DETAILED ACTION
I. This office action is in response to the correspondence filed on January 05, 2024. Claims 1-20 are pending and being examined.
Notice of Pre-AIA or AIA Status
II. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
III. The following is a statement of reasons for the indication of allowable subject matter:
Claim 9 may be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Claim 9 contains allowable subject matter because Li and Yeh do not teach receive a radio resource control (RRC) model request from a device, the RRC model request indicating a scenario, a channel, traffic, and a mobility status related to the device; select a function based on the scenario, the function being a real-time artificial intelligence function (RTAIF) or a non real-time artificial intelligence function (NRTAIF); use the function to identify an artificial intelligence/machine learning (AI/ML) model for the device based on the channel, the traffic, or the mobility status indicated by the RRC model request; generate an RRC model response that indicates a model identifier (ID) corresponding to the AI/ML model; and
Claims 18 and 20 are objected to as being dependent upon a rejected base claim, but may be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
IV. Claims 9-16 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 9 recites “receive a radio resource control (RRC) model request from a device, the RRC model request indicating a scenario, a channel, traffic, or a mobility status related to the device;
select a function based on the scenario, the function being a real-time artificial intelligence function (RTAIF) or a non real-time artificial intelligence function (NRTAIF); use the function to identify an artificial intelligence/machine learning (AI/ML) model for the device based on the channel, the traffic, or the mobility status indicated by the RRC model request” in lines 3-9. It is unclear what the identifying in lines 7-8 is based on because the claim earlier requires that only one of the scenario, channel, traffic, or mobility status related to the device may be indicated in the RRC request. The limitations render the claim indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
For purposes of examination the examiner will treat the following quotation from claim 1, “receive a radio resource control (RRC) model request from a device, the RRC model request indicating a scenario, a channel, traffic, or a mobility status related to the device” as “receive a radio resource control (RRC) model request from a device, the RRC model request indicating a scenario, a channel, traffic, and a mobility status related to the device”.
Claims 2-7 are dependent on claim 1 and are rejected for indefiniteness under 35 U.S.C. 112(b) for the same reasons given above.
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.
V. Claims 17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (US 2022/0038349 A1).
Regarding claim 17 Li teaches a radio access network (RAN) infrastructure comprising: a radio access network model repository function (RMRF) structure to store artificial intelligence/machine learning (AI/ML) models (see paragraph [0065] and claim 1, The central server (e.g., the gNB-CU/DU selects and trains a model with stored data (stored by the gNB). The gNB comprises processing circuitry configured to: update the AI/ML model based on the aggregated training model updated parameters and a memory to store the AI/ML model. The operations may operate in a number of components, modules, structures (see paragraphs [0043] – [0044]). This reads on a radio access network model repository function (RMRF) structure to store artificial intelligence/machine learning (AI/ML) models); a radio access network model coordination function (RMCF) structure to manage the AI/ML models (see claim 1, The gNB comprises processing circuitry configured to: update the AI/ML model based on the aggregated training model updated parameters and a memory to store the AI/ML model. This reads on a radio access network model coordination function (RMCF) structure to manage the AI/ML models); the RMRF structure and the RMCF structure arranged in a service based system architecture (see paragraphs [0014] & [0030]; and Fig. 1A, An architecture network in accordance with 4G, 5G, 6G network functions for user communications reads on the RMRF structure and the RMCF structure arranged in a service based system architecture) and addressable via hypertext transfer protocol (HTTP) signaling (see paragraph [0048], Instructions may be transmitted or received over hypertext transfer protocol (HTTP). This reads on addressable via hypertext transfer protocol (HTTP) signaling); and a base station coupled to the RMRF structure and the RMCF structure, the base station to communicate with one or more user equipments (UEs) to share the AI/ML models (see paragraphs [0066]; claim 9; Fig. 3B, The central server (e.g., the gNB-CU/DU) can deploy the trained model to local node (UE). This reads on a base station coupled to the RMRF structure and the RMCF structure, the base station to communicate with one or more user equipments (UEs) to share the AI/ML models).
Regarding claim 19 Li teaches a radio access network data repository function (RDRF) structure coupled to the RMRF structure and the RMCF structure in the service based system architecture, the RDRF structure to store data to be utilized for training the AI/ML models (paragraph [0066]; claim 9; Fig. 1A & Fig. 3B, The central server (e.g. the gNB-CU/DU) can select and train a suitable model with stored historical data (stored by the gNB). The gNB is configured with a memory to store the AI/ML model. The operations may operate in a number of components, modules, structures (see paragraphs [0043] – [0044]). This reads on radio access network data repository function (RDRF) structure coupled to the RMRF structure and the RMCF structure in the service based system architecture, the RDRF structure to store data to be utilized for training the AI/ML models); and a radio access network data coordination function (RDCF) structure coupled to the RMRF structure, the RMCF structure, and the RDRF structure in the service based system architecture, the RDCF structure to manage the data stored by the RDRF structure (paragraph [0066]; claim 9; claim 5p; Fig. 1A & Fig. 3B, The central server (e.g. the gNB-CU/DU) can select and train a suitable model with stored historical data (stored by the gNB). The gNB is further configured to train the suitable AI/ML model with the stored historical data and past local AI/ML models reported previously by other UEs. This reads on a radio access network data coordination function (RDCF) structure coupled to the RMRF structure, the RMCF structure, and the RDRF structure in the service based system architecture, the RDCF structure to manage the data stored by the RDRF structure).
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.
VI. Claims 1-3 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).
Regarding claim 1 Li teaches a method of operating a device comprising: generating a radio resource control (RRC) model request (see paragraph [0058]; 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. This reads on generating a radio resource control (RRC) model request); transmitting the RRC model request to a base station (see paragraphs [0058]; [0060]; claim 9 and Fig. 1A, 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 transmitting the RRC model request to a base station); 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, or a mobility status related to the device.
Yeh teaches a model that indicates a scenario, a channel, traffic, or a mobility status related to the device (see paragraph [0110], Information, such as UE moving trajectory, historical network traffic load, compute resource usage, can be incorporated into the AI/ML model. This reads on a model that indicates a scenario, a channel, traffic, or a mobility status 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 model request in Li adapt to indicate a scenario, a channel, traffic, or 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 7 Li teaches generating an RRC model update request for requesting an updated AI/ML model for the device (see paragraph [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. This reads on generating 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); transmitting the RRC model update request to the base station (see paragraphs [0060] – [0061]; claim 1; claim 9 and Fig. 1A, 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]). The UE can obtain a new updated model trained at the central server. This reads on transmitting the RRC model update request to the base station); 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).
VII. Claims 4-6 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) and Pateromichelakis (WO 2022/048746 A1).
Regarding claim 4 Li and Yeh teach the method of claim 1 except for wherein the RRC model request indicates a scenario that comprises channel state information (CSI) compression related to the device or mobility related to the device.
Pateromichelakis teaches wherein a request for an RRC model that indicates a scenario that comprises mobility related to the device (see paragraph [0079], a trained AI/MIL model (i.e., AI-traffic/mobility model 215) on the mobility for the respective UE reads on wherein an RRC model indicates a scenario that comprises mobility 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 and Yeh combination adapt to indicate a scenario that comprises channel state information (CSI) compression related to the device or mobility related to the device because it would allow for improved quality of service for the user (see Pateromichelakis, paragraph [0091]).
Regarding claim 5 Li and Yeh 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 and Yeh 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 and Yeh 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 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 Li and Yeh 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]).
Conclusion
VIII. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Imine Pub. No.: US 2021/0124986 A1 discloses image processing apparatus, control method thereof, and storage medium including selecting an AI function and determining a model to be used based on the selection (see paragraph [0078] and Fig. 10A and 10B).
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
December 17, 2025
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