1DETAILED 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) 1-8, 17-26, 29 and 30 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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-8, 1-27, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Document D1 (WO 2016095826 A1) in view of LIU (CN 112818596 A).
Regarding claim 1, 17, and 25, Document D1 teaches a method performed by a network
node/ an apparatus for wireless communication at a network node/a non-transitory computer-
readable medium storing a set of instructions for wireless communication, the set of instructions
comprising, comprising: a memory; and one or more processors coupled to the memory, the
memory and the one or more processors configured to:
obtaining a machine learning (ML) model trained to provide one or more predicted
channel interference (CI) indicators informative of channel interference between cells of a cellular network (see par. 0060: As described above, an analytics assisted fully automatic closed loop self-organizing network provides a general framework for solving large scale near real time network optimization problems (SON use cases) The optimization process disclosed herein learns online the environment via real-time feedback of UE MRs and cell KPIs using machine learning analytics to assign actionable metrics/labels to cells; par. 0061: The optimization process is self-driving in that it uses machine learned cell labels or blame metrics with engineering knowledge guided small step actions to extract quick initial gains in network performance; par. 0064: FIGURE 10 shows a process 1000 for determining cell states to adjust antenna configuration parameters; par. 0066: An interference analysis is performed at block 1010 to determine whether the cell is an interferer or non-interferer. An example of such an interference analysis is described in detail below with respect to FIGURES 18 and 19); calculating, using the ML model, the one or more predicted CI indicators using data characterizing a given cell and one or more neighbor cells of the given cell (see par. 0094: FIGURE 18 shows the interference states that can be assigned to a cell as determined in block 1010 of FIGURE 10. As shown in FIGURE 18, a cell may be considered as being a strong/multi-interferer 1802, a medium/single-interferer 1804, or a weak/non-interferer 1806. A first cell may be an interfering cell to a second cell if a RSRP associated with the first cell in a MR of a UE device 104 best served by the second cell is within a threshold range of an average RSRP reported by UE devices 104 best served by the second cell; par. 0095: FIGURE 19 shows a process for determining an interferer state of a cell. Process 1900 begins at block 1902 with the receipt of MRs from UE devices 104 for each cell. From the MRs, those UE devices 104 best served by each cell are identified in block 1904. At block 1906, a determination is made in each
cell if a RSRP associated with another cell is within a top k of RSRPs for the cell and/or within a reference range of an average RSRP in each cell. A cell having a RSRP within a top k of RSRPs for another cell may be an interferer to that cell. In block 1908, UE devices 104 best served by each cell as having a RS-SINR below a quality threshold due to a RSRP of another cell being within a threshold range of top RSRP values for the cell are identified. An interference blame counter is maintained in block 1910 for each cell as a cell pair with the other cells to record how many UE devices 104 are affected by a non-serving cell. A total blame counter for a cell is determined in block 1912 by summing interference blame counters over all affected cells ), the data comprising at least one of a latitude, a longitude, an azimuth of an antenna, a height of the antenna, a tilt of the antenna, or a beam configuration of the antenna of one or both of a given cell or one or more neighbor cells (see par. 00125: Aspects of this disclosure also provide embodiment SON optimization techniques that utilize an iterative learning approach to adjust wireless network configuration parameters. In particular, a controller iteratively generates and evaluates global solutions over a sequence of iterations. During this process, the controller uses experience obtained from evaluating global solutions during previous iterations when generating global solutions in subsequent iterations. This may be achieved by using the evaluation results to update parameters (e.g., topology model, traffic/usage patterns) of a heuristic/adaptive algorithm used to generate the global solutions. In this way, the controller learns more about the network (e.g., topology, conditions, traffic patterns, etc.) during each successive iteration, which ultimately allows the controller to more closely tailor global solutions to the network. As used herein, the term “global solution” refers to a set of local solutions for two or more wireless network coverage areas in a wireless network. Each “local solution” specifies one or more wireless configuration parameters for a particular wireless network coverage area. For example, in the context of CCO, a local solution may specify an antenna tilt of an access point in a wireless network coverage area and/or a transmit power level (e.g., uplink, downlink, or otherwise) for the wireless network coverage area); and providing the one or more predicted Cl indicators (see pars. 0056: UE devices 104 typically use RS-SINR to calculate a Channel Quality Indicator (CQI) reported to the network. RS-SINR indicates the power of measured usable signals, the power of measured signals or channel interference signals from other cells in the current system, and background noise related to measurement bandwidths and receiver noise coefficients. Though the present disclosure focuses on RSRP and RS-SINR, there are other parameters provided in the measurement reports that are used in operation of LTE network 100. par.0095: A check is made in block 1914 as to whether the total blame counter is greater than a first or second interference threshold. If the total blame counter is not greater than the first or second interference threshold, the cell is assigned a weak/non-interfering state at block 1916. If the total blame counter is greater than the first interference threshold but less than the second interference threshold, the cell is assigned a medium/single-interfering state at block 1918. If the total blame counter is greater than the second interference threshold, the cell is assigned a strong/multi-interferer state. The total blame counter may be normalized by the total number of UE devices 104 served by all cells in the neighborhood of the cell being assigned an interferer state). Document D1 does not teach the data used as input data to the ML model. LIU teaches the data used as input data to the ML model (3, effectively solving the uplink interference problem of the optimization engineer for long time; the problem is qualitative, intuitionistic presentation; the network optimization engineer improves the network service quality to give a valuable reference opinion; 4; Because of the data analysis of intelligent optimization, real time monitoring network operation condition, finding network problem in time, greatly reducing the workload of testing and checking interference, saving a lot of optimization and engineering investment, with high economic benefit, with the continuous upgrading and product of the simulation algorithm and applied to the actual work, it becomes the power tool of network optimization engineer ; 5; the wireless electric wave of each type of interference source has the frequency domain characteristic (frequency of interference signal, bandwidth, waveform) of high identification degree; time domain characteristic (frame structure), these characteristics in the original collected data (background detection data, field interference detection data) are with corresponding information, mathematical model modeling of each kind of interference in the prior known or project process; establishing a wireless interference data model library, extracting the characteristic algorithm of the common characteristic, realizing input data of low cost collection, input system, through algorithm matching interference database model, direct intelligent analysis, fast qualitative interference source, direct intelligent output interference source intelligent analysis result, qualitative, locating interference source; and give advice to solve the interference).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective the filling date of claimed invention (AIA ) to modify the data used as input data to the ML model of LIU to the method of SHIM et al. in order for locating interference source and giving advice to solve the interference.
Regarding claims 2, 18 and 26, Document D1 also teaches wherein the one or more predicted CI indicators indicate an impact of the given cell on the one or more neighbor cells, or an impact of the one or more neighbor cells on the given cell (see fig. 0094: (see par. 0094: FIGURE 18 shows the interference states that can be assigned to a cell as determined in block 1010 of FIGURE 10. As shown in FIGURE 18, a cell may be considered as being a strong/multi-
interferer 1802, a medium/single-interferer 1804, or a weak/non-interferer 1806. A first cell may be an interfering cell to a second cell if a RSRP associated with the first cell in a MR of a UE device 104 best served by the second cell is within a threshold range of an average RSRP reported by UE devices 104 best served by the second cell).
Regarding claims 3, 19 and 27, Document D1 also teaches configuring a cellular network based at least in part on the one or more predicted Cl indicators (see Figure 10, steps 1016, 1018; par. [0067 ]-[0069]).
Regarding claims 4 and 20, Document D1 also teaches configuring the cellular network further comprises at least one of. adding or removing a cell as a neighbor cell of the given cell, adding or removing a cell as a 5G-4G anchor, performing physical cell identifier planning, performing root sequence index planning, performing coverage and capacity optimization, performing mobility load balancing, performing self-healing, or performing non- terrestrial network planning (see Figure 10, steps 1016, 1018; par. [0067]-[0069]).
Regarding claims 5 and 21, Document D1 also teaches wherein configuring the cellular network further comprises identifying a placement of a cell based at least in part on the one or more predicted CI indicators (see Figure 10, steps 1016, 1018; par. [0067] -[0069]).
Regarding claims 6 and 22, Document D1 also teaches selecting the given cell and the one or more neighbor cells as a set of cells of interest, wherein calculating, using the ML model, the one or more predicted Cl indicators further comprises calculating the one or more predicted CI indicators for the given cell and the one or more neighbor cells based at least in part on the given cell and the one or more neighbor cells being the set of cells of interest (see par. 0071: FIGURE 12 shows an example of how a cell may be considered in a weak edge state 1106 and/or a weak interior/insufficient state 1108. A cell in a weak edge state 1106 has a certain
number/percentage of UE devices 104 that it serves with corresponding RSRP values below a coverage threshold. In addition, a cell in weak edge state 1106 has a certain number/percentage of UE devices 104 that it serves with RSRP values associated with one or more neighboring cells within a coverage reference range of an average RSRP value for the cell. In this scenario, a UE device 104 with a low RSRP value corresponding to the best serving cell coupled with a high enough RSRP value associated with a neighboring cell is most likely located near the edge of coverage provided by the best serving cell).
Regarding claims 7 and 23, Document D1 also teaches wherein selecting the given cell and the one or more neighbor cells as the set of cells of interest is based at last in part on a use case associated with configuring a cellular network including the given cell and the one or more neighbor cells (see par. 007 1: FIGURE 12 shows an example of how a cell may be considered in a weak edge state 1106 and/or a weak interior/insufficient state 1108. A cellin a weak edge state 1106 has a certain number/percentage of UE devices 104 that it serves with corresponding RSRP values below a coverage threshold. In addition, a cell in weak edge state 1106 has a certain number/percentage of UE devices 104 that it serves with RSRP values associated with one or more neighboring cells within a coverage reference range of an average RSRP value for the cell. In this scenario, a UE device 104 with a low RSRP value corresponding to the best serving cell coupled with a high enough RSRP value associated with a neighboring cell is most likely located near the edge of coverage provided by the best serving cell).
Regarding claims 8 and 24, Document D1 also teaches wherein calculating, using the ML model, the one or more predicted CI indicators further comprises calculating the one or more predicted CI indicators based at least in part on a transmission power of the given cell or the one or more neighbor cells (see par. 0071: FIGURE 12 shows an example of how a cell may be
considered in a weak edge state 1106 and/or a weak interior/insufficient state 1108. A cell in a weak edge state 1106 has a certain number/percentage of UE devices 104 that it serves with corresponding RSRP values below a coverage threshold. In addition, a cell in weak edge state 1106 has a certain number/percentage of UE devices 104 that it serves with RSRP values associated with one or more neighboring cells within a coverage reference range of an average RSRP value for the cell. In this scenario, a UE device 104 with a low RSRP value corresponding to the best serving cell coupled with a high enough RSRP value associated with a neighboring cell is most likely located near the edge of coverage provided by the best serving cell).
Regarding claim 29, Document D1 also teaches wherein the ML model is trained using a training set generated based at least in part on a model of a network of deployed cells, wherein the deployed cells are characterized by a location, a height, an antenna tilt, a carrier frequency, an operating bandwidth, or a transmission power cells (see par. 00125: For example, in the context of CCO, a local solution may specify an antenna tilt of an access point in a wireless network coverage area and/or a transmit power level (e.g., uplink, downlink, or otherwise) for
the wireless network coverage area).
Allowable Subject Matter
Claim 30 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 30, the prior art of record does not mention wherein the ML model comprises a first sub-model trained for serving cells operating in accordance with a 4G protocol, and a second sub-model trained for serving cells operating in accordance with a 5G protocol, the method further comprising: selecting the first sub-model or the second sub-model as the ML model based on an operating bandwidth of the given cell. Therefore, it is objected.
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 DAVID Q NGUYEN whose telephone number is (571)272-7844. The examiner can normally be reached Monday-Friday 7:00 AM - 3:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jinsong Hu can be reached at 5712723965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID Q NGUYEN/Primary Examiner, Art Unit 2643