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 .
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.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang 12057902 herein Huang in view of Pateromichelakis et al. 20230246724 herein Pateromichelakis.
Per claim 1, Huang discloses: A method for O-RAN optimization implemented in cooperation with a network system comprising one or more network infrastructure devices, server devices, or client devices, the method comprising: (fig. 1, col. 8; the overall architecture of the O-RAN is: building on the basis of the Centralized Unit (CU)/Distributed Unit (DU) architecture and functional virtualization of the wireless network, introducing a reference design of open interface and open hardware, and optimizing wireless control flow by artificial intelligence) receiving, via an interface between the O-Cloud orchestrator and the near- realtime RAN intelligent controller ("near-RT RIC"), policies related to O-Cloud workload optimization; (fig. 1col. 8; a service management and orchestration (SMO) entity 101: the SMO entity 101 is an entity for providing various management services and network management functions, which includes a Non-Real time RAN intelligent controller 101-1 (Non-RT RIC) and a data collector 101-2 (Collector); Non-RT RIC 101-1: the Non-RT RIC 101-1 has functions of microservice and policy management, wireless network analysis, and Artificial Intelligence (AI)/ Machine Learning (ML) model training, etc. In some embodiments, the microservice may be a service that can deploy application(s) and/or service(s) in a cloud network. The trained AI model may be directly deployed in the Non-RT RIC 101-1 or deployed in the Near-Real Time RAN Intelligent Controller 102-1 (Near-RT RIC) through an A1 interface for inference and executing) transmitting, from the near-RT RIC to the O-Cloud, instructions for one or more corrective actions; (fig. 2, col. 8; a Near-RT RIC 102-1 is introduced into the O-RAN network functional entity 102. The entities or functions related to the present disclosure in the internal functional nodes of the O-RAN network functional entity 102 also include: at least one of an O-RAN Centralized Unit 102-2 (O-CU) and an O-RAN Distributed Unit 102-3 (O-DU); Near-RT RIC 102-1: realizing near-real time control and optimization of wireless resources through near-real time data collection and processing. The Near-RT RIC 102-1 may also include training, inferring and updating of Artificial Intelligence (AI)/ Machine Learning (ML) models. The Near-RT RIC 102-1 may process near-real time services with a delay less than that of the non-real time services. The near-real time services may be services such as training, inferring and updating of AI/ML models, and optimization of wireless resources) and transmitting, from the one or more Xapps on the O-Cloud, confirmation of the execution of the one or more corrective actions; to be executed via a control application on the O-Cloud (fig. 2, col. 11 line 5; In step 206, the Non-RT RIC 101-1 determines whether congestion is alleviated based on the current congestion situation and a preset condition on congestion alleviation. If the congestion situation is alleviated (step 206, YES), the method proceeds to step 207; if the congestion situation is not alleviated (step 206, NO), the method proceeds to step 203. (47) In step 207, the Non-RT RIC transmits, to the O-RAN network functional entity 102, a message including information indicating to delete the congestion handling policy. For example, the Non-RT RIC may transmit, to the O-RAN network functional entity 102, a message including information indicating to delete the QoS policy of the network slices).
Huang discloses applications providing enrichment data but does not specifically disclose: subscribing, via one or more XApps on the near-RT RIC, to data from Infrastructure Management Services (IMS) and Deploymnet Management Services (DMS) subsystem of the O-Cloud, the data associated with the policies related to O-Cloud workload optimization.
However, Pateromichalekis discloses: subscribing, via one or more XApps on the near-RT RIC, to data from Infrastructure Management Services (IMS) and Deploymnet Management Services (DMS) subsystem of the O-Cloud, the data associated with the policies related to O-Cloud workload optimization (¶0095; In a first communication 536 transmitted from the service and/or management plane 502 to the second xAPP 518, the second xAPP 518 initially receives IM configuration policies (e.g., initial configuration of policies) from the service and/or management plane 502 including: a list of available IM policies (e.g., ICIC, eICIC, CoMP 1, CoMP 2), thresholds and/or criteria for access and BH metrics which will support the selection and/or update of an IM policy, preferences, whether a policy can be enforced by the second xAPP 518, a recommendation, a time of coverage, and/or an area of coverage. The IM configuration policies may be vertical specific (e.g., V2X, IIoT) or common configurations for all verticals using RAN resources. This IM configuration policies may provide required interactions among xAPPs for intra-vertical and cross-vertical cases if controlling the same RAN nodes; the examiner notes that the IMS and DMS isn’t defined and interpreted as merely services on the platform).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the teachings of Huang and Pateromichalekis’s model based predictive management to support decisions of parameter settings. Pateromichalekis improves resources usage efficiency (¶0071).
Per claim 2, Huang discloses: wherein the Xapps on the near-RT RIC are configured to receive data from the O-Cloud and from external sources (fig. 2, col. 15, line 15; the Near-RT RIC 102-1 collects near-real time internal data. The near-real time internal data refers to near-real time data from a functional node in the O-RAN network functional entity 102, for which FIG. 4 takes an E2 node corresponding to at least one of O-CU 102-2 or O-DU 102-3 as an example. The near-real time internal data may include at least one of the following three types of data: (1) capability information (data); (2) statistical information (data); and (3) configuration information (data)).
Per claim 3, Huang discloses: wherein an Xapp of the one or more XApps is configured monitoring the utilization of cells associated with a DU and interface with other O-Cloud resources to dynamically scale resources in response to detected traffic (fig. 2, col. 15, line 15; the Near-RT RIC 102-1 collects near-real time internal data. The near-real time internal data refers to near-real time data from a functional node in the O-RAN network functional entity 102, for which FIG. 4 takes an E2 node corresponding to at least one of O-CU 102-2 or O-DU 102-3 as an example. The near-real time internal data may include at least one of the following three types of data: (1) capability information (data); (2) statistical information (data); and (3) configuration information (data)).
Per claim 4, Huang discloses: wherein the one or more XApps (merely software running on RT RIC) comprises a plurality of XApps operatively linked to perform coordinated operations (fig. 2, col. 15, line 15; the Near-RT RIC 102-1 collects near-real time internal data. The near-real time internal data refers to near-real time data from a functional node in the O-RAN network functional entity 102, for which FIG. 4 takes an E2 node corresponding to at least one of O-CU 102-2 or O-DU 102-3 as an example. The near-real time internal data may include at least one of the following three types of data: (1) capability information (data); (2) statistical information (data); and (3) configuration information (data)).
Per claim 5, Huang discloses: wherein the one or more XApps (merely software running on RT RIC) are configured to interface with the IMS and DMS of the O-Cloud to perform resource management functions s (fig. 2, col. 9, line 18; The O-Cloud 103 (O-Cloud) is a cloud computing platform, which is composed of physical infrastructure nodes meeting requirements of the O-RAN to carry data of related O-RAN functions (such as Near-RT RIC, O-CU and O-DU) and support software components (such as operating systems, virtual machine monitors, container runtime, etc.) and appropriate management and orchestration functions. a 5G core network 104 (NG-core); an external system 105: the external system 105, such as servers of various applications APPs, provides enrichment data for the SMO entity).
Claims 6-10 are the controller claims corresponding to the method claims 1-5 and are rejected under the same reasons set forth in connection with the rejection of claims 1-5.
Claims 11-15 are the CRM claims corresponding to the method claims 1-5 and are rejected under the same reasons set forth in connection with the rejection of claims 1-5.
Claims 16-20 are the system claims corresponding to the method claims 1-5 and are rejected under the same reasons set forth in connection with the rejection of claims 1-5. The rejection of claim 1 is silent in regards to: generate a bridge between the plurality of hypervisors of the locally connected computing subdomains. However, Huang discloses: generate a bridge between the plurality of hypervisors of the locally connected computing subdomains (col. 10; Herein, each virtual logical network is defined as one network slice. Each network slice supports one type of services, and thus these network slices enable the same physical network to support different types of services. Since the 5G network will support diverse services, the network slice will play a key role in the 5G network. The network slice or slice mentioned in the present disclosure specifically includes a network slice controllable by the O-RAN, a network sub-slice of a RAN layer, or a network slice instance).
Response to Arguments
Applicant’s arguments, see remarks, filed 6/30/26, with respect to the rejection(s) of claim(s) 1, 6, 11 and 16 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Pateromichelakis.
Remark
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
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 BABOUCARR FAAL whose telephone number is (571)270-5073. The examiner can normally be reached M-F 8:30-5:30 EST.
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BABOUCARR . FAAL
Primary Examiner
Art Unit 2138
/BABOUCARR FAAL/Primary Examiner, Art Unit 2138
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