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 .
The instant application having Application No. 18/219,642 has a total of 12 claims pending in the application, there are 3 independent claims and 9 dependent claims, all of which are ready for examination by the examiner.
Oath/Declaration
The applicant’s oath/declaration has been reviewed by the examiner and is found to conform to the requirements prescribed in 37 C.F.R. 1.63.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Specification
The applicant’s specification submitted are acceptable for examination purposes.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 11-12 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Liu et al. (US 2023/0089481 A1).
For claim 1, Liu et al. teaches:
An anomaly detection method performed by an electronic device including one or more processors, a communication circuit which communicates with an external device, and one or more memories storing at least one instruction executed by the one or more processors [anomaly detection system, 0077: Liu], the method comprising: by the one or more processors, receiving target data for discriminating whether an anomaly occurs, wherein the target data includes a value for each of a plurality of features [receiving targeted and structured data for anomaly detection, 0018-0019: Liu]; inputting a value for at least one important feature among the plurality of features into an anomaly detection model, wherein the at least one important feature is determined by important feature information received from the external device [reception of node to node features for anomaly detection, 0018; reception of service to device, 0077: Liu]; and determining whether the target data is abnormal based on an output of the anomaly detection model [detection of abnormality, 0017: Liu].
For claim 2, Liu et al. teaches:
The anomaly detection method according to claim 1, wherein the external device determines at least one important feature among the plurality of features included in the target data based on an autoencoder model [feature determination from data based on autoencoder model, 0051-0059: Liu].
For claim 3, Liu et al. teaches:
The anomaly detection method according to claim 2, wherein the anomaly detection model is a low complexity model having a lower complexity than the autoencoder model [anomaly detection model with better performance than autoencoder model, 0020-0023: Liu].
For claim 4, Liu et al. teaches:
The anomaly detection method according to claim 1, wherein the anomaly detection model is a model learned based on at least one technique of isolation forest, principal component analysis (PCA), support vector machine (SVM), a density-based spatial clustering of applications with noise (DBSCAN), or normal distribution technique [anomaly modeling based on spatial modeling and normal distribution, 0019 and 0024: Liu].
For claim 5, Liu et al. teaches:
The anomaly detection method according to claim 1, wherein the determining of whether the target data is abnormal includes comparing an evaluation score calculated by the output of the anomaly detection model and a critical score, and determining the target data as anomaly data when the evaluation score is equal to or less than the critical score [anomaly scoring based on comparison to a reference score, 0017: Liu].
For claim 6, Liu et al. teaches:
An anomaly detection method performed by an electronic device including one or more processors, a communication circuit which communicates with an external device, and one or more memories storing at least one instruction executed by the one or more processors [anomaly detection system, 0077: Liu], the method comprising: by the one or more processors, acquiring an original data set constituted by data of the same format as target data to be subjected to anomaly detection [comparison and anomaly detection based on scoring using initial data with test data, 0051-0064: Liu]; determining at least one important feature among a plurality of features of data based on an autoencoder model and the original data set [features determined based on autoencoder model, 0051-0059: Liu]; and transmitting important feature information including information on the at least one important feature to the external device through the communication circuit [reception of node to node features for anomaly detection, 0018; reception of service to device, 0077: Liu].
For claim 8, Liu et al. teaches:
The anomaly detection method according to claim 6, wherein the determining of the at least one important feature includes calculating each of a first reconstruction error change amount for a specific feature in a normal data set included in the original data set [reconstruction errors based on network structure, 0059: Liu], and a second reconstruction error change amount for the specific feature in an anomaly data set included in the original data set [reconstruction errors based on node attributes, 0059: Liu].
For claim 11, Liu et al. teaches:
An electronic device comprising: a communication circuit which communicates with an external device; one or more processors; and one or more memories storing instructions which cause the one or more processors to perform a computation when being executed by the one or more processors [anomaly detection system, 0077: Liu], wherein the one or more processors are configured to receive target data for discriminating whether an anomaly occurs, wherein the target data includes a value for each of a plurality of features [receiving targeted and structured data for anomaly detection, 0018-0019: Liu], input a value for at least one important feature among the plurality of features into an anomaly detection model, wherein the at least one important feature is determined by important feature information received from the external device [reception of node to node features for anomaly detection, 0018; reception of service to device, 0077: Liu], and determine whether the target data is abnormal based on an output of the anomaly detection model [detection of abnormality, 0017: Liu].
For claim 12, Liu et al. teaches:
The electronic device according to The electronic device according to wherein the one or more processors are configured to acquire an original data set constituted by data of the same format as target data to be subjected to anomaly detection [comparison and anomaly detection based on scoring using initial data with test data, 0051-0064: Liu], determine at least one important feature among a plurality of features of data based on an autoencoder model and the original data set [features determined based on autoencoder model, 0051-0059: Liu], and transmit important feature information including information on the at least one important feature to the external device through the communication circuit [reception of node to node features for anomaly detection, 0018; reception of service to device, 0077: Liu].
Allowable Subject Matter
Claims 7 and 9-10 are in condition for allowance.
For dependent claims 7, 9 and 10, prior art, in current interpretation of the perceived language, calculating reconstruction error change for features like cited reference Liu et al. (US 2023/0089481 A1) but does not calculating a reconstruction error of the original data set by using the autoencoder model, calculating a reconstruction error of a modified data set in which a specific feature value of data included in the original data set is changed by using the autoencoder model, and calculating an importance level of the specific feature value based on the reconstruction error of the original data set and the reconstruction error of the modified data set.
Conclusion
The Examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting the application.
When responding to this Office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJITH M JACOB whose telephone number is (571)270-1763. The examiner can normally be reached on Monday-Friday: Flexible Hours.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AJITH JACOB/Primary Examiner, Art Unit 2161
2/16/2026