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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. 12057902 herein Huang.
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) determining one or more policy scenarios have occurred; (fig. 2) subscribing, via one or more XApps (merely software running on near RT RIC) on the near-RT RIC, to data associated with the policies related to O-Cloud workload optimization; (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) 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) executing, via a control application on the O-Cloud, one or more corrective actions consistent with the received instructions; (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) and transmitting, from the one or more Xapps on the O-Cloud, confirmation of the execution of the one or more corrective actions (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).
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).
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
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BABOUCARR . FAAL
Primary Examiner
Art Unit 2138
/BABOUCARR FAAL/Primary Examiner, Art Unit 2138