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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12250236. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application is anticipated by the said patent.
Instant App. 19070959
Patent No. 12250236
1. A method, comprising:receiving endpoint data from a computing device;receiving network data from a network access device, the network data being associated with the computing device;combining the endpoint data and the network data to generate event data associated with the computing device, the event data including a sequence of events between the computing device and the network access device;analyzing the event data associated with the computing device to detect an anomaly; andinitiating a mitigation procedure to address the anomaly.
2. The method of claim 1, further comprising:generating a timeline of the event data; andcausing the timeline to be provided on a display of a remote computing device.
3. The method of claim 2, wherein at least a portion of the timeline is selectable.
4. The method of claim 3, further comprising providing additional information associated with the timeline in response to receiving a selection of the at least the portion of the timeline.
5. The method of claim 1, wherein the mitigation procedure includes causing a blocking action to be performed on the anomaly.
6. The method of claim 1, wherein the endpoint data is endpoint detection and response (EDR) data.
7. A system, comprising:a processor; anda memory coupled to the processor and storing instructions that, when executed by the processor, perform operations, comprising:receiving endpoint data from a computing device;receiving network data from a network access device, the network data being associated with the computing device;combining the endpoint data and the network data to generate event data associated with the computing device, the event data including a sequence of events between the computing device and the network access device;analyzing the event data associated with the computing device to detect an anomaly;categorizing the anomaly based, at least in part, on the analyzing; andinitiating a mitigation procedure to address the anomaly.
8. The system of claim 7, further comprising instructions for:generating a timeline of the event data; andcausing the timeline to be provided on a display of a remote computing device.
9. The system of claim 8, wherein at least a portion of the timeline is selectable.
10. The system of claim 9, further comprising instructions for providing additional information associated with the timeline in response to receiving a selection of the at least the portion of the timeline.
11. The system of claim 7, wherein the mitigation procedure includes an initiating of a blocking action performed on the anomaly based, at least in part, on the categorization of the anomaly.
12. The system of claim 7, wherein the endpoint data is endpoint detection and response (EDR) data.
13. A method, comprising:receiving endpoint data from a first computing device;receiving network data associated with the first computing device; generating event data for the first computing device based, at least in part, on the endpoint data and the network data; analyzing the event data; identifying an anomaly in the event data; and initiating a mitigation procedure on the anomaly.
14. The method of claim 13, wherein the mitigation procedure is a blocking procedure.
15. The method of claim 13, wherein the network data includes one or more of a domain name system (DNS) lookup associated with the first computing device, a website that was visited by the first computing device, or an address resolution protocol (ARP) communication associated with the first computing device.
16. The method of claim 13, further comprising generating a timeline for display on a user interface, the timeline including one or more selectable events associated with the event data.
1. A method, comprising:
receiving automatically, by a stitching system, endpoint data of a computing device from the computing device, the endpoint data comprising activities and/or events that occurred on the computing device;
receiving automatically, by the stitching system, network data from a network access device, the network data being associated with the computing device, wherein the network data includes ethernet layer information and network layer information;
combining, by the stitching system and using a mapping system, the endpoint data and the network data to generate event data associated with the computing device based on associated and related endpoint actions and network actions, the event data including a sequence of events between the computing device and the network access device, wherein the generated event data is associated with a graph database;
analyzing the event data associated with the computing device to detect an anomaly; and
initiating, by an artificial intelligence system, a mitigation procedure to address the anomaly that includes deploying appropriate preventative measures based on a comparison between the detected anomaly and a global list of known anomalies.
2. (Original) The method of claim 1, further comprising:
generating a timeline of the event data; and
causing the timeline to be provided on a display of a remote computing device.
3. (Original) The method of claim 2, wherein at least a portion of the timeline is selectable.
4. (Original) The method of claim 3, further comprising providing additional information associated with the timeline in response to receiving a selection of the at least the portion of the timeline.
5. (Canceled)
6. (Canceled)
7. (Original) The method of claim 1, wherein the mitigation procedure includes causing a blocking action to be performed on the anomaly.
8. (Original) The method of claim 1, wherein the endpoint data is endpoint detection and response (EDR) data.
9. A system, comprising:
a processor; and
a memory coupled to the processor and storing instructions that, when executed by the processor, perform operations, comprising:
automatically receiving endpoint data of a computing device from the computing device, the endpoint data comprising activities and/or events that occurred on the computing device;
automatically receiving network data from a network access device, the network data being associated with the computing device, wherein the network data includes ethernet layer information and network layer information;
combining, using a mapping system, the endpoint data and the network data to generate event data associated with the computing device based on associated and related endpoint actions and network actions, the event data including a sequence of events between the computing device and the network access device, wherein the generated event data is associated with a graph database;
analyzing the event data associated with the computing device to detect an anomaly;
categorizing the anomaly based, at least in part, on the analyzing; and
initiating, by an artificial intelligence system, a mitigation procedure to address the anomaly that includes deploying appropriate preventative measures based on a comparison between the detected anomaly and a global list of known anomalies.
10. (Original) The system of claim 9, further comprising instructions for:
generating a timeline of the event data; and
causing the timeline to be provided on a display of a remote computing device.
11. (Original) The system of claim 10, wherein at least a portion of the timeline is selectable.
12. (Original) The system of claim 11, further comprising instructions for providing additional information associated with the timeline in response to receiving a selection of the at least the portion of the timeline.
13. (Canceled)
14. (Canceled)
15. (Original) The system of claim 9, wherein the mitigation procedure includes an initiating of a blocking action performed on the anomaly based, at least in part, on the categorization of the anomaly.
16. (Original) The system of claim 9, wherein the endpoint data is endpoint detection and response (EDR) data.
17. A method, comprising:
receiving automatically, by a stitching system, endpoint data of a first computing device from the first computing device, the endpoint data comprising activities and/or events that occurred on the first computing device;
receiving automatically, by the stitching system, network data associated with the first computing device, wherein the network data includes ethernet layer information and network layer information;
generating event data for the first computing device based, at least in part, on the endpoint data and the network data and associated and related endpoint actions and network actions;
analyzing the event data;
identifying an anomaly in the event data; and
initiating, by an artificial intelligence system, a mitigation procedure on the anomaly that includes deploying appropriate preventative measures based on a comparison between the detected anomaly and a global list of known anomalies.
18. (Original) The method of claim 17, wherein the mitigation procedure is a blocking procedure.
19. (Original) The method of claim 17, wherein the network data includes one or more of a domain name system (DNS) lookup associated with the first computing device, a website that was visited by the first computing device, or an address resolution protocol (ARP) communication associated with the first computing device.
20. (Original) The method of claim 17, further comprising generating a timeline for display on a user interface, the timeline including one or more selectable events associated with the event data.
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.
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.
Claim(s) 1 – 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivas et al (US 11,469,946), Sri and Giorgio et al (US 11,057,414), Gio.
Claim 1: Sri teaches a method, comprising: receiving endpoint data from a computing device; (C2L10-12: one or more collectors are configured to receive network traffic data from a plurality of network elements in the network; CL8-10, C10L37-57: wherein the high layer information is one or more of network users, network devices; C13L58-60: raw data include, but are not limited to, user information such as roles and associated policies, login status; C14L18-20: collect data from devices (IP address, credentials));
receiving network data from a network access device, the network data being associated with the computing device; (C9L17-21, C10L37-41: collector also receives data from other enterprise systems including identity management systems, network element controllers and the like; C2L25-34: The manager programs the programmable network element to send filtered network traffic data from the plurality of network elements to the collector ... collector is further configured to receive statistics about … input from one or more enterprise systems);
combining the endpoint data and the network data to generate event data associated with the computing device, the event data including a sequence of events between the computing device and the network access device; (C24L52-58: the remote network manager combines the network traffic data from the plurality of networks and the network management data from the plurality of enterprise systems... into combined cross-network data for simultaneous and central analysis of the combined cross-network data, C13L32-35: performance information is generated by the collector performing performance tests against the applications; C15L15: Number of different hosts a particular host interacts with; C18L5-6: a segment-by-segment analysis of a particular user/application/device's traffic);
and initiating a mitigation procedure to address the anomaly. (C4L46-49: using the information from the visibility part to enforce high-level policies and automatically remediating security and performance issues in the network).
Sri is not explicit about analyzing the event data associated with the computing device to detect an anomaly;
But analogous art Gio teaches analyzing the event data associated with the computing device to detect an anomaly; (C7L27-30: the number of times a particular endpoint or other graph node set element has been encountered in metadata events, etc. C9L61-62: applying streaming analytics in real (or replay) time, to determine anomalous activity; C4L20-25, 65-67: metadata events can contain a reference to a specific source and destination. The analytics can be based on the history of former bi-directional traffic flows between this pair. In addition, the analytics are supplemented with threat intelligence and enrichment information (TI&E); AHIMA utilizes the long-term historical information coupled with current metadata events to find malicious activity).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sri to include the idea of analysis of data and finding anomaly for an endpoint as taught by Gio so that enable fast execution, both for ingesting information and for responding to queries (C2L8-9).
Claim 2: the combination of Sri and Gio teaches the method of claim 1, further comprising: generating a timeline of the event data; and causing the timeline to be provided on a display of a remote computing device. (Sri: C11L17-20: dynamically controlled and programed to direct specific traffic during specific time intervals and network locations to the collector..., C4L42-43: and a ranking of responses can be computed and presented to the user).
Claim 3: the combination of Sri and Gio teaches the method of claim 2, wherein at least a portion of the timeline is selectable. (Sri: C12L38-40: Crawling refers to an act of dynamically selecting a different set of raw data for the collectors to examine at any given time; C15L7-9: each of the above features on a time slice by time slice basis is analyzed).
Claim 4: the combination of Sri and Gio teaches the method of claim 3, further comprising providing additional information associated with the timeline in response to receiving a selection of the at least the portion of the timeline. (Sri: C10L52-54: collects and stores this time series data, and analyzes the time series data for trends/patterns over time and other dimensions).
Claim 5: the combination of Sri and Gio teaches the method of claim 1, wherein the mitigation procedure includes causing a blocking action to be performed on the anomaly. (Sri: C20L23, 58-63: Block user X from accessing the network … the present system and method ties a particular anomalous traffic behavior to a specific user/ application/device, and further to particular IP/MAC addresses).
Claim 6: the combination of Sri and Gio teaches the method of claim 1, wherein the endpoint data is endpoint detection and response (EDR) data. (Gio: C5L10-15: AHIMA operates asynchronously, online, and simultaneously evaluates multiple enterprise endpoints for suspect activity based on arriving metadata events).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sri to include the idea of EDR data as taught by Gio so that enable fast execution, both for ingesting information and for responding to queries (C2L8-9).
Claim 7: Sri teaches a system, comprising: a processor; and a memory coupled to the processor and storing instructions that, when executed by the processor, perform operations, comprising: receiving endpoint data from a computing device; receiving network data from a network access device, the network data being associated with the computing device; combining the endpoint data and the network data to generate event data associated with the computing device, the event data including a sequence of events between the computing device and the network access device; and initiating a mitigation procedure to address the anomaly. (C2L10-12: one or more collectors are configured to receive network traffic data from a plurality of network elements in the network; CL8-10, C10L37-57: wherein the high layer information is one or more of network users, network applications, network devices, and network behaviors; C9L17-21: collector also receives data from other enterprise systems including identity management systems, network element controllers and the like; C13L58-60: raw data include, but are not limited to, user information such as roles and associated policies, login status; C14L18-20: collect data from devices (IP address, credentials); C2L25-34: The manager programs the programmable network element to send filtered network traffic data from the plurality of network elements to the collector ... collector is further configured to receive statistics about … input from one or more enterprise systems; C24L52-58: the remote network manager combines the network traffic data from the plurality of networks and the network management data from the plurality of enterprise systems... into combined cross-network data for simultaneous and central analysis of the combined cross-network data, C13L32-35: performance information is generated by the collector performing performance tests against the applications; C15L15: Number of different hosts a particular host interacts with; C18L5-6: a segment-by-segment analysis of a particular user/application/device's traffic; C4L46-49: using the information from the visibility part to enforce high-level policies and automatically remediating security and performance issues in the network).
Sri is not explicit about analyzing the event data associated with the computing device to detect an anomaly; categorizing the anomaly based, at least in part, on the analyzing;
But analogous art Gio teaches analyzing the event data associated with the computing device to detect an anomaly; categorizing the anomaly based, at least in part, on the analyzing; (C7L27-30: the number of times a particular endpoint or other graph node set element has been encountered in metadata events, etc. C9L61-62: applying streaming analytics in real (or replay) time, to determine anomalous activity; C4L20-25, 65-67: metadata events can contain a reference to a specific source and destination. The analytics can be based on the history of former bi-directional traffic flows between this pair. In addition, the analytics are supplemented with threat intelligence and enrichment information (TI&E); AHIMA utilizes the long-term historical information coupled with current metadata events to find malicious activity; Fig. 11 shows example possible states for various classification variables).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sri to include the idea of analysis of data and finding anomaly for an endpoint as taught by Gio so that enable fast execution, both for ingesting information and for responding to queries (C2L8-9).
Claim 8: the combination of Sri and Gio teaches the system of claim 7, further comprising instructions for: generating a timeline of the event data; and causing the timeline to be provided on a display of a remote computing device. (Sri: C11L17-20: dynamically controlled and programed to direct specific traffic during specific time intervals and network locations to the collector..., C4L42-43: and a ranking of responses can be computed and presented to the user).
Claim 9: the combination of Sri and Gio teaches the system of claim 8, wherein at least a portion of the timeline is selectable. (Sri: C12L38-40: Crawling refers to an act of dynamically selecting a different set of raw data for the collectors to examine at any given time; C15L7-9: each of the above features on a time slice by time slice basis is analyzed).
Claim 10: the combination of Sri and Gio teaches the system of claim 9, further comprising instructions for providing additional information associated with the timeline in response to receiving a selection of the at least the portion of the timeline. (Sri: C10L52-54: collects and stores this time series data, and analyzes the time series data for trends/patterns over time and other dimensions).
Claim 11: the combination of Sri and Gio teaches the system of claim 7, wherein the mitigation procedure includes an initiating of a blocking action performed on the anomaly based, at least in part, on the categorization of the anomaly. (Sri: C20L23, 59-62: Block user X from accessing the network … the present system and method ties a particular anomalous traffic behavior to a specific user/ application/device, and further to particular IP/MAC addresses).
Claim 12: the combination of Sri and Gio teaches the system of claim 7, wherein the endpoint data is endpoint detection and response (EDR) data. (Gio: C5L10-15: AHIMA operates asynchronously, online, and simultaneously evaluates multiple enterprise endpoints for suspect activity based on arriving metadata events).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sri to include the idea of EDR data as taught by Gio so that enable fast execution, both for ingesting information and for responding to queries (C2L8-9).
Claim 13: Sri teaches a method, comprising: receiving endpoint data from a first computing device; receiving network data associated with the first computing device; generating event data for the first computing device based, at least in part, on the endpoint data and the network data; and initiating a mitigation procedure on the anomaly. (C2L10-12: one or more collectors are configured to receive network traffic data from a plurality of network elements in the network; C9L17-21: collector also receives data from other enterprise systems including identity management systems, network element controllers and the like; CL8-10, C10L37-57: wherein the high layer information is one or more of network users, network applications, network devices, and network behaviors; C13L58-60: raw data include, but are not limited to, user information such as roles and associated policies, login status; C14L18-20: collect data from devices (IP address, credentials); C2L25-34: The manager programs the programmable network element to send filtered network traffic data from the plurality of network elements to the collector ... collector is further configured to receive statistics about … input from one or more enterprise systems; C24L52-58: the remote network manager combines the network traffic data from the plurality of networks and the network management data from the plurality of enterprise systems... into combined cross-network data for simultaneous and central analysis of the combined cross-network data, C13L32-35: performance information is generated by the collector performing performance tests against the applications; C15L15: Number of different hosts a particular host interacts with; C18L5-6: a segment-by-segment analysis of a particular user/application/device's traffic; C4L46-49: using the information from the visibility part to enforce high-level policies and automatically remediating security and performance issues in the network).
Sri is not explicit about analyzing the event data; identifying an anomaly in the event data;
But analogous art Gio teaches analyzing the event data; identifying an anomaly in the event data; (C7L27-30: the number of times a particular endpoint or other graph node set element has been encountered in metadata events, etc. C9L61-62: applying streaming analytics in real (or replay) time, to determine anomalous activity; C4L20-25, 65-67: metadata events can contain a reference to a specific source and destination. The analytics can be based on the history of former bi-directional traffic flows between this pair. In addition, the analytics are supplemented with threat intelligence and enrichment information (TI&E); AHIMA utilizes the long-term historical information coupled with current metadata events to find malicious activity; Fig. 11 shows example possible states for various classification variables).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Sri to include the idea of analysis of data and finding anomaly for an endpoint as taught by Gio so that enable fast execution, both for ingesting information and for responding to queries (C2L8-9).
Claim 14: the combination of Sri and Gio teaches the method of claim 13, wherein the mitigation procedure is a blocking procedure. (Sri: C20L23, 59-62: Block user X from accessing the network … the present system and method ties a particular anomalous traffic behavior to a specific user/ application/device, and further to particular IP/MAC addresses).
Claim 15: the combination of Sri and Gio teaches the method of claim 13, wherein the network data includes one or more of a domain name system (DNS) lookup associated with the first computing device, a website that was visited by the first computing device, or an address resolution protocol (ARP) communication associated with the first computing device. (Sri: C14L57-65: Examples of a flow-by-flow feature include, but are not limited to: Number of different types of DNS packets; Binary feature of whether DNS_Q was followed by
DNS_SUCC_RESP).
Claim 16: the combination of Sri and Gio teaches the method of claim 13, further comprising generating a timeline for display on a user interface, the timeline including one or more selectable events associated with the event data. (Sri: C12L38-40: Crawling refers to an act of dynamically selecting a different set of raw data for the collectors to examine at any given time; C15L7-9: each of the above features on a time slice by time slice basis is analyzed).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Badri Champakesan whose telephone number is (571)270-3867. The examiner can normally be reached M-F: 8.30am-4.30pm (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.
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/BADRINARAYANAN /Primary Examiner, Art Unit 2494.
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