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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/21/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin (US 2020/0022025 A1; “Qin”) in view of Armenta et al. (US 2023/0337057 A1; “Armenta”) in view of Sivakumar et al. (US 2022/0279451 A1; “Sivakumar”).
Regarding claim 1, Qin teaches a method for network agility traffic steering at a centralized management entity in a network system, the method comprising:
receiving load management information from a plurality of cells within a network cluster [Qin ¶¶ 0123-0124, Fig. 5: apparatus 200 (i.e. centralized management entity) acquires the predicted intra-frequency distributable amount b(111, 112) and the predicted non-distributable amount a(111) and a (112) from the cells 111 and 112, wherein cells 111 and 112 belong to a cluster; ¶ 0032, Fig. 1: with respect to one cell cluster of the at least one cell cluster included in the wireless communication network 100, a predicted non-distributable amount of non-distributable service of each cell in that cell cluster, and a predicted distributable amount of service of each cell in that cell cluster which is able to be distributed with other cells are acquired (here, the predicted amounts are analogous to load management information of cells within a cluster)];
evaluating the load management information to determine resource allocation for the plurality of cells within the network cluster [Qin ¶ 0035: based on the acquired predicted non-distributable amount and predicted distributable amount of each cell in the cell cluster, an uplink-downlink ratio configuration scheme of that cell cluster within a next ratio configuration adjusting period is determined in order to optimize the utilization efficiency of communication resources; ¶ 0051, Fig. 3: uplink-downlink radio configuration schemes in a TD-SCDMA/LTE TDD system];
generating traffic steering instructions based on the evaluation; transmitting the traffic steering instructions to the cells within the network cluster to dynamically adjust resource allocation and network performance [Qin ¶ 0125, Fig. 5: at the apparatus 200, the uplink-downlink ratio configuration scheme i1 is determined with respect to the cell cluster which the cells 111 and 112 belong to, and provided to the cells 111 and 112, and additionally may determine the power adjusting schemes for cells 111 and 112 at the apparatus 200, and provide the same to the cells 111 and 112 (here, a configuration message/instruction would inherently be generated prior to transmission)].
However, Qin does not explicitly disclose enabling clustering of cells through containerized microservices coordinated using Kubernetes, allowing for flexible and scalable deployment of network functions.
However, in a similar field of endeavor, teaches enabling clustering of cells [Armenta ¶ 0036, Fig. 2: the Local Data Center (LDC) 204 is a data center that can support multiple servers and multiple towers for cellular communications (i.e. a cell cluster)] through containerized applications coordinated using Kubernetes, allowing for flexible and scalable deployment of network functions [Armenta ¶ 0017 & 0019: Kubernetes cluster may be part of a set of nodes that run containerized applications (i.e. microservices), wherein the containerized applications (e.g., Kubernetes clusters) are used to deploy a RAN so that the virtual distributed unit (“vDU”) (also referred to herein as the “DU”) of the RAN is located at one cluster and the virtual central unit (“vCU”) (also referred to herein as the “CU”) is located at a remote location from the vDU, allowing for a more stable and flexible configuration for the RAN].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of proving cell clustering through used of containerized applications coordinated using Kubernetes as taught by Armenta. The motivation to combine these references would be to provide for stable and flexible RAN configuration [Armenta ¶ 0019].
However, Qin in view of Armenta does not explicitly disclose performing RAN control trough containerized microservices coordinated using Kubernetes.
However, in a similar field of endeavor, Sivakumar teaches performing RAN control trough containerized microservices coordinated using Kubernetes [Sivakumar ¶ 0042: 5G uses a cloud-native approach in which functional blocks are decomposed into microservices deployed as containers on x86 platforms, orchestrated by Kubernetes].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of proving RAN configuration through used of containerized applications coordinated using Kubernetes as taught by Armenta. The motivation to combine these references would be to provide efficiency and increased control in RAN deployment [Sivakumar ¶ 0005].
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Gibson (US 2020/0359224 A1; “Gibson”).
Regarding claim 2, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the network cluster is configured to utilize low-latency backhaul connections, such as eCPRI, to ensure rapid communication between the centralized management entity and the cells within the cluster.
However, in a similar field of endeavor, Gibson teaches wherein the network cluster is configured to utilize low-latency backhaul connections, such as eCPRI, to ensure rapid communication between the centralized management entity and the cells within the cluster [Gibson ¶ : multi-tenant small cell clusters can support termination of separate fiber optic and/or electrical connections into the cell site wherein the small cell site 200 (e.g., 5G small cell sites) and can support eCPRI terminations at a centralized base station controller allowing for a centralized management of the small cells].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of proving low-latency backhaul connections to small cell clusters as taught by Gibson. The motivation to combine these references would be to improve network quality [Gibson ¶ 0002].
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Ck et al. (US 2023/0030173 A1; “Ck”).
Regarding claim 3, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the centralized management entity further takes into account the signal strength of the different cells as seen from user equipment (UE) to enhance the accuracy of traffic steering decisions.
However, in a similar field of endeavor, Ck teaches wherein the centralized management entity further takes into account the signal strength of the different cells as seen from user equipment (UE) to enhance the accuracy of traffic steering decisions [Ck ¶¶ 0021-0022, Fig. 2: load is measured within each BS/AP (or “eNB”), which may include reporting from the UE, measurements of the UE environment taken by the UE, e.g., signal strength of signals received by the UE from various BS/APs in the enterprise network, and reporting BS/AP load conditions from the BS/AP to the LBr (i.e. centralized management entity); ¶ 0018: LBr configures a handover (HO) offset for each individual BS/AP based on the congestion levels associated with that BS/AP (here, traffic steering is determined according to signal strength observed by UE)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of determining load information of a cell based on signal strength observations at a UE within cell coverage as taught by Ck. The motivation to combine these references would be to enhance load balancing within wireless networks [Ck ¶ 0002].
Claim(s) 4-6, 9, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Kotaru et al. (US 2022/0408377 A1; “Kotaru”).
Regarding claim 4, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the centralized management entity is implemented using a near-real-time RAN Intelligent Controller (RIC) capable of handling multi-RAT environments.
However, in a similar field of endeavor, Kotaru teaches wherein the centralized management entity is implemented using a near-real-time RAN Intelligent Controller (RIC) capable of handling multi-RAT environments [Kotaru ¶ 0028: centralized controller 199 may include a radio access network (RAN) controller, for example, a radio access network intelligent controller (RIC) and may be applied to a multi-RAT CU].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing a multi-RAT RIC in centralized controller as taught by Kotaru. The motivation to combine these references would be to provide coordination between remote units in a distributed computing environment [Kotaru ¶ 0001].
Regarding claim 5, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the centralized management entity provides network agility traffic steering in a multi-RAT (Radio Access Technology) environment.
However, in a similar field of endeavor, Kotaru teaches wherein the centralized management entity provides network agility traffic steering in a multi-RAT (Radio Access Technology) environment [Kotaru ¶ 0028: centralized controller 199 may include a radio access network (RAN) controller, for example, a radio access network intelligent controller (RIC) and may be applied to a multi-RAT CU].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing a multi-RAT RIC in centralized controller as taught by Kotaru. The motivation to combine these references would be to provide coordination between remote units in a distributed computing environment [Kotaru ¶ 0001].
Regarding claim 6, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the cells operate on different RATs including at least 4G and 5G.
However, in a similar field of endeavor, Kotaru teaches wherein the cells operate on different RATs including at least 4G and 5G [Kotaru ¶ 0028: centralized controller 199 may include a radio access network (RAN) controller, for example, a radio access network intelligent controller (RIC) and may be applied to a multi-RAT CU (i.e. the CU supports multiple-RATs); ¶ 0032: RAN intelligent controller (MC) allowed 4G LTE logical radio node (eNBs) and 5G logical radio node (gNB) functionalities].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing a multi-RAT RIC in centralized controller as taught by Kotaru. The motivation to combine these references would be to provide coordination between remote units in a distributed computing environment [Kotaru ¶ 0001].
Regarding claim 9, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the centralized management entity provides resource allocation for at least two of 2G, 4G, and 5G.
However, in a similar field of endeavor, Kotaru teaches wherein the centralized management entity provides resource allocation for at least two of 2G, 4G, and 5G [Kotaru ¶ 0028: centralized controller 199 may include a radio access network (RAN) controller, for example, a radio access network intelligent controller (RIC) and may be applied to a multi-RAT CU (i.e. the CU supports multiple-RATs); ¶ 0032: RAN intelligent controller (MC) allowed 4G LTE logical radio node (eNBs) and 5G logical radio node (gNB) functionalities, RIC has been further developed to support older generation Open RAN standards, such as those for 2G, 3G, and 4G].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing a multi-RAT RIC in centralized controller as taught by Kotaru. The motivation to combine these references would be to provide coordination between remote units in a distributed computing environment [Kotaru ¶ 0001].
Regarding claim 11, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the network system integrates AI/ML policies and enrichment across different generations (Gs) to optimize network performance.
However, in a similar field of endeavor, Kotaru teaches wherein the network system integrates AI/ML policies and enrichment across different generations (Gs) to optimize network performance [Kotaru ¶ 0028: centralized controller 199 may include a radio access network intelligent controller (RIC) and may be applied to a multi-RAT CU (i.e. the CU supports multiple-RATs); ¶ 0032: RAN intelligent controller (MC) allowed 4G LTE logical radio node (eNBs) and 5G logical radio node (gNB) functionalities, RIC has been further developed to support older generation Open RAN standards, such as those for 2G, 3G, and 4G; ¶ 0039: Non-RT RIC acts as a configuration layer to DU and CU software as well as via the E2 standard interface, and can be learned with AI and/or abstracted to enable intelligent management and control the RAN with Near-RT RIC].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing a multi-RAT RIC in centralized controller as taught by Kotaru. The motivation to combine these references would be to provide coordination between remote units in a distributed computing environment [Kotaru ¶ 0001].
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Mishra et al. (US 2020/0260296 A1; “Mishra”).
Regarding claim 7, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the instructions are applicable to at least two of Option 7.2, Option 6, and Option 8 radio fronthaul splits.
However, in a similar field of endeavor, Mishra teaches wherein the instructions are applicable to at least two of Option 7.2, Option 6, and Option 8 radio fronthaul splits [Mishra ¶¶ 0041-0042: 5G native architecture having Unified Software Platform to support all the different protocol splits between distributed units (DUs) and centralized units (CUs) based on available backhaul/fronthaul options, e.g., Option 6, Option 7, Option 7.2, Option 8, etc.].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing a 5C native architecture thar supports different protocol splits based on available fronthaul/backhaul options as taught by Mishra. The motivation to combine these references would be to reduce complexity, simplify network maintenance, and improve resource utilization [Mishra ¶ 0042].
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Li et al. (WO 2022/217550 A1; citations refer to the attached machine translation retrieved from ip.com; “Li”).
Regarding claim 8, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the centralized management entity performs decision- making that takes into account energy savings by steering traffic to a smaller number of carriers, thereby reducing the number of active carriers at a given time.
However, Li teaches wherein the centralized management entity performs decision- making that takes into account energy savings by steering traffic to a smaller number of carriers, thereby reducing the number of active carriers at a given time [Li p. 9, last para.: network management is responsible for delivering carrier scheduling policies, such as: setting the time period for carrier shutdown, selection strategy for carrier shutdown, carrier service threshold (i.e. traffic steering by central management entity); p. 11, step 302: When the second carrier can carry the first load in the first coverage sub-area, and the third carrier can carry the second load in the second coverage sub-area, migrate the first load to the second carrier, and transfer the first load to the second carrier. The second load migrates to the third carrier and turns off the first carrier (here, carriers are selectively reduced based on load of the cells)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of selectively reducing a number of carriers based on load information of cell as taught by Li. The motivation to combine these references would be to improve load balancing accuracy thereby reducing network consumption [Li p. 9, 8th para.].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Harrang (US 2015/0189550 A1; cited in Applicant’s IDS submitted 05/21/2025; “Harrang”).
Regarding claim 10, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the network system performs proactive load balancing by rebalancing an entire cluster when a certain load threshold is reached, rather than waiting for a single cell to reach its maximum load.
However, in a similar field of endeavor, Harrang teaches wherein the network system performs proactive load balancing by rebalancing an entire cluster when a certain load threshold is reached, rather than waiting for a single cell to reach its maximum load [Harrang teaches ¶ 0132: the load balancing score calculated in process 1100 is compared to a threshold value, and if the load balancing score exceeds the threshold value, then a load balancing opportunity is present and load balancing is performed; ¶ 0107: load balancing score for the cluster may be used to qualify action for load balancing the cluster, wherein scores that exceed a threshold value are used to trigger load balancing manipulation of cell antenna configurations].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of determining to load balance a cluster of cells based on a cell cluster load balancing score exceeding a threshold as taught by Harrang. The motivation to combine these references would be to improve load balancing while mitigating coverage holes [Harrang ¶ 0004].
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Armenta in view of Sivakumar in view of Wang et al. (US 2020/0067800 A1; “Wang”).
Regarding claim 12, Qin in view of Armenta in view of Sivakumar teaches the method of claim 1, however, does not explicitly disclose wherein the network system uses a microservices-based architecture for high availability and scalability, enabling parallel processing and advanced monitoring.
However, in a similar field of endeavor, Wang teaches wherein the network system uses a microservices-based architecture for high availability and scalability, enabling parallel processing and advanced monitoring [Wang ¶ 0050: Service function chaining may involve configuring and/or allocating various virtual machines (VMs) to run these virtualized network functions, and may also involve steering traffic across one or more networks ¶ 0051: multiple microservices implemented within a resource orchestrator 108 and/or an SDN controller may work collaboratively to generate candidate solutions for mapping a service function chain to physical network resources (e.g., various network elements 112) in physical network 110, wherein instances of each of the microservices may operate in parallel to generate candidate mapping solutions in real time (or near real time) in response to receiving new or updated service function chain requests and/or initial or updated information about network resources available for satisfying service chain function requests].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of collecting load data from cells of a cluster and determining an uplink-downlink resource configuration for network optimization as taught by Qin, with the method of implementing parallel microservices for traffic steering across a network as taught by Wang. The motivation to combine these references would be to facilitate handling of dynamic loads and to further improve overall performance [Wang ¶ 0202].
Conclusion
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/BRIAN P COX/ Primary Examiner, Art Unit 2474
