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 Amendment
The Amendment filed on 9/30/2025 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to Claims have overcome the objection previously set forth in the Non-Final Office Action mailed on 6/30/2025.
Response to Arguments
Applicant’s arguments on pages 10-13 with respect to claims 1, 9 and 17 have been considered but are moot upon a further consideration and a new ground of rejection made under 35 U.S.C. 103 as being unpatentable over Huang (US PGPub 2023/0292291) in view of Atawia (US PGPub 2024/0015561).
Allowable Subject Matter
Claims 2, 5, 10, 13, 18 and 19 are 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.
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.
Claims 1, 3, 4, 6-9, 11, 12, 14-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang (US PGPub 2023/0292291) in view of Atawia (US PGPub 2024/0015561).
Regarding claims 1, 9 and 17, Huang teaches an apparatus for implementing an application hosted in a radio access network (RAN) Intelligent controller (RIC) within a Service Management and Orchestration (SMO) framework of a telecommunications network (Huang, see paragraph 0022, The resource adjustment device 26 logically comprises a Service Management and Orchestration (SMO), a Non-Real Time RAN Intelligent Controller (Non-RT RIC) and a Near-RT RAN Intelligent Controller (Near-RT RIC) complying with the O-RAN specifications), the apparatus comprising:
a memory storing instructions (Huang, see paragraph 0022, The resource adjustment device); and
at least one processor within the SMO framework for implementing the RIC (Huang, see paragraph 0022, The resource adjustment device 26 logically comprises a Service Management and Orchestration (SMO), a Non-Real Time RAN Intelligent Controller (Non-RT RIC) and a Near-RT RAN Intelligent Controller (Near-RT RIC) complying with the O-RAN specifications), the at least one processor configured to execute the instructions to:
receive data comprising at least one performance indicator from an Open RAN (O-RAN) centralized unit (O-CU) in an O-Cloud computing environment (Huang, see paragraphs 0024 and 0029, Obtain RAN training information RAN_Info_1-RAN_Info_t at a first time point. the RAN training information RAN_Info_1-RAN_Info_t obtained by the resource adjustment device 26 in Step 302 are time series composed of vectors including a plurality of features, and the plurality of features of each vector include radio resource data RR Metrics and computing resource data CR Metrics), wherein the at least one performance indicator comprises a performance indicator of the O-CU (Huang, see table 1 and table 2 on pages 3-4, the list of RR (radio resource) metrics and the list of CR (computing resource) metrics);
compare the least one performance indicator with a first predetermined threshold (Huang, see paragraph 0035, Determine whether the predicted value P_DL_PRB_Usg of the average utilization rate of downlink PRB of the O-RU 20_1 for user plane transmission is greater than 80%);
receive and evaluate a resource status of at least one physical host in the O-Cloud computing environment (Huang, see table 2 on page 4, the list of CR (computing resource) metrics); and
create a resource management policy for allocating O-Cloud computing resources of the at least one physical host to scale the O-CU in at least one physical location in the O-Cloud computing environment based on the comparing and the evaluating (Huang, see paragraphs 0037 and 0041, Adjust the computing resources of the O_DU 22 or the O-CU 24 according to the predicted value P_CPU_Usg. The method for adjusting the computing resources of the O-DU 22 or the O-CU 24 in Step 508 is using the Horizontal Pod Autoscaler (HPA) API provided by Kubernetes to adjust the number of Pods (replicas). When more computing resources are required, the number of Pods is increased to support the computations required by the O-DU 22 or the O-CU 24. Even though failing to expressly teach about creating a resource management policy, Huang teaches about utilizing the horizontal pod autoscaler (HPA) API of Kubernetes to adjust the number of pods allocated for the O-CU. Therefore, the limitation of creating a resource management policy is embedded in the disclosure of Huang).
Huang teaches the above yet fails to teach wherein the at least one physical location is selected from among plural physical locations based on the evaluating, according to location information of the plural physical locations.
Then Atawia teaches wherein the at least one physical location is selected from among plural physical locations based on the evaluating, according to location information of the plural physical locations (Atawia, see paragraph 0051, For delay measurements 331 that are outside delay threshold ranges, the controller 320 can perform scaling and placement determinations 323. Placement determinations can determine new cloud 340 placement locations for the network functions 330. Scaling determinations can determine cloud 340 resources to apply to network functions 330. Scaling and placement determinations 323 can result in scaling and placement decisions 324, comprising the location placement and scaling determinations generated by the processing conducted pursuant to scaling and placement determinations 323. The controller 320 can provide scaling and placement decisions 324 to the cloud 340, to thereby enable the cloud 340 to implement the scaling and placement decisions 324).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Huang with Location selection for disaggregated radio access network functions of Atawia, because doing so would make Huang more efficient in providing techniques accounting for inter-cell delay requirements of different RAN cells, to place network functions in a manner that supports effective coordination between multiple cells (Atawia, see paragraph 0020).
Regarding claims 3 and 11, Huang in view of Atawia teaches wherein:
based on the compared at least one performance indicator comprising a performance indicator of an O-CU control plane (CP), the resource management policy is for allocating O-Cloud computing resources to scale the O-CU CP (Huang, see paragraphs 0037 and 0041, Adjust the computing resources of the O_DU 22 or the O-CU 24 according to the predicted value P_CPU_Usg. The method for adjusting the computing resources of the O-DU 22 or the O-CU 24 in Step 508 is using the Horizontal Pod Autoscaler (HPA) API provided by Kubernetes to adjust the number of Pods (replicas). When more computing resources are required, the number of Pods is increased to support the computations required by the O-DU 22 or the O-CU 24); and
based on the compared at least one performance indicator comprising a performance indicator of an O-CU user plane (UP), the resource management policy is for allocating O-Cloud computing resources to scale the O-CU UP (Huang, see paragraph 0037, Adjust the computing resources of the O_DU 22 or the O-CU 24 according to the predicted value P_CPU_Usg).
Regarding claims 4 and 12, Huang in view of Atawia teaches wherein the performance indicator of the O-CUP CP comprises a number of connected devices, and the performance indicator of the O-CU UP comprises an amount of traffic or a number of active devices (Huang, see table 1 and table 2 on pages 3-4, the list of RR (radio resource) metrics and the list of CR (computing resource) metrics).
Regarding claims 6 and 14, Huang in view of Atawia teaches wherein the at least one processor is further configured to execute the instructions to:
based on N number of instantiated O-CUs in accordance with the resource management policy, implement a failsafe policy in which a single additional redundancy is instantiated as a failsafe for the N number of instantiated O-CUs, wherein N is a whole number greater than 1 (Huang, see paragraphs 0037 and 0041, Adjust the computing resources of the O_DU 22 or the O-CU 24 according to the predicted value P_CPU_Usg. The method for adjusting the computing resources of the O-DU 22 or the O-CU 24 in Step 508 is using the Horizontal Pod Autoscaler (HPA) API provided by Kubernetes to adjust the number of Pods (replicas). When more computing resources are required, the number of Pods is increased to support the computations required by the O-DU 22 or the O-CU 24).
Regarding claims 7, 15 and 20, Huang in view of Atawia teaches wherein the resource management policy is for upscaling the O-CU by instantiating an additional O-CU at a physical host at a different data center than a data center of the O-CU, based on the evaluated resource status of physical hosts in the data center of the O-CU (Huang, see paragraphs 0037 and 0041, Adjust the computing resources of the O_DU 22 or the O-CU 24 according to the predicted value P_CPU_Usg. The method for adjusting the computing resources of the O-DU 22 or the O-CU 24 in Step 508 is using the Horizontal Pod Autoscaler (HPA) API provided by Kubernetes to adjust the number of Pods (replicas). When more computing resources are required, the number of Pods is increased to support the computations required by the O-DU 22 or the O-CU 24).
Regarding claims 8 and 16, Huang in view of Atawia teaches wherein the resource status of the at least one physical host comprises at least one of a hardware processor load, a memory usage, and a hard disk drive usage of the at least one physical host (Huang, see table 2 on page 4, the list of CR (computing resource) metrics).
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 CHONG G KIM whose telephone number is (571)270-0619. The examiner can normally be reached Mon-Fri @ 9am - 5pm.
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/CHONG G KIM/Examiner, Art Unit 2443
/NICHOLAS R TAYLOR/Supervisory Patent Examiner, Art Unit 2443