Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is in response to application filed on 05/30/2024. Claims 1-20 were previously pending. Claims 1-20 are rejected.
Information Disclosure Statement
3. The information disclosure statement(s) (IDS) submitted on 05/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
Drawings
4.1. The drawings FIG.5 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) “506”, “508” mentioned in the description: [0075-76].
4.2. The drawings FIG.5 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) “Super Graph” not mentioned in the description [0075].
4.3. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
5. The disclosure is objected to because of the following informalities:
in page 9, missing paragraph [0044-45].
Claim Rejections - 35 USC § 103
6. 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 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.
6.1. 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 of this title, 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.
6.2. 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.
6.3. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Zanzi et al., ("Zanzi", WO 2024/217710 A1) in view of Orhan et al., (“Orhan”, US 2022/0124543 A1), and further in view of Kwan, (US 2017/0026888 A1).
Regarding Claim 1, Zanzi teaches, A method, comprising:
based on a federated learning process, determining, by a system comprising at least one processor, access network intelligent controller (Zanzi, FIG.4, federated learning manager (FLM), Open Radio Access Network (O-RAN), Non-Real-Time RAN Intelligent Controller (Non-RT RIC), Near-Real-Time RAN Intelligent Controller (Near-RT RIC), Abstract, Claim 8: deploying a FLM, in an O-RAN Non-RT RIC; coordinating by the FLM an involvement of the Non-RT RIC and an O-RAN Near-RT RIC, in at least one federated learning scheme; and executing the at least one federated learning scheme, based on a topology information and/or a system load. FIG.1, page 5, lines 23-34: shows communications between a group O-RU (O-RAN radio unit) and a Non-RT RIC and an O-RAN Near-RT RIC); and
(Zanzi, page 5, lines 23-34: desired in O-RAN to enable a more scalable and distributed and secure RAN management by distributing the learning and intelligence to the local nodes and saving the networking resources for transmitting large amounts of data to a central location. FIG.3, page 12. lines 10-20: enable the federated learning operation in O-RAN Non-RT RIC architecture. page 14, lines 15-33-page 15, lines 105, : The training profile may include one or more of the following non-exhaustive parameters: Traffic condition, Radio Access Network topology, Resource allocation, Energy consumption).
Zanzi does not expressly teach
based on the graphical representation, facilitating,
Orhan teaches
(Orhan, FIG.1, network 100, backhaul links 103, connection 105, UE 121, connection management function (CMF) 136, radio units (RUs) 130, distributed units (DUs) 131, centralized units (CUs) 132, [0024]: the edge network 100 includes a CMF 136 co-located with one or more CUs 132, DUs 131, RUs 130, and UEs 121. The CUs 132 and some DUs 131 have wired and/or wireless backhaul links 103 to the CMF 136 and supports wireless access 105 to the UEs 121. [0034]: the CMF 136 can be part of a RAN Intelligent Controller (RIC) such as a Non-Real Time (RT) RIC or a Near-RT RIC. FIG.2, graph 200, network access node (NAN) 231, [0046]: Graph also includes data elements for each existing/current connections 105 between individual UEs 221 and at least one of the NANs 231, [0234]: the CMF 136 generates an initial graph 200 of network including an initial arrangement of edges among a set of nodes. The set of nodes represent respective communication equipments (CEs) of the set of CEs (e.g., UEs 221 and/or NANs 231), and the edges in the initial arrangement of edges represent respective com-links (e.g., links 105 and/or 103) of the set of com-links); and
based on the graphical representation, facilitating, by the system, user equipment association that defines an action for a network traffic load balancing process according to a network energy savings criteria, (Orhan, S1604, [0235], [0301]: cause a graph neural network (GNN) to determine a set of candidate graphs based on a set of node features, the candidate arrangements different than the initial arrangement. [0309]: operate the GNN based on at least one of throughput of a communication network including the set of CEs, coverage of the communication network, or load balance among the set of CEs).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “graph neural networks (GNN) for load-aware connection management” of Orhan into the invention of Zanzi. The suggestion/motivation incorporates the connection management techniques based on GNN to make better decisions to balance network traffic load while network throughput is also maximized. Including the “GNN) for connection management” of Orhan into the invention of Zanzi was within the ordinary ability of one of ordinary skill in the art based on the teachings of Orhan.
Zanzi-Orhan does not expressly teach
Kwan teaches (Kwan, FIG.4, [0117]: if a particular small cell radio is experiencing a low level of traffic, one or more operations can be triggered to determine whether it may be more efficient to handover the UEs currently served by the small cell radio to another small cell radio; [0121]: determining a maximum energy savings can include either 1) solving an optimization problem to determine a set of small cell radios that can be turned off that will maximize energy savings the RAN within a given target macro cell radio load constraint for the RAN.).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “radio access point (RAP) load prediction” of Kwan into the invention of Zanzi -Orhan. The suggestion/motivation of involving calculating predicted network behavior for radio access network for potential user equipment handovers from source RAP to target RAPs. Including the “RAP load prediction” of Kwan into the invention of Zanzi -Orhan was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kwan.
Regarding Claim 2, Zanzi-Orhan-Kwan teaches, The method of claim 1, wherein the graphical representation is a first graphical representation, and wherein the method further comprises:
determining , by the system, a second graphical representation of a radio unit level of the communication network (Orhan, Claim 1: cause a graph neural network (GNN) to determine a set of candidate graphs (“second graphical representation”) based on a set of node features, respective ones of the candidate graphs including corresponding candidate arrangements of edges between corresponding ones of the set of nodes, the candidate arrangements different than the initial arrangement (“first graphical representative”)); and
based on information indicative of the second graphical representation of the communication network, determining the first graphical representation, wherein the first graphical representation is associated with a radio access network intelligence controller level of the communication network (Orhan, FIG.14, connection 1420, [0227]: Each connection 1420 may be assigned a weight that represents its relative importance. The weights may also be adjusted as learning proceeds. The weight increases or decreases the strength of the signal at a connection 1420).
Regarding Claim 3, Zanzi-Orhan-Kwan teaches, The method of claim 2, wherein the second graphical representation is an original graph of the communication network, and wherein the first graphical representation is a supergraph derived from the original graph (In graph theory and network modeling, an original graph consists of a distinct set of discrete elements and their specific relationships. A supergraph, by contrast, is a broader graph that encompasses the original graph, usually by grouping multiple nodes or subgraphs together to simplify or generalize the network's structure. Therefore, it would have been obvious to a person of ordinary skill in the art that a first graphical representation is a supergraph derived from the initial graph).
Regarding Claim 4, Zanzi-Orhan-Kwan teaches, The method of claim 2, wherein the information indicative of the second graphical representation comprises weighted values that represent communication links between radio units to radio access network intelligent controllers (Orhan, FIG.14, connection 1420, [0227]: Each connection 1420 may be assigned a weight that represents its relative importance. The weights may also be adjusted as learning proceeds. The weight increases or decreases the strength of the signal at a connection 1420).
Regarding Claim 5, Zanzi-Orhan-Kwan teaches, The method of claim 1, further comprising:
prior to determining the graphical representation, performing, by the system, the federated learning process that comprises:
training, by the system, a first model to a first defined confidence level, wherein the training of the first model comprises performing localized processing at a radio unit level of the communication network (Zanzi, FIG.4, Claim 12: the coordinating step and/or the executing of the at least one federated learning scheme comprises performing a collection, a validation and/or an update of at least one preferably local model (“first model”) in a federated manner);
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sending, by the system, information indicative of weighted values associated with the first model from the radio unit level to a radio access network intelligence controller level of the communication network (Zanzi, Claim 13, wherein at least one global model update is returned to at least one Near-RT RIC or target Near-RT RIC); and
training, by the system, a second model to a second defined confidence level, wherein the training of the second model comprises performing pooled processing at the radio access network intelligence controller level of the communication network (Zanzi, Claim 12: the coordinating step and/or the executing of the at least one federated learning scheme comprises performing a collection, a validation and/or an update of at least one preferably local model (“first model”) in a federated manner (“second model”). Claim 14, the FLM is a part of or is integrated in an Artificial Intelligence/Machine Learning, AI/ML function, entity or operation box).
Regarding Claim 6, Zanzi-Orhan-Kwan teaches, The method of claim 5, wherein user data associated with the user equipment is not included in the information indicative of the weighted values (Orhan, FIG.1, [0040]: To achieve intelligent and proactive connection management, a given network is abstracted as a graph, in which cells (e.g., RUs 130) and UE 121 are represented by nodes and the quality of the wireless links are given by edge weights).
Regarding Claim 7, Zanzi-Orhan-Kwan teaches, The method of claim 5, wherein the first model and the second model are graph neural network models (Orphan, [0059]: Graph Neural Networks (GNNs) are a framework to capture the dependence of nodes in graphs via message passing between the nodes).
Regarding Claim 8, Zanzi-Orhan-Kwan teaches, The method of claim 7, wherein the first model and the second model are message passing graph neural network models (Orphan, [0059]: Graph Neural Networks (GNNs) are a framework to capture the dependence of nodes in graphs via message passing between the nodes).
Regarding Claim 9, Zanzi-Orhan-Kwan teaches, The method of claim 7, wherein the training of the second model comprises training the second model to facilitate conformance with a network energy savings minimization criterion (Orhan, FIG.2, UE 221, NAN 231, [0082]: When a UE 221 is camped on a cell provided by a NAN 231, the UE 221 may regularly or periodically search for a better cell or beam according to cell or beam (re)selection criteria (“network energy savings minimization criterion”). For cell (re)selection, if a better cell is found, that cell or beam may be selected, and the UE 221 may tune to that cell's 230 control channel(s)).
Regarding Claim 10, Zanzi teaches, A system (Zanzi, FIG.4, federated learning manager (FLM), Open Radio Access Network (O-RAN), Non-Real-Time RAN Intelligent Controller (Non-RT RIC), Near-Real-Time RAN Intelligent Controller (Near-RT RIC), Abstract, Claim 8: deploying a FLM, in an O-RAN Non-RT RIC; coordinating by the FLM an involvement of the Non-RT RIC and an O-RAN Near-RT RIC, in at least one federated learning scheme; and executing the at least one federated learning scheme, based on a topology information and/or a system load), comprising:
performing a network traffic load balancing procedure according to an energy savings criteria, (Zanzi, page 5, lines 23-34: desired in O-RAN to enable a more scalable and distributed and secure RAN management by distributing the learning and intelligence to the local nodes and saving the networking resources for transmitting large amounts of data to a central location. FIG.3, page 12. lines 10-20: enable the federated learning operation in O-RAN Non-RT RIC architecture. page 14, lines 15-33-page 15, lines 105: The training profile may include one or more of the following non-exhaustive parameters: Traffic condition, Radio Access Network topology, Resource allocation, Energy consumption).
Zani does not expressly teach
at least one processor; and at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising:
based on information indicative of a first graphical construct that represents a radio unit level of the cellular communication network, determining a second graphical construct that represents a radio access network intelligence controller level of the cellular communication network; and
based on the second graphical construct of the cellular communication network, transferring the network traffic of the user equipment from the source cell to the specified target cell.
Orhan teaches
at least one processor; and at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising (Orhan, FIG.2, open radio access network (O-RAN) 100, RAN Intelligent Controller (RIC) such as a Non-Real Time (RT) RIC or a Near-RT RIC, [0034]: O-RAN includes RIC such as a Non-Real Time (RT) RIC or a Near-RT RIC.FIG.11, compute node 1150, processor 1152, memory 1154, storage 1158, non-transitory machine-readable medium (NTMRM) 1160, [0191]: the instructions 1181, 1182, 1183 provided via the memory 1154, the storage 1158, or the processor 1152 may be embodied as a NTMRM 1160 including code to direct the processor 1152 to perform electronic operations in the compute node 1150):
performing a network traffic load balancing procedure according to an energy savings criteria,
based on information indicative of a first graphical construct that represents a radio unit level of the cellular communication network, determining a second graphical construct that represents a radio access network intelligence controller level of the cellular communication network (Orhan, FIG.1, network 100, backhaul links 103, connection 105, UE 121, connection management function (CMF) 136, radio units (RUs) 130, distributed units (DUs) 131, centralized units (CUs) 132, [0024]: the edge network 100 includes a CMF 136 co-located with one or more CUs 132, DUs 131, RUs 130, and UEs 121. The CUs 132 and some DUs 131 have wired and/or wireless backhaul links 103 to the CMF 136 and supports wireless access 105 to the UEs 121. [0034]: the CMF 136 can be part of a RAN Intelligent Controller (RIC) such as a Non-Real Time (RT) RIC or a Near-RT RIC. FIG.2, graph 200, network access node (NAN) 231, [0046]: Graph also includes data elements for each existing/current connections 105 between individual UEs 221 and at least one of the NANs 231.[0234]: the CMF 136 generates an initial graph 200 of network including an initial arrangement of edges among a set of nodes. The set of nodes represent respective communication equipments (CEs) of the set of CEs (e.g., UEs 221 and/or NANs 231), and the edges in the initial arrangement of edges represent respective com-links (e.g., links 105 and/or 103) of the set of com-links); and
based on the second graphical construct of the cellular communication network, transferring the network traffic of the user equipment from the source cell to the specified target cell (Orhan, S1604, [0235], Claim 1: cause a graph neural network (GNN) to determine a set of candidate graphs based on a set of node features, the candidate arrangements different than the initial arrangement. Claim 5: operate the GNN based on at least one of throughput of a communication network including the set of CEs, coverage of the communication network, or load balance among the set of CEs).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “graph neural networks (GNN) for load-aware connection management” of Orhan into the invention of Zanzi. The suggestion/motivation incorporates the connection management techniques based on GNN to make better decisions to balance network traffic load while network throughput is also maximized. Including the “GNN) for connection management” of Orhan into the invention of Zanzi was within the ordinary ability of one of ordinary skill in the art based on the teachings of Orhan.
Zanzi-Orhan does not expressly teach
Kwan teaches (Kwan, FIG.4, [0117]: if a particular small cell radio is experiencing a low level of traffic, one or more operations can be triggered to determine whether it may be more efficient to handover the UEs currently served by the small cell radio to another small cell radio; [0121]: determining a maximum energy savings can include either 1) solving an optimization problem to determine a set of small cell radios that can be turned off that will maximize energy savings the RAN within a given target macro cell radio load constraint).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “radio access point (RAP) load prediction” of Kwan into the invention of Zanzi -Orhan. The suggestion/motivation of involving calculating predicted network behavior for radio access network for potential user equipment handovers from source RAP to target RAPs. Including the “RAP load prediction” of Kwan into the invention of Zanzi -Orhan was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kwan.
Regarding Claim 11, Zanzi-Orhan-Kwan teaches, The system of claim 10, wherein the information indicative of the first graphical construct comprises weighted values that represent communication links between radio units of the radio unit level of the cellular communication network to radio access network intelligent controllers of the radio access network intelligence controller level of the cellular communication network (Orhan, FIG.14, connection 1420, [0227]: Each connection 1420 may be assigned a weight that represents its relative importance. The weights may also be adjusted as learning proceeds. The weight increases or decreases the strength of the signal at a connection 1420).
Regarding Claim 12, Zanzi-Orhan-Kwan teaches, The system of claim 10, wherein the operations further comprise:
prior to the performing the network traffic load balancing procedure, training a machine learning model to a defined confidence level (Zani, page 14, lines 15-33-page 15, lines 105: The distributed nature of the RAN domain well matches with federated learning schemes in which ML-based local agents train local model instances based on local monitoring or measurement data, and only share the output of this process to a centralized entity in charge of collecting and aggregate the local model updates aiming to build a global and general knowledge from the environment).
Regarding Claim 13, Zanzi-Orhan-Kwan teaches, The system of claim 12, wherein the machine learning model is a graph neural network model (Orphan, [0059]: Graph Neural Networks (GNNs) are a framework to capture the dependence of nodes in graphs via message passing between the nodes).
Regarding Claim 14, Zanzi-Orhan-Kwan teaches, The system of claim 10, wherein the operations further comprise:
prior to the performing the network traffic load balancing procedure, performing a federated learning process, wherein the federated learning process comprises:
training a first model to a first defined confidence level, wherein the training of the first model comprises performing localized processing at the radio unit level of the cellular communication network; and based on information indicative of weighted values associated with the first model, training a second model to a second defined confidence level, wherein the training of the second model comprises performing pooled processing at the radio access network intelligence controller level of the cellular communication network (Zanzi, page 14, lines 15-33-page 15, lines 105: The distributed nature of the RAN domain well matches with federated learning schemes in which ML-based local agents train local model instances based on local monitoring or measurement data, and only share the output of this process to a centralized entity in charge of collecting and aggregate the local model updates aiming to build a global and general knowledge from the environment).
Regarding Claim 15, Zanzi-Orhan-Kwan teaches, The system of claim 14, wherein the training of the second model comprises training the second model to facilitate conformance with a network energy savings minimization criterion (Orhan, FIG.2, UE 221, NAN 231, [0082]: When a UE 221 is camped on a cell provided by a NAN 231, the UE 221 may regularly or periodically search for a better cell or beam according to cell or beam (re)selection criteria (“network energy savings minimization criterion”). For cell (re)selection, if a better cell is found, that cell or beam may be selected, and the UE 221 may tune to that cell's 230 control channel(s)).
Regarding Claim 16, Zanzi teaches,
based on a federated learning process, determining a graphical representation of a communication network, (Zanzi, FIG.4, federated learning manager (FLM), Open Radio Access Network (O-RAN), Non-Real-Time RAN Intelligent Controller (Non-RT RIC), Near-Real-Time RAN Intelligent Controller (Near-RT RIC), Abstract, Claim 8: deploying a FLM, in an O-RAN Non-RT RIC; coordinating by the FLM an involvement of the Non-RT RIC and an O-RAN Near-RT RIC, in at least one federated learning scheme; and executing the at least one federated learning scheme, based on a topology information and/or a system load. FIG.1, page 5, lines 23-34: shows communications between a group O-RU (O-RAN radio unit) and a Non-RT RIC and an O-RAN Near-RT RIC); and
(Zanzi, page 5, lines 23-34: desired in O-RAN to enable a more scalable and distributed and secure RAN management by distributing the learning and intelligence to the local nodes and saving the networking resources for transmitting large amounts of data to a central location. FIG.3, page 12. lines 10-20: enable the federated learning operation in O-RAN Non-RT RIC architecture. page 14, lines 15-33-page 15, lines 105: The training profile may include one or more of the following non-exhaustive parameters: Traffic condition, Radio Access Network topology, Resource allocation, Energy consumption).
Zanzi does not expressly teach
A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor of network equipment, facilitate performance of operations, wherein the operations comprise:
based on the graphical representation, user equipment from being connected to a source cell of a group of cells of the communication network to being connected to a target cell of the group of cells.
Orhan teaches
A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor of network equipment, facilitate performance of operations, wherein the operations comprise (Orhan, FIG.11, processor 1152, memory 1154, storage 1158, non-transitory machine-readable medium (NTMRM) 1160, the instructions 1181, 1182, 1183, [0191]: the instructions 1181, 1182, 1183 provided via the memory 1154, the storage 1158, or the processor 1152 may be embodied as a NTMRM 1160 including code to direct the processor 1152 to perform electronic operations in the compute node):
(Orhan, FIG.1, network 100, backhaul links 103, connection 105, UE 121, connection management function (CMF) 136, radio units (RUs) 130, distributed units (DUs) 131, centralized units (CUs) 132, [0024]: the edge network 100 includes a CMF 136 co-located with one or more CUs 132, DUs 131, RUs 130, and UEs 121. The CUs 132 and some DUs 131 have wired and/or wireless backhaul links 103 to the CMF 136 and supports wireless access 105 to the UEs 121. [0034]: the CMF 136 can be part of a RAN Intelligent Controller (RIC) such as a Non-Real Time (RT) RIC or a Near-RT RIC. FIG.2, graph 200, network access node (NAN) 231, [0046]: Graph also includes data elements for each existing/current connections 105 between individual UEs 221 and at least one of the NANs 231. [0234]: the CMF 136 generates an initial graph 200 of network including an initial arrangement of edges among a set of nodes. The set of nodes represent respective communication equipments (CEs) of the set of CEs (e.g., UEs 221 and/or NANs 231), and the edges in the initial arrangement of edges represent respective com-links (e.g., links 105 and/or 103) of the set of com-links);
based on the graphical representation, performing user equipment association that defines an action for a network traffic load balancing process that facilitates conformance to an energy savings criterion, (Orhan, S1604, [0235], Claim 1: cause a graph neural network (GNN) to determine a set of candidate graphs based on a set of node features, the candidate arrangements different than the initial arrangement. Claim 5: operate the GNN based on at least one of throughput of a communication network including the set of CEs, coverage of the communication network, or load balance among the set of CEs).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “graph neural networks (GNN) for load-aware connection management” of Orhan into the invention of Zanzi. The suggestion/motivation incorporates the connection management techniques based on GNN to make better decisions to balance network traffic load while network throughput is also maximized. Including the “GNN) for connection management” of Orhan into the invention of Zanzi was within the ordinary ability of one of ordinary skill in the art based on the teachings of Orhan.
Zanzi-Orhan does not expressly teach
Kwan teaches (Kwan, FIG.4, [0117]: if a particular small cell radio is experiencing a low level of traffic, one or more operations can be triggered to determine whether it may be more efficient to handover the UEs currently served by the small cell radio to another small cell radio; [0121]: determining a maximum energy savings can include either 1) solving an optimization problem to determine a set of small cell radios that can be turned off that will maximize energy savings the RAN within a given target macro cell radio load constraint for the RAN.).
Prior to the effective filing date of invention, it would have been obvious to a person of ordinary skill in the art to implement the “radio access point (RAP) load prediction” of Kwan into the invention of Zanzi -Orhan. The suggestion/motivation of involving calculating predicted network behavior for radio access network for potential user equipment handovers from source RAP to target RAPs. Including the “RAP load prediction” of Kwan into the invention of Zanzi -Orhan was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kwan.
Regarding Claim 17, Zanzi-Orhan-Kwan teaches, The non-transitory machine-readable medium of claim 16, wherein the operations further comprise:
prior to the determining of the graphical representation of the communication network, training a first model to a first defined confidence level, wherein the training of the first model comprises performing localized processing at a radio unit level of the communication network (Zanzi, FIG.4, Claim 12: the coordinating step and/or the executing of the at least one federated learning scheme comprises performing a collection, a validation and/or an update of at least one preferably local model (“first model”) in a federated manner); and
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based on information indicative of weighted values associated with the first model, training a second model to a second defined confidence level, wherein the training of the second model comprises performing pooled processing at a radio access network intelligence controller level of the communication network (Zanzi, Claim 12: the coordinating step and/or the executing of the at least one federated learning scheme comprises performing a collection, a validation and/or an update of at least one preferably local model (“first model”) in a federated manner (“second model”). Claim 14, the FLM is a part of or is integrated in an Artificial Intelligence/Machine Learning, AI/ML function, entity or operation box).
.
Regarding Claim 18, Zanzi-Orhan-Kwan teaches, The non-transitory machine-readable medium of claim 17, wherein the weighted values are non-zero weights that represent respective connectivities between the group of radio units and the radio access network intelligent controller (Orhan, [0303]: determining, using a Q function, a Q value for each candidate graph in the set of candidate graphs, wherein the Q value of each candidate graph is an expected reward value of rearranging the set of com-links according to the corresponding candidate arrangement from the initial arrangement according to a policy).
Regarding Claim 19, Zanzi-Orhan-Kwan teaches, The non-transitory machine-readable medium of claim 18, wherein the training of the second model comprises training the second model to facilitate conformance with a network energy savings minimization criterion (Orhan, FIG.2, UE 221, NAN 231, [0082]: When a UE 221 is camped on a cell provided by a NAN 231, the UE 221 may regularly or periodically search for a better cell or beam according to cell or beam (re)selection criteria (“network energy savings minimization criterion”). For cell (re)selection, if a better cell is found, that cell or beam may be selected, and the UE 221 may tune to that cell's 230 control channel(s)).
Regarding Claim 20, Zanzi-Orhan-Kwan teaches, The non-transitory machine-readable medium of claim 17, wherein the first model and the second model are message passing graph neural network models (Orphan, [0059]: Graph Neural Networks (GNNs) are a framework to capture the dependence of nodes in graphs via message passing between the nodes).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Soldati et al., US 2024/0243984 A1, method for monitoring performance of artificial intelligence/machine learning model in radio communication network, involves receiving message from network node, and determining performance metric based on historical data elements, [0136-138].
Zhang et al., US 2024/0089752 A1, method for artificial intelligence (AI) application in radio access network (RAN), involves transmitting first information associated with primary processing model to second wireless network node to accomplish wireless network activity, [0052]
8. Any inquiry concerning this communication or earlier communications from the examiner should
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1000.
/C.L/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446
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