# US 2026/0074960 A1 · Artificial intelligence various mode measurements procedure

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- Number: US 2026/0074960 A1
- Document ID: US20260074960A1
- Status: Published application
- Kind: A1
- Application number: 19/102,509
- Filed: 2023-08-07
- Published: 2026-03-12
- Earliest claimed: 2022-08-08
- Assignees: InterDigital Patent Holdings, Inc.
- Applicants: InterDigital Patent Holdings, Inc.
- Inventors: Tezcan Cogalan; Yugeswar Deenoo Narayanan Thangaraj; Oumer Teyeb; James Miller; Filipe Conceicao
- Web page: https://patentgrep.com/patent/US20260074960A1

## CPC

- G06N 20/00: Machine learning
- H04W 24/10: Scheduling measurement reports {; Arrangements for measurement reports}
- H04W 8/24: Transfer of terminal data
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products directed to artificial intelligence-specific idle/inactive/connected mode measurements procedure. In an embodiment, a method implemented by a wireless transmit receive unit (WTRU), the method comprising: receiving, from a network, a first message comprising a configuration about AI/ML model training and associated measurements and logging periodicity; performing minimization of drive test (MDT) measurements; selecting an AI/ML model for training based on the based on the first message; training the selected AI/ML model based on MDT measurements; and in response to accuracy of the trained model above a configured accuracy threshold, triggering transition to connected state and reporting to the network the trained AI/ML model identity and AI/ML model parameters.

## Claims

### Claim 1

A method implemented by a wireless transmit receive unit (WTRU) comprising:
receiving, from a network, a first message comprising first information indicating a configuration for artificial intelligence/machine learning, AI/ML, model training, and for measurements for AI/ML training, wherein the configuration for AI/ML model training comprises second information indicating any of one or more AI/ML models, one or more AI/ML selection criteria, a training data configuration, a conditional logging configuration, and a conditional training configuration;
performing, in an idle state, measurements configured for AI/ML model training;
selecting an AI/ML model for training based on the first message;
training the AI/ML model based on the performed measurements; and
in response to an accuracy level of the trained AI/ML model above a configured accuracy threshold level, triggering transition to connected state, and transmitting, to the network, a second message comprising third information indicating an identity of the trained AI/ML model and one or more AI/ML model parameters.

### Claim 2 (depends on 1)

The method of claim 1, wherein the accuracy level of the trained AI/ML model is determined based on the performed measurements.

### Claim 3 (depends on 1)

The method of claim 1, comprising:
prior to receiving the first message, transmitting, to the network, AI/ML capability information.

### Claim 4 (depends on 3)

The method of claim 3, wherein the AI/ML capability information includes any of one or more available AI/ML models, one or more accuracy levels of the one or more available AI/ML models, a computation capability for training, a computation capability for validation, and an AI/ML-dedicated memory capacity.

### Claim at position 5

5-6. (canceled)

### Claim 7 (depends on 1)

The method of claim 1, wherein the measurements configured for AI/ML model training are of a type of minimization of drive test (MDT) measurements.

### Claim 8 (depends on 7)

The method of claim 7, wherein performing the measurements configured for AI/ML model training comprises:
logging legacy MDT measurements or skipping MDT measurement logging occasion when one or more of the following conditions are met: (i) the AI/ML model is still in a training phase with existing logged MDT measurements data; (ii) current MDT measurements are used to validate the trained AI/ML model or (iii) the trained AI/ML model achieves the configured accuracy threshold level.

### Claim 9 (depends on 7)

The method of claim 7, wherein in response to logged MDT measurement data meeting the training data configuration and if one or more training criteria is met, the method comprising:
performing training of the AI/ML model; and
logging the one or more AI/ML model parameters.

### Claim 10 (depends on 1)

The method of claim 1, comprising:
receiving an AI/ML model from the network.

### Claim 11 (depends on 1)

The method of claim 1, wherein selecting an AI/ML model for training based on the first message comprises selecting the AI/ML model based on an indication of an AI model and the one or more AI/ML selection criteria.

### Claim 12 (depends on 1)

The method of claim 1, wherein in response to the accuracy level of the trained AI/ML model equal or below the configured accuracy threshold level, the method comprising:
reporting one or more logged measurements and a maximum trained AI/ML model accuracy level achieved during training.

### Claim 13

A wireless transmit/receive unit (WTRU) comprising a processor, a transceiver unit and a storage unit, and configured to:
receive, from a network, a first message comprising first information indicating a configuration for artificial intelligence/machine learning, AI/ML, model training, and for measurements for AI/ML training, wherein the configuration for AI/ML model training comprises second information indicating any of one or more AI/ML models, one or more AI/ML selection criteria, a training data configuration, a conditional logging configuration, and a conditional training configuration;
perform, in an idle state, measurements configured for AI/ML model training;
select an AI/ML model for training based on the first message;
train the AI/ML model based on the performed measurements; and
in response to an accuracy level of the trained AI/ML model above a configured accuracy threshold level, trigger transition to connected state, and transmit to the network a second message comprising third information indicating an identity of the trained AI/ML model and one or more AI/ML model parameters.

### Claim 14 (depends on 13)

The WTRU of claim 13, wherein the accuracy level of the trained AI/ML model is determined based on the performed measurements.

### Claim 15 (depends on 13)

The WTRU of claim 13, configured to:
transmit, to the network, AI/ML capability information.

### Claim 16 (depends on 15)

The WTRU of claim 15, wherein the AI/ML capability information indicates any of one or more available AI/ML models, one or more accuracy levels of the one or more available AI/ML models, a computation capability for training, a computation capability for validation, and an AI/ML-dedicated memory capacity.

### Claim at position 16

17-18. (canceled)

### Claim 19 (depends on 13)

The WTRU of claim 13, to wherein the measurements configured for AI/ML model training are of a type of minimization of drive test (MDT) measurements.

### Claim 20 (depends on 19)

The WTRU of claim 19, configured to:
log legacy MDT measurements or skip MDT measurement logging occasion when one or more of the following conditions are met: (i) the AI/ML model is still in a training phase with existing logged MDT measurements data; (ii) current MDT measurements are used to validate the trained AI/ML model or (iii) the trained AI/ML model achieves the configured accuracy threshold level.

### Claim 21 (depends on 19)

The WTRU of claim 19, and configured to, in response to logged MDT measurement data meeting the training data configuration and if one or more AI/ML training criteria is met:
perform training of the AI/ML model; and
log the one or more of AI/ML model parameters.

### Claim 22 (depends on 13)

The WTRU of claim 13, configured to:
receive an AI/ML model from the network.

### Claim 23 (depends on 13)

The WTRU of claim 13, to wherein being configured to select the AI/ML model for training based on the first message comprises being configured to select the AI/ML model based on an indication of a AI model and the one or more AI/ML selection criteria.

### Claim 24 (depends on 13)

The WTRU of claim 13, configured to:
in response to the accuracy level of the AI/ML trained model equal or below the configured accuracy threshold level, report one or more logged measurements and a maximum trained AI/ML model accuracy level achieved during training.