DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Amendment filed on 8/28/2025.
Claims 1-14 are pending. Claims 1 is independent.
The rejection of claims 13-14 under 35 USC § 101 has been withdrawn in view of the amendment.
The previous rejection of claims 1-14 under 35 USC § 103 have been withdrawn in view of the amendment.
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) 1-4, 8, 10-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chong et al. (Abnormal Event Detection in Videos using Spatiotemporal Autoencoder) in view of Kwak et al. (Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors) and Ahn et al. (Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems).
In regards to claim 1, Chong et al. a machine learning apparatus comprising a processing circuit configured to
train a first learning parameter of an extraction layer configured to extract, from input data, feature data of the input data, based on a plurality of training data (Chong et al. pg5 section3.2.1 para1, autoencoder trains encoder model to extract features), and
train a second learning parameter of a reconstruction layer configured to generate reconstructed data of the input data, based on a plurality of training feature data obtained by applying the trained extraction layer to the plurality of training data (Chong et al. pg5 section3.2.1 para1, autoencoder trains decoder model to minimize reconstruction error), and
the representative vectors as many as the dimension count are defined by a weighted sum of the plurality of training data (Chong et al. pg7 section3.2.4 para2, weight matrixes trained from input vectors).
Chong et al. does not explicitly disclose wherein: the second learning parameter represents representative vectors as many as a dimension count of the feature data.
However Kwak et al. substantially discloses wherein: the second learning parameter represents representative vectors as many as a dimension count of the feature data (Kwak et al. pg39998 section III.B. para1 vector has same dimension count (length) as number of channels).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the anomaly detection method of Kwak et al. in order to prevent loss of valuable channel information through reconstruction errors (Kwak et al. pg39995 abstract).
Chong et al. does not explicitly disclose the extraction layer and the reconstruction layer are included in a neural network configured to receive the input data and output a determination result of presence or absence of abnormality of the input data.
However Ahn et al. discloses The extraction layer and the reconstruction layer are included in a neural network configured to receive the input data and output a determination result of presence or absence of abnormality of the input data (Ahn et al. pg8 section 4 para2, a series of processes for anomaly detection using multidimensional time-series data include data preprocessing (including purification, integration, cleanup, and transformation), feature extraction using deep networks, and learning models represented by algorithms considering robustness and performance optimization).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have combined the abnormality detection method of Chong et al. with the deep generative models of Ahn et al. in order to determine if a system has deviated from the proper operating range due to a failure (Ahn et al. pg1 section 1 para1).
In regards to claim 2, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 1, wherein the processing circuit
calculates a false detection rate concerning abnormality detection based on the training feature data obtained by applying the trained extraction layer to the training data and training reconstructed data obtained by applying the trained reconstruction layer to the training feature data (Kwak et al. pg40001 section IV.B para para7, calculates false positive rate based on trained hyperparameters), and
displays the false detection rate on a display device (Kwak et al. table 2 pg40002 section IV.D para4, displays FPR results).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the anomaly detection method of Kwak et al. in order to prevent loss of valuable channel information through reconstruction errors (Kwak et al. pg39995 abstract).
In regards to claim 3, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 2, wherein the processing circuit
calculates a probability distribution of an error between the training feature data and the training reconstructed data (Chong et al. pg9 section 3.3, calculates the reconstruction error),
calculates, as the false detection rate, a probability that the error is not less than a threshold in the probability distribution (Chong et al. pg9 section 3.4.1, calculates at detection threshold where false positive rate is equal to false negative rate), and
displays a graph of the false detection rate for the threshold (Chong et al. fig. 4 pg13 section 4.3.2. para1-2, displays graph of regularity score).
In regards to claim 4, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 3, wherein the processing circuit sets a threshold used to determine presence/absence of abnormality of the input data using the neural network including the extraction layer and the reconstruction layer to a value designated via the graph by an operator (Kwak et al. pg39999 section III.B para3, user defines anomaly rate used to set the anomaly score value corresponding to the threshold to detect anomalies).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the anomaly detection method of Kwak et al. in order to prevent loss of valuable channel information through reconstruction errors (Kwak et al. pg39995 abstract).
In regards to claim 8, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 1, wherein the processing circuit trains the second learning parameter by minimizing an error between the training feature data and training reconstructed data obtained by applying the training feature data to the reconstruction layer (Chong et al. pg5 section 3.2.1 para1, model is trained to minimize reconstruction error).
In regards to claim 10, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 1, wherein the neural network including the extraction layer and the reconstruction layer includes a determination layer configured to output a determination result of presence/absence of abnormality of the input data based on comparison between a threshold and an error between the reconstructed data and the input data (Chang et al. pg9 section3.4.1, threshold determines how sensitive system is to identifying frame as anomalous based on a reconstruction error).
In regards to claim 11, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 1, wherein the representative vectors as many as the dimension count are defined by the weighted sum of the plurality of training data, and the weight has a value based on the plurality of training feature data (Chong et al. pg7 section3.2.4 para2, bias vectors have dimension based on trainable weight matrices).
In regards to claim 12, Chong et al. as modified Kwak and Ahn et al. discloses the apparatus according to claim 1, wherein the dimension count is decided in accordance with a storage capacity that is assigned to a memory of an apparatus in which the neural network including the extraction layer and the reconstruction layer is implemented, and is needed for the neural network (Chong et al. pg5 section 3.1 para2, reduces and normalizes dimensionality of data).
In regards to claim 13, Chong et al. as modified by Kwak et al. and Ahn et al. discloses a system comprising:
The machine learning apparatus according to claim 1; and
an abnormality detection apparatus comprising:
a processing circuit configured to:
extract feature data from the diagnostic data using the extraction layer trained by the machine learning apparatus (Chong et al. pg5 section3.2.1 para1, autoencoder trains encoder model to extract features),
generate reconstructed data from the feature data using the reconstruction layer trained by the machine learning apparatus, the reconstructed data being generated based on a weighted sum of the feature data (Chong et al. pg5 section3.2.1 para1, autoencoder trains decoder model to minimize reconstruction error), and
determine presence/absence of abnormality of the diagnostic data based on the diagnostic data and the reconstructed data (Chong et al. pg9 section3.4.1, determines if data is normal or anomalous based on anomaly threshold parameter).
Chong et al. does not explicitly disclose representative vectors as many as a dimension count of the feature data.
However Kwak et al. substantially discloses representative vectors as many as a dimension count of the feature data (Kwak et al. pg39998 section III.B. para1 vector has same dimension count (length) as number of channels).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the anomaly detection method of Kwak et al. in order to prevent loss of valuable channel information through reconstruction errors (Kwak et al. pg39995 abstract).
Chong et al. does not explicitly disclose obtain diagnostic data from a manufacturing machine for an abnormality determination target or an inspection device of the manufacturing machine.
However Ahn et al. discloses obtain diagnostic data from a manufacturing machine for an abnormality determination target or an inspection device of the manufacturing machine (Ahn et al. pg2 section 1 para3, Time-series data transmitted from each subsystem, part, and sensor are needed to diagnose the spacecrafts system’s mechanical condition).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have combined the abnormality detection method of Chong et al. with the deep generative models of Ahn et al. in order to determine if a system has deviated from the proper operating range due to a failure (Ahn et al. pg1 section 1 para1).
Claim 14 recites substantially similar limitations to claim 13. Thus claim 14 is rejected along the same rationale as claim 13.
Claim(s) 5-7, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chong et al. and Kwak et al. and Ahn et al. as applied to claim 1 and 8 above, and further in view of Xing et al. (US2020/0104984).
In regards to claim 5, Chong et al. as modified Kwak discloses the apparatus according to claim 1. Chong et al. does not explicitly disclose wherein if the training data includes only normal data, the processing circuit trains the first learning parameter such that positive correlation between an inner product of two normal data and an inner product of two feature data corresponding to the two normal data becomes high.
However Xing et al. discloses wherein if the training data includes only normal data, the processing circuit trains the first learning parameter such that positive correlation between an inner product of two normal data and an inner product of two feature data corresponding to the two normal data becomes high (Xing et al. fig. 8 a-c para[0089], correlation becomes positive for normal data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the dimension reduction method of Xing et al. in order to reduce a large number of variables into few comprehensive indicators (Xing et al. para[0004]).
In regards to claim 6, Chong et al. as modified Kwak discloses the apparatus according to claim 1. Chong et al. does not explicitly disclose wherein if the training data includes normal data and abnormal data, the processing circuit trains the first learning parameter such that negative correlation between an inner product of the normal data and the abnormal data and an inner product of feature data corresponding to the normal data and feature data corresponding to the abnormal data becomes high.
However Xing et al. discloses wherein if the training data includes normal data and abnormal data, the processing circuit trains the first learning parameter such that negative correlation between an inner product of the normal data and the abnormal data and an inner product of feature data corresponding to the normal data and feature data corresponding to the abnormal data becomes high (Xing et al. fig. 8 a-c para[0089], correlation becomes negative for abnormal data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the dimension reduction method of Xing et al. in order to reduce a large number of variables into few comprehensive indicators (Xing et al. para[0004]).
In regards to claim 7,Chong et al. as modified Kwak discloses the apparatus according to claim 1. Chong et al. wherein the processing circuit trains the first learning parameter by contrastive learning and decorrelation based on an inner product of the training data and an inner product of feature data corresponding to the training data.
However Xing et al. discloses wherein the processing circuit trains the first learning parameter by contrastive learning and decorrelation based on an inner product of the training data and an inner product of feature data corresponding to the training data (Xing et al. fig. 7 para[0074], generates correlation matrix from abnormal and normal sample image data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the dimension reduction method of Xing et al. in order to reduce a large number of variables into few comprehensive indicators (Xing et al. para[0004]).
In regards to claim 9, Chong et al. as modified Kwak discloses the apparatus according to claim 8. Chong et al. does not explicitly disclose wherein the reconstruction layer is a linear regression model.
Xing et al. discloses wherein the reconstruction layer is a linear regression model (Xing et al. para[0080], reconstruction layer is a ridge regression classification model).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the abnormality detection method of Chong et al. with the dimension reduction method of Xing et al. in order to reduce a large number of variables into few comprehensive indicators (Xing et al. para[0004]).
Response to Arguments
Applicant’s arguments, see pg8, filed 8/28/2025, with respect to 13-14 have been fully considered and are persuasive. The 101 rejection of 5/28/2025 has been withdrawn.
Applicant argues on page 10 that Chong et al. does not disclose “train a first learning parameter of an extraction layer configured to extract, from input data, feature data of the input data, based on a plurality of training data” and “train a second learning parameter of a reconstruction layer configured to generate reconstructed data of the input data, based on a plurality of training feature data obtained by applying the trained extraction layer to the plurality of training data.”
However Chong et al. discloses train a first learning parameter of an extraction layer configured to extract, from input data, feature data of the input data, based on a plurality of training data (Chong et al. pg5 section3.2.1 para1 and pg10 section 4.2 para1, normal input data is used to train weight parameters of the auto encoder to extract useful features);and train a second learning parameter of a reconstruction layer configured to generate reconstructed data of the input data, based on a plurality of training feature data obtained by applying the trained extraction layer to the plurality of training data (Chong et al. pg5 section3.2.1 para1 and pg10 section 4.2 para1, weight parameters of decoder model are trained through backpropagation to minimize reconstruction error).
Applicant argues on page 11 that Chong et al. does not teach “the representative vectors as many as the dimension count are defined by a weighted sub of the plurality of training”.
However Chong et al. discloses the representative vectors as many as the dimension count are defined by a weighted sum of the plurality of training data (Chong et al. pg7 section3.2.4 para2, weight matrix has same dimensions as input vectors).
Applicant argues on page 11 that Chong et al. does not teach “the second learning parameter represents representative vectors as many as a dimension count of the feature data”.
However Chong et al. as modified by Kwak et al. and Ahn et al. discloses wherein the second learning parameter represents representative vectors as many as a dimension count of the feature data (Kwak et al. pg39998 section III.B. para1, vector with the same length as the number of input channels used to tune the model’s architecture, pg39998 section III.A para6, error vector represent how each channel differs from a normal channel).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.H/Examiner, Art Unit 2141
/HOPE C SHEFFIELD/Primary Examiner, Art Unit 2141
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