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
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.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 44-49, 51-56, 58-63 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by PANTELIDOU et al (US 2023/0297882 A1, IDS).
Regarding claim 44, PANTELIDOU ‘882 discloses a method by a first radio node (fig. 4, see, UE coupled to the gNB or base station, the UE transmits Activate ML model response based on Activate ML model from the gNB, section 0105-0106, 0081-the UE and the gNB that are capable of operating with a support of ML models, section 081-0082) comprising: transmitting (fig. 4, see, the UE transmits Activate ML model Response (ML model m, problem pm, Accept) to the gNB, section 0105+0106, to a second radio node (fig. 4, see, the UE transmits Activate ML model Response (ML model m, problem pm, Accept) to the gNB, section 0105+0106), information indicating an activation or a deactivation of one or more Artificial Intelligence (AI) and/or Machine Learning (ML) models at the first radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0106).
Regarding claim 45, PANTELIDOU ‘882 discloses the method of claim 44, wherein prior to transmitting the information indicating the activation or deactivation of the one or more AI and/or ML models (fig. 4, the UE receives Activate ML model m ([ML model m, problem pm)], then transmits Activate ML model response, section 0105-0108), the method comprises: receiving, from the second radio node, information triggering the activation or the deactivation of the one or more AI and/or ML models at the first radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0111).
Regarding claim 46, PANTELIDOU ‘882 discloses the method of claim 45, wherein the information triggering the activation or the deactivation of the one or more AI and/or ML models (see, one can have different Activation type of an ML model, activation the ML model at reception of the Activation message, section 0105-0108, 0109-0111) comprises at least one of: model identification information; model functionality information; activation information (see, activation mode with respect to activate the ML model based time indication, section 0109-0112); deactivation information; at least one condition for the activation/deactivation of the one or more AI and/or ML models; model purpose information indicating whether the one or more AI and/or ML models are implemented for communication-related or performance evaluation/model retraining purposes; at least one model configuration parameter related to at least one of: frequency band, carrier, cell identifier, timing advance group parameter; a period of time during which the one or more AI and/or ML models are to activated or deactivated; an indication of whether a response message is expected; information indicating at least one change to the one or more AI and/or ML models at the first radio node; and information indicating at least one change to at least one AI and/or ML model at the second radio node.
Regarding claim 47, PANTELIDOU ‘882 discloses the method of claim 44, comprising receiving, from the second radio node (see, activation mode with respect to activate the ML model based time indication, section 0109-0112), or transmitting, to the second radio node, information indicating a configuration of the one or more AI and/or ML models for implementation at the first radio node (see, the activation mode could be triggered if certain event/measurement is observed by the UE, the UE informed the network/gNB on ML ability to train or execute the ML models,, section 0105-0115, fig. 4, see, Activate ML model Response).
Regarding claim 48, PANTELIDOU ‘882 discloses the method of claim 44, comprising: activating at least two AI and/or ML models during a duration of time (see, activation mode with respect to activate the ML model based time indication, section 0109-0112); and comparing model performance (see, activation of the ML model if the measured throughput drops below a threshold, or if the number of handover failures exceed a threshold, section 0111-0112) of the at least two AI and/or ML models (see, ML models 1, 2, x, section 0105-0106); and selecting one of the at least two AI and/or ML models (see, De-Activation of ML models based on network conditions (e.g., detection of suboptimal operation of ML model; m for a give problem m), section 0116-0118).
Regarding claim 49, PANTELIDOU ‘882 discloses the method of 48, comprising receiving, from the second radio node, information indicating the at least two AI and/or ML models for activation (see, the both the UE and the gNB have several Trained ML models, the UE may execute the trained ML models that have been trained by the network, section 0027-0028).
Regarding claim 51, Pantelidou ‘882 discloses the method of claim 44, wherein the first radio node is a base station or a UE (fig. 4, see, UE coupled to the gNB or base station, the UE transmits Activate ML model response based on Activate ML model from the gNB, section 0105-0106, 0081-the UE and the gNB that are capable of operating with a support of ML models, section 081-0082), or wherein the second radio node is a base station or a UE (fig. 4, see, the gNB sends to UE a message Activate ML model with which network activates a ML model to solve a problem, section 0105-0108).
Regarding claim 52, Pantelidou ‘882 discloses a method by a second radio node (fig. 4, see, the gNB sends to UE a message Activate ML model with which network activates a ML model to solve a problem, section 0105-0108) comprising: transmitting (fig. 4, the GNB sends to UE a message Activate ML model, section 0105-0108), to at least one other radio node (fig. 4, the UE which receives the Activate ML model from the gNB, section 0105+-0108), information (fig. 4, activation message to Activate ML model or activation field in the configuration to the UE, section 0105-0108) for triggering an activation or a deactivation (fig. 4, activation message to Activate ML model or activation field in the configuration to the UE, section 0105-0108) of one or more Artificial Intelligence, AI, and/or Machine Learning, ML, models for implementation at the at least one other radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0108, 0109-0112-Trigger based activation by the UE based on Activate message from the gNB) .
Regarding claim 53, Pantelidou ‘882 discloses the method of claim 52, comprising receiving, from the at least one other radio node, information indicating the activation or the deactivation of the one or more AI and/or ML models at the at least one other radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0111).
Regarding claim 54, Pantelidou ‘882 discloses the method of claim 52, comprising transmitting, to the at least one other radio node, or receiving, from the at least one other radio node, a configuration of the one or more AI and/or ML models for implementation at the at least one other radio node fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0108, 0109-0112-Trigger based activation by the UE based on Activate message from the gNB) .
Regarding claim 55, Pantelidou ‘882 discloses the method of claim 54, wherein the configuration comprises at least one condition associated with the activation and/or deactivation of the one or more AI and/or ML models at the at least one other radio node (see, deactivation of ML model based on suboptimal for given task/problem, section 0088-0090, 0116-0118-the UE deactivates due to suboptimal condition).
Regarding claim 56, Pantelidou ‘882 discloses the method of claim 55, wherein the configuration comprises a modified configuration of the one or more AI and/or ML models at the at least one other radio node (see, the gNB/network node sends a De-activate ML model message to the UE based on suboptimal operation or changed network conditions, section 0114-0118).
Regarding claim 58, Pantelidou ‘882 discloses the method of claim 52, wherein the information transmitted to the at least one other radio node for triggering the activation or the deactivation (see, deactivation of ML model based on suboptimal for given task/problem, section 0088-0090, 0116-0118-the UE deactivates due to suboptimal condition) of one or more AI and/or ML models indicates at least two AI and/or ML models (see, the both the UE and the gNB have several Trained ML models, the UE may execute the trained ML models that have been trained by the network, section 0027-0028) to be activated during a duration of time for comparison of model performance (see, activation mode with respect to activate the ML model based time indication, section 0109-0112, ee, RSRP, interference receiver power exceeds a threshold with respect to ML model, section 0031-0032 ).
Regarding claim 59, Pantelidou ‘882 discloses the method of claim 54, wherein the second radio node is a base station or a UE (fig. 4, see, UE coupled to the gNB or base station, the UE transmits Activate ML model response based on Activate ML model from the gNB, section 0105-0106, 0081-the UE and the gNB that are capable of operating with a support of ML models, section 081-0082),, or wherein the at least one other radio node includes a UE or a base station (fig. 4, see, the gNB sends to UE a message Activate ML model with which network activates a ML model to solve a problem, section 0105-0108).
Regarding claim 60, Pantelidou ‘882 discloses a first radio node (fig. 4, see, UE coupled to the gNB or base station, the UE transmits Activate ML model response based on Activate ML model from the gNB, section 0105-0106, 0081-the UE and the gNB that are capable of operating with a support of ML models, section 081-0082) adapted to: transmit (fig. 4, see, the UE transmits Activate ML model Response to the gNB, section 0105-0106) to a second radio node (fig. 4, see, UE coupled to the gNB or base station, the UE transmits Activate ML model response based on Activate ML model from the gNB, section 0105-0106, 0081-the UE and the gNB that are capable of operating with a support of ML models, section 081-0082), information indicating an activation or a deactivation of one or more Artificial Intelligence (AI) and/or Machine Learning (ML) models at the first radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0106) .
Regarding claim 61, Pantelidou ‘882 discloses the first radio node of claim 60, wherein the first radio node is adapted to, prior to transmitting the information indicating the activation or deactivation of the one or more AI and/or ML models (fig. 4, the UE receives Activate ML model m ([ML model m, problem pm)], then transmits Activate ML model response, section 0105-0108), receive, from the second radio node, information triggering the activation or the deactivation of the one or more AI and/or ML models at the first radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0106).
Regarding claim 62, Pantelidou ‘882 discloses a second radio node (fig. 4, see, the gNB sends to UE a message Activate ML model with which network activates a ML model to solve a problem, section 0105-0108) adapted to: transmit (fig. 4, the gNB sends to UE a message Activate ML model, section 0105-0108), to at least one other radio node (fig. 4, the UE which receives the Activate ML model from the gNB, section 0105+-0108), information (fig. 4, activation message to Activate ML model or activation field in the configuration to the UE, section 0105-0108) for triggering an activation or a deactivation of one or more Artificial Intelligence, AI, and/or Machine Learning, ML, models for implementation at the at least one other radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0108, 0109-0112-Trigger based activation by the UE based on Activate message from the gNB) .
Regarding claim 63, Pantelidou ‘882 discloses the second radio node of claim 62, wherein the second radio node is adapted to receive, from the at least one other radio node, information indicating the activation or the deactivation of the one or more AI and/or ML models at the at least one other radio node (fig. 4, Activate ML model Response ([ML model m, problem pm, Accept]), and ML state indication from the UE to the gNB section 0105-0108, 0109-0112-Trigger based activation by the UE based on Activate message from the gNB) .
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.
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.
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.
Claims 50, 57 are rejected under 35 U.S.C. 103 as being unpatentable over PANTELIDOU et al (US 2023/0297882 A1, IDS) in view of LEE et al (US 2025/0048432 A1, Foreign Priority: November 30, 2021).
PANTELIDOU ‘882 discloses all the claim limitations as set forth above but fails to explicitly disclose: Regarding claim 50, the method of claim 48, wherein comparing the model performance comprises comparing a block error rate of the at least two AI and/or ML models, and wherein selecting the one of the at least two AI and/or ML models comprises selecting the one of the at least two AI and/or ML models that has a best block error rate.
However, LEE et al (US 2025/0048432 A1, Foreign Priority: November 30, 2021) from s similar field of endeavor discloses: section 0118, 0123-service degradation in AI/ML model, 0127, 0018-AI/ML models, 0020-highest RSRP to the base station to change a beam, 0118-measurement report (e.g., RSRSP, RSRQ, SINR), 0126
Regarding claim 50, the method of claim 48, wherein comparing the model performance comprises comparing a block error rate of the at least two AI and/or ML models (section 0138-0142, 00149-discloses implicit comparison of AI/ML models by the UE in relation to transmitting model performance feedback (e.g., CQI, RSRP, RSRQ, SINR) relating to each AI/ML model group), section 0020, 0118, 0123, 0158-0161), and wherein selecting the one of the at least two AI and/or ML models comprises selecting the one of the at least two AI and/or ML models that has a best block error rate (section 0160-0161, 0163-0164, 0171, see, the terminal notifies information on beams with highest RSRP to the network, the AI/ML model is update accordingly, noted: service degradation can implicitly mean block or packet or frame error rate or packet lost).
In view of the above, it 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 to implement the method and the apparatus for updating the AI/ML models based on performance evaluation (e.g., CSI, RSRSP, RSRQ, SINR) of each AI/ML model group and comparison between model performance as taught by LEE ‘432 into the method and apparatus for activating and deactivation of ML model of PANTELIDOU ‘882. The motivation would have been to provide mobile optimization as suggested in section 0126.
Regarding claim 57, Pantelidou ‘882 as modified by LEE ‘432 discloses the method of claim 52, comprising: activating at least two AI and/or ML models during a duration of time (see, activation mode with respect to activate the ML model based time indication, section 0109-0112, noted: the UE and he network/gNB can have several models, section 0026-0027, 0077); comparing model performance of the at least two AI and/or ML models (see, RSRP, interference receiver power exceeds a threshold with respect to ML model, section 0031-0032, LEE, section 0138-0142, 00149-discloses implicit comparison of AI/ML models by the UE in relation to transmitting model performance feedback (e.g., CQI, RSRP, RSRQ, SINR) relating to each AI/ML model group), section 0020, 0118, 0123, 0158-0161),); and selecting one of the two AI and/or models for activation at the at least one other radio node, and wherein the information transmitted to the at least one other radio node for triggering the activation or the deactivation of one or more AI and/or ML models indicates the selected one of the two AI and/or ML models for activation at the at least one other radio node (LEE, section 0160-0161, 0163-0164, 0171, see, the terminal notifies information on beams with highest RSRP to the network, the AI/ML model is update accordingly).
In view of the above, it 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 to implement the method and the apparatus for updating the AI/ML models based on performance evaluation (e.g., CSI, RSRSP, RSRQ, SINR) of each AI/ML model group and comparison between model performance as taught by LEE ‘432 into the method and apparatus for activating and deactivation of ML model of PANTELIDOU ‘882. The motivation would have been to provide mobile optimization as suggested in section 0126.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Narayannan et l (US 2024/0187127 A1) discloses updating of AI model based on AI/ML model performance (i.e., RSRQ, CSI feedback, RSRP, SINR) including activating /deactivation of Ail models (section 0263) and switching from first one model to the another model (section 0075-0262, 0265-0271).
LEE et al (US 2024/0106507 A1) discloses activating of AI algorithm based on CSI feedback (section 0317-0318).
YAGHMOUR et al (US 2022/0158709 A1) discloses using AI/ML model to select antennas, block error rate (section 0111-0122).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kwang B Yao can be reached at 571 272-3182. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CANDAL ELPENORD/Primary Examiner, Art Unit 2473
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