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
This office action is in response to amendment/reconsideration filed 10/9/2025, the amendment/reconsideration has been considered. Claims 1-4, 6-15 and 17-20 are pending for examination.
Response to Arguments
Applicant's arguments are moot in light of the new ground of rejections set forth below.
Claim Rejections - 35 USC § 103
3. 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.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
6. Claims 1-4, 6-9, 11-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hegar et al (US 11632326) in view of Parastar et al (“On-device ML For the Current and the Emerging Networks: A Survey on Current Approaches and Challenge”).
As to claim 12, Hegar discloses an apparatus, comprising:
a processor coupled to the one or more network interfaces and configured to execute one or more processes (col. 18, last paragraph); and
a memory configured to store a process that is executable by the processor, the process (col. 18, last paragraph) when executed configured to:
obtain telemetry data associated with application traffic for an online application (col. 18, lines 28-40, “In some embodiments, probing is halted after a port 510 is selected, and then probing is again performed to identify a new port when there is a failure on port 510 or if sender 502 otherwise determines a new port should be used for transmitting multimedia stream content”; col. 23, paragraph 1, “if transmission of the first data packet was not successful (e.g., NACK'd or no ACK received within maximum round trip time), then a retransmission of the lost data packet may be made using an updated port to exercise a different network path”. Here, the data reflecting the failure of data packet transmission are obtained telemetry data associated with the multimedia traffic (see col. 2, paragraph 2, wherein data packets are disclosed for carrying and transmitting multimedia content) for the respective multimedia application accessible via network therefore an online application. The obtained telemetry data can also include network condition metrics obtained via prior probing, see col. 22, paragraph 2);
classify, based on applying a machine algorithm to the telemetry data, a network path as potentially exhibiting transient events that occur when a particular performance metric of the network path is degraded, wherein the transient events are undetected at a current probing frequency for the network path, and wherein the machine learning model classifies the network path as potentially exhibiting transient event based on telemetry data that is different from the particular performance metric (see citation above, wherein the transmission failure of a data packet is the telemetry data. See col. 18, lines 28-40, “In some embodiments, probing is halted after a port 510 is selected, and then probing is again performed to identify a new port when there is a failure on port 510 or if sender 502 otherwise determines a new port should be used for transmitting multimedia stream content. Variations on the techniques described above may include identifying K ports that meet a threshold level of reliability (e.g., 99.9% reliability over the past hour and no lost packets within the most recent minute) and transmitting data packets for multimedia stream 512 over the K ports in a round-robin or random fashion. Sending data packets over K ports (e.g., K network paths) rather than a single port may provide more resilience against a single point of failure”; col. 22, lines 25-38, “if network reliability ( e.g., as measured by rate of lost packets, rate of change of lost packets, etc.) is above a threshold, then probing is halted; but if the measured network reliability is below the threshold, then probing may be initiated or re-initiated due to the potential need to find a new port for the data content due to poor network conditions. In some embodiments, the rate at which probe packets are sent is tuned based on measured network reliability-for example, as the rate of lost packets or rate of change of lost packets increases, the rate at which probes are sent is increased. The probe rate may be directly proportional to an error rate and/or inversely proportional to a success rate”; Col. 16, last paragraph to col. 17, paragraph 1, “identify reliable network paths based on detecting that network conditions are degrading”; see col. 1, paragraph 1, “adverse network conditions (e.g., network outages) may simultaneously affect several network paths. If a failure is detected in the transmission of a data packet along a first network path”.
Here, the failed transmission of a data packet carrying multimedia content (instead of a probe packet) is the telemetry data that is of another type (i.e., failure/loss of a data packet carrying multimedia content) than a different particular performance metric (the port’s reliability level or success rate as measured by the probe packets, wherein the probe packets do not carry multimedia content (see col. 21, paragraph 3, “a probe packet may include information that is used by a sender and receiver to determine current network conditions, and may be in contrast to a data packet that encodes content that is intended for use by a receiver or a downstream entity, such as multimedia content.”) therefore correspond to a different performance metric).
As cited above. the failure of the data packets is based on to identify a network path (a port) as potentially exhibiting transient events (adverse network conditions such as network outage, see col. 1, paragraph 1), that occur when the different performance metric is degraded (reliability level or success rate of the port measured by probe packets goes down). The halted probing indicates a probing frequency of ZERO, and the re-initiated probing indicates a new probing frequency, wherein the transient events (adverse network conditions such as network outage) that occurs when the different performance metric (reliability level or success rate of the port as measured by the probe packets) are undetected at the current probing frequency of ZERO. The new probing frequency (the re-initiated probing rate) is determined and re-initiated to detect adverse network conditions for the port and for selecting a new port that does not have such adverse network condition to the level of below threshold success rate, said adverse network conditions measured by probing are undetected during a halted probing with a frequency of ZERO. Also see col. 32, lines 30-40, “In some embodiments, the rate at which probe packets are sent is tuned based on measured network reliability-for example, as the rate of lost packets or rate of change of lost packets increases, the rate at which 35 probes are sent is increased. The probe rate may be directly proportional to an error rate and/or inversely proportional to a success rate.”);
determine a new probing frequency for the network path, higher than the current probing frequency, to detect the transient events that occur when the particular performance metric of the network path is degraded and that are undetected at the current probing frequency (see citation and explanation in the immediately preceding limitation); and
cause probes to be sent along the network path according to the new probing frequency (see citation and explanation in the two preceding limitations).
However, Hegar does not expressly disclose that the machine algorithm is a machine learning model. Parastar discloses a concept to use machine learning model to predict degraded performance metric based on telemetry data (Page 13, right column, “ SDN can make network probing more adaptable and intelligent. Probes are test transactions that are used to track network activity and collect data from network elements. Due to the increasing number of devices, the variety of characteristics to measure, and the short time intervals to log data, determining the ideal probe rate in future networks will be prohibitively expensive. Aggressive probing can raise traffic overhead significantly, resulting in network performance loss. Conservative probing, on the other hand, runs the risk of overlooking important abnormalities or network events. As a result, it’s critical to adjust probing rates to keep traffic overhead under control while reducing performance degradation. SDN can use machine learning techniques to provide the ideal framework for adaptive probing. The SDN controller, for example, can investigate suspected devices more quickly after forecasting a fault or detecting an anomaly. Similarly, when the network is overburdened, the controller may lower the probing rate and rely on regression to estimate the value of the recorded parameters”).
Before the effective filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Hgear with Parastar. The suggestion/motivation of the combination would have been to provide adaptive probing to detect anomaly (Parastar, Page 13, right column).
As to claim 1, see similar rejection to claim 12.
As to claim 20, see similar rejection to claim 12.
As to claim 13, Hegar-Parastar discloses the apparatus as in claim 12, wherein the telemetry data comprises results from probing the network path at the current probing frequency (Hegar, see col. 22, paragraph 2).
As to claim 2, see similar rejection to claim 13.
As to claim 14, Hegar-Parastar discloses the apparatus as in claim 12, wherein the apparatus obtains the telemetry data from the online application (Hegar, see citation in rejection to limitation 1 of claim 1, wherein the transmission failure of data packets are obtained from the receiver such as the multimedia application, see Hegar, col. 3, “A failure may occur in 35 transmission of the first data packet from a sender computing entity to a receiver computing entity. In response to such a failure, one or more retransmission attempts may be made according to the second mode of operation…, a negative acknowledgement (e.g., NACK) may also be used to positively identify ports (and associated network paths) that are not working properly.” ).
As to claim 3, see similar rejection to claim 14.
As to claim 15, Hegar-Parastar discloses the apparatus as in claim 12, wherein the apparatus identifies the network path in part based on input from a user interface designating the network path as important (Hegar, col. 18, lines 28-40, “In some embodiments, probing is halted after a port 510 is selected, and then probing is again performed to identify a new port when there is a failure on port 510 or if sender 502 otherwise determines a new port should be used for transmitting multimedia stream content”, wherein the combination of the multiple ports can also be considered a network path).
As to claim 4, see similar rejection to claim 15.
As to claim 17, Hegar-Parastar discloses the apparatus as in claim 12, wherein the apparatus identifies the network path using a machine learning classifier to classify the network path (Hegar, see citation in rejection to claim 1, and Hegar, col. 7, last paragraph, “the network reliability metrics will have been updated for every source port showing whether they
delivered packets successfully”. This process is not disclosed to require human classification).
As to claim 6, see similar rejection to claim 17.
As to claim 7, Hegar-Parastar discloses the method as in claim 6, further comprising:
obtaining, by the device, feedback regarding the probes sent along the network path according to the new probing frequency; and updating, by the device, the machine learning classifier based on the feedback (Hegar, see citation in rejection to claim 1, and Hegar, col. 7, last paragraph, “the network reliability metrics will have been updated for every source port showing whether they delivered packets successfully”).
As to claim 9, Hegar-Parastar discloses the method as in claim 1, wherein the telemetry data is indicative of at least one of: packet loss (Hegar, see citation in rejection to claim 1, where the transmission failure of the data packet is a packet loss), latency, jitter, or throughput.
As to claim 18, Hegar-Parastar discloses the apparatus as in claim 12, wherein the process when executed is further configured to: provide the new probing frequency for display (Hegar, see citation in rejection to claim 1, and Hegar, col, 31, lunes 30-38, “or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting” indicating that the decisions does not preclude user input or prompting, therefore the new probing frequency is provided to enable or for the purpose of displaying. It is to be noted that the claimed limitation does not require displaying the new probing frequency, but instead, merely “for displaying”).
As to claim 8, see similar rejection to claim 18.
As to claim 19, Hegar-Parastar discloses the apparatus as in claim 12, wherein the apparatus causes probes to be sent along the network path according to the new probing frequency by:
sending an instruction to a networking device associated with the network path that indicates the new probing frequency (Hegar, see citation to rejection to claim 1 wherein sending an instruction to a device that carries out the new probing frequency is implied. It is to be noted that the claim does not require a specific type of device therefore Examiner interprets any device).
As to claim 11, see similar rejection to claim 19.
Claim Rejections - 35 USC § 103
7. 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.
8. 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 of this title, 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.
9. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
10. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hegar-Parastar, as applied to claim 1 above, and further in view of Kim et al (US 2014/0146695).
As to claim 10, Hegar-Parastar discloses the claimed invention substantially as discussed in claim 1, but does not expressly disclose that the telemetry data is indicative of a maximum concealment time for the online application. Kim discloses a concept for telemetry data to be indicative of a maximum concealment time for an online application (see [0144], wherein telemetry data such as lost packet is indicative of a maximum concealment time. See [0034] and [0048], wherein the voice and video communication based on a VoIP are online applications. It is to be noted that the claim does not require a specific way to “indicate”, nor does the claim specify what is considered as maximum, lacking a controlling definition in the specification)
Before the effective filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Hegar-Parastar with Kim. The suggestion/motivation of the combination would have been to compensate for lost packets (Kim, [0144]).
Conclusion
11. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/HUA FAN/ Primary Examiner, Art Unit 2458