DETAILED ACTION
This Final Office Action is in response to application number 17/726,985 filed on April 22nd,2022. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statements
The information disclosure statements (IDS), submitted on October 20th, 2023 and November 2nd, 2022 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
In the applicant arguments dated 02/02/2026 the applicant contends that “Kozat merely states that separate data streams correspond to separate traffic flows of different user equipment (UE). These separate data streams in Kozat correspond to separate data flows, but they are not comprised in a single service flow as required by the claim.”
The Examiner respectfully disagrees as Kozat Page 3 Lines 1-2 disclose “Optionally, in any of the preceding aspects, each of the plurality of data stream includes a traffic flow transmitted by different user equipment (UEs) over uplink channels of the 5G network.” Here it is clearly stated that each data stream includes traffic flow from different UEs, hence the data stream carries or contains a plurality of traffic flows from different UEs. This is in alignment with the claim 1 limitation which states that “wherein a service flow of the M service flows includes data flows from a plurality of user terminal devices, the data flows including a data flow of a first user terminal device and a data flow from a second user terminal device, wherein the first user terminal device is a terminal device different than the second user terminal device;” Hence applicants’ characterization of Kozat in stating that “that separate data streams correspond to separate traffic flows of different user equipment (UE)” is not agreed to by the Examiner. Page 10-11 lines 36-37 and 1-10 further emphasize the fact that the data stream addresses a single service flow, “In some embodiments, end to end slices may be modeled and realized with multiple slice segments each covering a different resource domain. A resource domain may refer to a set of resources located (e.g., physically, logically or virtually) at a specific location, and administrated by an associated administrator ( e.g., physically, logically or virtually). Examples of a resource domain may include a RAN domain, or a core network (CN) domain. Slice segments covering a RAN domain may be referred to as RAN segments, and slice segments covering a CN domain may be referred to as CNsegments. Different functional splitting and disaggregation options considered in a slice type may lead to a different number of segments. A slice segment herein refers to one or more virtual or physical processing elements or functions for processing or servicing traffic. For example, a slice segment may be a cascade of one or more virtual or physical processors, a baseband processing unit (BBU) instance (supports multiple UE flows), or a chain of one or more UP functions in 5G networks. Depending on actual functional split, types of slice segments may be differentiated.”
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1,3-10,13,15-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Liu et al. (EP2849386-A1) in view of Kozat et al. (WO2019075479-A1).
Regarding claims 1,13 and 20, Liu et al. disclose a data processing method, comprising: obtaining first bandwidth adjustment information and a bandwidth adjustment result of M service flows in a first exploration period, wherein the bandwidth adjustment result is obtained after the M service flows are adjusted based on the first bandwidth adjustment information (Paragraph 0023, step 101 and step 103 disclose the allocation of bandwidth to each user and the calculation of the bandwidth utilization rate for each user, whereby the user is equivalent to the service flow, the allocation of bandwidth is equivalent to the bandwidth adjustment information and the calculation of the utilization rate for each user is equivalent to the bandwidth adjustment result); determining second bandwidth adjustment information of the M service flows in a first decision period based on the first bandwidth adjustment information and the bandwidth adjustment result; and adjusting the M service flows based on the second bandwidth adjustment information (Paragraph 0023-0025 step 104 and step 105 disclose the classification of users with bandwidth utilization rates higher/lower than a preset value and the reallocation of bandwidth to users, whereby the reallocation of bandwidth is based on the initial bandwidth allocation value and the utilization rate, this is equivalent to the second bandwidth adjustment information of the service flows based on the first bandwidth adjustment information and the bandwidth adjustment result).
Liu et al. fail to explicitly disclose a service flow of the M service flows includes data flows from a plurality of user terminal devices, the data flows including a data flow of a first user terminal device and a data flow from a second user terminal device, wherein the first user terminal device is a terminal device different than the second user terminal device;
However, in an analogous art Kozat et al. teach a service flow of the M service flows includes data flows from a plurality of user terminal devices, the data flows including a data flow of a first user terminal device and a data flow from a second user terminal device, wherein the first user terminal device is a terminal device different than the second user terminal device; (Page 3 Lines 1-2 disclose “Optionally, in any of the preceding aspects, each of the plurality of data stream includes a traffic flow transmitted by different user equipment (UEs) over uplink channels of the 5G network.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Liu et al. to incorporate the teachings of Kozat et al. to map multiple data flows from different users on a single service flow, in order to simply and optimize the data flows with common objectives in a complex network.
Regarding claim 3 and 15, Liu et al disclose the method according to claim 1, wherein the first exploration period comprises N exploration time periods, the first bandwidth adjustment information comprises a bandwidth adjustment parameter for each service flow in each exploration time period, the bandwidth adjustment result comprises N utility value sets, an nth utility value set comprises a utility value of each service flow in an nth exploration time period, 1<=n<=N, M<=N, the second bandwidth adjustment information comprises a first bandwidth adjustment parameter and a second bandwidth adjustment parameter, the first bandwidth adjustment parameter is for a first service flow in the first decision period, the second bandwidth adjustment parameter for a second service flow in the first decision period, and the M service flows comprise the first service flow and the second service flow (Paragraph 0027-0028 and Fig. 2 disclose the bandwidth-even allocation unit, the traffic statistical unit and the bandwidth dynamic-allocation unit. The bandwidth even-allocation unit allocates initial bandwidth equivalent to the first bandwidth adjustment information, the traffic usage statistical unit in conjunction with the bandwidth utilization rate calculation subunit and the redundant bandwidth allocation subunit continuously assess real time traffic and preset bandwidth values to calculate utility rate and determine the second bandwidth adjustment information. The feedback loop within the bandwidth dynamic-allocation unit supports an iterative bandwidth allocation process equivalent to the N time periods over which bandwidth adjustment information, results and utility are evaluated); wherein before the determining second bandwidth adjustment information of all the M service flows in a-the first decision period based on the first bandwidth adjustment information and the bandwidth adjustment result, the method further comprises: determining the first service flow based on the N utility value sets; and wherein adjusting all the M service flows based on the second bandwidth adjustment information comprises: adjusting the first service flow based on the first bandwidth adjustment parameter, and adjusting the second service flow based on the second bandwidth adjustment parameter (Paragraph 0023-0025 step 104 and step 105 disclose the classification of users with bandwidth utilization rates higher/lower than a preset value and the reallocation of bandwidth to users, whereby the reallocation of bandwidth is based on the initial bandwidth allocation value and the utilization rate).
Regarding claim 4, 16, Liu et al. disclose the method according to claim 3, wherein the determining the first service flow based on the N utility value sets comprises: determining a first exploration time period from the N exploration time periods, wherein the first exploration time period corresponds to a first utility value set in the N utility value sets, and a sum of all utility values in the first utility value set is greater than a sum of all utility values in any utility value set other than the first utility value set in the N utility value sets; obtaining bandwidth adjustment parameters for the M service flows in the first exploration time period; and using a service flow corresponding to a maximum value in the bandwidth adjustment parameters for M service flows in the first exploration time period as the first service flow (Paragraph 0030 Step 306 discloses the allocation of redundant bandwidth to users or service flows with bandwidth utilization rates higher than the preset utilization rate. This is equivalent to using and allocating more bandwidth to service flows corresponding to the maximum value in the bandwidth adjustment parameter).
Regarding claims 5 and 17, Liu et al. disclose the method according to claim 4, wherein the determining second bandwidth adjustment information of the M service flows in the first decision period comprises: determining the first bandwidth adjustment parameter and the second bandwidth adjustment parameter based on a utility value of the first service flow in the first exploration time period and a bandwidth adjustment parameter for the first service flow in the first exploration time period (Paragraph 0030 and FIG. 3 discloses determining the second bandwidth adjustment information (bandwidth allocation) for the service flows (users) through steps 301-302 the allocation of the initial bandwidth to a user and steps 303- 306 the reallocation of bandwidth to a user based on traffic usage and the calculated bandwidth utilization rate).
Regarding claims 6 and 18, Liu et al. disclose the method according to claim 5, wherein the determining the first bandwidth adjustment parameter and the second bandwidth adjustment parameter comprises: determining the first bandwidth adjustment parameter based on the utility value of the first service flow in the first exploration time period and the bandwidth adjustment parameter for the first service flow in the first exploration time period; and determining the second bandwidth adjustment parameter based on the first bandwidth adjustment parameter (Paragraph 0030 and FIG. 3 disclose determining the first bandwidth adjustment parameter and the second bandwidth adjustment parameter based on a utility value in a time period through steps 303-306 the reallocation of bandwidth to a user based on traffic usage and the calculated bandwidth utilization rate. As per the claim the preset utility rate is compared to the current calculated utility rate to determine the bandwidth allocation or the bandwidth adjustment information for the following time period).
Regarding claims 7 and 19, Liu et al. disclose the method according to claim 6, wherein the determining the first bandwidth adjustment parameter based on the utility value of the first service flow in the first exploration time period and the bandwidth adjustment parameter for the first service flow in the first exploration time period comprises: determining a first value based on the utility value of the first service flow in the first exploration time period and the bandwidth adjustment parameter for the first service flow in the first exploration time period; and if the first value is less than or equal to a preset threshold, using the first value as the first bandwidth adjustment parameter (Paragraph 0030 step 305 discloses users with bandwidth utilization rates equivalent to the first value that is lower than the preset lower limit value of the utilization rate, in this instance as in the claim the lower utilization rate is allocated equivalent to the first bandwidth adjustment parameter).
Regarding claim 8, Liu et al disclose the method according to claim 7, wherein the method further comprises: if the first value is greater than the preset threshold, using the preset threshold as the first bandwidth adjustment parameter (Paragraph 0009 discloses evenly dividing and allocating the available bandwidth to each connected user whereby the allocated bandwidth is equivalent to the threshold value).
Regarding claim 9, Liu et al. disclose the method according to claim 7, wherein the first value is determined based on a second value and a third value, and the second value is a difference between the utility value of the first service flow in the first exploration time period and a utility value of the first service flow in any exploration time period other than the first exploration time period in the N exploration time periods; and the third value is a difference between the bandwidth adjustment parameter for the first service flow in the first exploration time period and a bandwidth adjustment parameter for the first service flow in the any exploration time period other than the first exploration time period in the N exploration time periods (Paragraphs 0023-0025 and FIG 1. disclose the evaluation of the difference between the utility values in subsequent time periods for a user to determine the allocation of the bandwidth parameter for the following time period).
Regarding claim 10, Liu et al. disclose the method according to claim 3, wherein the adjusting the first service flow based on the first bandwidth adjustment parameter, and adjusting the second service flow based on the second bandwidth adjustment parameter comprises: adjusting bandwidth allocation information, in first bandwidth allocation information, for the first service flow based on the first bandwidth adjustment parameter, and adjusting bandwidth allocation information, in the first bandwidth allocation information, for the second service flow based on the second bandwidth adjustment parameter, wherein the first bandwidth allocation information is bandwidth allocation information for the M service flows in a second decision period (Paragraph 0023-0025 step 104 and step 105 disclose the classification of users with bandwidth utilization rates higher/lower than a preset value and the reallocation (adjustment) of bandwidth to users, whereby the reallocation (adjustment) of bandwidth is based on the initial bandwidth allocation value and the utilization rate, this is equivalent to the second bandwidth adjustment information of the service flows based on the first bandwidth adjustment information and the bandwidth adjustment result).
Claims 2 and 14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Liu et al. (EP2849386-A1) in view of Kozat et al. (WO2019075479-A1) further in view Barth et al. (US20180006962-A1).
Regarding claims 2 and 14, Liu et al. and Kozat et al. teach the method according to claim 1.
Liu et al. and Kozat et al. fail to explicitly teach determining second bandwidth adjustment information of the M service flows in the first decision period comprises: determining the second bandwidth adjustment information of M service flows in the first decision period by using a machine learning algorithm based on the first bandwidth adjustment information and the bandwidth adjustment result.
However, in an analogous art, Barth et al disclose, determining second bandwidth adjustment information of the M service flows in the first decision period comprises: determining the second bandwidth adjustment information of M service flows in the first decision period by using a machine learning algorithm based on the first bandwidth adjustment information and the bandwidth adjustment result (Barth et al. Paragraph 0063 discloses the traffic profiling device determining policy based on techniques such as machine learning, whereby known traffic behaviors and the corresponding bandwidth values are used to predict future traffic behaviors and corresponding bandwidth values).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Liu et al. and Kozat et al. to incorporate the teachings of Barth et al., to employ machine learning techniques on bandwidth adjustment information and results to determine the second bandwidth adjustment information of M service flows in the first decision period in order to decrease the time and resources required to obtain the optimal bandwidth parameters for multiple flows with different objectives in a network and to maximize network utility.
Claims 11 and 12 are rejected under 35 U.S.C. 103(a) as being unpatentable over Liu et al. (EP2849386-A1) in view of Kozat et al. (WO2019075479-A1) further in view of Nagaraj et al. (NUMFabric: Fast and Flexible Bandwidth Allocation in Datacenters).
Regarding claim 11, Liu et al. and Kozat et al. teach the method according to claim 3.
Liu et al. and Kozat et al. fail to explicitly teach obtaining a reference utility sum in the first decision period, wherein the reference utility sum in the first decision period is a sum of utility values of the M service flows in the second decision period; and if a sum of all utility values in at least one of the N utility value sets is greater than or equal to the reference utility sum, performing determining the first service flow based on the N utility value sets.
However, in an analogous art, Nagaraj et al. teaches obtaining a reference utility sum in the first decision period, wherein the reference utility sum in the first decision period is a sum of utility values of the M service flows in the second decision period; and if a sum of all utility values in at least one of the N utility value sets is greater than or equal to the reference utility sum, triggering the step of performing determining the first service flow based on the N utility value sets (Nagaraj et al. Page 188 Column 2 Paragraph 2 states, “NUMFabric then realizes the bandwidth allocation that maximizes the sum of the utility functions in completely distributed fashion”, this implies that the nth utility value set chosen is the one that has the highest utilization amongst the N utility values in a in time period).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Liu et al. and Kozat et al. to incorporate the teachings of Nagaraj et al., to obtain a utility sum in a time period that maximizes value of the utility sum, as the maximum utility sum for the M flows in a network yields the maximum network utility or usage and the optimal bandwidth parameter for the flow, this ensures that the network resources are efficiently used and the best available service experience is offered to the end user of the service flow.
Regarding claim 12, Liu et al. and Kozat et al. teach the method according to claim 11.
Liu et al. and Kozat et al. fails to explicitly teach if a sum of all utility values in each of the N utility value sets is less than the reference utility sum, obtaining the bandwidth allocation information for the M service flows in the second decision period; and using the bandwidth allocation information as bandwidth allocation information for the M service flows in the first decision period.
However, in an analogous art, Nagaraj et al. teaches if a sum of all utility values in each of the N utility value sets is less than the reference utility sum, obtaining the bandwidth allocation information for the M service flows in the second decision period; and using the bandwidth allocation information as bandwidth allocation information for the M service flows in the first decision period (Nagaraj et al. Page 188 Column 2 Paragraph 2 states, “NUMFabric then realizes the bandwidth allocation that maximizes the sum of the utility functions in completely distributed fashion”, this implies that the nth utility value set chosen is the one that has the highest utilization amongst the N utility values in a in multiple time periods as the objective of NUMFabric is to identify the set of utility values that provides highest value in terms of the sum ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Liu et al. and Kozat et al. to incorporate the teachings of Nagaraj et al., to obtain a utility sum in a time period that maximizes value of the utility sum, as the maximum utility sum for the M flows in a network yields the maximum network utility or usage and the optimal bandwidth parameter for the flow, this ensures that the network resources are efficiently used and the best available service experience is offered to the end user of the service flow.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samuel Dilan Rutnam whose telephone number is 703-756-1374. The examiner can normally be reached between 8:30am-5:00pm Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy Kundu can be reached on 571-272-8586.
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/Samuel Dilan Rutnam/
Patent Examiner, Art Unit 2471
/SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471
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