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 statements (IDS) submitted on April 30, 2025, August 13, 2025 and November 12, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 74-76, 80 and 81 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 74, line 1, “the data” lacks proper antecedent basis.
In claim 75, line 1, “the indication of the capability of the consumer NF to evaluate the training of the ML model” lacks proper antecedent basis.
Claims 80 and 81 are indefinite since they depend on canceled claim 1. For examination purposes, they will be considered as dependent on claim 70.
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)(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 70-83 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by 3GPP, "3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Study of Enablers for Network Automation for 5G, 5G System (5GS); Phase 3 (Release 18), 3GPP TR 23.700-81 V1.1.0 (2022-10), pp. 1-276.
Regarding claim 70, the 3GPP document teaches on page 61 a method for determining whether a consumer network function (NF) is approved to participate in training a machine learning (ML) model, the method implemented by a network node functioning as the consumer NF including:
sending a discovery request (Figure 6.13.2-1, Step 2) to a registry (NRF) to discover a server Network Data Analytics Function (NWDAF), wherein the discovery request indicates a capability of the consumer NF to support participation in training an ML model (Page 61, the discovery request may include in the request ML model file serialization format supported per Analytics ID(s) by the NWDAF containing AnLF);
subscribing to the server NWDAF (Step 4); and
receiving a subscription response message from the server NWDAF (Step 5), wherein the subscription response message indicates whether the consumer NF is permitted to participate in the training of the ML model (Page 64, MTLF can verify that the requesting AnLF instance is allowed to retrieve the ML model, otherwise it can reject the request).
Regarding claim 71, wherein the 3GPP document further shows that sending the discovery request to the registry to discover the server NWDAF comprises the consumer NF sending an Nnrf_NFDiscoveryRequest service message (Step 2) to the registry (NRF).
Regarding claim 72, the 3GPP document further teaches the discovery request further identifies one or more ML model training participation modes supported by the consumer NF (See page 81, Solution #21, the discovery request includes the Analytics ID that allows the NRF to select the NWDAF containing MTLF that supports the Analytics ID and the Area of Interest to train the ML model).
Regarding claim 73, the 3GPP document further teaches wherein the discovery request further indicates one or more of:
an availability of data for use by the consumer NF in the training of the ML model;
a capability of the consumer NF to evaluate the training of the ML model; and
one or more ML model training participation modes supported by the consumer NF (as described above with respect to claim 72).
Regarding claim 74, the data for use by the consumer NF in the training of the ML model comprises one or more of:
actual data used by the ML model (i.e., the NWDAF may determine whether existing trained ML Model(s) can be used for the request);
test data used to test the ML model; and
validation data used to validate the ML model.
Regarding claim 75, the 3GPP document further teaches that the consumer NF is capable of testing (See page 45, step 4, the NWDAF can rate the ML model) or validating an accuracy of the ML model.
Regarding claim 76, the 3GPP document further teaches wherein the ML model is one of:
an initial trained ML model (See page 83, Initial ML model training);
an intermediate trained ML model; and
a final ML model.
Regarding claim 77, the 3GPP document further shows wherein the one or more ML model training participation modes supported by the consumer NF comprises:
a first participation mode in which the consumer NF participates in evaluating a status of the ML model;
a second participation mode in which the consumer NF substantially continuously participates in the training of the ML model to evaluate the ML model;
a third participation mode in which the consumer NF periodically participates in the training of the ML model to evaluate the ML model (See page 81, Solution #21, the discovery request includes the Analytics ID that allows the NRF to select the NWDAF containing MTLF that supports the Analytics ID and the Area of Interest to train the ML model);
a fourth participation mode in which the consumer NF is triggered by the server NWDAF to participate in the training of the ML model to evaluate the ML model; and
a fifth participation mode in which the consumer NF provides a final evaluation of the ML model.
Regarding claim 78, the 3GPP document further teaches wherein in the first participation mode, the consumer NF evaluates the ML model and provides an ML model status to the server NWDAF (See page 41, step 4).
Regarding claim 79, the 3GPP document further shows the subscription response message received from the server NWDAF comprises one or both of:
an indication that the consumer NF is approved to participate in the training of the ML model (Page 61, Step 5, providing address of the Model file); and
a selected ML model training participation mode for the consumer NF to use in training the ML model, wherein the selected ML model training participation mode is selected by the server NWDAF from the one or more ML model training participation modes included in the discovery request.
Regarding claim 80, the 3GPP document further shows in Figure 6.13.2-1 wherein the consumer NF comprises an Analytics Logical Function (AnLF) and wherein the server NWDAF comprises a Model Training Logical Function (MTLF).
Regarding claim 81, the 3GPP document further shows in Figure 6.13.2-1 wherein the registry comprises a Network Repository Function (NRF).
Regarding claims 82 and 83, they are the network node and computer readable medium claims corresponding to method claim 70, and are therefore rejected for the same reasons above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ouyang (WO 2021/179176) is cited to show federated learning in a telecom communication system.
Xin et al (U.S. Patent Publication 2023/0083982) is cited to show data analytics in a network.
Sung (U.S. Patent Publication 2025/0039064) is cited to show supporting reinforced learning in a mobile communication system.
Pestana (U.S. Patent 12,640,992) are cited to show a machine learning communication network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM R KORZUCH whose telephone number is (571)272-7589. The examiner can normally be reached Mon.-Fri. 8:00-4:00.
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/WILLIAM R KORZUCH/Supervisory Patent Examiner, Art Unit 2491
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