DETAILED ACTION
This Action is responsive to Claims filed 09/02/2025.
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 .
Status of the Claims
Claims 1-2, 8-12, 14, 16-18, 21, and 24 have been amended. Claims 4, 5, and 19 have been cancelled. Claims 1-3, 6-18, and 20-24 are currently pending.
Response to Arguments
Applicant’s arguments, see Pages 11-15, filed 09/02/2025, with respect to claims 1, 4-14, 16-17, and 19-24 have been fully considered and are persuasive. The 35 U.S.C. 102(a)(1) rejection of claims 1, 4-14, 16-17, and 19-24 has been withdrawn. However, new grounds of rejection have been raised under 35 U.S.C. 102(a)(1) over Agarwal et al. (Inductive Anomaly Detection on Attributed Networks, 01/07/02021).
Applicant’s arguments with respect to the 35 U.S.C. 103 prior art rejection(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-18, and 20-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ding, Li, Agarwal, and Liu (Inductive Anomaly Detection on Attributed Networks, 01/07/02021), hereinafter Agarwal.
Applicant has provided evidence in this file showing that the claimed invention and the subject matter disclosed in the prior art reference were owned by, or subject to an obligation of assignment to, the same entity as Kaize Ding and Huan Liu not later than the effective filing date of the claimed invention, or the subject matter disclosed in the prior art reference was developed and the claimed invention was made by, or on behalf of one or more parties to a joint research agreement in effect not later than the effective filing date of the claimed invention. However, although reference Agarwal has been excepted as prior art under 35 U.S.C. 102(a)(2), it is still applicable as prior art under 35 U.S.C. 102(a)(1) that cannot be excepted under 35 U.S.C. 102(b)(2)(C).
Applicant may rely on the exception under 35 U.S.C. 102(b)(1)(A) to overcome this rejection under 35 U.S.C. 102(a)(1) by a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application, and is therefore not prior art under 35 U.S.C. 102(a)(1). Alternatively, applicant may rely on the exception under 35 U.S.C. 102(b)(1)(B) by providing evidence of a prior public disclosure via an affidavit or declaration under 37 CFR 1.130(b).
In regards to claim 1: The present invention recites: “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to: receive, at the processor, a graph indicative of a network that includes a plurality of nodes;” Agarwal Section 3, Page 1289 “Problem 1 Inductive Anomaly Detection on Attributed Networks: Given a partially observed attributed network G = (A,X) for training and a newly observed (sub)network G’ = (A’, X’ ) for testing, the task is to rank all the nodes in G’ according to the degree of abnormality, such that abnormal nodes should be ranked on higher positions.”
“and generate, by an encoder network of a graph differentiative network in association with the processor, a set of learned node representations indicative of a plurality of features of the plurality of nodes of the graph, the encoder network including a plurality of graph differentiative layers configured to:” Agarwal Section 4.1, Pages 1289-1290 “We will start by describing a single graph differentiative layer (Figure 1(a)) used to construct any graph differentiation networks (GDNs) for inductive anomaly detection. Apart from the existing GNNs, GDN is capable of learning anomaly aware node representations from arbitrary-order neighborhoods. Specifically, a GDN layer has an attention-based hierarchical structure described as follows”
“compute, for each node and for each neighborhood order of a plurality of neighborhood orders, a node-level embedding of a plurality of node-level embeddings by applying a node-level attention mechanism to feature difference vectors between the node and one or more neighboring nodes located at the neighborhood order,” Agarwal Section 4.1, Page 1290 describes this limitation regarding node-level embeddings using a node-level attention mechanism to capture feature differences.
“and generate the set of learned node representations by applying a neighborhood-level attention mechanism to the plurality of node-level embeddings generated across the plurality of neighborhood orders.” Agarwal Section 4.1, Page 1290 describes this limitation regarding neighbor-level embeddings using a neighborhood-level attention mechanism across multiple neighborhoods.
In regards to claim 2: The present invention claims: “generate, by a discriminator network of a generative adversarial network in association with the processor and based on the set of learned node representations associated with a node of the plurality of nodes, an output value indicative of a quantitative assessment of a normalcy of the node;” Agarwal Sections 4.2 and 4.3, Pages 1290-1291 describe the use of the generative adversarial network in conjunction with the aforementioned embeddings.
“and apply an anomaly scoring function to the output value generated by the discriminator network that results in an anomaly score for each respective node of the plurality of nodes.” Agarwal Section 4.4, Page 1291 describes the output of the anomaly score.
In regards to claim 3: The present invention claims: “generate a list that includes the plurality of nodes, wherein the list ranks each node of the plurality of nodes based on the anomaly score for each respective node of the plurality of nodes.” Agarwal Section 3, Page 1289 “the task is to rank all the nodes in G’ according to the degree of abnormality, such that abnormal nodes should be ranked on higher positions.”
In regards to claim 6: The present invention claims: “wherein the graph is indicative of a newly observed network.” Agarwal Section 4.3, Page 1291 “After the model converges on the training network, the discriminator D learns the distribution of normal nodes, and can be directly used to detect anomalies on any newly observed nodes or (sub)networks.”
In regards to claim 7: The present invention claims: “train the encoder network of the graph differentiative network using a decoder network of the graph differentiative network, the decoder network being a neural network and operable to decode the set of learned node representations and the graph being indicative of a training network.” Agarwal Section 4.2, Page 1290 and Figure 1, Page 1290 describe the decoder and its function in conjunction with the encoder.
In regards to claim 8: The present invention claims: “train the discriminator network of the generative adversarial network using a generator network of the generative adversarial network, the generator network being a neural network and operable to generate one or more informative potential anomalies and the graph being indicative of a training network.” Agarwal Section 4.2, Page 1291 “With the learned anomaly-aware node representations, the second phase aims to train a generative adversarial network (w.r.t., Ano-GAN) that can accurately model the distribution of normal data. Specifically, the generator G takes noises sampled from a prior distribution p(˜z) as input, and attempts to generate informative potential anomalies.”
In regards to claim 9: The present invention claims: “apply the discriminator network to the one or more informative potential anomalies and one or more learned node representations of the set of learned node representations of the graph;” Agarwal Section 4.2, Page 1291 describes the learning process of the discriminator network.
“determine, by the discriminator network, a distribution of normal nodes based on the one or more informative potential anomalies and the set of learned node representations of the graph;” Agarwal Section 4.2, Page 1291 describes the distribution learned by the discriminator network.
“and determine, by the discriminator network, a decision boundary that encloses the distribution of normal nodes.” Agarwal Section 4.2, Page 1291 describes the decision boundary determined by the discriminator network.
In regards to claims 10 and 11: Claims 10 and 11 recites similar limitations to claims 1-3 and 6-9, with the exception of “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:” of claim 10; therefore, both claims are similarly rejected.
In regards to claim 12: The present invention claims: “minimize a first loss that optimizes the generator network on an output of the generator network and an output of the discriminator network; and minimize a second loss that optimizes the discriminator network on the output of the generator network and the output of the discriminator network, the second loss incorporating the first loss such that a result of the second loss increases when a result of the first loss decreases.” Agarwal Section 4.3, Page 1291 and Algorithm 1 describes both loss functions.
In regards to claim 13: The present invention claims: “jointly optimize the first loss and the second loss.” See above where Agarwal describes optimizing both loss functions Equations 7 and 8.
In regards to claim 14: Claim 14 recites similar limitations to claims 1-3 and 6-9, with the exception of “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:” of claim 10; therefore, both claims are similarly rejected.
In regards to claim 15: The present invention claims “minimize a reconstruction loss between the encoder network and the decoder network.” Agarwal Section 4.3, Page 1291 (left column describes the reconstruction loss of the GDN-AE.
In regards to claim 16: Claim 16 recites similar limitations to claims 1-3 and 6-9, with the exception of “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:” of claim 10; therefore, both claims are similarly rejected.
In regards to claims 17-18 and 20-24: Claims 17-18 and 20-24 recites similar limitations to claims 1-3 and 6-9, with the exception of “A method, comprising:” of claim 17; therefore, both claims are similarly rejected.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 6, 10, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (SPAGAN: Shortest Path Graph Attention Network, 2021), hereinafter Yang, and Boschetti et al. (TVi: A Visual Querying System for Network Monitoring and Anomaly Detection, 2011), hereinafter Boschetti.
In regards to claim 1: The present invention recites: “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to: receive, at the processor, a graph indicative of a network that includes a plurality of nodes;” See Yang Figure 2 (Page 2) at least for their method operating on a graph made of nodes.
“and generate, by an encoder network of a graph differentiative network in association with the processor, a set of learned node representations indicative of a plurality of features of the plurality of nodes of the graph, the encoder network including a plurality of graph differentiative layers configured to:” Yang teaches “Our contribution is therefore a novel high-order graph attention network, that explicitly conducts path-based attention within each layer, allowing for an effective and intact encoding of the graphical structure into the attention coefficients and thus into the features of nodes.”
“compute, for each node and for each neighborhood order of a plurality of neighborhood orders, a node-level embedding of a plurality of node-level embeddings by applying a node-level attention mechanism…between the node and one or more neighboring nodes located at the neighborhood order,” See Yang Section 4.1, Page 4, specifically Equation 6-8 for the attention mechanism being applied to a node and its neighbors of a given order.
“and generate the set of learned node representations by applying a neighborhood-level attention mechanism to the plurality of node-level embeddings generated across the plurality of neighborhood orders.” See Yang Section 4.1, Page 4, specifically Equation 9 “In the second level, we focus on aggregating features of paths with different lengths and applying the attention mechanism to obtain the embedded features for the center node”
Yang fails to explicitly teach “…to feature difference vectors…” However, Boschetti teaches “In order to detect an anomalous behaviour, a metric (measure of distance) is requested. We used the Euclidean norm of the difference vector as an anomaly indicator:” in their anomaly detection algorithm (Section 3.1.3, Pages 3-4).
Boschetti teaches “Monitoring, anomaly detection and forensics are essential tasks that must be carried out routinely for every computer network” (Abstract). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to compare feature differences, rather than feature influence, when attempting to detect anomalous nodes in a network in a combination of Yang and Boschetti.
In regards to claim 6: The present invention claims: “wherein the graph is indicative of a newly observed network.” Yang evaluates their system on multiple graphs/networks (Pages 5-6, Sections 5.2-6).
In regards to claim 10: Claim 10 recites similar limitations to claims 1, with the exception of “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:” of claim 10; therefore, both claims are similarly rejected.
In regards to claim 17 and 20: Claims 17 and 20 recite similar limitations to claims 1 and 6, with the exception of “A method, comprising…” of claim 17, therefore, both claims are similarly rejected.
Claim(s) 2-3, 7-9, 11-14, 16, 18, and 21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang and Boschetti as applied to claims 1, 10, and 17 above, and further in view of Wang et al. (Adversarial Defense Framework for Graph Neural Networks, 2019), hereinafter Wang, and Ding et al. (Interactive Anomaly Detection on Attributed Networks, 2019), hereinafter Ding1.
In regards to claim 2: The Combination of Yang and Boschetti fails to explicitly teach “generate, by a discriminator network of a generative adversarial network in association with the processor and based on the set of learned node representations associated with a node of the plurality of nodes, an output value indicative of a quantitative assessment of a normalcy of the node;” However, Wang, in a similar field of endeavor of graph attention and anomaly detection teaches an Adversarial Contrastive Learning generator to learn the graph’s aforementioned embedding and output a scoring matrix (Pages 4-5, Section 3.2 and Figure 1).
Wang highlights the training data volume and security threats with GNNs “In particular, we first investigate the latent vulnerabilities in every layer of GNNs and propose corresponding strategies including dual-stage aggregation and bottleneck perceptron. Then, to cope with the scarcity of training data, we propose an adversarial contrastive learning method to train the GNN in a conditional GAN manner by leveraging the high-level graph representation.” (Abstract). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to improve the quantity of training data and robustness against adversarial attacks by combining elements of Yang, Boschetti, and Wang.
The combination of Yang, Boschetti, and Wang fails to explicitly teach an anomaly score as explicitly claimed in “and apply an anomaly scoring function to the output value generated by the discriminator network that results in an anomaly score for each respective node of the plurality of nodes.” However, Ding1 teaches “we assume that the anomalous nodes ranked by these unsupervised methods will be presented to the human expert one by one according to the descending order of the anomaly scores. Thus we can also compute the results in terms of the aforementioned three evaluation metrics.” (Page 363, Left Column).
Ding1 highlights the importance and varied challenges of performing anomaly detection on attributed networks, including an evolving threat space and interacting with the environment being modeled (Abstract, Introduction). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to combine the teachings of Yang, Boschetti, Wang, and Ding1 to overcome said challenges.
In regards to claim 3: The present invention claims: “generate a list that includes the plurality of nodes, wherein the list ranks each node of the plurality of nodes based on the anomaly score for each respective node of the plurality of nodes.” See above where a combination of Wang and Ding1 (Page 363) would read on a ranked list of nodes and scores, and how a combination of Yong Boschetti, Wang, and Ding1 would have been obvious to one of ordinary skill in art.
In regards to claim 7: The present invention claims: “train the encoder network of the graph differentiative network using a decoder network of the graph differentiative network, the decoder network being a neural network and operable to decode the set of learned node representations and the graph being indicative of a training network.” Wang teaches that a typical GNN includes training based on a decoder (Page 2, Section 2). These decode functions appear in Figure 1.
In regards to claim 8: The present invention claims: “train the discriminator network of the generative adversarial network using a generator network of the generative adversarial network, the generator network being a neural network and operable to generate one or more informative potential anomalies and the graph being indicative of a training network.” Wang teaches “However, the vanilla GAN does not take any constraint into account for the generator, and the generator is not tailored to the graph data. This may cause suboptimal quality of the generated adversarial samples since the adversarial latent space can not be effectively explored. This limitation may reduce the performance of the feature learning. To overcome it, we adopt a conditional GAN configuration, which extends a conditional model where the generator and discriminator are both conditioned on some extra information.” (Page 4, Right Column).
In regards to claim 9: The present invention claims: “apply the discriminator network to the one or more informative potential anomalies and one or more learned node representations of the set of learned node representations of the graph; determine, by the discriminator network, a distribution of normal nodes based on the one or more informative potential anomalies and the set of learned node representations of the graph; and determine, by the discriminator network, a decision boundary that encloses the distribution of normal nodes.” Wang teaches their model performing adversarial contrastive learning on the generated samples and generating a distribution of the learned samples in Section 3.2, Page 4-5, Right and Left Columns respectively.
In regards to claim 11: Claim 11 recites similar limitations to claims 1-3 and 6-9, with the exception of “A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:” of claim 10; therefore, both claims are similarly rejected.
In regards to claim 12: The present invention claims: “minimize a first loss that optimizes the generator network on an output of the generator network and an output of the discriminator network; and minimize a second loss that optimizes the discriminator network on the output of the generator network and the output of the discriminator network, the second loss incorporating the first loss such that a result of the second loss increases when a result of the first loss decreases.” Section 3.2 of Wang teaches the joint training of the generator and the discriminator networks, with the discriminator attempting to correctly guess the generated samples while the generator attempts to make better and better samples.
In regards to claim 13: The present invention claims: “jointly optimize the first loss and the second loss.” See the rejection of claim 12 above where Wang would read on this optimization.
In regards to claim 14: The present invention claims: “sample, by the generator network, a prior distribution value from a prior distribution; and generate, by the generator network and using the prior distribution value, the one or more informative potential anomalies.” Wang teaches “The generative model G captures the data distribution, and the discriminative model Dw estimates the probability that a sample comes from the training data…” (Page 4, Right Column, mapping to sampling prior data, by Applicant’s Specification [0034]).
In regards to claim 16: Claim 16 recites similar limitations to claims 1 and 6-9, therefore both claims are similarly rejected.
In regards to claims 18 and 21-24: Claims 18 and 21-24 recite similar limitations to claims 1 and 7-9, with the exception of “A method, comprising…” of claim 17, therefore, both sets of claims are similarly rejected.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang and Boschetti as applied to claim 10 above, and further in view of Ding et al. (Deep Anomaly Detection on Attributed Networks, 2019), hereinafter Ding2.
In regards to claim 15: The combination of Yang and Boschetti fails to explicitly teach “minimize a reconstruction loss between the encoder network and the decoder network.” However, Ding2 teaches “The reconstruction errors of nodes following the encoder and decoder phases are then leveraged for spotting anomalous nodes on attributed networks.” (Page 595, Left Column).
Ding2 highlights the importance and varied challenges of performing anomaly detection on attributed networks, including adequately capturing the complexity of attributed networks (Abstract, Introduction). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to combine the teachings of Yang, Boschetti, and Ding2 to overcome said challenges.
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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/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121