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 § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over SZILAGYI et al. (US 20190364457 A1, hereinafter, "SZILAGYI") in view of VASUDEVAN et al. (US 20210204152 A1, hereinafter, "VASUDEVAN").
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 a plurality of content categories 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 a content category of the traffic flow from the plurality of content categories;
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).
SZILAGYI fails to explicitly disclose information regarding, “and monitoring the traffic flow for any changes in a traffic pattern of the traffic flow,”, “wherein the traffic pattern is analyzed by statistics comprising: bytes per second, time between peaks, width or density of transfers that would indicate a change in the content category of the traffic flow;”, and “and when a change in the traffic pattern is detected, evaluating the packet parameters to determine a new content category from the plurality of content categories.”
However, in analogous art, VASUDEVAN teaches and monitoring the traffic flow for any changes in a traffic pattern of the traffic flow,
VASUDEVAN writes, “In some implementations, a communication system is configured to classify data traffic, including data that is encrypted. The classification determined can be used to assign a quality of service class or priority for service in the network. The communication system can be configured to classify encrypted and unencrypted traffic in real-time based on measured traffic characteristics. The system can be configured to automatically update models used for traffic classification, to ensure the models do not degrade in performance when new traffic patterns are encountered” (paragraph 0003).
wherein the traffic pattern is analyzed by statistics comprising: bytes per second, time between peaks, width or density of transfers that would indicate a change in the content category of the traffic flow;
VASUDEVAN writes, “A classifier can use statistical patterns of data traffic to identify types of data flows that are present, even if the data flows are encrypted. These patterns can include packet-level statistics, such as frequency and consistency of packet transmissions, amounts of packets in a flow, and so on. A machine learning model can be trained, based on examples in a set of training data, to detect common types of data flows, e.g., corresponding to real-time calls (e.g., voice over Internet protocol (VOIP) traffic), web page transfers and other interactive situations, file transfers, media streaming, and so on. However, when the trained machine learning model is deployed and used, the model may encounter data traffic flows that do not fit the types or classes of data flows that the model has been trained to detect” (paragraph 0005).
and when a change in the traffic pattern is detected, evaluating the packet parameters to determine a new content category from the plurality of content categories.
VASUDEVAN writes, “To address this situation and allow the system to learn to detect new types of data, a portion of the system can be configured to detect anomalous data flows. When traffic is identified that differs from previously established data flow types, the data can be labelled and collected, and then provided to one or more other devices for further training of machine learning models. Updated machine learning models can then be provided that can account for the previously unrecognized data flow characteristics. For example, the updated model(s) may be trained to assign the data that was previously unrecognizable to a new class or category of data traffic or to assign the data to an existing class or category that is most appropriate. In this manner, there is a flow of communication between the traffic classifiers and model training system, allowing the classification models to be automatically updated and refined as new traffic patterns are encountered” (paragraph 0006).
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 SZILAGYI to include aspects described by VASUDEVAN that relates to “provid[ing] the service needed for different applications, data streams may be handled differently according to the type of data being transferred, e.g., a web page, a streaming video, a file upload, a video conference call, etc.” VASUDEVAN provides the motivation for modification stating, “The techniques can be used to provide one or more of the following advantages. For example, satellite terminals having classification models that operate and are updated as described herein may provide better user experience by more accurately servicing traffic according to the correct QoS class. This also allows optimizing overall system performance to better allocate bandwidth at the system level. The techniques can help provide accurate, reliable, and low-latency traffic classification, which can result in better service offered to more QoS sensitive traffic. This can be especially useful at times of traffic congestion (e.g., busy hours or other peak utilization times)” (paragraph 0011).
Regarding claim 2, SZILAGYI and VASUDEVAN teach 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 and VASUDEVAN teach 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 and VASUDEVAN teach 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 and VASUDEVAN teach the method according to claim 1,
Additionally, VASUDEVAN 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.
VASUDEVAN writes, “Machine learning and rule-based models can be used. The models, such as the ML-based traffic classifier 176, can be configured to make predictions about connections based on only on packet and object statistics (e.g., size, timing, etc.) and not on the content or destination of the packets. Changes in protocols, such as from TCP to QUIC, can be addressed by collecting new data and training the model by adjusting the model parameters or adjusting the rules and/or conditions according to the new traffic patterns” (paragraph 0067). VASUDEVAN continues, “In some cases, rule-based approaches formulate rudimentary conditions that must be satisfied for assignment to a specific traffic class. The set of rules and conditions are often very simple, and so do not require a lot of computational resources to execute and can be reliably run on an embedded system” (paragraph 0068).
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.
Claims 11-19 have been cancelled by the applicant, respectfully.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Christopher A. Reyes/Examiner, Art Unit 2475 9/14/2026
/ABDULLAHI AHMED/Examiner, Art Unit 2475
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