DETAILED ACTION
This action is in response to communication filed on 7/21/2026.
Claims 1-14 are pending.
Claims 1-4, 7-11 and 14 have been amended.
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 .
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-14 over Pezeshki in view of Brown (and further in view of Duan or Larsson) have been considered but are moot in view of the new ground of rejection.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 4-9, 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2023/0075276) in view of Li et al. (US 2024/0349082).
Regarding claim 1, Zhu discloses a method performed by a user equipment (UE), the method comprising:
transmitting, to a base station, information on requested artificial intelligence (AI) / machine learning (ML) model including a list of AI/ML models (Zhu discloses the UE transmits capability information and a machine learning request to the network/base station that includes a list of potential / supported machine learning models and an indication that the UE may request machine learning; [0005] “The UE may transmit capability information to the network. The capability information may include one or more of a list of potential neural network functions, a list of potential machine learning models, or an indication of whether or not the UE may request machine learning” and [0006] “The UE may send a message to the network requesting to implement machine learning … The message may include an indication of a neural network function, a neural network model, and a corresponding parameter set”),
wherein an AI/ML model included in the list of AI/ML models is identified by the AI/ML model identifier (ID) (Zhu discloses the reported list is a list of model IDs and the request identifies the model by model ID; [0084] “The capability information may include a list of neural network functions supported by the UE 115-b, a list of neural network models (e.g., a list of model IDs) supported by the UE 115b” and [0086] “The machine learning request message may include one or more of the model ID whose condition was satisfied (e.g., the second model ID), a neural network function of the one or more neural network functions, or an indication of the condition that was satisfied”);
receiving, from the base station, at least one AI/ML model information indicating an activation of at least one AI/ML model (Zhu discloses the base station transmits an activation message for the machine learning model; [0089] “the other network nodes may send a model activation response message to the UE 115-b via a MAC-CE or RRC signaling activating the machine learning at the UE 115-b” and [0124] “receiving, from the base station, an activation message for the machine learning model, the neural network function, or both”),
wherein the at least one AI/ML model to be activated is selected based on the list of AI/ML models and a UE profile associated with the UE (Zhu discloses the network selects model IDs from the reported list based on the UE’s capability information, and each model is applicable according to a UE-specific scope including UE type, RRC state, service, and location; [0005] “Based on the capability information, the network may select a set of neural network functions, a set of machine learning models, and sets of corresponding parameters” and [0085] “Each model ID of the one or more model IDs indicated to the UE 115-b at 325 may be associated with a condition (or applicable scope). The condition may be a location … a type of UE, RRC states, a type of service … or a configuration” and [0087] “the network … may select a neural network function … and a machine learning model (e.g., select a machine learning model corresponding to the model ID indicated in the machine learning request message received at 335) and configure the UE 115-b with the machine learning model as well as a corresponding set of parameters at 340”); and
activating the at least one AI/ML model based on the received at least one AI/ML model information (Zhu discloses the UE implements / utilizes the model after receiving the activation message; [0006] “the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks” and [0089] “activating the machine learning at the UE 115-b”).
However, the prior art does not explicitly disclose wherein the list of AI/ML models includes an AI/ML model operation associated with an AI/ML model identifier (ID) deployed at UE side.
Li in the field of the same endeavor discloses enhanced UE–NG-RAN collaboration to configure machine learning models on the UE, including UE reporting of per-model operations for models deployed at the UE. In particular, Li teaches the following:
wherein the list of AI/ML models includes an AI/ML model operation associated with an AI/ML model identifier (ID) deployed at UE side (Li discloses the UE reports, as ML capability information, the type of machine learning model the UE supports and, for each such model, a training capability and an inference capability, and further discloses that the machine learning model is deployed at the UE; [0021] “For machine learning capability, it is used to indicate: what type of machine learning model UE can support (e.g. CNN, RNN, RL, Classification, regression, etc.) and for each machine learning model, it includes: maximum model size, training capability, e.g. supported SW library, inference capability, e.g. supported SW library, etc”. Li further discloses that the network selects the ML model and configuration based on that UE capability / hardware profile and that the UE then executes the model ([0067] “the network device 102 may select a ML model and a ML configuration for the ML model for the service based on the UE capability information 112”, [0068] “The UE device 104 may execute the ML model based on the ML configuration 114”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to modify Zhu with the teaching of Li. One would have been motivated because including Li’s per-model training/inference operation in Zhu’s model-ID list would allow the network to activate only an operation that the UE-side model can actually perform given the UE’s reported capabilities, yielding the predictable result of matching a requested, ID’d, UE-deployed model and its operation to the UE’s profile without undue experimentation.
Regarding claim 2, Zhu-Li discloses the method of claim 1, wherein the at least one AI/ML model information is received via a radio resource control (RRC) signaling (Zhu [0089] “send a model activation response message to the UE 115-b via a MAC-CE or RRC signaling activating the machine learning at the UE 115-b” and Li [0069] “The machine learning configuration may be sent via RRC signaling from the NG-RAN to the UE”).
Regarding claim 4, Zhu-Li discloses the method of claim 1, further comprising: transmitting, to the base station, AI/ML model information supported by the UE (Zhu [0084] “a list of neural network models (e.g., a list of model IDs) supported by the UE”; Li [0051] the ML report may be sent via RRC signaling from UE to NG-RAN. The information uploaded/reported from UE to RAN includes machine learning model parameter updates, prediction results, action space, feedback (e.g., model performance feedback and/or wireless feedback (e.g., system KPI, e.g. throughput/latency, etc.)) via a new message type “MachineLearningReport””).
Regarding claim 5, Zhu-Li discloses the method of claim 1, wherein the UE is in an RRC connected state (Zhu [0085] model scope includes “RRC states”).
Regarding claim 6, Zhu-Li discloses the method of claim 1, wherein the activating of the indicated at least one AI/ML model comprises: activating at least one AI/ML model allowed by a base station (Zhu discloses the network selects the model and sends the activation message, and further discloses a whitelist of model IDs the UE is allowed to request / implement [0087] network selects the machine learning model corresponding to the indicated model ID; [0085] “the UE 115-b may be able to request to implement models in the whitelist”, [0133] “a second set of machine learning models included in a whitelist”).
Regarding claim(s) 7-8, 14 and 9, 11-13, do(es) not teach or further define over the limitation in claim(s) 1, 2 and 4-6 respectively. Therefore claim(s) 7-8, 14 and 9, 11-13 is/are rejected for the same rationale of rejection as set forth in claim(s) 1, 2 and 4-6 respectively.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2023/0075276) in view of Li et al. (US 2024/0349082) in view of Duan (US 2024/0281719).
Regarding claim 3, Zhu-Li discloses the invention substantially, however the prior art does not explicitly disclose the method of claim 1, wherein the at least one AI/ML model information is received via a non-access stratum (NAS) signaling.
Duan in the field of the same endeavor discloses techniques for updating a global machine learning model using local model parameters from at least one UE. In particular, Duan discloses the following:
wherein the at least one AI/ML model information is received via a non-access stratum (NAS) signaling (Duan discloses the UE receives a NAS message containing information on the second model file of the updated global machine learning model; [0391]-[0392] “where the second NAS message includes the information on the second model file, and the information on the second model file may include the updated global model parameter, or the second model file including the updated global model parameter, or the address information of the second model file”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine the prior art with Duan. One would have been motivated because Duan enables the network to send updated model information to the UE in NAS signaling in order to dynamically maintain and improve model accuracy and performance while retaining centralized network control.
Regarding claim(s) 10, do(es) not teach or further define over the limitation in claim(s) 3 respectively. Therefore claim(s) 10 is/are rejected for the same rationale of rejection as set forth in claim(s) 3 respectively.
Conclusion
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.
For the reason above, claims 1-14 have been rejected and remain pending.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIMMY H TRAN whose telephone number is (571)270-5638. The examiner can normally be reached Monday-Friday 9am-5pm PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Parry can be reached at 571-272-8328. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JIMMY H TRAN
Primary Examiner
Art Unit 2451
/JIMMY H TRAN/Primary Examiner, Art Unit 2451
Prosecution Insights