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 .
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5-12, and 14-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The metes and bounds of the claimed invention are not clear and hence the claims are indefinite. The language of the claim was given a broadest reasonable interpretation. The boundaries of the protected subject matter are not clearly delineated and the scope is unclear. Because claims delineate the patentee’s right to exclude, the patent statute requires that the scope of the claims be sufficiently definite to inform the public of the bounds of the protected invention, i.e., what subject matter is covered by the exclusive rights of the patent. (See MPEP 2173.02).
Claims 1, 10, and 19 each recite "wherein the deep learning module is retrained according to a predetermined time interval and on demand based on a high traffic volume." The term "high" is a relative term which renders the claims indefinite. The term "high" is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification recites the limitation only at paragraph 0042, in language identical to the claims, and supplies no threshold, value, or point of comparison by which a traffic volume is determined to be high.
Claim 20 recites "the loading metric," for which there is insufficient antecedent basis. Claim 19 recites radio condition metrics and a congestion metric and does not recite a loading metric.
Appropriate correction is required.
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) 1-3, 5, 6, 8-12, 15, 16, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0377804 A1 herein Sivaraj in view of US 2023/0135872 A1 herein Singh.
Claim 1, as analyzed with respect to the limitations as discussed in claim 10.
Sivaraj discloses a method for data load allocation in a wireless network (0016, 0022, Method), and comprising:
scheduling the first fractional portion and the second fractional portion of the data load for transmission via the first cell and the second cell in a dual-connectivity configuration (0105, incoming PDUs are queued in the logical buffer waiting to get scheduled and transmitted over frequency-time resources to the UE).
Claim 2, Sivaraj discloses the method of claim 1, wherein the first fractional portion of the data load is sent using a primary cell (Pcell) (0088, the MeNB Cell ID IE gives information about the ECGI of the primary E-UTRA cell (PCell) for the UE within the MeNB; 0132, 65% of the PDCP PDUs go over the LTE interface).
Claim 3, Sivaraj discloses the method of claim 2, wherein the first fractional portion of the data load allocated is sent using low band frequencies (0049, LTE operates at 700 MHz or 2 GHz or sub-6 GHz).
Claim 5, Sivaraj discloses the method of claim 1, wherein the second fractional portion of the data load is an amount of data remaining to be allocated after the first fractional portion of the data load has been determined (0046, the gNB decides how large a fraction of the UE subscribed traffic can be sent over the NR interface and forwards the remaining portion of the traffic to the eNB over the X2 interface).
Claim 6, Sivaraj discloses the method of claim 5, wherein the second fractional portion of the data load is sent using a secondary cell (Scell) (0088, the SgNB to MeNB Container IE gives information about the primary NR serving cell, the secondary NR cells and SpCell of the secondary cell group of the SgNB to serve the UE).
Claim 8, as analyzed with respect to the limitations as discussed in claim 20.
Claim 9, as analyzed with respect to the limitations as discussed in claim 20.
Claim 10, Sivaraj discloses a system for data load allocation (0026, System), 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 for receiving radio condition metrics and transmitting data load allocations (0029, network nodes can have an antenna mast and multiple antennas for performing various transmission operations; 0068, policy establishes which cell within an MN/SN should serve as a primary cell and secondary cells across LTE MN and NR SN nodes), and a processor (0175, processing unit 1004), the processor configured to:
determine radio condition metrics for the at least one primary cell and the at least one secondary cell (0088, IEs identify the LTE and NR cells to which the UE is associated and receive the UE's RRC signal strength measurements; the UE NR Measurement Report IE gives cell-wide RSRP, RSRQ, SINR for serving and neighbor NR 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 (0095, IEs identify the LTE and NR cell load in terms of number of UEs and bandwidth utilization; the Radio Resource Status IE indicates the usage of physical resource blocks and control channel elements for each cell);
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, generate a data load allocation decision that divides a data load into multiple portions and assign a first fractional portion of the data load to the at least one primary cell (0132, the traffic split function in the EN-DC xAPP uses RLDT to predict buffer occupancy using input features like signal strength, instantaneous buffer size, PRB utilization for the UE and other RAN KPls; the DC xAPP directs 65% of the PDCP PDUs over the LTE interface) and a second fractional portion of the data load to the at least one secondary cell (0046, the gNB forwards the remaining portion of the traffic to the eNB over the X2 interface),
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 (0062, predictive intelligence techniques used in xApps count on historical RAN state information from the nodes and the R-NIB can maintain finite persistence), and
wherein the data load allocation decision is generated based on the learned traffic patterns (0131, the DC xAPP can change the fraction of the traffic split across the two RAT interfaces over time, based on predicted eNB and gNB buffer sizes in near-RT and in the near future), and
wherein the first fractional portion and the second fractional portion are transmitted via the at least one primary cell and the at least one secondary cell in a dual-connectivity configuration (0072, the near-RT RIC can perform data-driven optimization to optimally divide split-bearer user-plane data traffic across the LTE MN and NR SN for a given UE; 0045, the UE can access a 5G NR connection if and only if it is also simultaneously connected to an LTE base station, called EUTRA-NR Dual Connectivity).
Sivaraj may not explicitly disclose 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 each of a neural network and a deep learning module, wherein the deep learning module is retrained according to a predetermined time interval and on demand based on a high traffic volume.
Singh discloses 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 each of a neural network and a deep learning module (0054, a load prediction model and a power saving model, both models being updatable after retraining a corresponding neural network based model; 0060, a deep neural network is an artificial neural network comprising multiple hidden layers between the input layer and the output layer; 0068, load metrics comprise the volume of traffic arriving at or delivered by various cells, physical resource blocks required to deliver the traffic, and number of devices connected to various cells), wherein the deep learning module is retrained according to a predetermined time interval (0091, the RAN-n continues sending periodic RAN data at requested time granularity, for example once in a minute, in 15 minutes, and the PSM retrains the power saving Q learning model using that additional data) and on demand based on a high traffic volume (0104, the retraining may be triggered by detecting that the observed throughput has diverged more than a preset threshold from the assumed throughput used for calculating the reward). 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 Sivaraj to include two neural network based models receiving the network metrics and a deep learning model retrained both periodically and upon a triggering condition, as taught by Singh, so as to learn from the environment without pre-set thresholds and enhance network performance (0134).
Claim 11, as analyzed with respect to the limitations as discussed in claim 3.
Claim 12, as analyzed with respect to the limitations as discussed in claim 5.
Claim 15, as analyzed with respect to the limitations as discussed in claim 20.
Claim 16, as analyzed with respect to the limitations as discussed in claim 20.
Claim 18, Sivaraj discloses the system of claim 10, wherein the first fractional portion is transmitted by a scheduler (0105, incoming PDUs are queued in the logical buffer waiting to get scheduled and transmitted over frequency-time resources to the UE).
Claim 19, as analyzed with respect to the limitations as discussed in claim 10. Sivaraj discloses a non-transitory computer storage media storing computer-useable instructions that, when used by one or more processors, cause the processors to (0172, tangible and non-transitory media which can be used to store desired information; 0175, processing unit 1004), and comprising:
continuously observe, by the neural network, traffic associated with the plurality of cells and learn traffic patterns over time (0062, the RAN nodes can send their state information over E2 either following a triggered event or at regular periodic intervals, and predictive intelligence techniques used in xApps may count on historical RAN state information);
and based on the learned traffic patterns 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 and a second fractional portion of the data load to a second cell of the plurality of cells (0131, the DC xAPP can change the fraction of the traffic split across the two RAT interfaces over time, based on predicted eNB and gNB buffer sizes).
Claim 20, Sivaraj discloses the non-transitory computer storage media of claim 19, wherein the radio conditions metrics are based on at least one of a signal-to-interference and noise (SINR) measurement or a reference signal received power (RSRP) measurement (0088, the UE NR Measurement Report IE gives RSRP, RSRQ, SINR) and
the loading metric is based on a utilization rate of physical resource blocks (PRBs) (0095, the Radio Resource Status IE indicates the usage of physical resource blocks for scheduling downlink and uplink traffic for each cell) and the congestion metrics are based on at least one of: a number of UEs connected to a primary cell (Pcell) and a number of UEs connected to a secondary cell (Scell) (0095, cell load in terms of number of UEs), a number of UEs using an access point hosting a Pcell and a Scell, and a traffic metric based on a time of day.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj in view of Singh and further in view of US 11,071,028 B1 herein Kim.
Claim 7, Sivaraj in view of Singh discloses the method of claim 6. Sivaraj may not explicitly disclose wherein the second fractional portion of the data load sent to the Scell uses mid-band time division duplex (TDD) frequencies of the spectrum.
Kim discloses wherein the second fractional portion of the data load sent to the Scell uses mid-band time division duplex (TDD) frequencies of the spectrum (Col. 3: 20-25, band class B41 using time-division duplexing and comprising frequencies around 2.5 GHz). 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 Sivaraj to include a mid-band TDD band class for the secondary cell, as taught by Kim, so as to provide better capacity.
Claim 14, as analyzed with respect to the limitations as discussed in claim 7.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj in view of Singh and further in view of US 11,064,425 81 herein Cui.
Claim 17, Sivaraj in view of Singh discloses the system of claim 16. Sivaraj may not explicitly disclose wherein the traffic metric is based on a time of day.
Cui discloses wherein the traffic metric is based on a time of day (Col. 14: 25-30, location data can be correlated to time data associated with a specific UE, UE 102 is in/near macro-cell at 8 am most mornings). 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 Sivaraj to include a traffic metric correlated to time of day, as taught by Cui, so as to detect and predict UE mobility and network patterns.
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
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 Mehmood B. Khan whose telephone number is (571)272-9277. The examiner can normally be reached M-F 9:30 am-6:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Asad Nawaz can be reached on (571) 272-3988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mehmood B. Khan/ Primary Examiner, Art Unit 2468
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