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 .
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) 1-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SZILAGYI, et al. (US 20190364457 A1, hereinafter, "SZILAGYI").
Regarding claim 1, SZILAGYI teaches a method for classifying application and content in a computer network, the method comprising:
SZILAGYI writes, “In some example embodiments, there may be provided a method. The method may include receiving, at an adaptive quality controller, an indication of a classification of an application, when a session of the application is detected…” (paragraph 0004). SZILAGYI adds, “In some example embodiments, the system 400 may include an application detection and classification module 405. The application detection and classification module 405 may detect and classify an application session as being of a certain type. For example, at the initiation of a session, the application detection and classification module 405 may detect that an application is a video streaming session related to a certain OTT application. The applications may be classified into for example one of a plurality of application types, such as (A) real-time multimedia, (B) stored media, (C) interactive data, (D) messaging/transactional, (e) Background, and/or other types of classification” (paragraph 0034).
determining an application associated with a traffic flow;
SZILAGYI writes, “The application detection/classification module 405 may detect applications in a variety of ways. For example, for a set of popular applications, application detection/classification module 405 may include detection logic for user plane packet monitoring (e.g., by the correlation of IP addresses, DNS query/response messages, TLS handshake information, URLs, and/or the like). This may lead to the detection of a specific application (e.g., YouTube), which can be mapped to a specific application classification. Non-dedicated applications may be classified based on the traffic patterns generated by the application itself (e.g., specific attributes of HTTP adaptive streaming, VoIP/video calls, messaging, etc. can be identified without knowing the exact identity of the application). Additionally or alternatively, the application's behavior may be matched to already profiled applications to identify that a new application generates the same kind of traffic as a known one thus it is likely that the similar characteristics apply” (paragraph 0054).
determining at least one type of content category associated with the application;
SZILAGYI writes, “In some example embodiments, the system 400 may include an application detection and classification module 405. The application detection and classification module 405 may detect and classify an application session as being of a certain type. For example, at the initiation of a session, the application detection and classification module 405 may detect that an application is a video streaming session related to a certain OTT application. The applications may be classified into for example one of a plurality of application types, such as (A) real-time multimedia, (B) stored media, (C) interactive data, (D) messaging/transactional, (e) Background, and/or other types of classification” (paragraph 0034).
reviewing packet parameters to determine the content category of the traffic flow;
SZILAGYI writes, “The application detection/classification module 405 may detect applications in a variety of ways. For example, for a set of popular applications, application detection/classification module 405 may include detection logic for user plane packet monitoring (e.g., by the correlation of IP addresses, DNS query/response messages, TLS handshake information, URLs, and/or the like). This may lead to the detection of a specific application (e.g., YouTube), which can be mapped to a specific application classification. Non-dedicated applications may be classified based on the traffic patterns generated by the application itself (e.g., specific attributes of HTTP adaptive streaming, VoIP/video calls, messaging, etc. can be identified without knowing the exact identity of the application). Additionally or alternatively, the application's behavior may be matched to already profiled applications to identify that a new application generates the same kind of traffic as a known one thus it is likely that the similar characteristics apply” (paragraph 0054).
and monitoring the traffic flow for any changes to the packet parameters that would indicate a change in the content category of the traffic flow.
SZILAGYI writes, “In some example embodiments, the system 400 may include a context module 405. The context module 405 may gather or receive a context for application sessions. When an application is initiated, the context module 405 may obtain (for example, monitor, receive, and/or the like) context information regarding the application, user, network, traffic, and/or the like. Moreover, the context module 405 may continue to obtain context information while the session for an application is running to determine the current context of the application session. For example, at the initiation of a session, the context module 405 may obtain context information indicating that a video streaming application session has a context of 720 progressive video quality, the network state or traffic, and/or the like. Later, the context module 405 may determine that the context of the session for the video streaming application session has changed (for example, the user may have selected 1080 progressive video quality and frequent “buffering” causing interruption in the viewing of the video, the network is now congested, and/or the like). In this example, the context module 405 would obtain the current context of the session for the video streaming application, and provide that current context to the QoS/QoE definition function module 499” (paragraph 0035). SZILAGYI indicates the context module may obtain context information to determine the current context of the application session. SZILAGYI previously stated that the application detection and classification module may detect and classify an application session as being of a certain type. The application context may obtain, for example, monitor, context information regarding the application and traffic. The context module may continue to obtain context information while the session for an application is running to determine the current context of the application session. SZILAGYI provides an example and points out the context module may determine that the context of the session for the video streaming application session has changed. Therefore, SZILAGYI, indicates the application context monitors the traffic flow to obtain current context of the application session, including any changes, that may indicate a change in the content category.
Regarding claim 2, SZILAGYI teaches the method according to claim 1,
Additionally, SZILAGYI teaches wherein the packet parameters comprise signatures of the traffic flow and determining the content category comprises matching the signature of the traffic flow with a previously stored signature of the content category.
SZILAGYI writes, “Non-dedicated applications may be classified based on the traffic patterns generated by the application itself (e.g., specific attributes of HTTP adaptive streaming, VoIP/video calls, messaging, etc. can be identified without knowing the exact identity of the application). Additionally or alternatively, the application's behavior may be matched to already profiled applications to identify that a new application generates the same kind of traffic as a known one thus it is likely that the similar characteristics apply” (paragraph 0054).
Regarding claim 3, SZILAGYI teaches the method according to claim 1,
Additionally, SZILAGYI teaches wherein the packet parameters comprise bincode entry functions and determining the content category comprises reviewing the bincode and a bitrate of the traffic flow.
SZILAGYI writes, “The QoS/QoE definition module 497 may quantify QoE targets, such as download time, bitrate, and/or the like, and this quantification may be based on pre-defined attributes and/or on-the-fly detected attributes... Moreover, certain QoE targets may require the detection of session metadata, such as the media rate of the video in order to quantify the amount of bandwidth it requires for smooth playback. This may dictate obtaining session establishment metadata for the network (e.g., from protocols such as SIP or RTSP), control-plane signaling (for native services), packet metadata (e.g., video media rate and codec information from manifests or from the metadata section of the video file being downloaded), or from any external source (e.g., signaling from the content provider or from the consumer application)” (paragraph 0055).
Regarding claim 4, SZILAGYI teaches the method according to claim 1,
Additionally, SZILAGYI teaches further comprising: monitoring the traffic flow for a predetermined evaluation time prior to determining an application associated with the traffic flow.
SZILAGYI writes, “...real time traffic profiling mechanisms may be used that are coupled with enforcement actions. Such actions may temporarily provide the application with sufficient resources (referred to as incubation) so that the application exhibits traffic delivery patterns that are characteristic to the particular session. An example of this is the HTTP(S) streaming that is used by most of the stored multimedia services. These applications download multimedia data as it is consumed by the player (e.g., with a rate that is close to the media rate in order to avoid pre-buffering excessive amount of data). Incubation enables the multimedia session to establish a download rate that is comfortable for the specific content, which can be measured during the incubation period and enforced later on” (paragraph 0056).
Regarding claim 5, SZILAGYI teaches the method according to claim 1,
Additionally, SZILAGYI teaches wherein the monitoring of the traffic flow comprises waiting for a predetermined number of packets before evaluating whether there has been a change in the content category.
SZILAGYI writes, “...real time traffic profiling mechanisms may be used that are coupled with enforcement actions. Such actions may temporarily provide the application with sufficient resources (referred to as incubation) so that the application exhibits traffic delivery patterns that are characteristic to the particular session. An example of this is the HTTP(S) streaming that is used by most of the stored multimedia services. These applications download multimedia data as it is consumed by the player (e.g., with a rate that is close to the media rate in order to avoid pre-buffering excessive amount of data). Incubation enables the multimedia session to establish a download rate that is comfortable for the specific content, which can be measured during the incubation period and enforced later on” (paragraph 0056).
Claims 6-10 are system claims corresponding to the method claims 1-5 that have already been rejected above. The applicant’s attention is directed to the rejection of claim 1-5. Claims 6-10 are rejected under the same rational as claims 1-5.
Claim(s) 11-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WANG, et al. (US 20190044869 A1, hereinafter, "WANG").
Regarding claim 11, WANG teaches a method for determining a confidence score of an application or content classification of network traffic comprising:
WANG writes, “The method 300 begins in block 302, in which the virtual router 212 determines whether to classify network traffic based on flow (i.e., perform a flow classification operation on received network traffic)” (paragraph 0043). WANG adds, “It should be appreciated that the classification algorithm design selector 220 may also apply a level of confidence to each tested candidate classification algorithm, relative to the performance data collected, such that the classification algorithm design selector 220 does not select the preferred classification algorithm design until a sufficient level of confidence has been achieved for each tested candidate classification algorithm” (paragraph 0042).
determining an application or content classification of a traffic flow;
WANG writes, “The method 300 begins in block 302, in which the virtual router 212 determines whether to classify network traffic based on flow (i.e., perform a flow classification operation on received network traffic)” (paragraph 0043). WANG adds, “It should be appreciated that the classification algorithm design selector 220 may also apply a level of confidence to each tested candidate classification algorithm, relative to the performance data collected, such that the classification algorithm design selector 220 does not select the preferred classification algorithm design until a sufficient level of confidence has been achieved for each tested candidate classification algorithm” (paragraph 0042).
determining a test matrix for the application or content classification;
WANG writes, “The classification algorithm design selector 220 is configured to select a preferred classification algorithm design for flow classification that is to be deployed (e.g., by the classification algorithm deployment manager 216) across one or more processors/cores. To do so, the classification algorithm design selector 220 is configured to rank each candidate classification algorithm based on the performance data (e.g., as may be collected by the classification algorithm performance monitor 218) collected for each tested candidate classification algorithm. To select the preferred classification algorithm design, the classification algorithm design selector 220 is configured to select the highest ranking algorithm that exceeds a predetermined performance threshold. It should be appreciated that the classification algorithm design selector 220 may also apply a level of confidence to each tested candidate classification algorithm, relative to the performance data collected, such that the classification algorithm design selector 220 does not select the preferred classification algorithm design until a sufficient level of confidence has been achieved for each tested candidate classification algorithm” (paragraph 0042).
determining test results based on the test matrix;
WANG writes, “The classification algorithm design selector 220 is configured to select a preferred classification algorithm design for flow classification that is to be deployed (e.g., by the classification algorithm deployment manager 216) across one or more processors/cores. To do so, the classification algorithm design selector 220 is configured to rank each candidate classification algorithm based on the performance data (e.g., as may be collected by the classification algorithm performance monitor 218) collected for each tested candidate classification algorithm. To select the preferred classification algorithm design, the classification algorithm design selector 220 is configured to select the highest ranking algorithm that exceeds a predetermined performance threshold. It should be appreciated that the classification algorithm design selector 220 may also apply a level of confidence to each tested candidate classification algorithm, relative to the performance data collected, such that the classification algorithm design selector 220 does not select the preferred classification algorithm design until a sufficient level of confidence has been achieved for each tested candidate classification algorithm” (paragraph 0042).
and determining a confidence score based on the test matrix.
WANG writes, “The classification algorithm design selector 220 is configured to select a preferred classification algorithm design for flow classification that is to be deployed (e.g., by the classification algorithm deployment manager 216) across one or more processors/cores. To do so, the classification algorithm design selector 220 is configured to rank each candidate classification algorithm based on the performance data (e.g., as may be collected by the classification algorithm performance monitor 218) collected for each tested candidate classification algorithm. To select the preferred classification algorithm design, the classification algorithm design selector 220 is configured to select the highest ranking algorithm that exceeds a predetermined performance threshold. It should be appreciated that the classification algorithm design selector 220 may also apply a level of confidence to each tested candidate classification algorithm, relative to the performance data collected, such that the classification algorithm design selector 220 does not select the preferred classification algorithm design until a sufficient level of confidence has been achieved for each tested candidate classification algorithm” (paragraph 0042).
Regarding claim 12, WANG teaches a method according to claim 11 further comprising:
Additionally, WANG teaches determining any increase or decrease to the confidence score in comparison to a previously determined confidence score for the application or content classification;
WANG writes, “...if all of the candidate algorithms have been tested, and to an acceptable degree of confidence, if applicable, the method 300 proceeds to block 330. In block 330, the virtual router 212 ranks each tested classification algorithm based on the associated performance level. In block 332, the virtual router 212 selects the candidate classification algorithm with the highest ranked performance level (e.g., the highest throughput level)” (paragraph 0047). WANG adds, “In block 416, the network appliance 106 constructs a learned data structure (e.g., a lookup table, a state machine, etc.) based the performance model. To construct the learned data structure, in block 418, the network appliance 106 uses various features as inputs, including network traffic patterns, rule patterns, etc. Additionally, in block 420, the network appliance 106 constructs the learned data structure using a predicted performance for each of the different candidate classification algorithms as output. In an illustrative example, to mitigate the costs of real-time machine learning overhead, a lookup table can be constructed using the offline learning process by using impact factors (e.g., rules, flows, etc.) as inputs and a predicted performance (e.g., based on the performance model) for different algorithm designs as outputs” (paragraph 0053).
and determining any changes to any traffic policies based on the increase or decrease of the confidence score.
WANG writes, “In some embodiments, during real-time virtual router operation, the virtual router may continuously sample traffic characteristics. Accordingly, in such embodiments, when the performance level drops below the performance threshold, the virtual router can perform a lookup operation on the lookup table and obtain the best algorithm design to achieve optimal performance, as opposed to performing the real-time performance testing for different algorithm designs as described above” (paragraph 0053).
Regarding claim 13, WANG teaches a method according to claim 12
Additionally, WANG teaches further comprising preparing a summary with details as to the increase or decrease in the confidence score.
WANG writes, “In block 340, the virtual router 212 transmits a report to a controller/administrator that indicates the performance level of the selected candidate classification algorithm does not exceed the performance threshold” (paragraph 0049).
Regarding claim 14, WANG teaches a method according to claim 11
Additionally, WANG teaches wherein determining an application or content classification comprises determining whether the application is a top used application.
WANG writes, “In block 330, the virtual router 212 ranks each tested classification algorithm based on the associated performance level. In block 332, the virtual router 212 selects the candidate classification algorithm with the highest ranked performance level (e.g., the highest throughput level)” (paragraph 0047).
Regarding claim 15, WANG teaches a method according to claim 14
Additionally, WANG teaches wherein if the application is a top used application determining a priority level for each test in the test matrix.
WANG writes, “In block 330, the virtual router 212 ranks each tested classification algorithm based on the associated performance level. In block 332, the virtual router 212 selects the candidate classification algorithm with the highest ranked performance level (e.g., the highest throughput level)” (paragraph 0047).
Regarding claim 16, WANG teaches a method according to claim 14
Additionally, WANG teaches wherein if the application is a top used application, determining test results comprises:
WANG writes, “In block 330, the virtual router 212 ranks each tested classification algorithm based on the associated performance level. In block 332, the virtual router 212 selects the candidate classification algorithm with the highest ranked performance level (e.g., the highest throughput level)” (paragraph 0047). WANG indicates the highest ranked performance level algorithm will be selected, and the highest ranked performance level algorithm may be, in certain cases, the top used application.
determining a pass or fail result per test in the test matrix;
WANG writes, “It should be further appreciated that there are many factors that impact flow classification performance. Accordingly, by using domain knowledge and ranking the classification algorithms, the most important factors that need to be considered in the adaptive algorithm can be identified. For example, such factors may include a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.” (paragraph 0050). WANG indicates there are many factors that impact the flow classification performance including a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.
determining a consistency factor fear each test;
WANG writes, “It should be further appreciated that there are many factors that impact flow classification performance. Accordingly, by using domain knowledge and ranking the classification algorithms, the most important factors that need to be considered in the adaptive algorithm can be identified. For example, such factors may include a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.” (paragraph 0050). WANG indicates there are many factors that impact the flow classification performance including a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.
and determining if any planned test in the test matrix was not run.
WANG writes, “It should be further appreciated that there are many factors that impact flow classification performance. Accordingly, by using domain knowledge and ranking the classification algorithms, the most important factors that need to be considered in the adaptive algorithm can be identified. For example, such factors may include a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.” (paragraph 0050). WANG indicates there are many factors that impact the flow classification performance including a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.
Claim Rejections - 35 USC § 103
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.
Claim(s) 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG in view of SZILAGYI.
Regarding claim 17, WANG teaches a method according to claim 14
Additionally, WANG teaches wherein if the application is not a top used application determining a test matrix comprises:
WANG writes, “In block 330, the virtual router 212 ranks each tested classification algorithm based on the associated performance level. In block 332, the virtual router 212 selects the candidate classification algorithm with the highest ranked performance level (e.g., the highest throughput level)” (paragraph 0047). WANG indicates the highest ranked performance level algorithm will be selected, and the highest ranked performance level algorithm may not be, in certain cases, the top used application.
and determine a ticket count for the application or content classification.
WANG writes, “It should be further appreciated that there are many factors that impact flow classification performance. Accordingly, by using domain knowledge and ranking the classification algorithms, the most important factors that need to be considered in the adaptive algorithm can be identified. For example, such factors may include a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.” (paragraph 0050). WANG indicates there are many factors that impact the flow classification performance including a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.
WANG fails to explicitly disclose information regarding, “determining a signature adaptability of the application or content classification;” and “determining trend analysis of the application or content classification;”
However, in analogous art, SZILAGYI teaches determining a signature adaptability of the application or content classification;
SZILAGYI writes, “The application detection/classification module 405 may detect applications in a variety of ways. For example, for a set of popular applications, application detection/classification module 405 may include detection logic for user plane packet monitoring (e.g., by the correlation of IP addresses, DNS query/response messages, TLS handshake information, URLs, and/or the like). This may lead to the detection of a specific application (e.g., YouTube), which can be mapped to a specific application classification. Non-dedicated applications may be classified based on the traffic patterns generated by the application itself (e.g., specific attributes of HTTP adaptive streaming, VoIP/video calls, messaging, etc. can be identified without knowing the exact identity of the application). Additionally or alternatively, the application's behavior may be matched to already profiled applications to identify that a new application generates the same kind of traffic as a known one thus it is likely that the similar characteristics apply” (paragraph 0054).
determining trend analysis of the application or content classification;
SZILAGYI writes, “In some example embodiments, the system 400 may include a context module 405. The context module 405 may gather or receive a context for application sessions. When an application is initiated, the context module 405 may obtain (for example, monitor, receive, and/or the like) context information regarding the application, user, network, traffic, and/or the like. Moreover, the context module 405 may continue to obtain context information while the session for an application is running to determine the current context of the application session. For example, at the initiation of a session, the context module 405 may obtain context information indicating that a video streaming application session has a context of 720 progressive video quality, the network state or traffic, and/or the like. Later, the context module 405 may determine that the context of the session for the video streaming application session has changed (for example, the user may have selected 1080 progressive video quality and frequent “buffering” causing interruption in the viewing of the video, the network is now congested, and/or the like). In this example, the context module 405 would obtain the current context of the session for the video streaming application, and provide that current context to the QoS/QoE definition function module 499” (paragraph 0035).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method and invention of WANG to include aspects described by SZILAGYI that “relates to wireless communications.” SZILAGYI provides the motivation for modification stating, “A goal of the combined QoE/resource management may be to arbitrate the resource allocations in a way that no application session gets under-allocated (which may cause compromised QoE) due to serving another session above their demand” (paragraph 0048).
Regarding claim 19, WANG and SZILAGYI teach a method according to claim 17
Additionally, WANG teaches wherein the ticket count is determined by subscriber tickets, internal tickets and external tickets.
WANG writes, “It should be further appreciated that there are many factors that impact flow classification performance. Accordingly, by using domain knowledge and ranking the classification algorithms, the most important factors that need to be considered in the adaptive algorithm can be identified. For example, such factors may include a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.” (paragraph 0050). WANG indicates there are many factors that impact the flow classification performance including a number of active flows, access pattern of flows (e.g., bursty or non-bursty, sequential or random), a number of rules, access pattern of rules, rule format distribution (e.g., many wildcard rules or same exact match format), an update rate, etc.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG and SZILAGYI as applied to claim 17 above, and further in view of KERUR, et al. (US 20230246972 A1, hereinafter, "KERUR").
Regarding claim 18, WANG and SZILAGYI teach a method according to claim 17
WANG and SZILAGYI fail to explicitly disclose information regarding, “wherein the ticket count is reviewed a plurality of consecutive time periods.”
However, in analogous art, KERUR teaches wherein the ticket count is reviewed a plurality of consecutive time periods.
KERUR writes, “This process can be repeated for each of a plurality of distinct traffic flows or distinct queues of packets to be classified” (paragraph 0101).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method and invention of WANG and SZILAGYI to include aspects described by KERUR that “relates to classification of network traffic.” KERUR provides the motivation for modification stating, “...described herein are systems and methods for traffic classification that integrate a plurality of different traffic classifiers to take advantage of their strengths, to mitigate their respective shortcomings, and/or to improve classification results. The resulting traffic classifications can be used to determine a preferred or desirable link between nodes on a network where the network includes different links or network paths between the nodes. The disclosed systems and methods may be particularly advantageous where the different network paths have different characteristics such as latency, capacity, congestion, cost, bandwidth, etc.” (paragraph 0054).
Conclusion
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/Christopher A. Reyes/Examiner, Art Unit 2475 12/16/2025
/KHALED M KASSIM/supervisory patent examiner, Art Unit 2475