DETAILED ACTION
This action is in response to communications filed December 11th, 2025.
Claims 1-28, 41, and 61 are currently pending. Claims 1, 5-6, 12, 21, 25-26, 41, and 61 are currently amended.
The present application claims priority to provisional application no. 63/511,817, filed on July 3rd, 2023.
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 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.
Claims 1-28, 41, and 61 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (U.S. Patent Application Publication no. 2024/0196376, hereinafter Wang) in view of Guo et al (U.S. Patent Application Publication no. 2021/0211942, hereinafter Guo).
With respect to claims 1, 21, 41, and 61, Wang discloses a device (paragraph [0048], user equipment) associated with service management of a wireless network (paragraph [0046], Service and Orchestration Framework 10), comprising:
a processing system that includes processor circuitry and memory circuitry that stores code (paragraph [0046]), the processing system configured to cause the device to:
receive a request associated with a network slice in the wireless network (paragraph [0063], lines 1-9, fullfill SLA in the radio access network), the request indicating one or more parameters associated with a service level agreement of the network slice (paragraph [0063], lines 1-9, including new parameters);
select, in accordance with the one or more parameters associated with the service level agreement, a respective physical resource block (PRB) allocation of the network slice for each cell of a set of cells of the wireless network (paragraph [0068], number of Physical Resource Blocks (PRBs) that the slice can accommodate); and
output, to a network slice management function and in accordance with a respective PRB utilization at each cell of the set of cells and the respective PRB allocation of the network slice for each cell of the set of cells, a recommendation associated with the network slice (paragraph [0070], handover optimization xApp is applied).
But Wang does not discloses receiving a request to admit a network slice in the wireless network.
However, Guo discloses receiving a request to admit a network slice in the wireless network (paragraph [0049], lines 1-8, whether that slice request can be admitted), and
Outputting the recommendation (paragraph [0049], lines 1-8, whether that slice request can be admitted) comprising an approval or rejection of the network slice to the wireless network (paragraph [0049], lines 8-11, rejecting certain new/modified slice requests).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the improvements in and relating to cell configuration and control of Wang with the communication system of Guo. The motivation to combine being to improve the deployment of network slices. The deployment of network slices being improved by admitting network slices based on network performance requirements (abstract: Guo).
With respect to claim 2 and 22, the combination of Wang and Guo discloses the device of claims 1 and 21, Wang further discloses wherein, to select the respective PRB allocation of the network slice for each cell of the set of cells, the processing system is further configured to cause the device to:
predict the respective PRB allocation of the network slice for each cell of the set of cells in accordance with the one or more parameters associated with the service level agreement (paragraph [0070], lines 5-13, predicted resource usage).
With respect to claim 3 and 23, the combination of Wang and Guo discloses the device of claims 2 and 22, Wang further discloses wherein, to predict the respective PRB allocation of the network slice for each cell of the set of cells, the processing system is further configured to cause the device to:
predict the respective PRB allocation of the network slice for each cell of the set of cells in accordance with both the one or more parameters associated with the service level agreement and observed network conditions at the set of cells (paragraph [0070], lines 5-13, predicted resource usage, and number or UEs at the cell).
With respect to claims 4 and 24, the combination of Wang and Guo discloses the device of claims 2 and 22, Wang further discloses wherein the processing system is further configured to cause the device to:
scan the set of cells of the wireless network in accordance with respective summations of respective PRB utilizations at the set of cells and respective PRB allocations of the network slice for the set of cells (paragraph [0093], report Slice Resource utilization), wherein outputting the recommendation associated with the network slice is in accordance with scanning the set of cells (paragraph [0117], lines 5-13, achieve SLA assurance according the context of the cells).
With respect to claims 5 and 25, the combination of Wang and Guo discloses the device of claims 4 and 24, Wang further discloses wherein, to output the recommendation associated with the network slice, the processing system is further configured to cause the device to:
output, in accordance with greater than or equal to a threshold quantity of cells of the set of cells be able to accommodate the respective summations, an approval of the network slice (paragraph [0164], based on a predicted load exceeding threshold); or
output, in accordance with fewer than the threshold quantity of cells of the set of cells be able to accommodate the respective summations, a rejection of the network slice (paragraph [0164], based on a predicted load exceeding threshold).
With respect to claims 6 and 26, the combination of Wang and Guo discloses the device of claims 4 and 24, Wang further discloses wherein, to output the recommendation associated with the network slice, the processing system is further configured to cause the device to:
output, in accordance with cells of the set of cells that be able to accommodate the respective summations serving greater than or equal to a threshold quantity of intended users, an approval of the network slice (paragraph [0164], based on a predicted load exceeding threshold); or
output, in accordance with the cells of the set of cells that be able to accommodate the respective summations serving fewer than the threshold quantity of intended users, a rejection of the network slice (paragraph [0164], based on a predicted load exceeding threshold).
With respect to claims 7 and 27, the combination of Wang and Guo discloses the device of claims 2 and 22, Wang further discloses wherein, to predict the respective PRB allocation of the network slice for each cell of the set of cells, the processing system is further configured to cause the device to:
predict a first PRB allocation of the network slice for a first cell of the set of cells in accordance with the one or more parameters associated with the service level agreement and first observed network conditions at the first cell (paragraph [0117], lines 5-13, predicted resource usage); and
predict a second PRB allocation of the network slice for a second cell of the set of cells in accordance with the one or more parameters associated with the service level agreement and second observed network conditions at the second cell (paragraph [0117], lines 5-13, predicted resource usage).
With respect to claims 8 and 28, the combination of Wang and Guo discloses the device of claims 1 and 21, Wang further discloses wherein the processing system is further configured to cause the device to:
train a machine learning model to output the recommendation associated with the network slice in accordance with one or more of a prediction of the respective PRB allocation of the network slice for each cell of the set of cells (paragraph [0068]), the respective PRB utilization at each cell of the set of cells, one or more radio frequency metrics associated with the set of cells, or a morphology associated with the set of cells, wherein the prediction is associated with observed network conditions at the set of cells of the wireless network (paragraphs [0084]-[0085] and [0088], lines 5-13, predicted resource usage).
With respect to claim 9, the combination of Wang and Guo discloses the device of claim 8, Wang further discloses wherein, to train the machine learning model, the processing system is further configured to cause the device to:
provide, as a training set associated with the machine learning model, a plurality of network snapshots (paragraph [0068]), wherein each network snapshot of the plurality of network snapshots corresponds to a suitable PRB allocation to a requested network slice and is associated with a unique permutation of one or more cell types, one or more cluster sizes, one or more cell physical characteristics, one or more cell load conditions, or one or more cell channel quality distributions, one or more interference levels, or any combination thereof (paragraphs [0121]-][0125]).
With respect to claim 10, the combination of Wang and Guo discloses the device of claim 8, Wang further discloses wherein, to train the machine learning model, the processing system is further configured to cause the device to:
receive, at the device associated with the service management of the wireless network and in accordance with deployment of the network slice in the wireless network, information indicative of one or more performance indicators associated with the network slice (paragraph [0091], KPI report); and
update the machine learning model in accordance with the one or more performance indicators (paragraph [0088], ML optimized).
With respect to claim 11, the combination of Wang and Guo discloses the device of claim 10, Wang further discloses wherein the one or more performance indicators include an actual PRB usage by the network slice at each of the set of cells, and wherein, to update the machine learning model, the processing system is further configured to cause the device to:
update the machine learning model in accordance with a delta between the respective PRB allocation of the network slice for each cell of the set of cells and the actual PRB usage by the network slice for each cell of the set of cells (paragraph [0088], ML optimized).
With respect to claim 12, the combination of Wang and Guo discloses the device of claim 8, Wang further discloses wherein the observed network conditions at the set of cells include one or more of a channel quality distribution for each cell of the set of cells, a morphology associated with the set of cells, a traffic behavior at each cell of the set of cells, or a frequency band associated with the set of cells (paragraphs [0084]-[0085] and [0088], lines 5-13, predicted resource usage).
With respect to claim 13, the combination of Wang and Guo discloses the device of claim 1, Wang further discloses wherein the processing system is further configured to cause the device to:
trigger a resource estimation application of the device in accordance with receiving the request, the resource estimation application configured to output the respective PRB allocation of the network slice for each cell of the set of cells (paragraphs [0093], lines 5-13, PRB usage per slice);
store information indicative of the respective PRB allocation of the network slice for each cell of the set of cells at the device (paragraphs [0093], lines 5-13, PRB usage per slice); and
trigger a feasibility application of the device in accordance with storing the information indicative of the respective PRB allocation of the network slice for each cell of the set of cells, the feasibility application configured to output the recommendation associated with the network slice (paragraphs [0095], how the slice portion could be controlled).
With respect to claim 14, the combination of Wang and Guo discloses the device of claim 13, Wang further discloses wherein a set of inputs to the feasibility application include one or more of a set of radio frequency performance metrics, a capacity assessment associated with the set of cells, the respective PRB allocation of the network slice for each cell of the set of cells, an output of a machine learning model trained to assist in a determination of the recommendation, a traffic forecast associated with the set of cells, a coverage evaluation associated with the set of cells, or a slice admission policy (paragraph [0088], ML optimized).
With respect to claim 15, the combination of Wang and Guo discloses the device of claim 1, Wang further discloses wherein the processing system is further configured to cause the device to:
predict a future respective PRB utilization at each cell of the set of cells in accordance with a traffic forecast, wherein the recommendation associated with the network slice is in accordance with the respective PRB utilization at each cell of the set of cells, a prediction of the future respective PRB utilization at each cell of the set of cells, and a prediction of the respective PRB allocation of the network slice for each cell of the set of cells (paragraph [0164], predicted resource usage).
With respect to claim 16, the combination of Wang and Guo discloses the device of claim 1, Wang further discloses wherein the one or more parameters associated with the service level agreement are indicative of one or more of a throughput expectation, a latency constraint, a bit error rate, or a quantity of intended users at each cell of the set of cells (paragraphs [0090] and [0093]).
With respect to claim 17, the combination of Wang and Guo discloses the device of claim 1, Wang further discloses wherein the one or more parameters associated with the service level agreement are indicative of a slice admission policy associated with the network slice, and wherein the recommendation is in accordance with the slice admission policy (paragraph [0093], policy service).
With respect to claim 18, the combination of Wang and Guo discloses the device of claim 1, Wang further discloses wherein the set of cells are located within a geographic coverage area associated with the network slice (Figure 10).
With respect to claim 19, the combination of Wang and Guo discloses the device of claim 1, Wang further discloses wherein the set of cells, in accordance with which the recommendation associated with the network slice is output, includes one or more cells of a larger set of cells (paragraph [0070], handover optimization xApp is applied).
With respect to claim 20, the combination of Wang and Guo discloses the e device of claim 19, Wang further discloses wherein the processing system is further configured to cause the device to: train a machine learning model to output the recommendation associated with the network slice in accordance with the larger set of cells, wherein outputting the recommendation associated with the network slice is in accordance with training the machine learning model (paragraph [0070], handover optimization xApp is applied).
Response to Arguments
Applicant’s arguments with respect to claims 1-28, 41, and 61 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar Pat. Pub. 2025/0080423
Pateromichelakis Pat. Pub. 2023/0403543
Chong Pat. Pub. 2021/0337553
Guo Pat. Pub. 2021/0211942
Sciancalepore Pat. Pub. 2018/0317133
Sciancalepore Pat. Pub. 2020/0059521
Thyagaturu Pat. Pub. 2023/0006889
Samdanis Pat. Pub. 2019/0174498
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 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 BLAKE J RUBIN whose telephone number is (571)270-3802. The examiner can normally be reached on Monday - Friday, 9am - 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ario Etienne can be reached on 571-272-4001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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2/25/26
/BLAKE J RUBIN/Examiner, Art Unit 2457