ML can learn patterns that humans might not be aware of, so you there might be certain things that happen that show you will be on a mission to East Asia for a couple days.

Only when data is supplied to it to match the trained pattern,

ML is pattern recognition. Anything outside of that is still AI, but it isn't ML. I can think of very few feature sets we could supply to help predict someone will be deployed to East Asia for a few days other than scraping calendars and mail for religious and military organizations.

From a design perspective, Nest and others are either additively learning in situ to enhance a base model or they are working from a base model that doesn't directly learn, just classifies workflow to categorize observations on a base model. I doubt heavy training is occurring where the Nest and similar is treated as the central compute node.