Detection of meaningful context changes for adaptive control of electronic devices

A system to detect a meaningful context change in a human experience, comprising:

• one or more processing units;
• a memory;
• and
• interface circuitry, wherein the system is structured as:
• a short term forecast model that predicts a next action and a next intent from context data received from one or more electronic devices, the context data describing aspects of the human experience wherein the aspects include the next action and the predicted next intent;
• a first variational autoencoder that receives prediction values output from the short term forecast model and generates a latent first state of each aspect;
• a predictor that receives output of the first variational autoencoder, wherein the predictor applies a high-level model to the latent first state of each aspect to predict a next value of the latent first state;
• a compatibility detector that compares the predicted next value of the latent first state and a latent next state to determine a distance between the latent next state and the predicted next value of the latent first state;
• and
• a context change detector that receives output of the predictor to detect a meaningful context change when the distance is greater than a threshold, wherein the meaningful context change distinguishes a temporary change of intent and context from a change of intent and context for which a machine learning model is to be changed.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for controlling operation of one or more electronic devices based on detection of a meaningful context change in context data describing a human experience, the method comprising:

• receiving context data from one or more electronic devices, the context data describing aspects of the human experience, wherein the aspects comprise an action and an intent;
• predicting a next action and a next intent related to a task based on the context data;
• generating, by a distributional representation, for the predicted next action, the predicted next intent and one or more further aspects, a corresponding latent first state;
• predicting, by a predictor, a next value of the latent first state for each latent first state;
• comparing a latent next state and the predicted next value of the latent first state for each latent first state by determining a distance between the latent next state and the predicted next value of the latent first state;
• determining a meaningful context change in the performance of the task when the distance is greater than a threshold, wherein the meaningful context change distinguishes a temporary change of intent and context from a change of intent and context for which a machine learning model running on at least one of the electronic devices is to be changed;
• and
• in response to determining the meaningful context change, changing the machine learning model running on at least one of the electronic devices.