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 responsive to: an original application filed on 14 November 2024.
2. Claims 1-20 are currently pending and claims 1, 8 and 15 are independent claims.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 14 November 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
4. Priority claimed from parent application has been noted.
Drawings
5. The drawings filed on 14 November 2024 are accepted by the examiner.
Double Patenting
6. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents /process/ file/efs/guidance /eTD-info-I.jsp.
Claims 1-20 are rejected under the grounds of non-statutory obviousness-type double patenting, as they are deemed unpatentable over claims 1-20 of US Patent application No. 18/376,912. Although the conflicting claims are not identical, they are considered not patentably distinct from one another, as they convey the same inventive concept. Specifically, both sets of claims disclose a method classifying network device by analyzing sequence of behavior using State Machine. Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of the invention’s filing, to employ this approach to prevent and protect data from rouge device that attempts to join in a network, thereby rendering the claims unpatentable.
Claim Rejections - 35 USC § 102
7. 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 –
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 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.
(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 1-20 are rejected 35 U.S.C §102 (a)(1) as being anticipated by Georgios Apostolopoulos (US Publication No. 20180219888), hereinafter Apostolopoulos.
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 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).
and classifying, responsive to detecting the second entity coming onto the network, the second entity based on the profile of the first entity (Apostolopoulos, ¶139, ¶65), wherein the graph generator 710 can compare the action to the table of identifiable actions, identify one or more relationships between the entities and system detect access request from multiple entities.
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 based on the profile of the first entity comprises classifying the second entity based on the state machine (Apostolopoulos, ¶153).
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 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).
and classify, responsive to detecting the second entity coming onto the network, the second entity based on the profile of the first entity (Apostolopoulos, ¶139, ¶65), wherein the graph generator 710 can compare the action to the table of identifiable actions, identify one or more relationships between the entities and system detect access request from multiple entities.
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 based on the profile of the first entity, the processing device is to classify the second entity based on the state machine (Apostolopoulos, ¶153).
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 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 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).
and classify, responsive to detecting the second entity coming onto the network, the second entity based on the profile of the first entity (Apostolopoulos, ¶139, ¶65), wherein the graph generator 710 can compare the action to the table of identifiable actions, identify one or more relationships between the entities and system detect access request from multiple entities.
Regarding claim 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:
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 based on the profile of the first entity, the instructions, when executed by the processing device, cause the processing device to classify the second entity based on the state machine (Apostolopoulos, ¶153).
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Monjour Rahim whose telephone number is (571)270-3890.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shewaye 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.
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/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