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
1. This action is in response to the amendment and argument field on 14 May 2026.
2. Claims 1, 6, 8, 13, 15 and 20 have been amended.
3. Claims 1-20 remain Pending and rejected.
Responses to the Argument
4. The applicant’s arguments filed on 14 May 2026 are moot in view of new ground of rejection rendered.
Claim Rejections - 35 USC § 103
5. 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 1-20 are rejected under 35 U.S.C §103 as being unpatentable over Georgios Apostolopoulos (US Publication No. 20180219888), hereinafter Apostolopoulos and in view of Martin et al. (US Publication No. 20180004948), hereinafter Martin.
Regarding claim 1:
accessing data associated with one or more communications of a first entity on a network (Apostolopoulos, ¶101), wherein threat indicators and threats are escalations of events of concern. As an example of scale, hundreds of millions of packets of incoming event data from various data sources may be analyzed to yield 100 anomalies, which may be further analyzed to yield 10 threat indicators, which may again be further analyzed to yield one or two threats.
determining one or more behaviors based on the data associated with the one or more communications of the first entity (Apostolopoulos, ¶147, ¶152) wherein composite graph enables the security platform to perform analytics on entity behaviors, which can be a sequence of activities and identify entity behaviors and event patterns that are not previously known to security experts.
determining one or more sequences of the one or more behaviors of the first entity (Apostolopoulos, ¶154), wherein a machine learning model in the ML-based CEP engine can perform entity-specific behavioral analysis, time series analysis of event sequences, graph correlation analysis of entity activities.
determining, by a processing device, a profile of the first entity based on the one or more sequences of the one or more behaviors (Apostolopoulos, ¶153), wherein, it makes predictions based on historical sequence of events. In another example, the ML-based CEP engine can train a state machine. Not only is the state machine trained based on a historical sequences of events, but it is also applied based on a historical sequence of events. For example, when the ML-based CEP engine processes event feature sets corresponding to an entity wherein the profile comprises a classification of the first entity (Apostolopoulos, ¶177), wherein Each model instance may be of a particular model type configured to detect a particular category of anomalies based on incoming event data.
detecting a second entity, different from the first entity, coming onto the network (Apostolopoulos, ¶38, ¶148), wherein detecting patterns of risky activity that spans across multiple days and/or multiple entities (e.g., users or devices).
Apostolopoulos does not explicitly suggest, and classifying, responsive to detecting the second entity coming onto the network, the second entity as the same classification of the first entity by applying the profile of the first entity to second one or more behaviors of the second entity; however, in a same field of endeavor Martin discloses this limitation (Martin, abstract, ¶50, ¶53).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to include the method of event profiling of Apostolopoulos with the identifying similarity disclosed in Martin to identify historical similar behavior, stated by Martin at para.53.
Regarding claim 2:
wherein the profile further comprises at least one static attribute associated with the first entity (Apostolopoulos, ¶105), wherein geo attribute data is static, inherently.
Regarding claim 3:
wherein the one or more communications associated with the first entity are accessed from at least one of a log, traffic data, information from an external system, or classification information (Apostolopoulos, ¶117).
Regarding claim 4:
wherein the classification information is based on an attribute associated with the first entity (Apostolopoulos, ¶137).
Regarding claim 5:
wherein the one or more sequences of the one or more behaviors comprises a plurality of behaviors, wherein each of the plurality of behaviors is associated with a period of time (Apostolopoulos, ¶44).
Regarding claim 6:
further comprising: determining a state machine based on the profile of the first entity, wherein classifying the second entity as the same classification as the first entity is performed based on the state machine (Apostolopoulos, ¶153, ¶173, ¶171).
Regarding claim 7:
further comprising: uploading the profile to a remote system; and validating the profile for accuracy (Apostolopoulos, ¶67, ¶106).
Regarding claim 8:
a memory (Apostolopoulos, ¶53); and a processing device, operatively coupled to the memory, to (Apostolopoulos, ¶53): access data associated with one or more communications of a first entity on a network (Apostolopoulos, ¶101), wherein threat indicators and threats are escalations of events of concern. As an example of scale, hundreds of millions of packets of incoming event data from various data sources may be analyzed to yield 100 anomalies, which may be further analyzed to yield 10 threat indicators, which may again be further analyzed to yield one or two threats.
determine one or more behaviors based on the data associated with the one or more communications of the first entity (Apostolopoulos, ¶147, ¶152) wherein composite graph enables the security platform to perform analytics on entity behaviors, which can be a sequence of activities and identify entity behaviors and event patterns that are not previously known to security experts.
determine one or more sequences of the one or more behaviors of the first entity (Apostolopoulos, ¶154), wherein a machine learning model in the ML-based CEP engine can perform entity-specific behavioral analysis, time series analysis of event sequences, graph correlation analysis of entity activities.
determine a profile of the first entity based on the one or more sequences of the one or more behaviors, (Apostolopoulos, ¶153), wherein, it makes predictions based on historical sequence of events. In another example, the ML-based CEP engine can train a state machine. Not only is the state machine trained based on a historical sequences of events, but it is also applied based on a historical sequence of events. For example, when the ML-based CEP engine processes event feature sets corresponding to an entity wherein the profile comprises a classification of the first entity (Apostolopoulos, ¶177), wherein Each model instance may be of a particular model type configured to detect a particular category of anomalies based on incoming event data.
detect a second entity, different from the first entity coming onto the network (Apostolopoulos, ¶38, ¶148), wherein detecting patterns of risky activity that spans across multiple days and/or multiple entities (e.g., users or devices).
Apostolopoulos does not explicitly suggest, and classify, responsive to detecting the second entity coming onto the network, the second entity as the same classification of the first entity by applying the profile of the first entity to second one or more behaviors of the second entity; however, in a same field of endeavor Martin discloses this limitation (Martin, abstract, ¶50, ¶53).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to include the method of event profiling of Apostolopoulos with the identifying similarity disclosed in Martin to identify historical similar behavior, stated by Martin at para.53.
Regarding claim 9:
wherein the profile further comprises at least one static attribute associated with the first entity (Apostolopoulos, ¶105), wherein geo attribute data is static, inherently.
Regarding claim 10:
wherein the one or more communications associated with the first entity are accessed from at least one of a log, traffic data, information from an external system, or classification information (Apostolopoulos, ¶117).
Regarding claim 11:
wherein the classification information is based on an attribute associated with the first entity (Apostolopoulos, ¶137).
Regarding claim 12:
wherein the one or more sequences of the one or more behaviors comprises a plurality of behaviors, wherein each of the plurality of behaviors is associated with a period of time (Apostolopoulos, ¶44).
Regarding claim 13:
wherein the processing device is further to: determine a state machine based on the profile of the first entity, wherein to classify the second entity as the same classification as the first entity is performed based on the state machine (Apostolopoulos, ¶153, ¶171, ¶173).
Regarding claim 14:
wherein the processing device is further to: upload the profile to a remote system; and validate the profile for accuracy (Apostolopoulos, ¶67, ¶106).
Regarding claim 15:
A non-transitory computer readable medium having instructions encoded thereon that, when executed by a processing device, cause the processing device to (Apostolopoulos, ¶53, ¶252): access data associated with one or more communications of a first entity on a network (Apostolopoulos, ¶101), wherein threat indicators and threats are escalations of events of concern. As an example of scale, hundreds of millions of packets of incoming event data from various data sources may be analyzed to yield 100 anomalies, which may be further analyzed to yield 10 threat indicators, which may again be further analyzed to yield one or two threats.
determine one or more behaviors based on the data associated with the one or more communications of the first entity (Apostolopoulos, ¶147, ¶152) wherein composite graph enables the security platform to perform analytics on entity behaviors, which can be a sequence of activities and identify entity behaviors and event patterns that are not previously known to security experts.
determine one or more sequences of the one or more behaviors of the first entity (Apostolopoulos, ¶154), wherein a machine learning model in the ML-based CEP engine can perform entity-specific behavioral analysis, time series analysis of event sequences, graph correlation analysis of entity activities.
determine, by a processing device, a profile of the first entity based on the one or more sequences of the one or more behaviors (Apostolopoulos, ¶153), wherein, it makes predictions based on historical sequence of events. In another example, the ML-based CEP engine can train a state machine. Not only is the state machine trained based on a historical sequences of events, but it is also applied based on a historical sequence of events. For example, when the ML-based CEP engine processes event feature sets corresponding to an entity wherein the profile comprises a classification of the first entity (Apostolopoulos, ¶177), wherein Each model instance may be of a particular model type configured to detect a particular category of anomalies based on incoming event data.
detect a second entity, different from the first entity, coming onto the network (Apostolopoulos, ¶38, ¶148), wherein detecting patterns of risky activity that spans across multiple days and/or multiple entities (e.g., users or devices).
Apostolopoulos does not explicitly suggest, and classify, responsive to detecting the second entity coming onto the network, the second entity as the same classification of the first entity by applying the profile of the first entity to second one or more behaviors of the second entity; however, in a same field of endeavor Martin discloses this limitation (Martin, abstract, ¶50, ¶53).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to include the method of event profiling of Apostolopoulos with the identifying similarity disclosed in Martin to identify historical similar behavior, stated by Martin at para.53.
Regarding claim 16:
16. The non-transitory computer readable medium of claim 15, wherein the profile further comprises at least one static attribute associated with the first entity (Apostolopoulos, ¶105), wherein geo attribute data is static, inherently.
Regarding claim 17:
17. The non-transitory computer readable medium of claim 15, wherein the one or more communications associated with the first entity are accessed from at least one of a log, traffic data, information from an external system, or classification information (Apostolopoulos, ¶117).
Regarding claim 18:
wherein the classification information is based on an attribute associated with the first entity (Apostolopoulos, ¶137).
Regarding claim 19:
wherein the one or more sequences of the one or more behaviors comprises a plurality of behaviors, wherein each of the plurality of behaviors is associated with a period of time (Apostolopoulos, ¶44).
Regarding claim 20:
wherein the instructions, when executed by the processing device, cause the processing device further to: determine a state machine based on the profile of the first entity, wherein to classify the second entity as the same classification as the first entity (Apostolopoulos, ¶153, ¶173, ¶171).
Conclusion
6. 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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (See form “PTO-892 Notice of reference cited).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONJUR RAHIM whose telephone number is (571)270-3890.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shewye Gelagay can be reached on 571-272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Monjur Rahim/
Patent Examiner
United States Patent and Trademark Office
Art Unit: 2436; Phone: 571.270.3890
E-mail: monjur.rahim@uspto.gov
Fax: 571.270.4890
Prosecution Insights