CTNF 18/444,156 CTNF 87245 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-21 are pending. Claim Objections 07-29-01 AIA Claim 2 is objected to because of the following informalities: -- and hardware constraint -- should be -- the hardware constraints -- in claim 2 . Appropriate correction is required. Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Also ML and RAN is abbreviated without reciting the full form. The disclosure is objected to because of the following minor informalities: -- “10 ms to seconds” -- appears to be missing number of seconds in [0049]. Similar deficiency exist in [0050]. Appropriate correction is required. 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. Claims 1-21 are rejected under 35 U.S.C. 112 (b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or joint inventor regards as the invention. Claim 1 recites “real-time” and “near-real time” without clearly reciting the distinguishing feature of real-time and near-real time i.e. what is considered real-time and near-real time e.g. latency requirements, because these are relative terms and are not definite. Claim 1 recites “far-edge” and “near-edge” without clearly reciting what is considered far-edge and what is near-edge, because these are relative terms and are not definite. Claim 1 recites “conflict in the computing resources predicted by a Bayesian optimizer among the plurality of application”. It is unclear conflict is between the application, or between the computing resources, or in the computing resources. Claim 4 recites “resource usage for a dominant demand of a feasible combination”. It is unclear if dominant demand is for a particular resource or particular resource type or multiple resources or multiple resource types for the feasible combination (i.e. what is being referred by dominant demand). Claim 7 recites “select an allocation of the resources in conflict”. It is unclear if the conflicting resources are selected or one or more of non-conflicting resources from the conflicting resource are selected for allocation. Similar deficiency exist in claim 9. Claims 10 and 18 recite elements of claim 1 and have similar deficiency as claim 1. Therefore, they are rejected for the same rational. Remaining dependent claims 2-9, 11-17 and 19-21 are also rejected due to their dependency on the rejected independent claims. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-3, 6-11, 14-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ranganath et al. (US 2024/0259879 A1, hereafter Ranganath) in view of Chauhan et al. (US 2024/0276298 A1, hereafter Chauhan) . Ranganath was cited in the IDS filed on 06/26/2025. As per claim 1, Ranganath teaches the invention substantially as claimed including a system for executing applications for radio interface controller (RIC) management, comprising ([0002] RAN, RIC implementations, RIC-based application): one or more far-edge datacenters each including first computing resources configured to execute a radio access network (RAN) function and a real-time RIC ([0433] edge data center, smallest, house multiple high-performance compute and data storage nodes [0024] ORAN, xApps, RAN operations; fig. 8 O-RAN network functions 804, Non-RT RIC 812 Near-RT RIC 814); one or more near-edge datacenters each including second computing resources configured to execute a core network function and at least one of a near-real-time RIC or a non-real-time RIC (fig. 1 near-RT RIC 114 Non-RT RIC 112 [0433] cloud data center, regional data center, house multiple high-performance compute and data storage nodes; fig. 1 near-RT RIC 114 app layer fig. 3a 114 330 310 320 [0026] SMO 102 policy, configuration, inventory, design i.e. core service and Non-RT RIC 112 ; core services extensible by xApps; fig. 8 NG-core 808); and a central controller configured to (fig. 1 SMO 102 RIC 114; fig. 3C 3c02 3c14): receive inputs of application requirements, hardware constraints , and a capacity of the first computing resources and the second computing resources ([0047] applications, one or more constraints e.g. latency, data rate, bandwidth, application requirements [0103] edge compute nodes, resources e.g. memory CPU, GPU, orchestration of multiple applications, containers, VEs, VMs, servers [0067] data processing, specific application, service, requirements, network capabilities, high data rates, traffic densities, service availability, low latency, capabilities specified based on SLA between the mobile operator and their customers/subscribers [0297] receive input) for a plurality of applications to be executed ([0047] MO 3c02, applications 3c32, application rules and requirements) on the one or more far-edge datacenters or the one or more near-edge datacenters in one or more processing pipelines ( [0433] cloud data center, regional data center, house multiple high-performance compute and data storage nodes [0202] AI/ML support function, provide pipeline, data pipeline, data ingestion and preparation services for applications [0057] vRAN processors, vRAN accelerators, accelerator pipeline); enumerate a plurality of feasible combinations of application locations and configurations that satisfy the application requirements and hardware constraints ([0068] AI/ML models 3c24, uses specific performance metrics as well as telemetry data or profiling information, determine resource allocations for individual xApps 410 or rApps911 [0070] observation data, insights /knowledge, AI/ML models, measurement data and/or platform telemetry data, analyze data, determine HW/SW, and /or NW resource allocations for individuals xApps; determining to scale up or down HW, SW and/or NW resources i.e. multiple feasible allocations [0074] enforcing dynamic xApp resource allocation and/or QoS within the near-RT RIC 414 [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083] [0025] resource allocations to be relevant to existing network conditions); allocate computing resources from the first computing resources or the second computing resources to a feasible combination that would produce a deployment having a greatest utility based on a utility function applied to a quant of the computing resources or to a conflict in the computing resources predicted by a Bayesian optimizer among the plurality of applications ([0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083] manage resource allocations for multiple RAN nodes and/or cells [0172] conflict management functions, security functions to resolve potential conflicts and/or overlaps, request from xApps [0189] conflict mitigation function, conflicting interactions between different xApps [0190] conflicts, control, conflict involves total requested resources from different xApps, exceed the limitation of RAN system i.e. utility function [0191] conflict mitigation function, anticipate, possible conflict [0193] utility metrics, defined, incorporating, relative importance, metrics targeted by xApps, importance of the optimization [0031] optimization functions and/or other predictive algorithms, optimal performance [0507] Bayesian optimization); and deploy each of the plurality of applications to the real-time RIC, the near-real-time RIC, or the non-real-time RIC based on the deployment ([0031] deploy and/or migrate workloads [0088] determine allocations, apps, fed back into controller/orchestrator, manage the resource allocations at various levels e.g. local/global levels, apply different resource allocations to different apps of the nearRT-RICs and/or other RICs; fig. 5 515 520 525 410). Ranganath doesn’t specifically teach Real-Time RIC; capacity of computing resources. Chauhan, however, teaches Real-Time RIC ([0010] implementing real-time RIC [0024] data center); capacity of computing resources for a plurality of applications ([0033] zApps, RT RIC applications [0054] coverage and capacity requirement [0065] [0006] RIC, applications [0080] different applications, KPI). It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Ranganath with the teachings of Chauhan of implementing a real-time radio interface controller, capacity requirement corresponding to different applications to improve efficiency and provide Real-Time RIC; and required capacity of computing resources to the method of Ranganath as in the instant invention. The combination of Ranganath and Chauhan would have been obvious because applying known method of implementing real-time radio interface controller and coverage and capacity requirement associated with applications as taught by Chauhan to the resource management method of near-real time and/or non-real time RIC taught by Ranganath to yield expected result to reduce latency and improve efficiency. As per claim 2, Ranganath teaches wherein the feasible combinations satisfy the application requirements and hardware constraints ([0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083]). As per claim 3, Ranganath teaches wherein to allocate the computing resources, the central controller is configured to incrementally allocate a quant of the first computing resources or the second computing resources to the feasible combination that would produce a greatest utility from the quant based on a utility function in view of previous allocations until all of the first computing resource and the second computing resources are allocated (fig. 1 SMO 102 RIC 114; fig. 3C 3c02 3c14 [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083] [0190] conflicts, control, conflict involves total requested resources from different xApps, exceed the limitation of RAN system i.e. utility function [0191] conflict mitigation function, anticipate, possible conflict [0193] utility metrics, defined, incorporating, relative importance, metrics targeted by xApps, importance of the optimization [0031] optimization functions and/or other predictive algorithms, optimal performance [0507] Bayesian optimization [0071] customize HW/SW and/or NW resource per network slice, per network service, and/or per xApp; provides platform performance improvements and efficiencies, and opportunity for privileged services utilizing platform telemetry, provides closed control loop functions in real-time (or near real-time) by running/operating AI/ML models 3 c 24 to identify or determine increases or decreases in KPIs, KPMs, SLA requirements, and/or QoS requirements of individual network/service slices, and dynamically adjust i.e. incrementally assigned and/or allocated HW, SW, and/or NW resources and/or power levels allocated to individual xApps 410). As per claim 6, Ranganath teaches wherein the utility function measures accuracy of the plurality of applications and efficiency of communications between the plurality of applications ([0193] individual xApps, utility metrics, relative importance of each of the metrics [0082] latency, reliability). As per claim 7, Ranganath teaches wherein to allocate the computing resources, the central controller is configured to: predict, using the Bayesian optimizer, a next conflict in the computing resources among the plurality of applications ([0507] Bayesian optimization, [0190] conflict, total requested resources from different xApps, exceed the limitations of the RAN system [0191] anticipate the possible conflict, e.g. antenna tilts [0192] implicit conflict, different apps, optimize different metrics, reconfigure different parameters); and select an allocation of the resources in conflict that optimizes the utility function ( [0191] conflict mitigation function, anticipate possible conflicts, take action to mitigate [0507] Bayesian optimization [0193] utility metrics, relative importance of each of the metrics, importance of optimization [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements). As per claim 8, Ranganath teaches wherein the Bayesian optimizer is configured with an objective function that indicates an aggregated utility of the applications ([0507] Bayesian optimization [0193] utility metrics, relative importance of each of the metrics, importance of optimization). Claim 9 recites elements similar to combination of claims 1, 3, 7 and 8. Therefore, it is rejected for the same rational. As per claim 10, Ranganath teaches the invention substantially as claimed including a method for executing applications for radio interface controller (RIC) management, comprising ([0002] RAN, RIC implementations, RIC-based application): receive inputs of application requirements, hardware constraints ( [0047] applications, one or more constraints e.g. latency, data rate, bandwidth, application requirements [0103] edge compute nodes, resources e.g. memory CPU, GPU, orchestration of multiple applications, containers, VEs, VMs, servers [0067] data processing, specific application, service, requirements, network capabilities, high data rates, traffic densities, service availability, low latency, capabilities specified based on SLA between the mobile operator and their customers/subscribers [0297] receive input) for a plurality of applications to be executed (fig. 3C apps 3c32 fig. 5 xApps 410) in one or more processing pipelines ([0202] AI/ML support function, provide pipeline, data pipeline, data ingestion and preparation services for applications [0057] vRAN processors, vRAN accelerators, accelerator pipeline) on one more far-edge datacenters having a first computing resources configured to execute a radio access network (RAN) function ([0433] edge data center, smallest, house multiple high-performance compute and data storage nodes [0024] ORAN, xApps, RAN operations; fig. 8 O-RAN network functions 804, Non-RT RIC 812 Near-RT RIC 814) and a real-time RIC or one more near-edge datacenters having a second computing resources configured to execute a core network function and at least one or a near-real-time RIC (fig. 1 near-RT RIC 114 Non-RT RIC 112 [0433] cloud data center, regional data center, house multiple high-performance compute and data storage nodes; fig. 1 near-RT RIC 114 app layer fig. 3a 114 330 310 320 [0026] SMO 102 policy, configuration, inventory, design i.e. core service and Non-RT RIC 112 ; core services extensible by xApps; fig. 8 NG-core 808); enumerate a plurality of feasible combinations of application locations and configurations that satisfy the application requirements and hardware constraints ([0068] AI/ML models 3c24, uses specific performance metrics as well as telemetry data or profiling information, determine resource allocations for individual xApps 410 or rApps911 [0070] observation data, insights /knowledge, AI/ML models, measurement data and/or platform telemetry data, analyze data, determine HW/SW, and /or NW resource allocations for individuals xApps; determining to scale up or down HW, SW and/or NW resources i.e. multiple feasible allocations [0074] enforcing dynamic xApp resource allocation and/or QoS within the near-RT RIC 414 [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083] [0025] resource allocations to be relevant to existing network conditions); incrementally allocating a quant of the first computing resources or the second computing resources ([0071] customize HW/SW and/or NW resource per network slice, per network service, and/or per xApp; provides platform performance improvements and efficiencies, and opportunity for privileged services utilizing platform telemetry, provides closed control loop functions in real-time (or near real-time) by running/operating AI/ML models 3 c 24 to identify or determine increases or decreases in KPIs, KPMs, SLA requirements, and/or QoS requirements of individual network/service slices, and dynamically adjust i.e. incrementally assigned and/or allocated HW, SW, and/or NW resources and/or power levels allocated to individual xApps 410) to a feasible combination that would produce a first proposed deployment having a greatest utility based on a utility function applied to a quant of the computing resources or to a conflict in the computing resources predicted by a Bayesian optimizer among the plurality of applications ([0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083] manage resource allocations for multiple RAN nodes and/or cells [0172] conflict management functions, security functions to resolve potential conflicts and/or overlaps, request from xApps [0189] conflict mitigation function, conflicting interactions between different xApps [0190] conflicts, control, conflict involves total requested resources from different xApps, exceed the limitation of RAN system i.e. utility function [0191] conflict mitigation function, anticipate, possible conflict [0193] utility metrics, defined, incorporating, relative importance, metrics targeted by xApps, importance of the optimization [0031] optimization functions and/or other predictive algorithms, optimal performance [0507] Bayesian optimization); and deploying each of the plurality of applications to the real-time RIC, the near-real-time RIC, or the non-real-time RIC based on the deployment ([0031] deploy and/or migrate workloads [0088] determine allocations, apps, fed back into controller/orchestrator, manage the resource allocations at various levels e.g. local/global levels, apply different resource allocations to different apps of the nearRT-RICs and/or other RICs; fig. 5 515 520 525 410). Ranganath doesn’t specifically teach Real-Time RIC. Chauhan, however, teaches Real-Time RIC ([0010] implementing real-time RIC [0024] data center). It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Ranganath with the teachings of Chauhan of implementing a real-time radio interface controller to improve efficiency and provide Real-Time RIC to the method of Ranganath as in the instant invention. The combination of Ranganath and Chauhan would have been obvious because applying known method of implementing real-time radio interface controller as taught by Chauhan to the resource management method of near-real time and/or non-real time RIC taught by Ranganath to yield expected result to reduce latency and improve efficiency. Claim 11 recites elements similar to claim 2. Therefore, it is rejected for the same rationale. Claim 14 recites elements similar to claim 6. Therefore, it is rejected for the same rationale. Claim 15 recites elements similar to claim 7. Therefore, it is rejected for the same rationale. Claim 16 recites elements similar to part of claim 1 and 9 in combination. Therefore, it is rejected for the same rationale. Claim 17 recites elements similar to claim 8. Therefore, it is rejected for the same rationale. As per claim 18, Ranganath teaches the invention substantially as claimed including a non-transitory computer-readable medium storing computer-executable instructions for radio interface controller (RIC) management, comprising instructions that when executed by a processor of a central controller of a network cause the central controller to (fig. 1 SMO 102 RIC 114; fig. 3C 3c02 3c14): receive inputs of application requirements, hardware constraints ( [0047] applications, one or more constraints e.g. latency, data rate, bandwidth, application requirements [0103] edge compute nodes, resources e.g. memory CPU, GPU, orchestration of multiple applications, containers, VEs, VMs, servers [0067] data processing, specific application, service, requirements, network capabilities, high data rates, traffic densities, service availability, low latency, capabilities specified based on SLA between the mobile operator and their customers/subscribers [0297] receive input) for a plurality of applications to be executed (fig. 3C apps 3c32 fig. 5 xApps 410) in one or more processing pipelines ([0202] AI/ML support function, provide pipeline, data pipeline, data ingestion and preparation services for applications [0057] vRAN processors, vRAN accelerators, accelerator pipeline) on one more far-edge datacenters having a first computing resources configured to execute a radio access network (RAN) function ([0433] edge data center, smallest, house multiple high-performance compute and data storage nodes [0024] ORAN, xApps, RAN operations; fig. 8 O-RAN network functions 804, Non-RT RIC 812 Near-RT RIC 814) and a real-time RIC or one more near-edge datacenters having a second computing resources configured to execute a core network function and at least one or a near-real-time RIC (fig. 1 near-RT RIC 114 Non-RT RIC 112 [0433] cloud data center, regional data center, house multiple high-performance compute and data storage nodes; fig. 1 near-RT RIC 114 app layer fig. 3a 114 330 310 320 [0026] SMO 102 policy, configuration, inventory, design i.e. core service and Non-RT RIC 112 ; core services extensible by xApps; fig. 8 NG-core 808); enumerate a plurality of feasible combinations of application locations and configurations that satisfy the application requirements and hardware constraints ([0068] AI/ML models 3c24, uses specific performance metrics as well as telemetry data or profiling information, determine resource allocations for individual xApps 410 or rApps911 [0070] observation data, insights /knowledge, AI/ML models, measurement data and/or platform telemetry data, analyze data, determine HW/SW, and /or NW resource allocations for individuals xApps; determining to scale up or down HW, SW and/or NW resources i.e. multiple feasible allocations [0074] enforcing dynamic xApp resource allocation and/or QoS within the near-RT RIC 414 [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. The generated resource allocations 525 can provide optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0083] [0025] resource allocations to be relevant to existing network conditions); predict, using a Bayesian optimizer, a next conflict in resources among the plurality of applications ([0507] Bayesian optimization [0190] conflict, total requested resources from different xApps, exceed the limitations of the RAN system [0191] anticipate the possible conflict, e.g. antenna tilts [0192] implicit conflict, different apps, optimize different metrics, reconfigure different parameters); select an allocation of resources in conflict that optimizes a utility function for a first deployment ( [0191] conflict mitigation function, anticipate possible conflicts, take action to mitigate [0507] Bayesian optimization [0193] utility metrics, relative importance of each of the metrics, importance of optimization [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements); and deploying each of the plurality of applications to the real-time RIC, the near-real-time RIC, or the non-real-time RIC based on the deployment ([0031] deploy and/or migrate workloads [0088] determine allocations, apps, fed back into controller/orchestrator, manage the resource allocations at various levels e.g. local/global levels, apply different resource allocations to different apps of the nearRT-RICs and/or other RICs; fig. 5 515 520 525 410). Ranganath doesn’t specifically teach Real-Time RIC. Chauhan, however, teaches Real-Time RIC ([0010] implementing real-time RIC [0024] data center). It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Ranganath with the teachings of Chauhan of implementing a real-time radio interface controller to improve efficiency and provide Real-Time RIC to the method of Ranganath as in the instant invention. The combination of Ranganath and Chauhan would have been obvious because applying known method of implementing real-time radio interface controller as taught by Chauhan to the resource management method of near-real time and/or non-real time RIC taught by Ranganath to yield expected result to reduce latency and improve efficiency. Claim 19 recites elements similar to claim 8. Therefore, it is rejected for the same rationale. Claim 20 recites elements similar to part of claims 1 and 9 in combination. Therefore, it is rejected for the same rationale. As per claim 21, Ranganath teaches wherein the RAN function is a 5G network function ([0057] 5G vRAN workloads [0108] communication networks, 5G e.g. RAN nodes) . 07-21-aia AIA Claim s 4-5 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ranganath in view of Chauhan, as applied to above claims, and further in view of Yao et al. (US 2021/0224135 A1, hereafter Yao) . As per claim 4, Ranganath teaches wherein the quant is a fraction of resource usage for a dominant demand of a feasible combination with a maximum of resource usage among the first computing resources and the second computing resources at each application location ([0068] resource usage/allocations, individual xApps 420 [0070] adjust/determine resource usage/allocations according to service requirement [0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements [0081] HW/SW/NW metrics, calculated in the forms of maximum). Ranganath and Chauhan, in combination, do not specifically teach dominant demand. Yao, however, teaches resource usage for dominant demand ([0005] dominant resource, resource that occupies a maximum proportion in provided resources allocated [0016] [0006] share proportion of the type of resource required by the cloud user). It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Ranganath and Chauhan with the teachings of Yao of dominant resource that occupies the maximum proportion in the provided resources allocated to a user to improve efficiency and allow resource usage for dominant demand to the method of Ranganath and Chauhan as in the instant invention. The combination of Ranganath, Chauhan and Yao would have been obvious because applying known method of determining the dominant resource allocated to certain user as taught by Yao to the resource management method of near-real time and/or non-real time RIC taught by Ranganath and Chauhan to yield expected result and improve efficiency. As per claim 5, Ranganath teaches wherein the central controller is configured to sort the feasible combinations in ascending order of the dominant demand to select the resource to allocate ([0082] combines the observation data 515, 415 with the KPIs, KPMs, SLA requirements, and the like to determine appropriate HW, SW, and/or NW resource allocations 525 for individual xApps 410 on a real-time or near real-time basis. optimized performance in terms of e2e QoS or quality of experience (QE) or otherwise adhere to the KPIs KPMs, and SLA requirements). Yao teaches remaining claim elements of the dominant demand ([0005] dominant resource, resource that occupies a maximum proportion in provided resources allocated [0006] share proportion of the type of resource required by the cloud user). Claim 12 recites elements similar to claim 4. Therefore, it is rejected for the same rationale. Claim 13 recites elements similar to claim 5. Therefore, it is rejected for the same rationale. Examiners Note Applicant is further reminded of that the cited paragraphs and in the references as applied to the claims above for the convenience of the applicant(s) and although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider all of the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner . Allowable Subject Matter Claims 1+4+9 are objected to, but would be allowable if rewritten in independent form to overcome the rejections set forth in this office action. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chen (US 2023/0112534 A1) teaches artificial intelligence planning method and real-time radio access network intelligence controller Doshi; Kshitij et al. (US 20250071023 A1) teaches methods, system, articles of manufacture, and apparatus to manage telemetry data in an edge environment Guim Bernat; Francesc et al. (US 20240236017 A1) teaches automated node configuration tuning in EDGE systems Maciocco; Christian et al. (US 20210014133 A1) teaches methods and apparatus to coordinate EDGE platforms Subramani Jayavelu; Giridhar et al. (US 20230069604 A1 ) teaches use of CRDS as descriptors for applications, application components, deployments, clouds, AI/ML Models, and RTE in an O-RAN system Yeh et al. (US 2022/0124560 A1) teaches resilient radio resource provisioning for network slicing Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABU GHAFFARI whose telephone number is (571)270-3799. The examiner can normally be reached on Monday-Thursday 14:00 - 15:00 Hrs. 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, Meng-Ai AN can be reached on 571-272-3756. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ABU ZAR GHAFFARI/Primary Examiner, Art Unit 2195 Application/Control Number: 18/444,156 Page 2 Art Unit: 2195 Application/Control Number: 18/444,156 Page 3 Art Unit: 2195 Application/Control Number: 18/444,156 Page 4 Art Unit: 2195 Application/Control Number: 18/444,156 Page 5 Art Unit: 2195 Application/Control Number: 18/444,156 Page 6 Art Unit: 2195 Application/Control Number: 18/444,156 Page 7 Art Unit: 2195 Application/Control Number: 18/444,156 Page 8 Art Unit: 2195 Application/Control Number: 18/444,156 Page 9 Art Unit: 2195 Application/Control Number: 18/444,156 Page 10 Art Unit: 2195 Application/Control Number: 18/444,156 Page 11 Art Unit: 2195 Application/Control Number: 18/444,156 Page 12 Art Unit: 2195 Application/Control Number: 18/444,156 Page 13 Art Unit: 2195 Application/Control Number: 18/444,156 Page 14 Art Unit: 2195 Application/Control Number: 18/444,156 Page 15 Art Unit: 2195 Application/Control Number: 18/444,156 Page 16 Art Unit: 2195 Application/Control Number: 18/444,156 Page 17 Art Unit: 2195 Application/Control Number: 18/444,156 Page 18 Art Unit: 2195 Application/Control Number: 18/444,156 Page 20 Art Unit: 2195 Application/Control Number: 18/444,156 Page 23 Art Unit: 2195
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