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
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 and 11-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Solution for KI #8: Federated Learning Group creation” (hereinafter, “Solution for KI #8”).
Note: Solution for KI #8 was supplied by applicant with the Information Disclosure Statement (IDS) filed September 24, 2025, along with a Supplementary European Search Report that explains its relevance.
Regarding claim 1, Solution for KI #8 shows an operation method of a central network data analytics function (NWDAF) comprising:
receiving an analytics request message from a network function (“The NF sends Nnwdaf_AnalyticsInfo_Request to the NWDAF”: see section 6.X.1);
determining, on the basis of the analytics request message, a local NWDAF included in a federated learning (FL) group ("5. Federated Learning Group participation request 6. Federated Learning Group participation response", "2. The NWDAF 1 determines to perform federated learning for the received Analytics request if the request message from the NF contains Indication to perform federated learning or if it is locally configured to perform federated learning for the requested Analytics ID (e.g. based on the operator policy). The NWDAF 1 also determines to act as Central NWDAF for federation learning for the requested analytics in step 1 if it has Federated Learning Model Aggregation capability (i.e., capability of aggregating local training models and calculating the global model from then). 3. The NWDAF invokes the Nnrf_NFDiscovery_Request service operation from the NRF to find other NWDAFs that are able to perform federated learning as local NWDAFs for the Analytics request in step 1. The Nnrf_NFDiscovery_Request message includes Analytics ID in step 1, ML model information and ML model sharing capability that is required to perform Federation Learning 4. The NRF returns multiple local NWDAF candidates matching the requested capabilities and Analytics ID.", 6.X Solution #X: Federated learning group creation, 6.X.1 Description);
transmitting an FL local behavior instruction to the local NWDAF ("5. The central NWDAF requests the local NWDAF candidates to join the federated learning group for the Analytics ID received in step 1. The request message includes Analytics ID and ML model information for the federated learning operation.", 6.X Solution #X: Federated learning group creation, 6.X.1 Description);
receiving a response message from the local NWDAF in response to the FL local behavior instruction ("6. The local NWDAF candidates reply to the NWDAF with the indication on whether to accept the federation learning group participation request or not.", 6.X Solution #X: Federated learning group creation, 6.X.1 Description);
receiving, on the basis of the FL local behavior instruction, a local model generated by means of the local NWDAF from the local NWDAF (Upon receiving the responses from local NWDAF candidates in step 6, the central NWDAF determines a federated learning group consisting of NWDAFs that indicate to accept to the the federation learning group participation request in step 6. The central NWDAF assigns federated learning group ID for the determined group and creates federated learning group information that includes federated learning group ID, list of local NWDAF information (identifiers and addresses of NWDAFs), and federated learning operation information. The federated learning operation information includes Analytics ID, ML model information, Central NWDAF address as target address where local training model is sent, transmission expiration time information, and local learning processing rule information.", "The local learning processing rule information includes either local processing rule ID or local ML model training rule e.g. how many iterations for local model training should be performed for one epoch before providing the local model to the Central NWDAF.", 6.X Solution #X: Federated learning group creation, 6.X.1 Description);
generating a global model on the basis of the local model ("The transmission expiration time information indicates a deadline before which the local ML model should be transmitted to the Central NWDAF to generate global ML model for an epoch. For example, the deadline can be represented by timestamp and timer value respectively indicating start time of federated learning operation and expiry time period.", 6.X Solution #X: Federated learning group creation, 6.X.1 Description); and
transmitting the global model to the NWDAF ("8. The central NWDAF provides the local NWDAFs (i.e., federated learning group member local NWDAFs) with federated learning group ID and federated learning operation information.", "9. Local NWDAFs acknowledge to the central NWDAF to indicate if the local NWDAF is able to perform federate learning according to the federated learning operation information.", 6.X Solution #X: Federated learning group creation, 6.X.1 Description).
Regarding claim 2, Solution for KI #8 shows the limitations of claim 1 as applied above and further shows wherein the FL local behavior instruction includes delay situation occurrence event notification request information, a parameter required to generate the global model, and epoch time requirement information (see Solution for KI #8: items 5-9 under 6.X.2).
Regarding claim 3, Solution for KI #8 shows the limitations of claim 2 as applied above and further shows wherein the epoch time requirement information includes expected epoch time, expected local machine learning (ML) model training and transmission time, and local ML model training time requirement (see Solution for KI #8: items 5-9 under 6.X.2).
Regarding claim 4, Solution for KI #8 shows the limitations of claim 3 as applied above and further shows wherein the response message includes an indicator indicating whether the expected epoch time, or the expected local ML model training and transmission time, or the local ML model training time requirement is able to be met (see Solution for KI #8: items 5-9 under 6.X.2).
Regarding claim 5, Solution for KI #8 shows the limitations of claim 4 as applied above and further shows wherein the response message includes a new expected epoch time, a new expected local ML model training and transmission time, or a new local ML model training time requirement in a case where the expected epoch time, or the expected local ML model training and transmission time, or the local ML model training time requirement is not able to be met (see Solution for KI #8: items 5-9 under 6.X.2).
Regarding claim 6, Solution for KI #8 shows the limitations of claim 5 as applied above and further shows transmitting epoch time requirement information updated on the basis of the response message to the local NWDAF in response to the response message (see Solution for KI #8: items 5-9 under 6.X.2).
Claims 11-16 correspond to claims 1-6 and are rejected for the reasons given above, mutatis mutandis.
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 7-10 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Solution for KI #8 (“Solution for KI #8: Federated Learning Group creation”) in view of Yue (US Pub. No. 2025/0247776).
Regarding claim 7, Solution for KI # 8shows the limitations of claim 1 as applied above but does not explicitly show transmitting an acknowledgement message to the local NWDAF in response to the received local model.
Yue shows transmitting an acknowledgement message in response to received information (see Fig. 5, item 9a, [0055]-[0066]).
It would have been obvious to one of ordinary skill in the art to modify Solution for KI #8 with the teachings of Yue in order to keep the local NWDAF apprised of the status of any in-flight operations.
Regarding claim 8, the combination shows the limitations of claim 7 as applied above and further shows receiving a delay situation occurrence event report message in response to the acknowledgement message (see Yue, [0180]-[0202]).
Regarding claim 9, the combination shows the limitations of claim 8 as applied above and further shows wherein the global model is generated on the basis of the delay situation occurrence event report message and the local model (see Yue, [0180]-[0202]).
Regarding claim 10, the combination shows the limitations of claim 8 as applied above and further shows receiving a local model transmitted late from the local NWDAF on the basis of the delay situation occurrence event report message (see Yue, [0180]-[0202]).
Claims 17-20 correspond to claims 7-10 and are rejected for the reasons given above, mutatis mutandis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher D. Biagini whose telephone number is (571)272-9743. The examiner can normally be reached weekdays from 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar Louie can be reached at (571) 270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Christopher D. Biagini
Primary Examiner
Art Unit 2445
/Christopher Biagini/Primary Examiner, Art Unit 2445
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