In the Internet of Things, and particularly in the sub-domain called the smart home, there is so much information flowing in all directions that just dealing with exceptions or trying to derive insights are no longer sufficient for the realization of all the potential value that exists. True utility is dependent on devices and systems being acutely aware of where they are, where you are, what else is going on that might affect what should happen next, and knowing what happened before when a similar situation existed.
That’s called contextual intelligence, and the best way to describe it is with an illustration. Here’s how I want my contextually aware system to “think:”
The garage door just opened. Based on their smartphone locations, the husband and his wife are in the car and leaving. We should save electricity by turning down the air conditioning, but it’s 118 degrees outside [should have mentioned I live in a desert] so I don’t want to turn it off completely. I’ll raise it to 85, and will also make sure all the lights are off, including the outside lights because it’s the middle of the day, and I’ll set the alarms. I’ll also forward the land line to one of their cell phones. Later in the day, geofencing tells me they’re nearing home. When they’re ten minutes out I’ll crank the temperature down to 77. The garage door is opening and smartphone locations tell me it’s certainly the homeowners, so I’ll disarm the alarm and unforward the phone. Now they’re in the house and the husband goes upstairs while the wife stays downstairs. In ten minutes I’ll raise the downstairs temperature by two degrees because that’s what the wife always does. And if she walks upstairs, I’ll raise the temperature up there as well because I figured out long ago who wears the pants in this family.
As you might imagine, it takes a lot of intelligence to pull off a scenario like that, and the more you think about it, the more you realize that it’s far more complicated than it might seem at first blush. The system figured out that the homeowners like an initial blast of cold in the house but then turn up the temp when they get used to it. But it also knows that, if they’ve just been out on a bike ride – detectable by their smartphone-reported average 15-20 mph speed over a route they’ve done many times before – they want the temp left down low for a longer period of time. And just because the husband turned it up himself earlier than that once or twice doesn’t necessarily mean that anything needs to change in the default scene.
The same concepts that apply to using home automation also apply to the task of supporting people who use it. It’s becoming fairly well-understood that difficulties in installing and configuring smart home devices and systems is a key impediment to adoption. The approaches of the past won’t work; the new paradigm needs to rely on context.
Again, an example. (Full disclosure: This comes from work that Support.com is currently doing in this area.) A homeowner is having trouble adjusting her connected thermostat. She picks up her smartphone and goes to the app she uses to control all the devices in her home. The system has already detected that there’s something wrong with the thermostat, and has flagged it, indicating that the problem is local to the device and not an issue with the overall home ecosystem.
She presses a HELP button embedded in the app and, instead of making her answer a bunch of questions to diagnose the problem, the app immediately takes her to a series of steps telling her how to change the batteries. Unbeknownst to her, the cloud-based system behind her home automation configuration has figured out that the batteries have been losing power over the past several days, and the unit finally went offline that morning. The system was therefore able to figure out, based on those contextual clues, that the batteries had died and suggested the most likely solution.
Without ever leaving the app, she follows the recommended steps, replaces the batteries, but the thermostat still won’t power up. She then requests help from a live agent.
Now, at this point, traditionally, the agent would ask her to described the problem and then probably drag her through all the steps she’s already tried. If he’s lucky, she won’t hang up and ship the thermostat back to whomever she bought it from, but this is not going to be a happy consumer.
If the agent is instead using contextually aware tools, he won’t do either of those things. He’ll immediately see not only that the batteries have died, he’ll also know that she was presented with some suggested steps, followed them, and got stuck when she snapped the cover back on and the unit still didn’t power up. It’s as though he’d been right there with her all along. Contextual intelligence allows him to skip over the useless, frustrating steps and dig into the heart of the problem. The system might suggest to him, “Let’s use her smartphone as a remote camera so you can see how she put the batteries in.” Sure enough, one is upside down and he can tell her to turn it. Voilà: The problem is solved.
Let’s face it: In the main, there’s not much difference between the individual devices offered by various manufacturers. How much “better” can you turn the temperature up or switch a light bulb from green to purple?
The real differentiation lies in how intelligently you can orchestrate the interaction of those devices with each other and with the environment, and how seamlessly you can step in and help users who are having difficulties.
In both of those cases, context is king. And contextual intelligence is the next frontier in the Internet of Things.