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 final office action is prepared in response to amendments and arguments filed by Applicant on September 2, 2025 as a reply to the non-final office action mailed on June 2, 2025.
Claim 17 is newly added.
Claims 1-17 are pending.
Claims 1-17 are rejected.
Response to Arguments
The claim amendments and Applicant’s arguments filed on September 2, 2025 have been carefully considered but deemed unpersuasive in view of the new rejection rationale as set forth in the section “Claim rejection – 35 U.S.C. 103” below, necessitated by Applicant’s substantial amendments to the claims.
The rejection of claim 16 under 35 U.S.C. 112(b) has been withdrawn in view of the claim amendments that resolve the issue.
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).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 16, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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.
Claims 1-17 are rejected under 35 U.S.C. 103 as being obvious over Schiocchet et al. (U.S. 2024/0284256) in view of Vasseur et al. (U.S. 9,800,506).
Regarding claim 1, Schiocchet disclosed a method for managing a data communication network, the method comprising:
detecting a bursty data transfer on the data communication network (Schiocchet, Fig. 9, step 910 and [0089], “At 910, the UE 902 may identify, based on the burst threshold, a set of bursts in the data stream”);
inferring a flow burst and pause profile of the bursty data transfer using measurements of the bursty data transfer (Schiocchet, Fig. 9, step 912 and [0090], “At 912, the UE 902 may detect, based on the set of bursts, a pattern in the data stream.” Schiocchet, Fig. 9, step 916 and [0092], “ At 916, the UE 902 may estimate, based on the pattern and the burst threshold, at least one subsequent burst in the data stream”); and
managing the data communication network according to the flow burst and pause profile (Schiocchet, Fig. 9, steps 918 and 920, “monitor for the at least one subsequent burst in the data stream after being configured to estimate the at least one subsequent burst” and “output an indication of the at least one subsequent burst … to the base station 904”).
Schiocchet might not have explicitly disclosed that the managing was done by “adapting … a data policy responsible for performance optimization and managing a data transfer according to the adapted data policy”.
However, Schiocchet disclosed in the Abstract and paragraphs [0024, 0095, 0105, 0125] that “the predicted data bursts allow the transmission resource and power to be adaptively arranged to significantly reduce power consumption and improve transmission efficiency.” Even though Schiocchet did not explicitly use the word “data policy”, Schiocchet’s disclosure of “allow(ing) the transmission resource and power to be adaptively arranged” is often known by one of ordinary skill in the art as adaptively arranging the data policy for transmission resource and power.
Examiner would also like to note that the claim element “adapting … a data policy responsible for performance optimization and managing a data transfer according to the adapted data policy” is not explicitly disclosed in the application specification. The written description that is most relevant to this claim element is found in “Backgraound/Summary”, where it is stated that
“bursty data performance measurement and optimization may be used to identify and adapt data transport policies responsible for performance measurement and optimization”
Therefore, Schiocchet’s disclosure would have made the claim element “adapting a data policy responsible for performance optimization and managing a data transfer according to the adapted data policy” obvious.
Schiocchet might not have explicitly disclosed that the inferring was done using machine learning.
However, Vasseur disclosed a method that uses predictive time allocation scheduling for TSCH networks (Vasseur, Abstract). In particular, Vasseur disclosed in col. 5, lines 8-12 that “routing process 244 and/or channel hopping process 248/248a may utilize machine learning techniques, to predict a future state of the network (e.g., predict routing changes, predict time slot usage by nodes, etc.)” and in col. 13, lines 47-67 and col. 14, lines 1-67 additional details on how a machine-learning based architecture is used to make time slot allocation changes based on predicted traffic changes.
One of ordinary skill in the art would have been motivated to combine Schiocchet and Vasseur because both references disclosed the methods for detecting traffic patterns and predicting future bursts (Schiocchet, Abstract; Vasseur, col. 13, lines 47-67) and Schiocchet disclosed in a general manner that Artificial Intelligence/Machine Learning techniques could be used in its method without the specific details while Vasseur provides the details.
Therefore, Schiocchet’s teaching, when combined with that of Vasseur’s would have made it obvious that the inferring of flow burst and pause profile/pattern could be done using machine learning.
Claim 12 lists substantially the same elements as claim 1 in computer readable medium form rather than method form. Therefore, the rejection rationale for claim 1 applies equally as well to claim 12.
Regarding claims 2 and 13, Schiocchet and Vasseur disclosed the subject matter of claims 1 and 12, respectively.
Schiocchet further disclosed wherein detecting the bursty data transfer comprises detecting that a data transfer that was a continuous data transfer has become a bursty data transfer (Schiocchet, Fig. 5 disclosed a bursty data transmission pattern that burst detector 614 and burst end detector 616 shown in Fig. 6 must be able to detect, which implies that the burst detector and burst end detector would have been able to detect that the data transfer that was continuous has become bursty).
Regarding claims 3 and 14, Schiocchet and Vasseur disclosed the subject matter of claims 1 and 12, respectively.
Vasseur further disclosed wherein inferring, using machine learning, the flow burst and pause profile comprises providing the measurements of the bursty data transfer to a neural network, the neural network configured to produce the flow burst and pause profile (Vasseur, col. 14, lines 56-60, “Based on the traffic send during the allocated time slots between parent node 32 and child nodes 41 and 42, parent node 32 may then generate a time slot usage report that quantifies how heavily the nodes use the time slots.” and Vasseur, col. 15, lines 24-32, “Based on the traffic send during the allocated time slots between parent node 32 and child nodes 41 and 42, parent node 32 may then generate a time slot usage report that quantifies how heavily the nodes use the time slots.” Said time slot usage report is a measurement of the bursty data transfer and the flow burst and pause profile).
The motivation for combining Schiocchet and Vasseur is that same as that presented in the rejection of claim 1.
Regarding claims 4 and 15, Schiocchet and Vasseur disclosed the subject matter of claims 3 and 14, respectively.
Vasseur further disclosed wherein inferring, using machine learning, the flow burst and pause profile comprises providing value determined by one or more previously inferred flow burst and pause profiles of the bursty data transfer to the neural network (Vasseur, col. 15, lines 24-32, “Based on the traffic send during the allocated time slots between parent node 32 and child nodes 41 and 42, parent node 32 may then generate a time slot usage report that quantifies how heavily the nodes use the time slots. Detecting a time-based pattern in increased delays may also be treated by the prediction engine as a sign of seasonality and used by the prediction engine to allocate more time slots for the affected nodes at the specific period of time.”).
The motivation for combining Schiocchet and Vasseur is that same as that presented in the rejection of claim 1.
Regarding claim 5, Schiocchet and Vasseur disclosed the method of claim 3.
Schiocchet and Vasseur further disclosed wherein managing the data transfer according to the adapted data policy comprises managing the bursty data transfer according to the flow burst and pause profile (Schiocchet, Abstract, [0024, 0105, 0125]; Vasseur, col. 15, lines 24-32).
The motivation for combining Schiocchet and Vasseur is that same as that presented in the rejection of claim 1.
Regarding claim 6, Schiocchet and Vasseur disclosed the method of claim 3.
Schiocchet further disclosed wherein managing the data transfer according to the adapted data policy comprises managing a data transfer other the bursty data transfer according to data policy (Schiocchet, Abstract, [0024, 0095], “The predicted data bursts allow the transmission resource and power to be adaptively arranged to significantly reduce power consumption and improve transmission efficiency”).
Regarding claim 7, Schiocchet and Vasseur disclosed the method of claim 3.
Schiocchet further disclosed wherein the measurements of the bursty data transfer include one or more burst duration measurements, one or more burst size measurements, or both (Schiocchet, Fig. 5 shows the bursty traffic pattern; Schiocchet further disclosed in Fig. 9, step 910 and Fig. 11, step 1104 of “identify based on the burst threshold a set of bursts in the data stream”).
Regarding claim 8, Schiocchet and Vasseur disclosed the method of claim 3.
Schiocchet further disclosed wherein the measurements of the bursty data transfer include one or more pause duration measurements (Schiocchet, [0084], “actual gap duration”).
Regarding claim 9, Schiocchet and Vasseur disclosed the method of claim 1.
Vasseur further disclosed wherein the inferred flow burst and pause profile includes an inference of a data type being carried by the bursty data transfer (Vasseur, col. 5, lines 53-55, “an ANN may be trained to identify deviations in the behavior of a network that could indicate the presence of a network attack. (e.g., a change in packet losses, link delays, number of requests, etc.)” said teaching by Vasseur means that the machine learning can detect the type of data indicative of a network attack).
The motivation for combining Schiocchet and Vasseur is that same as that presented in the rejection of claim 1.
Regarding claim 10, Schiocchet and Vasseur disclosed the method of claim 3.
Schiocchet further disclosed wherein the inferred flow burst and pause profile includes an inferred target burst duration, an inferred target burst size, or both for the bursty data transfer (Schiocchet, [0078], “Burst Volume Percentage (BVP)” and “window duration”).
Regarding claim 11, Schiocchet and Vasseur disclosed the method of claim 3.
Schiocchet further disclosed wherein the inferred flow burst and pause profile includes an inferred target pause duration for the bursty data transfer (Schiocchet, [0083], “ If a periodically stable pattern is present, the minimum time gap duration until the next burst will be predicted. The predicted time gap duration may be based on the gap duration distribution (e.g., the mean m.sub.gap and the standard deviation δ.sub.gap of the gaps, obtained by the pattern detector 612)”).
Claim 16 lists substantially the same elements as claim 1 in system form rather than method form. Therefore, the rejection rationale for claim 1 applies equally as well to claim 16.
Regarding claim 17, Schiocchet and Vasseur disclosed the method of claim 1.
Schiocchet further disclosed
wherein adapting the data policy includes adjusting a target burst transfer rate according to the flow burst and pause profile (Schiocchet, Abstract, [0024, 0095], “The predicted data bursts allow the transmission resource and power to be adaptively arranged to significantly reduce power consumption and improve transmission efficiency”).
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 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY X ZHANG whose telephone number is (571)270-5012. The examiner can normally be reached 8:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon H Hwang can be reached at 571-272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIRLEY X ZHANG/Primary Examiner, Art Unit 2447