We're combining data from different sensors, so if Point hears broken glass, and then notices the temperature changing or street noise being louder than usual we can more accurately detect broken windows.
OK, this is actually a really cool approach to what I gather is a common problem!
Does Point try to learn a baseline for temperature/acoustics/etc., and trigger when a deviation occurs? Or do you just say "when glass breaks + these other thresholds are tripped, ..."
Because the latter would probably solve the problem fairly well, but the former would be outstanding.
