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 .
Response to Amendment
The amendment filed February 27, 2026 has been entered. Claims 1, 4-7, and 10-15 remain pending in the application.
Claim Objections
Claims 14-15 are objected to because of the following informalities:
Claims 14-15 are objected because of capital letters within body of claims, as in “F1 score”. Capital letter should only be used for first letter of claim or abbreviation.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the Step 1 of the Section 101 analysis, Claims 1-6 and 13 are drawn to a method which is within the four statutory categories (i.e., a process), and Claims 7-12 are drawn to a device which is within the four statutory categories (i.e. a machine).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-13 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1 and 7:
Claims 1 and 7 are drawn to an abstract idea without significantly more. The claims recite “performing mapping learning of first embedded features corresponding to learning data onto an embedding space through a network function, wherein the learning data have at least one normal data and at least one auxiliary data; mapping second embedded features corresponding to input data onto the embedding space by inputting the input data to the network function subjected to the learning; calculating anomaly scores based on the distances between the second embedded features and at least one or more first embedded features proximal to the second embedded features in the embedding space; and determining whether the input data are normal, based on the calculated anomaly scores, wherein the at least one auxiliary data have classes that are non-overlapping with the at least one normal data, and wherein in the performing the mapping learning, the network function learns to: map the first embedded features produced from the learning data having a same class as one another onto positions proximal to one another; and map the first embedded features produced from the learning data having different classes from one another onto positions distant from one another.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Certain Methods Of Organizing Human Activity such as Mathematical Concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations. For example, but for the “network” language, the underlined limitations in the context of this claim encompass the mathematical concepts. The series of steps belong to a typical mathematical calculations. Additionally, mapping features onto positions also belong to a typical mathematical calculations.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “An anomaly detection method comprising:”, “An anomaly detection device comprising: a memory for storing a program for anomaly detection; and a processor for executing the program and configured to:”, and “network”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claims 4-6 and 10-15:
Dependent claims 2-6 and 8-13 only further elaborate the abstract idea and do not recite additional elements.
Dependent claims 14-15 include additional limitations, for example, “network” (Claims 14-15), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-6 and 8-13 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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, 4-7, and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoon (KR102088509B1; already of record in IDS) in view of Aizawa (WO 2018148493 A1).
Regarding Claims 1 and 7, Yoon teaches An anomaly detection method comprising (Yoon: Abstract): An anomaly detection device comprising: a memory for storing a program for anomaly detection; and a processor for executing the program and configured to (Yoon: Page 3, lines 20-24; 8/16-35):
performing mapping learning of first embedded features corresponding to learning data onto an embedding space through a network function, wherein the learning data have at least one normal data and at least one … (Yoon: 5/19-27; 5/37 ~ 6/2; 2/13-19 teach(es) the embedding unit may map the collected log into a multi-dimensional space as a vector of a predetermined size using a deep learning-based sentence representation method; It is a natural language processing (NLP) field that vectorizes the meaning of the sentence itself in a multidimensional space; a method of classifying the behavior of a computer system into normal behavior and abnormal behavior using machine learning based on supervised learning on a data set. It vectorizes each data using frequency-based and TF-IDF, and classifies the vector into normal behavior and abnormal behavior using SVM (Support Vector Machine) and ANN (Artificial Neural Networks) models); mapping second embedded features corresponding to input data onto the embedding space by inputting the input data to the network function subjected to the learning (Yoon: 5/19-27; 5/37 ~ 6/2; 2/13-19, as stated above); calculating anomaly … based on distances between the second embedded features and at least one or more first embedded features proximal to the second embedded features in the embedding space; and determining whether the input data are normal, based on the calculated anomaly … (Yoon: 5/7-18; 6/26-32; 7/1-13 teach(es) the anomaly detection unit may detect that an anomaly has occurred in the computer system related to the call sequence log when the distance between the distribution region of the known normal call sequence and the vector of the call sequence log is greater than or equal to a predetermined value; the anomaly detection unit may apply the collected call sequence log (or the transformed vector) to the machine learning algorithm to determine whether the collected call sequence log represents a normal state or an abnormal behavior), ….
However, Yoon does not explicitly teach anomaly scores, auxiliary data, and wherein the at least one auxiliary data have classes that are non-overlapping with the at least one normal data, and wherein in the performing the mapping learning, the network function learns to: map the first embedded features produced from the learning data having a same class as one another onto positions proximal to one another; and map the first embedded features produced from the learning data having different classes from one another onto positions distant from one another.
Aizawa from same or similar field of endeavor teaches anomaly scores (Aizawa: Paragraph(s) 0081, 0060, 0004-0005 teach(es) Comparing these first embedding and the second embedding using a visual similarity metric generates a score representing the distance in feature space between the embeddings), auxiliary data (Aizawa: Paragraph(s) 0020, 0056, 0046, 0048-0049 teach(es) Mathematically, neural networks represent a class of non-linear and linear mapping functions of inputs to outputs; to classify the training data as anchor, similar, and dissimilar images based on the key word), and wherein the at least one auxiliary data have classes that are non-overlapping with the at least one normal data, and wherein in the performing the mapping learning (Aizawa: Paragraph(s) 0020, 0056, 0046, 0048-0049 teach(es) Mathematically, neural networks represent a class of non-linear and linear mapping functions of inputs to outputs; to classify the training data as anchor, similar, and dissimilar images based on the key word), the network function learns to: map the first embedded features produced from the learning data having a same class as one another onto positions proximal to one another; and map the first embedded features produced from the learning data having different classes from one another onto positions distant from one another (Aizawa: Paragraph(s) 0020, 0056, 0046, 0048-0049, as stated above).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yoon to incorporate the teachings of Aizawa for anomaly scores, auxiliary data, and wherein the at least one auxiliary data have classes that are non-overlapping with the at least one normal data, and wherein in the performing the mapping learning, the network function learns to: map the first embedded features produced from the learning data having a same class as one another onto positions proximal to one another; and map the first embedded features produced from the learning data having different classes from one another onto positions distant from one another.
There is motivation to combine Aizawa into Yoon because Aizawa’s teachings of anomaly scores would facilitate the machine learning or neural network (Aizawa: Paragraph(s) 0020).
Regarding Claims 4 and 10, the combination of Yoon and Aizawa teaches all the limitations of claims 1 and 7 above; however the combination does not explicitly teach wherein in the performing the mapping learning, the network function performs the learning based on at least one or more loss functions selected from Triplet loss, Max margin, NT-Xent, and NT-Logistic.
Aizawa further teaches wherein in the performing the mapping learning, the network function performs the learning based on at least one or more loss functions selected from Triplet loss, Max margin, NT-Xent, and NT-Logistic (Aizawa: Paragraph(s) 0029 teach(es) The neural network might be optimized using a metric learning objective function (e.g., triplet loss, n-pair loss, or another suitable metric learning objective) that uses relative positive and negative pairings of training examples).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yoon to incorporate the teachings of Aizawa for wherein in the performing the mapping learning, the network function performs the learning based on at least one or more loss functions selected from Triplet loss, Max margin, NT-Xent, and NT-Logistic.
There is motivation to combine Aizawa into Yoon because Aizawa’s teachings of a metric learning objective function would facilitate the machine learning or neural network (Aizawa: Paragraph(s) 0029).
Regarding Claims 5 and 11, the combination of Yoon and Aizawa teaches all the limitations of claims 1 and 7 above; however the combination does not explicitly teach wherein the calculating the anomaly scores comprises: detecting the at least one or more first embedded features in order of proximity to the second embedded features; and calculating a sum or an average of the distances between the second embedded features and the detected first embedded features.
Aizawa further teaches wherein the calculating the anomaly scores comprises: detecting the at least one or more first embedded features in order of proximity to the second embedded features; and calculating a sum or an average of the distances between the second embedded features and the detected first embedded features (Aizawa: Paragraph(s) 0076, 0081).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yoon to incorporate the teachings of Aizawa for wherein the calculating the anomaly scores comprises: detecting the at least one or more first embedded features in order of proximity to the second embedded features; and calculating a sum or an average of the distances between the second embedded features and the detected first embedded features.
There is motivation to combine Aizawa into Yoon because Aizawa’s teachings of the distance in feature space between the embeddings would facilitate the machine learning or neural network (Aizawa: Paragraph(s) 0081).
Regarding Claims 6 and 12, the combination of Yoon and Aizawa teaches all the limitations of claims 5 and 11 above; however the combination does not explicitly teach wherein the calculating the anomaly scores calculates the anomaly scores based on a K-Nearest Neighbor.
Aizawa further teaches wherein the calculating the anomaly scores calculates the anomaly scores based on a K-Nearest Neighbor (KNN) function (Aizawa: Paragraph(s) 0029 teach(es) To produce a recommendation, an example can be projected into this vector space and using nearest neighbor search, the nearest projection in this vector subspace is selected for recommendation).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yoon to incorporate the teachings of Aizawa for wherein the calculating the anomaly scores calculates the anomaly scores based on a K-Nearest Neighbor.
There is motivation to combine Aizawa into Yoon because Aizawa’s teachings of the nearest neighbor search would facilitate the machine learning or neural network (Aizawa: Paragraph(s) 0081).
Regarding Claim 13, the combination of Yoon and Aizawa teaches all the limitations of claim 1 above; and Yoon further teaches A computer program stored in a non-transitory recording medium to execute the method (Yoon: 3/16-19).
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoon in view of Aizawa, as applied to claims 6 and 12 above, and in further view of Kawanoue (US 20190286085 A1).
Regarding Claim 14, the combination of Yoon and Aizawa teaches all the limitations of claim 6 above; however the combination does not explicitly teach wherein the input data is determined to be abnormal if the calculated anomaly scores are greater than an optimal threshold value, and the optimal threshold value is a highest F1 score value calculated by inputting validation data into the learned network function.
Kawanoue further teaches wherein the input data is determined to be abnormal if the calculated anomaly scores are greater than an optimal threshold value, and the optimal threshold value is a highest F1 score value calculated by inputting validation data into the learned network function (Kawanoue: Paragraph(s) 0118-0120 teach(es) The abnormality score is a value indicating a possibility that one or a set of plural feature values of an evaluation target is an outlier or an abnormal value. The larger the value, the higher the probability of an abnormal value becomes (however, an abnormality score may be set to indicate a lower value when a value is more likely to be an abnormal value); the calculated abnormality score is compared to a predetermined threshold value, and thereby whether an abnormality has occurred in the monitoring target is determined).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Yoon and Aizawa to incorporate the teachings of Kawanoue for wherein the input data is determined to be abnormal if the calculated anomaly scores are greater than an optimal threshold value, and the optimal threshold value is a highest F1 score value calculated by inputting validation data into the learned network function.
There is motivation to combine Kawanoue into the combination of Yoon and Aizawa because Kawanoue’s teachings of abnormality score and predetermined threshold value would facilitate to detect and process the abnormal value (Kawanoue: Paragraph(s) 0118-0120).
Regarding Claim 15, the combination of Yoon and Aizawa teaches all the limitations of claim 12 above; however the combination does not explicitly teach wherein the input data is determined to be abnormal if the calculated anomaly scores are greater than an optimal threshold value, and the optimal threshold value is a highest F1 score value calculated by inputting validation data into the learned network function.
Kawanoue from same or similar field of endeavor teaches wherein the input data is determined to be abnormal if the calculated anomaly scores are greater than an optimal threshold value, and the optimal threshold value is a highest F1 score value calculated by inputting validation data into the learned network function (Kawanoue: Paragraph(s) 0118-0120, as stated above with respect to claim 14).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Yoon and Aizawa to incorporate the teachings of Kawanoue for wherein the input data is determined to be abnormal if the calculated anomaly scores are greater than an optimal threshold value, and the optimal threshold value is a highest F1 score value calculated by inputting validation data into the learned network function.
There is motivation to combine Kawanoue into the combination of Yoon and Aizawa because Kawanoue’s teachings of abnormality score and predetermined threshold value would facilitate to detect and process the abnormal value (Kawanoue: Paragraph(s) 0118-0120).
Response to Arguments
Applicant's arguments filed February 27, 2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument under Claim Rejections - 35 USC § 101 that “Unlike conventional machine learning-based anomaly detection methods that struggle to distinguish between normal and abnormal data when training with small datasets, the present claims introduce an enhanced training mechanism utilizing auxiliary data with classes that do not overlap with normal data. This claimed method inherently improves the clustering degree and feature extraction quality of the AI model, resulting in enhanced accuracy and efficiency of anomaly detection. (See paragraphs [0003]-[0006] of the publication),” examiner respectfully argues that the auxiliary data has never been recited or used explicitly in the other steps of calculating, determining, and the network function’s learning to map the first embedded features, not providing any improvements or integrating the abstract idea into a practical application. In addition, the argued features including “improves the clustering degree and feature extraction quality of the AI model, resulting in enhanced accuracy and efficiency of anomaly detection”, “The degree of clustering of normal data 710 is significantly enhanced through the joint learning process of using both normal data and auxiliary data”, “When the learning is performed using both normal and auxiliary data, the distribution variance among the normal data decreases”, “the auxiliary data allows the model to learn a greater abundance of differential features for identifying the normal data, improving the effectiveness of abnormal data determination”, etc. are not recited or reflected in the claims.
Regarding applicant’s argument under Claim Rejections - 35 USC § 103 that “The claimed method trains the network function using both normal data and auxiliary data with classes with non-overlapping classes. Through this process, the network function learns to map embedded features produced from the learning data having the same class more proximal to each other, while mapping embedded features produced from the learning data having different classes more distant from one another,” examiner respectfully argues that, as discussed above, the auxiliary data has never been recited or used explicitly in the other steps of calculating, determining, and the network function’s learning to map the first embedded features, not providing any improvements or integrating the abstract idea into a practical application. In addition, the argued features including “The auxiliary data is also distinguished from the target anomaly data of the detection process and serves as a distinct reference data introduced to define the boundaries more precisely”, “a contrastive learning mechanism of mapping the same classes closer together and mapping different classes”, “the claimed method significantly enhances the cluster density of normal data and enables the effective identification of even minute outliers from the normal data, which is difficult to achieve using conventional methods”, “improving normal data clustering and separating minute outliers by using auxiliary data”, etc. are not recited or reflected in the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gfeller (US 20210056980 A1) teaches Self-Supervised Audio Representation Learning For Mobile Devices, including formulating one or more auxiliary tasks using unlabeled data, and mapping the input data to the embedding space.
Muhistein (US 20230091110 A1) teaches Joint Embedding Content Neural Networks, including triplet loss, mapping, embedding, and k nearest neighbors.
Chen (US 20210110275 A1) teaches System And Method Of Machine Learning Using Embedding Networks, including triplet, neighbour, and nearest neighbors in a feature embedding space.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLAY LEE whose telephone number is (571)272-3309. The examiner can normally be reached Monday-Friday 8-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neha Patel can be reached at (571)270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CLAY C LEE/Primary Examiner, Art Unit 3699
Prosecution Insights