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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-4,7-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (us 2023/0254719) (hereinafter Kim) in view of Karampatsis et al (us 2024/0232708) (hereinafter Kar).
As regarding claim 1, Kim discloses determining, by a model training function node, a federated learning (FL) server node (see Kim 0089-0100, local NWDAF send a discovery request message to NRF to request information about the C-NWDAF and receive a response including information about the C-NWDAF and other local NWDAF participating in federated learning).
Kim is silent in regard to the concept of sending, by the model training function node, a first request message to the FL server node, wherein the first request message is used to trigger the FL server node to perform federated learning to obtain a target model; and receiving, by the model training function node, information about the target model that is sent by the FL server node.
Kar teaches the concept of sending, by the model training function node, a first request message to the FL server node, wherein the first request message is used to trigger the FL server node to perform federated learning to obtain a target model (see Kar 0003,0044, 0092 network function sends request to network unit that support aggregation of model parameters using federated learning); and receiving, by the model training function node, information about the target model that is sent by the FL server node (see Kar 0045, network unit/aggregator sends a aggregated parameters trained model in response to the request to the network function).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Kar to Kim because they're analogous art. A person would have been motivated to modify Kim with Kar’s teaching for the purpose of efficiently training model using federated learning (see Kar 0002).
As regarding claim 2, Kim-Kar discloses the determining, by a model training function node, an FL server node comprises: sending, by the model training function node, a node discovery request message to a network repository function network element, wherein the node discovery request message is used to request a network node that participates in federated learning training (see Kim 0089-0100, local NWDAF send a discovery request message to NRF to request information about the C-NWDAF and receive a response including information about the C-NWDAF and other local NWDAF participating in federated learning); and receiving, by the model training function node, a response message sent by the network repository function network element, wherein the response message comprises information about the FL server node (see Kim 0089-0100, receive a response including information about the C-NWDAF and other local NWDAF participating in federated learning).
As regarding claim 3, Kim-Kar discloses the node discovery request message comprises at least one of the following: an analytics identifier, area of interest (AOI) information, time of interest information, model description mode information, model shareability information, model performance information, model algorithm information, model training speed information, federated learning indication information, federated learning type information, FL server node type indication information, FL client node type indication information, first service information, and second service information, wherein the federated learning indication information is used to indicate that the network node requested by the request message needs to support federated learning; the first service information is used to indicate that the network node requested by the request message needs to support a service of a federated learning server and the second service information is used to indicate that the network node requested by the request message needs to support a service of a federated learning member (see Kim 0089-0098, analytic ID, supported model information, model ID etc.,).
As regarding claim 4, Kim-Kar discloses the federated learning type information is used to indicate that a federated learning type that the network node requested by the request message needs to support is at least one of the following: a horizontal federated learning type; or a vertical federated learning type (see Kar 0061-0062, horizontal federated learning, vertical federated learning). The same motivation was utilized in claim 1 applied equally well to claim 4.
As regarding claim 7, Kim-Kar discloses the first request message comprises at least one of the following: federated learning indication information or a model identifier, wherein the federated learning indication information is used to request the FL server node to trigger federated learning to obtain the target model; and the model identifier is used to uniquely identify the target model (see Kim 0091-0099, federated learning indicator, model information, etc.,).
As regarding claim 8, Kim-Kar discloses obtaining, by the model training function node, the model identifier (see Kim 0091-0099, model ID sends in request, it is obvious that before sending model ID in the request the node acquired the model ID).
As regarding claim 9, Kim-Kar discloses the first request message comprises information about an FL client node that participates in federated learning; or, wherein the information about the target model comprises at least one of the following information corresponding to the target model: a model identifier, federated learning indication information, a model file, or address information of the model file, wherein the federated learning indication information is used to indicate that the target model is a model obtained through federated learning; and the model identifier is used to uniquely identify the target model; or, wherein the method further comprises: sending, by the model training function node, the information about the target model to a model inference function node (see Kim 0091-0099, supported model ID, supported…local NWDAF participating in federated learning group…).
As regarding claim 10, Kim-Kar discloses before the determining, by a model training function node, an FL server node, the method further comprises: determining, by the model training function node, that federated learning needs to be performed to obtain the target model (see Kim 0084-0094, determine that federated learning needs to be performed).
As regarding claim 11, Kim-Kar discloses the determining, by the model training function node, that federated learning needs to be performed to obtain the target model comprises: in a case that the model training function node determines that all or some of training data for generating the target model is unable to be obtained, determining, by the model training function node, that federated learning needs to be performed to obtain the target model (see Kim 0084-0094, determine that federated learning needs to be performed, and determine that the local node does not have sufficient data set for federated learning).
As regarding claim 12, Kim-Kar discloses receiving, by a federated learning (FL) server node, a first request message sent by a model training function node, wherein the first request message is used to trigger the FL server node to perform federated learning to obtain a target model (see Kar 0003,0044, 0092 network function sends request to network unit that support aggregation of model parameters using federated learning); performing, by the FL server node, federated learning with an FL client node based on the first request message to obtain the target model (see Kim 0108-0109, C-NWDAF create the federated learning group that includes other local nodes to perform federated learning); and sending, by the FL server node, information about the target model to the model training function node (see Kar 0045, network unit/aggregator sends a aggregated parameters trained model in response to the request to the network function). The same motivation was utilized in claim 1 applied equally well to claim 12.
As regarding claim 13, Kim-Kar discloses the first request message comprises at least one of the following: federated learning indication information or a model identifier, wherein the federated learning indication information is used to request the FL server node to trigger federated learning to obtain the target model; and the model identifier is used to uniquely identify the target model (see Kim 0091-0099, federated learning indicator, model information, etc.,).
As regarding claim 14, Kim-Kar discloses the first request message comprises information about an FL client node that participates in federated learning (see Kim characteristic of model related to participating nodes in federated learning).
As regarding claim 15, Kim-Kar discloses the method further comprises: determining, by the FL server node, an FL client node that participates in federated learning (see Kim 0108-0109, C-NWDAF create the federated learning group that includes other local nodes to perform federated learning).
As regarding claim 16, Kim-Kar discloses the determining, by the FL server node, an FL client node that participates in federated learning comprises: sending, by the FL server node, a node discovery request message to a network repository function network element, wherein the node discovery request message is used to request an FL client node that participates in federated learning and receiving, by the FL server node, a response message sent by the network repository function network element, wherein the response message comprises information about an FL client node that participates in federated learning (see Kar 0109-0115, communication between aggregator and NRF to discover other nodes that can participated in the federated learning and receiving from NRF information about other nodes that participated in federated learning). The same motivation was utilized in claim 1 applied equally well to claim 16.
As regarding claim 17, Kim-Kar discloses the information about the target model comprises at least one of the following information corresponding to the target model: a model identifier, federated indication information, a model file, or address information of the model file, wherein the federated learning indication information is used to indicate that the target model is a model obtained through federated learning; and the model identifier is used to uniquely identify the target model; or, wherein the model identifier is obtained by the model training function node for the target model (see Kim 0091-0099, ML model information/ID, federated ID, etc.,).
As regarding claim 18, Kim-Kar discloses obtaining, by the FL server node, the model identifier for the target model (see Kim 0138, C-NWDAF includes information such as model ID and other information send to NRF, it is obvious that D-NWDAF obtain that information before sending).
As regarding claims 19-20, the limitations of claims 19-20 are similar to limitations of rejected claims 1,12 above, therefore rejected for the same rationale.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Kim-Kar as applied to claim 2 above and further in view of Lee et al (us 2022/0108214) (hereinafter Lee).
As regarding claim 5, Kim-Kar discloses the invention as claims in claim 2 above and further discloses the response message comprises information about N network nodes, the N network nodes comprise the FL server node, and N is a positive integer (see Kim 0108-0109, provide FL group and associated FL information to local NWDAF, such as a list of address and other nodes that participated in FL).
Kim-Kar is silent in regard to the concept of information about each network node comprises at least one of the following: a fully qualified domain name (FQDN), identification information, or address information.
Lee teaches information comprises at least one of the following: a fully qualified domain name (FQDN), identification information, or address information (see Lee 0184, FQDN information).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee to Kim-Kar because they're analogous art. A person would have been motivated to modify Kim-Kar with Lee’s teaching for the purpose allow the node to easily access the model using the model’s file address.
As regarding claim 6, Kim-Kar-Lee discloses wherein the N network nodes further comprise an FL client node; or, wherein the information about each network node further comprises: type information, wherein the type information is used to indicate a type of a network node, and the type is one of an FL server node or an FL client node (see Kim 0108-0109, provide FL group and associated FL information to local NWDAF, such as a list of address and other nodes that participated in FL).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUYEN MY DOAN whose telephone number is (571)272-4226. The examiner can normally be reached (571)272-4226.
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/DUYEN M DOAN/Primary Examiner, Art Unit 2459