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 .
DETAILED ACTION
The reply filed 5/4/2026 was received and considered.
Claims 1-15 are pending.
Response to Arguments
Applicant's arguments filed 5/4/2026 have been considered but they are not fully persuasive.
Applicant’s remarks (p. 8, regarding rejections under 35 U.S.C. §101) are persuasive in view of the amendments.
Applicant’s remarks (p. 8, regarding rejections under 35 U.S.C. §112) are persuasive in view of the amendments.
Applicant’s remarks (pp. 9-10) suggests that Stahlberg does not disclose that the local model is a “machine learning-based model of a backend threat detection mechanism”. However, Stahlberg discloses that local agents build behavioral models (¶88), report the models to the security server backend (¶91), where the security server backend can also share behavioral models to the network nodes (¶91) and teaches that a common model of normal behavior can be created at the security server backend (¶72), which then redistributes the common learnings to cope with updates (¶73). The Examiner respectfully maintains that, as the security server backend utilizes the received local and received/generated common models, the limitation “machine learning-based model of a backend threat detection mechanism” is met by the prior art.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-3, 6 and 12-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2022/0191224 A1 to Stahlberg et al. (Stahlberg).
Regarding claim 1, Stahlberg discloses a method of threat detection in a threat detection network (threat detection network, ¶36), the threat detection network comprising interconnected nodes (5a – 5h) (nodes 5a-5h, ¶38 and Fig. 1) and a backend system (2) (security backend/server 2, Fig. 1, ¶37), wherein the backend system (2) utilizes a backend threat detection mechanism (backend can utilize methods like federated learning to combine knowledge from multiple endpoints and consolidate models of users across multiple endpoints and/or also utilize hierarchical modelling approaches to learn from behaviors of similar users, ¶64; backend provides correlation and analysis of the data sent from the multitude of individual intelligent sensors and can also share behavioral models to the network nodes, ¶91; see also ¶22, comparing at the backend system the anomalous data with other behavior models, e.g. with other behavior models in the same organization and/or behavior models of known malicious users), and at least part of the nodes (5a – 5h) comprise security agent modules (6a – 6h) (nodes comprise security agent modules, ¶38) which collect data related to the respective node (security agent modules, 6a-6h, 4a collect various types of data at the nodes 5a-5h, ¶38), and wherein the nodes (5a – 5h) utilize at least one local threat detection model which comprises a machine learning-based model of a backend threat detection mechanism (agents continuously monitor, build behavioral models and detect anomalies, ¶89), wherein the method comprises: collecting data related to the node (5a – 5h) by the security agent module (6a – 6h) at the node (security agent modules, 6a-6h, 4a collect various types of data at the nodes 5a-5h, ¶38), applying the local threat detection model to the collected data (agents continuously monitor, build behavioral models and detect anomalies, ¶88; known threat can be detected based on the user behavior when comparing the detected behavior to the behavior model, ¶89), and making a security related decision at the node (5a – 5h), based on results of the local threat detection model (if the agent already has the means for response, that action may be taken, ¶89).
Regarding claim 12, the claim is similar in scope to claim 1 and is therefore rejected using a similar rationale (the claimed node is found in nodes 5a-5h, ¶38 and Fig. 1).
Regarding claim 13, the claim is similar in scope to claim 1 and is therefore rejected using a similar rationale.
Regarding claim 2, Stahlberg discloses wherein the local threat detection model which comprises a machine learning-based model of the backend threat detection mechanism (agents build behavioral model, utilizing a machine learning model, ¶63) is an approximation of the backend threat detection mechanism (common model of normal behavior may be generated by the security server backend of the computer network, ¶72; these common learnings are redistributed to cope for example operating system updates or new application versions which may be global but changing and would otherwise cause problems for such models, ¶73).
Regarding claim 3, Stahlberg discloses wherein the local threat detection model comprises at least one misuse detection model (behavior models, ¶65) which is based on at least one machine learning model for finding events that are likely to contribute to detections of a cyber incident (behavior models can then be used to monitor the activity of the same user and to notice changes in behavior which may be due to automation, attacks or simply another user using the same account-all potential threat scenarios, ¶65).
Regarding claim 6, Stahlberg discloses wherein the local threat detection model further comprises at least one anomaly detection model which is based on at least one machine learning model for finding uncommon events that are likely to contribute to threat detection (behavior models can then be used to monitor the activity of the same user and to notice changes in behavior which may be due to automation, attacks or simply another user using the same account-all potential threat scenarios, ¶65), intelligence and/or hunting purposes, and/or the at least one anomaly detection model is trained in unsupervised, supervised or semi-supervised learning fashion at the backend system or at the node (if the anomaly is determined to be a false positive e.g. by deeper analysis models or by a human analyst, the logic and/or behavior model is trained not to detect similar and corresponding case again as anomalous, ¶78).
Regarding claim 14, Stahlberg discloses a threat detection network comprising: at least one node (5a – 5h) according to claim 12 (see rejection of claim 12), and at least one backend system (2) (security backend/server 2, Fig. 1, ¶37), the backend system comprising at least one server which comprises at least one or more processors (security backend/server 2 comprising processors, Fig. 1, ¶37 and ¶22), and the backend system (2) is configured to utilize a backend threat detection mechanism (backend can utilize methods like federated learning to combine knowledge from multiple endpoints and consolidate models of users across multiple endpoints and/or also utilize hierarchical modelling approaches to learn from behaviors of similar users, ¶64; backend provides correlation and analysis of the data sent from the multitude of individual intelligent sensors and can also share behavioral models to the network nodes, ¶91; see also ¶22, comparing at the backend system the anomalous data with other behavior models, e.g. with other behavior models in the same organization and/or behavior models of known malicious users) and further configured to train and/or provide to nodes (5a – 5h) a local threat detection model (backend system can create a common model, ¶72 that are redistributed to nodes, ¶73), comprising an anomaly detection model and/or a misuse detection model (behavior models can then be used to monitor the activity of the same user and to notice changes in behavior which may be due to automation, attacks or simply another user using the same account-all potential threat scenarios, ¶65).
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 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.
Claims 4, 5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Stahlberg, as applied to claims 3 or 1, in view of US 2023/0231871 A1 to Jiao.
Regarding claim 4, Stahlberg lacks wherein the misuse detection models are trained at the backend system in supervised learning fashion for a classification problem. However, Jiao, in an analogous art (training a machine learning detector to detect threats, abstract), teaches that it was known to employ federated learning, including training a misuse detection model (gateway uses misuse detection model to detect malicious traffic, ¶112, ¶114) at a backend system (federated learning is employed such that the server aggregates parameters from gateways to train gateway models, ¶135) in supervised learning fashion for a classification problem (models are trained using malicious sample and normal sample, ¶121), gaining the benefit of improved model training (¶¶137-139). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg such that the misuse detection models are trained at the backend system in supervised learning fashion for a classification problem. One of ordinary skill in the art would have been motivated to perform such a modification to utilize federated learning to improve the models of the nodes, as taught by Jiao.
Regarding claim 5, Stahlberg, as modified, teaches wherein the misuse detection model comprises a training set comprising complementary subsets of existing events (malicious samples) that are proven to be relevant for confirmed and existing cyber incidents and/or cyber-attacks and/or represent typical benign (normal) behaviours (per the modification described with respect to claim 4, models are trained in a supervised manner using malicious samples and normal samples, Jiao, ¶121).
Regarding claim 11, Stahlberg discloses wherein preparation of the machine learning based threat detection model comprises defining local threat detection model features (agent builds a behavior model of a user, e.g. a “computer user behavioral persona”, ¶44), and training the local threat detection model based on training data (agent at the network node, e.g. an endpoint agent, locally collects and analyzes data which us used to build a behavior model of a user, e.g. a “computer user behavioral persona”, ¶44). Stahlberg, as modified, lacks defining backend threat detection mechanism features. However, Jiao, in an analogous art (training a machine learning detector to detect threats, abstract), teaches that it was known to employ federated learning, including training a misuse detection model (gateway uses local misuse detection model to detect malicious traffic, ¶112, ¶114) at a backend system (federated learning is employed such that the server aggregates parameters from gateways to train gateway models, ¶135) in supervised learning fashion for (models are trained using malicious sample and normal sample, ¶121), gaining the benefit of improved model training (¶¶137-139). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg to include defining local threat detection model features. One of ordinary skill in the art would have been motivated to perform such a modification to utilize federated learning to improve the models of the nodes, as taught by Jiao.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Stahlberg, as applied to claims 3 and 1, respectively, in view of US 2033/0114260 A1 to Udupi Raghavendra et al. (Raghavendra).
Regarding claim 7, Stahlberg does not explicitly teach wherein training of the misuse detection and/or an anomaly detection model is carried out regularly and/or once the training process is over, a new model is transmitted to nodes (5a – 5h) and used locally by the nodes (5a – 5h). However, Raghavendra, in an analogous art (collecting telemetry data to train an anti-malware system, abstract), teaches that it was known to collect telemetry data on non-malicious processes on a regular basis/periodically to adapt to changes of the input data over time (¶19). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg such that training of the misuse detection and/or anomaly detection model is carried out regularly and/or once the training process is over, a new model is transmitted to nodes (5a – 5h) and used locally by the nodes (5a – 5h). One of ordinary skill in the art would have been motivated to perform such a modification to adapt the models used by the nodes to changes over time, as taught by Raghavendra.
Regarding claim 15, Stahlberg discloses a computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method according to claim 1 (¶24), but lacks a non-transitory computer readable storage medium having instructions stored therein. However, Raghavendra, in an analogous art (collecting telemetry data to train an anti-malware system, abstract), teaches that it was known to utilize a non-transitory computer readable storage medium having instructions stored therein to implement the computer functions disclosed (¶¶123-124). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg such that the instructions are stored on a non-transitory computer readable storage medium. One of ordinary skill in the art would have been motivated to perform such a modification to utilize a known manner of storing and executing instructions, as taught by Raghavendra.
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Stahlberg, as applied to claim 1, in view of US 2024/0419785 A1 to Bartling et al. (Bartling).
Regarding claim 8, Stahlberg, as modified, is silent regarding wherein the agent (6a – 6h) of the node (i) uses the local threat detection model for obtaining scores for a stream of observed local events in a timely manner and/or (ii) aligns observed events over a timeline in the order of their appearance and combines their scores assigned by the local threat detection model to the timeline. However, Bartling, in an analogous art (detecting threats using behavioral models, abstract), teaches that it was known to perform local threat detection on a stream of observed local events (ingests a sequence of events in a stream, ¶16) in a timely manner (correlating multiple events within a period of time, ¶14; events are analyzed as a temporal sequence to determine if the events are undesirable, ¶17; events are recorded and a timer is started, ¶31) and combines their scores assigned by the local threat detection model to the timeline (scores are recorded with a confidence level with respect to a defined category, ¶24, where known malicious behaviors can have corresponding high scores, ¶25; scores are combined with new scores, ¶¶33-35). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg such that the agent (6a – 6h) of the node uses the local threat detection model for obtaining scores for a stream of observed local events in a timely manner and/or aligns observed events over a timeline in the order of their appearance and combines their scores assigned by the local threat detection model to the timeline. One of ordinary skill in the art would have been motivated to perform such a modification to utilize the temporal sequence of events in the threat determination, as taught by Bartling.
Regarding claim 9, Stahlberg is silent regarding wherein an anomaly detection model and/or the misuse detection model are applied to events observed on each node and overlaid on a timeline graph as a set of respective time series, and/or wherein every new event gets a score or set of scores from the anomaly detection model and/or misuse detection model. However, Bartling, in an analogous art (detecting threats using behavioral models, abstract), teaches that it was known to perform local threat detection on a stream of observed local events (ingests a sequence of events in a stream, ¶16) and observed events are recorded with a score (machine learning model assigns scores, ¶46, which are recorded with a confidence level with respect to a defined category, ¶24, where known malicious behaviors can have corresponding high scores, ¶25) on a timeline (events are analyzed as a temporal sequence to determine if the events are undesirable, ¶17; events are recorded and a timer is started, ¶31). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg such that the anomaly detection model and/or misuse detection model are applied to events observed on each node and overlaid on a timeline graph as a set of respective time series, and/or wherein every new event gets a score or set of scores from the anomaly detection model and/or misuse detection model. One of ordinary skill in the art would have been motivated to perform such a modification to utilize the temporal sequence of events in the threat determination, as taught by Bartling.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Stahlberg, as applied to claim 1, in view of “A contextual anomaly detection approach to discover zero-day attack” by AlEroud et al. (AlEroud).
Regarding claim 10, Stahlberg is silent regarding wherein the node utilizes both anomaly detection and misuse detection models at the node (5a – 5h) and uses the models together by analyzing at relations between score patterns between the anomaly detection model and the misuse detection model. However, AlEroud, in an analogous art (detection malicious actions in network-connected devices), teaches that it was known to utilizes both anomaly detection and misuse detection models at a node (Fig. 1, anomaly detection module and misuse detection model) and to use the models together by analyzing at relations between score patterns between the anomaly detection model and the misuse detection model (generating connection record profile similarity scores for misuse detection, p. 41, §III-A, generating an anomaly score for anomaly detection, p. 42, §III-B and apply the scores in conjunction, p. 42, §III-B; see also p. 43, ¶2). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Stahlberg such that the node utilizes both anomaly detection and misuse detection models at the node (5a – 5h) and uses the models together by analyzing at relations between score patterns between the anomaly detection model and the misuse detection model. One of ordinary skill in the art would have been motivated to perform such a modification to gain the benefits of both anomaly and misuse detection while minimizing the dependency on anomaly detection, as taught by AlEroud (see at least p. 43, ¶2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240244064 A1 (Roy; Kaushik et al.) teaches training local security models at clients, producing a new global model based on the local models and distributing the global model to clients for use (¶¶10-11).
KR20210064848A (KIM TAE HUN et al.) teaches federated learning in threat detection, including a lower monitoring system receiving a new threat detection model generated by an upper monitoring system (translation, ¶10).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SIMITOSKI whose telephone number is (571)272-3841. The examiner can normally be reached Monday - Friday, 7:00-3:00.
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/Michael Simitoski/ Primary Examiner, Art Unit 2493
June 23, 2026
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