and system use neural networks to automate identification of minority-class data samples from data that lacks minority-class labels. The identified minority-class data samples can be labeled (e.g., using a human labeler) and added to a training dataset, to correct for skew in the class distribution of the labeled data samples in the training dataset (¶0005). The method may include computing the minority-class threshold by: forward propagating a subset of validation activations, from the set of inner-layer activations, through the trained recalibration neural network and the trained autoencoder to obtain a set of reconstructed validation activations; computing a set of minority-class scores based on quality of reconstruction of the set of reconstructed validation activations; pairing each minority-class score with a corresponding class label; and identifying, from the pairings, a numerical value for the minority-class threshold representing a boundary between the minority-class score for a minority-class data sample and the minority-class score for a majority-class data sample (¶0019; ¶0063; ¶0087).
The closest prior art, Wang et al. (US 2022/0303288 A1) discloses that a detector uses a neural network based auto-encoder. The auto-encoder, at a high level, maps input data to itself (i.e., the input data) through a latent space representation of the input data. To that end, the auto-encoder encodes the input data into a latent space. Further, the auto-encoder decodes the encoded input data from the same latent space. A reconstruction loss between input data to the auto-encoder (or the encoder) and the reconstructed data is estimated. The anomaly detector may be trained based on the reconstruction loss to detect anomaly in the input data. Thus, the proposed anomaly detector can be trained without labeled data (i.e., the anomaly detector is trained using the reconstruction loss).
The prior art, considered individually or in combination, does not teach or suggest the subject matter recited in the independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee (US 11,922,301), Feinstein et al. (US 11,829,866), Ristoski et al. (US 2022/0051128), Chakraborty et al. (US 11,593,641) disclose method and system for efficiently and accurately distinguishing between anomalous members of a majority class and members of a target minority class, a method and system for efficiently and accurately distinguishing between anomalous members of a majority class and members of a target minority class more accurately is provided. Therefore, the disclosed embodiments provide a technical solution to the long standing technical problem of efficiently and accurately distinguishing between anomalous members of a majority class and members of a target minority class.
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/JUNGWON CHANG/Primary Examiner, Art Unit 2454 June 3, 2026
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