> How detailed are the maps? I'm assuming they're using SLAM. [1] If so, the maps aren't that detailed A few years ago I would have probably agreed. But moder

> How detailed are the maps? I'm assuming they're using SLAM. [1] If so, the maps aren't that detailed A few years ago I would have probably agreed. But modern machine learning can draw spooky inferences from seemingly sparse data, on a scale that makes it practical. As the roomba bumps around a room it will learn where obstacles are but not what those obstacles are. But the size and shape of the footprint of those obstacles probably contains enough information to make guesses at what those might be. Approximate identification of some obstacles as couches is probably possible, just from the size and shape, and likely the position of those obstacles in the room. That won't tell you how old the couch is, or whether it's worn out or not, but if the roomba can't find any probable couches in that person's house, advertising couches to that person may pay off. Also, did the roomba start finding a new room recently, but the other rooms have remained the same? That person may have build an addition to their house, or started furnishing their basement. Did all the rooms change but the roomba is still paired to the same smartphone? They may have moved into a new home. In either case, if the new room[s] don't have couch-shaped obstacles, advertise couches. And this is just with the bumpers. If Amazon adds a camera to roombas, the snooping potential is obvious.