Model id-based ai/ml model update management framework and its use

An apparatus comprising:

• a processor;
• and
• a memory comprising computer-executable instructions that, when executed by the processor, cause the apparatus at least to perform:
• based on a determination that a performance of a first artificial intelligence model falls below a quality threshold value and that no other accessible artificial intelligence model is suitable, receiving, in a wireless network from a network element in the wireless network, configuration indicating the apparatus is to aid in performing retuning of the first artificial intelligence model being used by the apparatus;
• receiving, from the network element, configuration for data collection for retuning to trigger apparatus to collect data to aid in model retuning of the first artificial intelligence model;
• based on the configuration, collecting measurements for the first artificial intelligence model, the measurements comprising:
• reference symbol measurements (e.g., CSI RS, PRS);
• sensor measurements (e.g., barometric pressure, velocity, acceleration);
• and non-RAT measurements (e.g., GNSS location);
• upon collecting a threshold amount sample measurements and a threshold amount of different types of measurements,
• performing operations to aid in the performing retuning of the first artificial intelligence model, wherein the retuning creates a second artificial intelligence model that is a retuned version of the first artificial intelligence model, wherein the first and second artificial intelligence models are from a same lineage of artificial intelligence models;
• using the collected measurements, retuning the first artificial intelligence model to create the second artificial intelligence model;
• switching from the first artificial intelligence model to the second artificial intelligence model;
• and
• sending, to the network element, the following in binary format:
• metadata for the second artificial intelligence model, an indication of a unique identification for the second artificial intelligence model, a delta between the first artificial intelligence model and the second artificial intelligence model, and information required to reconstruct the second artificial intelligence model.

An apparatus comprising:

• a processor;
• and
• a memory comprising computer-executable instructions that, when executed by the processor, cause the apparatus at least to perform:
• based on a determination that a performance of a first machine learning model falls below a quality threshold value and that no other accessible machine learning model is suitable, receiving, in a wireless network from a network element in the wireless network, configuration indicating the apparatus is to aid in performing retuning of the first machine learning model being used by the apparatus;
• receiving, from the network element, configuration for data collection for retuning to trigger apparatus to collect data to aid in model retuning of the first machine learning model;
• based on the configuration, collecting measurements for the first machine learning model, the measurements comprising:
• reference symbol measurements (e.g., CSI RS, PRS);
• sensor measurements (e.g., barometric pressure, velocity, acceleration);
• and non-RAT measurements (e.g., GNSS location);
• upon collecting a threshold amount sample measurements and a threshold amount of different types of measurements,
• performing operations to aid in the performing retuning of the first machine learning model, wherein the retuning creates a second machine learning model that is a retuned version of the first machine learning model, wherein the first and second machine learning models are from a same lineage of machine learning models;
• using the collected measurements, retuning the first machine learning model to create the second machine learning model;
• switching from the first machine learning model to the second machine learning model;
• and
• sending, to the network element, the following in binary format:
• metadata for the second machine learning model, an indication of a unique identification for the second machine learning model, a delta between the first machine learning model and the second machine learning model, and information required to reconstruct the second machine learning model.

A system comprising:

• an apparatus:
• a processor;
• and
• a memory comprising computer-executable instructions that, when executed by the processor, cause the apparatus at least to perform:
• based on a determination that a performance of a first machine learning model falls below a quality threshold value and that no other accessible machine learning model is suitable, receiving, in a wireless network from a network element in the wireless network, configuration indicating the apparatus is to aid in performing retuning of the first machine learning model being used by the apparatus;
• receiving, from the network element, configuration for data collection for retuning to trigger apparatus to collect data to aid in model retuning of the first machine learning model;
• based on the configuration, collecting measurements for the first machine learning model, the measurements comprising:
• reference symbol measurements (e.g., CSI RS, PRS);
• sensor measurements (e.g., barometric pressure, velocity, acceleration);
• and non-RAT measurements (e.g., GNSS location);
• upon collecting a threshold amount sample measurements and a threshold amount of different types of measurements,
• performing operations to aid in the performing retuning of the first machine learning model, wherein the retuning creates a second machine learning model that is a retuned version of the first machine learning model, wherein the first and second machine learning models are from a same lineage of machine learning models;
• using the collected measurements, retuning the first machine learning model to create the second machine learning model;
• switching from the first machine learning model to the second machine learning model;
• and
• sending, to the network element, the following in binary format:
• metadata for the second machine learning model, an indication of a unique identification for the second machine learning model, a delta between the first machine learning model and the second machine learning model, and information required to reconstruct the second machine learning model.