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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/05/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
(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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by KONTES et al. (US20260074959A1, which claims priority from European Application No. 23173270.2, filed May 14, 2023, hereinafter, KONTES).
Regarding claim 1, KONTES discloses:
A user equipment (UE) for wireless communication (Fig. 15B;[0105] the apparatus may, e.g., be the user equipment. The apparatus may, e.g., be configured to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models... at a network unit of the wireless communication system), comprising:
one or more memories ([0350] FIG. 16 illustrates an example of a computer system 600. The units or modules as well as the steps of the methods performed by these units may execute on one or more computer systems 600. The computer system 600 includes one or more processors 602; [0349] aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding apparatus); and
one or more processors, coupled to the one or more memories ([0351] The computer programs... are stored in main memory 606 and/or secondary memory 608... The computer program... when executed, enables processor 602 to implement the processes of the present invention, such as any of the methods described herein), configured to cause the UE to:
receive (Fig. 13; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one), based at least in part on one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed), assistance information ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0107] The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one) associated with an expected performance of the AI model ([0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model; [0327] the UE will select a model for evaluation, based on this indication will evaluate the expected performance; [0036] let's assume that the UE shown in FIG. 1... the active model performs as expected (achieves the set performance target/constraint) in certain areas (e.g. Cells 1 and 2), but... other areas (e.g. Cell 3)... is needed for fulfilling the performance target. In this case, on top of the typical measurements for handover management (e.g., received RSRP), a prediction on the estimated performance of models/functionalities in target cells, is also crucial); and
assess, based at least in part on the assistance information ([0048]; [0107];), the expected performance of the AI model ([0327] the apparatus is to evaluate at least one functionality or model for the purpose of selection, activation, deactivation or switching... Wherein the UE will select a model for evaluation, based on this indication will evaluate the expected performance or a parameter indicative of the selected performance).
Regarding claim 2, KONTES further discloses:
wherein the one or more processors ([0350]; [0351]), to cause the UE to receive the assistance information (Fig. 13; [0107];), are configured to cause the UE to receive the assistance information without transmitting a request for the assistance information ([0039] In some cases, the request for the UE to transmit or receive may be sent by the network: [0040] 1) The network entity indicates to the UE one or more configurations of reference signals that the UE may be expected to receive and or transmit, through a higher layer signalling mechanism, such as RRC signalling or LPP signalling).
Regarding claim 3, KONTES further discloses:
wherein the one or more processors are further configured to cause ([0350]; [0351]) the UE to:
transmit a request for the assistance information ([0042] the UE may: [0043] 1) Request the network entity to transmit a reference signal; [0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring), wherein the one or more processors, to cause the UE to receive the assistance information ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring), are configured to cause the UE to receive the assistance information ([0045] request the NW to provide the UE with an on-demand configuration for a reference signal that the UE can receive, indicating at least one parameter describing the reference signal, such as periodicity, bandwidth, subcarrier spacing, spatial direction) based at least in part on the request for the assistance information ([0045]; [0046] Perform the measurement on the reference signal transmitted by the network).
Regarding claim 4, KONTES further discloses:
wherein the request for the assistance information ([0042] the UE may: [0043] 1) Request the network entity to transmit a reference signal) includes an indication of one or more target conditions associated with the UE ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity (see FIG. 3); [0036] the UE shown in FIG. 1 has an active AI/ML model that performs beam management. Let's also assume that the active model performs as expected (achieves the set performance target/constraint) in certain areas (e.g. Cells 1 and 2), but for beam management in other areas (e.g. Cell 3), a different model (or functionality) is needed for fulfilling the performance target. In this case, on top of the typical measurements for handover management (e.g., received RSRP), a prediction on the estimated performance of models/functionalities in target cells, is also crucial).
Regarding claim 5, KONTES further discloses:
wherein the one or more processors are further configured to cause ([0350]; [0351]) the UE to:
select the AI model based at least in part on the assistance information ([0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity (see FIG. 3); [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Regarding claim 6, KONTES further discloses:
wherein the one or more processors are further configured to cause ([0350]; [0351]) the UE to:
transmit, based at least in part on the assistance information ([0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity; [0046]; [0047];), an AI model switching request ([0107]the apparatus may, e.g., be the user equipment. The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one of the two or more AI/ML models); and
receive, based at least in part on the AI model switching request ([0107] the apparatus may... be configured to request allowance from a network unit), a configuration associated with the AI model ([0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Regarding claim 7, KONTES further discloses:
wherein the assistance information includes data ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model) associated with the one or more performance metrics ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed).
Regarding claim 8, KONTES further discloses:
wherein the UE is a first UE, and wherein the one or more performance metrics are associated with a use of the AI model ([0092] the apparatus (first UE) may, e.g., be configured to determine the metric for the AI/ML model and/or for the functionality thereof depending on at least one of the following: [0093] information on which functionality/model is active now and its properties, [0094] information on the performance or associated QoS of the current active functionality/model) by one or more second UEs ([0101] high-level features/post-processed information on the UE state, for example, UE orientation/position/velocity, predicted future UE trajectory, [0102] side information from the network, on the general properties of the radio environment, reported problems from other UEs).
Regarding claim 9, KONTES further discloses:
wherein the one or more performance metrics ([0092] the apparatus (first UE) may, e.g., be configured to determine the metric for the AI/ML model and/or for the functionality thereof depending on at least one of the following) are one or more model-level performance metrics ([0156] Once the model is deployed, it needs to be continuously monitored to detect any performance degradation or errors. This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed).
Regarding claim 10, KONTES further discloses:
wherein the one or more performance metrics ([0092] the apparatus (first UE) may, e.g., be configured to determine the metric for the AI/ML model and/or for the functionality thereof depending on at least one of the following) are one or more system-level performance metrics ([0242] construct an estimator that for selected inactive functionalities/models it predicts the expected benefit of activating the functionality/model, taking into account the expected performance/QoS the model will bring, as well as the cost due to selection/activation/deactivation/switching to the candidate functionality/model. [0243] 2. The estimator should not only provide one-step predictions, i.e., the immediate performance/cost estimation, but should encode in the prediction the long-term performance/cost trade-off of activating a specific model, taking into account short-term requirements for model (re-) switching, based on the available model and radio environment properties; [0269] The estimators Q.sub.r and Q.sub.c are trained in a way that encodes that selecting a model at time t=1 does not only affect the states/performance/cost at time t=3, but could have a long-term effect (e.g., a switching to a model of a different functionality was needed eventually, so maybe a better decision is to start with a model from that functionality). This can be achieved, by e.g., training the Q functions using future performance/cost values from the entire UE trajectory/experience (potentially weighted to give higher importance/confidence to immediate outcomes)).
Regarding claim 11, KONTES further discloses:
wherein the data ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0046]; [0047];) associated with the one or more performance metrics ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed;) include one or more conditions ([0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity; [0036] the UE shown in FIG. 1 has an active AI/ML model that performs beam management. Let's also assume that the active model performs as expected (achieves the set performance target/constraint) in certain areas (e.g. Cells 1 and 2), but for beam management in other areas (e.g. Cell 3), a different model (or functionality) is needed for fulfilling the performance target) associated with one or more measurements of the one or more performance metrics ([0156]; [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model).
Regarding claim 12, KONTES further discloses:
wherein the one or more performance metrics ([0156];) are associated with a training dataset of the AI model ([0090] Different models/functionalities (e.g., AI models) might have different monitoring requirements. For example, a monitoring configuration for monitoring the input/output of a model to determine if it is close to the training data distribution can have minimal overhead compared to a different monitoring configuration that facilitates measuring all beams in the codebook in frequent time intervals; [0171] An evaluation based on input data distribution. This can be: It is noticed that the data being measured, seems to be the training data used to train the model, so the model should perform well here).
Regarding claim 13, KONTES further discloses:
wherein the assistance information ([0048]; [0107];) includes one or more identifiers of one or more AI models ([0166] An AI/ML model may, e.g., have a model ID with associated/meta information at least for some AI/ML operations... indication of model selection/activation/deactivation/switching/fallback is based on individual model IDs) including the AI model ([0107] The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one).
Regarding claim 14, KONTES further discloses:
wherein the one or more processors ([0350]; [0351]), to cause the UE to receive the assistance information (Fig. 9; Fig. 13; [0107];), are configured to cause the UE to receive the assistance information in a radio resource control (RRC) message ([0039] In some cases, the request for the UE to transmit or receive may be sent by the network: [0040] 1) The network entity indicates to the UE one or more configurations of reference signals that the UE may be expected to receive and or transmit, through a higher layer signalling mechanism, such as RRC signalling or LPP signalling), a media access control (MAC) control element ([0041] Alternatively, a MAC-CE or physical layer DCI or sidelink DCI may be used to indicate the UE to receive or transmit such signalling. Alternatively, the MAC-CE may be used to switch inactive models and also indicate the UE to receive or transmit signals again), or an over-the-top message ([0344] the OTT can align the information received from the UE with the information received from the network to train data for the model/functionality or train the estimator to predict the performance of the model; [0339] In an example, the model may be stored at the OTT server and/or at the network entity. Delivery of the model may be subject to authorization and subscription of entities. Therefore, when the UE makes the request to the network, the network entity (e.g. LMF) may need to to interact with UDM/AUSF to check whether the requested model is authorized and/or within the subscription of the UE).
Regarding claim 15, KONTES discloses:
A network entity for wireless communication (Fig. 9; Fig. 15B; [0002] The present invention relates to the field of wireless communication systems or networks, in particular to AI/IL enabled communication networks, and, more particularly to an apparatus and a method for performance prediction of models in AI/ML enabled communication networks), comprising:
one or more memories ([0350] FIG. 16 illustrates an example of a computer system 600. The units or modules as well as the steps of the methods performed by these units may execute on one or more computer systems 600. The computer system 600 includes one or more processors 602; [0349] aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding apparatus); and
one or more processors, coupled to the one or more memories ([0351] The computer programs... are stored in main memory 606 and/or secondary memory 608... The computer program... when executed, enables processor 602 to implement the processes of the present invention, such as any of the methods described herein), configured to cause the network entity to:
identify (Fig. 9; [0322] if the UE predicts that a model A3 activation is beneficial, the UE requests from the NW activation for Model A3. The UE can also provide the expected benefit information to the NW) one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed); and
transmit, based at least in part on the performance metrics, assistance information associated ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models) with an expected performance of the AI model ([0045] Request the network entity to transmit a reference signal... that the UE can receive [0046] 2) Perform the measurement on the reference signal transmitted by the network. [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model; [0036] let's assume that the UE shown in FIG. 1... the active model performs as expected (achieves the set performance target/constraint) in certain areas (e.g. Cells 1 and 2), but... other areas (e.g. Cell 3)... is needed for fulfilling the performance target. In this case, on top of the typical measurements for handover management (e.g., received RSRP), a prediction on the estimated performance of models/functionalities in target cells, is also crucial).
Regarding claim 16, KONTES further discloses:
wherein the one or more processors ([0350]; [0351]), to cause the network entity to transmit the assistance information (0048);, are configured to cause the network entity to transmit the assistance information without obtaining a request for the assistance information ([0039] In some cases, the request for the UE to transmit or receive may be sent by the network: [0040] 1) The network entity indicates to the UE one or more configurations of reference signals that the UE may be expected to receive and or transmit, through a higher layer signalling mechanism, such as RRC signalling or LPP signalling).
Regarding claim 17, KONTES further discloses:
wherein the one or more processors are further configured ([0350]; [0351]) to cause the network entity (Fig. 9) to:
obtain a request for the assistance information ([0042] the UE may: [0043] 1) Request the network entity to transmit a reference signal), wherein the one or more processors, to cause the network entity to transmit the assistance information ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring), are configured to cause the network entity to transmit the assistance information ([0046] Perform the measurement on the reference signal transmitted by the network) based at least in part on the request for the assistance information ([0042];).
Regarding claim 18, KONTES further discloses:
wherein the one or more processors are further configured ([0350]; [0351]) to cause the network entity (Fig. 9) to:
obtain, based at least in part on the assistance information ([0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity; [0048]; [0107];), an AI model switching request ([0107]the apparatus may, e.g., be the user equipment. The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one of the two or more AI/ML models); and
transmit, based at least in part on the AI model switching request ([0107] the apparatus may... be configured to request allowance from a network unit), a configuration associated with the AI model ([0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Regarding claim 19, KONTES further discloses:
wherein the assistance information includes data ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model) associated with the performance metrics ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed).
Regarding claim 20, KONTES further discloses:
wherein the assistance information ([0048]; [0107];) includes one or more identifiers of one or more AI models ([0166] An AI/ML model may, e.g., have a model ID with associated/meta information at least for some AI/ML operations... indication of model selection/activation/deactivation/switching/fallback is based on individual model IDs) including the AI model ([0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Regarding claim 21, KONTES further discloses:
wherein the network entity (Fig. 9) is a base station, a core network entity ([0330] FIG. 10 depicts the components of the 3GPP wireless communication system (or 5G System (5GS)). The system consists of user equipment (UE), access network (AN), core network (CN), and data network (DN). A UE registers itself with the AMF via either the NG-RAN node (such as gNB) using 3GPP defined radio access technology, such as NR or via a non-3GPP access method (such as WiFi) via the non-3GPP interworking function (N3IWF).), or a server (Fig. 10 – AF, OTT).
Regarding claim 22, KONTES further discloses:
wherein the one or more processors ([0350]; [0351]), to cause the network entity to transmit the assistance information ([0048]), are configured to cause the network entity to transmit the assistance information in a radio resource control (RRC) message ([0039] In some cases, the request for the UE to transmit or receive may be sent by the network: [0040] 1) The network entity indicates to the UE one or more configurations of reference signals that the UE may be expected to receive and or transmit, through a higher layer signalling mechanism, such as RRC signalling or LPP signalling), a media access control (MAC) control element ([0041] Alternatively, a MAC-CE or physical layer DCI or sidelink DCI may be used to indicate the UE to receive or transmit such signalling. Alternatively, the MAC-CE may be used to switch inactive models and also indicate the UE to receive or transmit signals again), or an over-the-top message ([0344] the OTT can align the information received from the UE with the information received from the network to train data for the model/functionality or train the estimator to predict the performance of the model; [0339] In an example, the model may be stored at the OTT server and/or at the network entity. Delivery of the model may be subject to authorization and subscription of entities. Therefore, when the UE makes the request to the network, the network entity (e.g. LMF) may need to to interact with UDM/AUSF to check whether the requested model is authorized and/or within the subscription of the UE).
Regarding claim 23, KONTES discloses:
A method of wireless communication ([0002] an apparatus and a method for performance prediction of models in AI/ML enabled communication networks) performed by a user equipment (UE) (Fig. 15B;[0105] the apparatus may, e.g., be the user equipment. The apparatus may, e.g., be configured to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models... at a network unit of the wireless communication system), comprising:
Receiving (Fig. 13; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one), based at least in part on one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed), assistance information associated ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0107] The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one) with an expected performance of the AI model ([0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model; [0327] the UE will select a model for evaluation, based on this indication will evaluate the expected performance; [0036] let's assume that the UE shown in FIG. 1... the active model performs as expected (achieves the set performance target/constraint) in certain areas (e.g. Cells 1 and 2), but... other areas (e.g. Cell 3)... is needed for fulfilling the performance target. In this case, on top of the typical measurements for handover management (e.g., received RSRP), a prediction on the estimated performance of models/functionalities in target cells, is also crucial); and
assessing, based at least in part on the assistance information ([0048]; [0107];), the expected performance of the AI model ([0327] the apparatus is to evaluate at least one functionality or model for the purpose of selection, activation, deactivation or switching... Wherein the UE will select a model for evaluation, based on this indication will evaluate the expected performance or a parameter indicative of the selected performance).
Regarding claim 24, KONTES further discloses:
wherein the assistance information includes data ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model) associated with the one or more performance metrics ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed).
Regarding claim 25, KONTES further discloses:
wherein the assistance information ([0048]; [0107];) includes one or more identifiers of one or more AI models including the AI model [0166] An AI/ML model may, e.g., have a model ID with associated/meta information at least for some AI/ML operations... indication of model selection/activation/deactivation/switching/fallback is based on individual model IDs) including the AI model ([0107] The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one).
Regarding claim 26, KONTES further discloses:
selecting the AI model based at least in part on the assistance information ([0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity (see FIG. 3); [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Regarding claim 27, KONTES further discloses:
transmitting, based at least in part on the assistance information ([0037] monitoring entity can decide to switch between models depending, e.g., on expected performance vs model complexity; [0046]; [0047];), an AI model switching request ([0107]the apparatus may, e.g., be the user equipment. The apparatus may, e.g., be configured to request allowance from a network unit of the wireless communication system to select and/or to activate one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to another one of the two or more AI/ML models); and
receiving, based at least in part on the AI model switching request ([0107] the apparatus may... be configured to request allowance from a network unit), a configuration associated with the AI model ([0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Regarding claim 28, KONTES discloses:
A method of wireless communication performed by a network entity (Fig. 9; Fig. 15B; [0002] The present invention relates to the field of wireless communication systems or networks, in particular to AI/IL enabled communication networks, and, more particularly to an apparatus and a method for performance prediction of models in AI/ML enabled communication networks), comprising:
identifying (Fig. 9; [0322] if the UE predicts that a model A3 activation is beneficial, the UE requests from the NW activation for Model A3. The UE can also provide the expected benefit information to the NW) one or more performance metrics associated with an artificial intelligence (AI) model associated with wireless communication ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed); and
transmitting, based at least in part on the performance metrics, assistance information associated ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models) with an expected performance of the AI model ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models) with an expected performance of the AI model ([0045] Request the network entity to transmit a reference signal... that the UE can receive [0046] 2) Perform the measurement on the reference signal transmitted by the network. [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model; [0036] let's assume that the UE shown in FIG. 1... the active model performs as expected (achieves the set performance target/constraint) in certain areas (e.g. Cells 1 and 2), but... other areas (e.g. Cell 3)... is needed for fulfilling the performance target. In this case, on top of the typical measurements for handover management (e.g., received RSRP), a prediction on the estimated performance of models/functionalities in target cells, is also crucial).
Regarding claim 29, KONTES further discloses:
wherein the assistance information includes data ([0048] The network may have provided configurations and/or assistance data indicating certain configuration of reference signals may be requested for AI/ML monitoring; [0046] 2) Perform the measurement on the reference signal transmitted by the network; [0047] 3) Monitor the performance of an active ML model and/or at least one inactive ML model) associated with the performance metrics ([0156] Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model... This stage involves tracking model performance (monitoring) metrics, detecting data drift, and retraining the model if needed).
Regarding claim 30, KONTES further discloses:
wherein the assistance information ([0048]; [0107];) includes one or more identifiers of one or more AI models including the AI model ([0166] An AI/ML model may, e.g., have a model ID with associated/meta information at least for some AI/ML operations... indication of model selection/activation/deactivation/switching/fallback is based on individual model IDs) including the AI model ([0107] the apparatus may, e.g., be configured, when receiving the allowance from the network unit to select and/or to activate said one of the two or more AI/ML models and/or to switch from one of the two or more AI/ML models to said other one of the two or more AI/ML models).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. PTO-892 form.
JUNG et al. (US 20260082257 A1) teaches measurement reporting based on machine learning in wireless communications.
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/THERESA NGUYEN/Examiner, Art Unit 2418
/Moo Jeong/Supervisory Patent Examiner, Art Unit 2418
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