DETAILED ACTION
Notice of Pre-AIA or AIA Status
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
II. This action is in response to applicants amendment/arguments filed on March 20, 2026. This action is made FINAL.
Allowable Subject Matter
III. The following is a statement of reasons for the indication of allowable subject matter:
Claims 9 and 17 contain allowable subject matter based on the amendments to the claims (see Amendments to the Claims, pages 2-6) and for the reasons given in applicant arguments/remarks (see Remarks, pages 7-11) received in the March 20, 2026 response to the Non-Final Office Action dated December 22, 2025.
Claims 10-16 and 19-20 contain allowable subject matter based on their dependence on independent claims 9 and 17.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
IV. Claims 1-4 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2022/0038349 A1) in view of Yeh et al. (US 2022/0124560 A1) and Esswie et al. (US 2024/0196230 A1).
Regarding claim 1 Li teaches a method of operating a device comprising: generating, for transmission to a base station, a radio resource control (RRC) model request (see paragraphs [0058]; [0060]; claim 9; and Fig. 1A, The UE includes processing circuitry configured to encode, for transmission to a central server in a 5th generation (5G) network, a service request for an AI/ML model. The UE includes processing circuitry configured to transmit to a central server in a 5th generation (5G) network , a service request for an AI/ML model. The central server may be (gNB-DU/gNB-CU, see paragraph [0060]). This reads on generating, for transmission to a base station, a radio resource control (RRC) model request); identifying an RRC model response received from the base station with an indication of an artificial intelligence/machine learning (AI/ML) model for the device (see claim 9, The UE includes processing circuitry configured to decode, from the central server, the AI/L model. This reads on identifying an RRC model response received from the base station with an indication of an artificial intelligence/machine learning (AI/ML) model for the device); and implementing the AI/ML model indicated by the RRC model response (see claim 9, The UE includes processing circuitry configured to decode, from the central server, the AI/L model. The UE obtains data for the AI/ML model and train AI/ML based on the data. This reads on implementing the AI/ML model indicated by the RRC model response).
Li does not specifically teach the request indicates a scenario, a channel, traffic, and a mobility status related to the device.
Yeh teaches an indication of traffic and a mobility status related to the device (see paragraph [0110], Information, such as UE moving trajectory, historical network traffic load, etc. can be incorporated into the AI/ML model. This reads on an indication of traffic and a mobility status related to the device).
Esswie teaches an indication of a scenario and a channel related to the device (see paragraph [0097], For AI/ML learning various parameters and metrics may be considered, analyzed, or evaluated depending on the nature of the problem being solved. Radio functions such as channel state information (CSI) compression or channel estimation read on an indication of a scenario and a channel related to the device because CSI compression indicates properties of an existing wireless transmission path from a receiver to transmitter and channel estimation indicates properties such as signal strength and delay of a channel).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the model request in Li adapt to indicate a scenario, a channel, traffic, and a mobility status related to the device because it would further improve AI/ML techniques for managing traffic in communications networks (see Li, paragraphs [0002] – [0003] and Yeh, paragraph [0001]).
Regarding claim 2 Li teaches wherein the RRC model response includes a model identifier (ID), a model structure, or parameters that indicate the AI/ML model to be implemented by the device (see paragraph [0075] and claim 3, The UE is configured to communicate and exchange the model and/or model parameters with the central server. The central server is configured to broadcast AI/ML service information via a SIB and/or an RRC message. This reads on wherein the RRC model response includes parameters that indicate the AI/ML model to be implemented by the device).
Regarding claim 3 Li teaches wherein the indication of the AI/ML model is supplied by a radio access network repository function (RMRF) including within a radio access network (RAN) infrastructure corresponding to the base station (see paragraphs [0066] and claim 9, The central server (e.g., the gNB-CU/DU) can select and train a suitable model. The trained model can be deployed to the UE. The operations may operate in a number of components, modules, structures (see paragraphs [0043] – [0044]). This indicates the Ai/ML model is included within the central server (e.g., the gNB-CU/DU) and reads on wherein the indication of the AI/ML model is supplied by a radio access network repository function (RMRF) including within a radio access network (RAN) infrastructure corresponding to the base station).
Regarding claim 4 Esswie teaches a scenario that comprises channel state information (CSI) compression related to the device or mobility related to the device (see paragraph [0097], For AI/ML learning various parameters and metrics may be considered, analyzed, or evaluated depending on the nature of the problem being solved. Radio functions such as channel state information (CSI) compression read on an indication of a scenario that comprises channel state information (CSI) compression related to the device).
Regarding claim 7 Li teaches generating, for transmission to the base station, an RRC model update request for requesting an updated AI/ML model for the device (see paragraphs [0060] - [0061]; claim 1; claim 9; and Fig. 1A, The UE includes processing circuitry configured to encode, for transmission to a central server in a 5th generation (5G) network, a service request for an AI/ML model. The UE can obtain a new updated model trained at the central server. The central server may be (gNB-DU/gNB-CU, see paragraph [0060]). This reads on generating, for transmission to the base station, an RRC model update request for requesting an updated AI/ML model for the device), the RRC model update request indicating a model identifier (ID) corresponding to the AI/ML model (see claim 9, The UE request for AI/ML model is encoded which indicates an identifier of the of the AI/ML model is included. This reads on the RRC model update request indicating a model identifier (ID) corresponding to the AI/ML model); identifying an RRC model update response received from the base station that indicates the updated AI/ML model for the device (see paragraph [0061] and claim 9, The UE includes processing circuitry configured to decode, from the central server, the AI/L model. The UE can obtain a new updated model trained at the central server. This reads on identifying an RRC model update response received from the base station that indicates the updated AI/ML model for the device); and implementing the updated AI/ML model indicated by the RRC model update response (see paragraph [0061] and claim 9, The UE includes processing circuitry configured to decode, from the central server, the AI/L model. The UE obtains data for the AI/ML model and train AI/ML based on the data. The UE can obtain a new updated model trained at the central server. This reads on implementing the updated AI/ML model indicated by the RRC model update response).
Regarding claim 8 Li teaches wherein the RRC model update request further indicates one or more trained model parameters related to the device (see claim 1 and claim 9, The gNB can update the AI/ML model based on updated parameters and store the AI/ML parameter (see claim 1). The UE can request the updated AI/ML model from the central server (e.g., gNB) (see claim 9). This reads on wherein the RRC model update request further indicates one or more trained model parameters related to the device).
V. Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2022/0038349 A1) in view of Yeh et al. (US 2022/0124560 A1), Esswie et al. (US 2024/0196230 A1) and Pateromichelakis (WO 2022/048746 A1).
Regarding claim 5 Li, Yeh and Esswie teach the method of claim 1 except for wherein the RRC model request indicates traffic, and wherein the traffic comprises a traffic type related to the device and a traffic loading related to the device.
Pateromichelakis teaches RRC model request indicates traffic (see paragraph [0079], A request for AI/ML model on the expected traffic of the target cell reads on RRC model request indicates traffic), and wherein the traffic comprises a traffic type related to the device (see paragraphs [0062] & [0079], Each mobile data connection utilizes a specific network slice. A network slice refers to a portion of the core network optimized for a certain traffic type. The AI/ML traffic model includes information on this traffic type because it refers to the data connections of the UE. This reads on wherein the traffic comprises a traffic type related to the device, and a traffic loading related to the device (see paragraph [0079], A request for AI/ML model on the expected traffic of the target cell for the respective UE reads on a traffic loading related to the device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the RRC model request in the Li, Yeh, and Esswie combination adapt to indicates traffic, and wherein the traffic comprises a traffic type related to the device and a traffic loading related to the device because it would allow for improved quality of service for the user (see Pateromichelakis, paragraph [0091]).
Regarding claim 6 Li, Yeh, and Esswie teach the method of claim 1 except for wherein the RRC model request indicates a mobility status, and wherein the mobility status comprises mobility speed related to eh device or mobility parameters related to the device.
Pateromichelakis teaches wherein the RRC model request indicates a mobility status, and wherein the mobility status comprises mobility speed (see paragraphs [0079] & [0097], A request is made for a trained AI/ML model on the mobility for the respective UE. UE context parameters include mobility/velocity. This reads on indicates a mobility status, and wherein the mobility status comprises mobility speed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the RRC model request in the 6 Li, Yeh, and Esswie combination adapt to indicates a mobility status, and wherein the mobility status comprises mobility speed related to eh device or mobility parameters related to the device because it would allow for improved quality of service for the user (see Pateromichelakis, paragraph [0091]).
Response to Arguments
VI. Applicant’s arguments with respect to claims 1-8 have been considered but are moot in view of the new grounds of rejection.
Conclusion
VII. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J MILLER whose telephone number is (571)272-7869. The examiner can normally be reached M-F.
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/BRANDON J MILLER/ Primary Examiner, Art Unit 2647
May 21, 2026
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