DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/13/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot because of the new ground of rejection.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11071028 B1 herein Kim in view of US 20190280845 A1 herein Bedekar in view of US 11064425 B1 herein Cui in view of US 10841853 B1 herein Yousefi.
Claim 1, as analyzed with respect to the limitations as discussed in claim 10. Kim discloses a method for data allocation (Col 1: 36-40, method).
Claim 2, as analyzed with respect to the limitations as discussed in claim 11.
Claim 3, as analyzed with respect to the limitations as discussed in claim 11.
Claim 4, as analyzed with respect to the limitations as discussed in claim 12.
Claim 5, as analyzed with respect to the limitations as discussed in claim 12.
Claim 6, as analyzed with respect to the limitations as discussed in claim 13.
Claim 7, as analyzed with respect to the limitations as discussed in claim 14.
Claim 8, as analyzed with respect to the limitations as discussed in claim15.
Claim 9, as analyzed with respect to the limitations as discussed in claims 16 and 17.
Claim 10, Kim discloses a system for data load allocation (Col 1: 36-40), comprising:
a base station having at least one primary cell and at least one secondary cell, the at least one secondary cell, the at least one primary cell and the at least one secondary cell having one or more antennas (Fig. 1A, Col 3: 27 – 46, access node with transmission of more than one band; Band1 and Band2 with antennas thus primary and secondary cell) for receiving radio condition metrics and transmitting data load allocations (Col 3: 27-46), and a processor (Fig. 1A: 110, access node, i.e. base station, since an access node thus a processor), the processor configured to:
determine radio condition metrics for the at least one primary cell and the at least one secondary cell (Col 3: 48-66, signal levels of the two bands, thus radio condition metrics for the primary and secondary cells);
determine a first congestion metric for the at least one primary cell and a second congestion metric for the at least one secondary cell (Col 3: 48-66; load imbalance of the two frequency bands, thus congestion metrics for the primary and secondary cells);
input the radio condition metric for the at least one primary cell, the radio condition metric for the at least one secondary cell, the first congestion metric and the second congestion metric [[to at least one of a neural network and a deep learning module]] (Col 8: 60 – Col 9: 5, adjustment of signal level based on load thresholds of the different cells); and
based on the radio condition metric for the at least one primary cell, the radio condition metric for the at least one secondary cell, the first congestion metric and the second congestion metric, [[and an output from at least one of the neural network and the deep learning module,]] allocate a first faction [[of a data load]] to the at least one primary cell (Figs. 5 and 6, Col 9: 33 – Col 10: 49, adjusting the threshold signal levels at the different bands/cells to adjust the detected load imbalance; Figs. 8A and 8B, Col 11: 53 – Col 12: 27, adjustments of the thresholds).
Kim may not explicitly disclose input to at least one of a neural network and a deep learning module and an output from at least one of the neural network and the deep learning module; generate a data load allocation decision that divides a data load of the UE into multiple portions and assigns a first fractional portion of the data load to a first cell of the plurality of cells; wherein the neural network continuously observes traffic associated with the at least one primary cell and the at least one secondary cell and learns traffic patterns over time, and wherein the data load allocation decision is generated based on the learned traffic patterns.
Bedekar discloses disclose input to at least one of a neural network and a deep learning module (0037, 0055, 0066, a controller utilizing deep learning in a neural network uses inputs of CQI/RSRQ/RSRP and load levels of SCells) and an output from at least one of the neural network and the deep learning module (0037, 0055, 0066, 0108, determining a preferred SCell). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to include neural networks and machine learning as taught by Bedekar so as to efficient load-balancing and resource utilization (0124).
Kim in view of Bedekar may not explicitly disclose generating a data load allocation decision that divides a data load of the UE into multiple portions and assigns a first fractional portion of the data load to a first cell of the plurality of cells; wherein the neural network continuously observes traffic associated with the at least one primary cell and the at least one secondary cell and learns traffic patterns over time, and wherein the data load allocation decision is generated based on the learned traffic patterns.
Cui discloses generating a data load allocation decision that divides a data load of the UE into multiple portions and assigns a first fractional portion of the data load to a first cell of the plurality of cells (Col 6: 53 – Col 7: 9, An MeNB packet data convergence protocol (PDCP) flow controller can split the flow across the MeNB/SeNB for the UE and decide a proper percentage of traffic running through the MeNB and the selected SeNB based on service characteristics, mobility state, delay/jitter requirements, and/or the MeNB and SeNB load conditions). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to include dividing data loads of UEs as taught by Cui so as to meet jitter and delay requirements (Col 6: 53 – Col 7: 9).
Kim may not explicitly disclose wherein the neural network continuously observes traffic associated with the at least one primary cell and the at least one secondary cell and learns traffic patterns over time, and wherein the data load allocation decision is generated based on the learned traffic patterns.
Yousefi discloses wherein the neural network continuously observes traffic associated with the at least one primary cell and the at least one secondary cell (Col 2: 53-59, Multi-Layer Perceptron Deep Learning (MLPDL) structure that can be trained iteratively with real network measurement data collected from multiple towers, thus primary and secondary cells) and learns traffic patterns over time (Col 2: 53-67, learning number of users active across multiple channels on cellular towers; Col 3: 38-42, importing of cellular tower information such as traffic demand and traffic carried, throughput, etc.), and wherein the data load allocation decision is generated based on the learned traffic patterns (Col 4: 4-47, selecting learning algorithm, MLPDL, and considering based on traffic demand/throughput of towers, thus learned traffic patterns, and redistributing traffic across towers based on an optimization algorithm). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to include load optimization as taught by Yousefi so as to minimize congestion while maintaining minimum quality experienced by users (Col 2: 65-67).
Claim 11, Kim in view of Bedekar discloses the system of claim 10, wherein the first fraction of the data load is sent using low band frequencies (Col 3: 27-35).
Claim 12, Kim in view of Bedekar discloses the system of claim 10, further comprising allocating a second fraction of the data load, wherein the second fraction of the data load is an amount of data remaining to be allocated after the first fraction of the data load has been allocated (Col 9: 33 – Col 10: 49).
Claim 13, Kim in view of Bedekar discloses the system of claim 12, wherein the second fraction of the data load is allocated to the at least one secondary cell (Col 3: 27-35).
Claim 14, Kim in view of Bedekar discloses the system of claim 13, wherein the second fraction of the data is sent using mid-band time division duplex (TDD) frequencies (Col 10: 22-29).
Claim 15, Kim in view of Bedekar discloses the system of claim 10. Kim discloses wherein the radio condition metrics comprise at least one of: signal-to-interference and noise (SINR), reference signal received power (RSRP), and reference signal received quality (RSRQ) (Col 11: 30-36).
Claim 16, Kin in view of Bedekar discloses the system of claim 10, wherein the congestion metrics comprise at least one of. a number of user equipments (UEs) connected to the at least one primary cell, a number of UEs connected to the at least one secondary cell, and a traffic metric (Col 2: 37-50).
Claim 17, Kim in view of Bedekar discloses the system of claim 16, wherein the traffic metric is based on a time of day (Col 3: 27-35).
Claim 18, Kim in view of Bedekar discloses the system of claim 10. Kim may not explicitly disclose wherein the first fraction allocation is transmitted by a scheduler.
Bedekar discloses wherein the first fraction allocation is transmitted by a scheduler (0040, scheduler allocates resources on its PCell and SCell). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to include neural networks and machine learning as taught by Bedekar so as to efficient load-balancing and resource utilization (0124).
Claim 19, as analyzed with respect to the limitations as discussed in claim 10. Kim discloses a non-transitory computer storage media storing computer-useable instructions that, when used by one or more processors (Col 5: 5-25, storge devices on nodes, and processors).
Claim 20, as analyzed with respect to the limitations as discussed in claims 15 and 16.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20210258866 A1 - An apparatus for use in a RAN node includes processing circuitry coupled to a memory. To configure the RAN node for network slice subnet instance (NSSI) configuration in an Open RAN (O-RAN) network, the processing circuitry is to perform training of a machine learning (ML) model using historical performance measurements associated with prior use of network resources of the O-RAN network by a plurality of NSSIs to generate a trained ML model. Current performance measurements associated with current use of the network resources are decoded by the plurality of NSSIs. A prediction of a usage pattern for the network resources is generated using the ML model based on the current performance measurements. An optimization action is performed to adjust allocation of the network resources to the plurality of NSSIs based on the prediction.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mehmood B. Khan whose telephone number is (571)272-9277. The examiner can normally be reached M-F 9:30 am-6:30 pm.
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/Mehmood B. Khan/ Primary Examiner, Art Unit 2468