An Intelligent, Situation-aware 'Do Not Disturb' mode for Mobile Devices
Submitted by George Krasadakis
Signed on Ethereum on 10/21/2019
An advanced software component (at the O/S level) for smart phones which can automatically understand if the situation is 'noise sensitive' in order to minimize the disturbance and annoyance from mobile ringtones, and the actual chats. Users benefit by auto-compliance of the audience and responsible use of smart phones when the user enters a place or joins a certain social gathering.
Ever been in theater, cinema or other noise-sensitive social situations where sounds from mobile notifications can spoil the moment? The natural move for a context-aware user is to set the mobile in silent or ‘Do not disturb’ mode. Although obvious, this is not the case for everybody: there are always those few who either by mistake or disrespectfully skip this.

What if there was a way for the audience to seamlessly self-organize?
‘The system’ could identify the situation as requiring ‘silent mode’ and notify the members of the audience to silent their mobiles (those who haven’t already); Or, in a more aggressive scenario, automatically set the phones into silent/vibration mode.
This could happen seamlessly with no controlling system or particular rules: Assuming a number of people are at a particular place — within a specific radius and possibly around a particular known location; each time a mobile device is set to ‘silent mode’ by a user, an event is triggered which sends location and mode data into an appropriate database; this centralized store of data allows the identification of ‘concurrent’ transitions to ‘silent mode’ within the same radius.

Multiple human-originated transitions to ‘silent mode’ which are time-aligned and within the same radius, indicate a self-adjusting behavior (people set their mobile phones to ‘silent mode’ at the same time and possibly for the same reason).

If this behavior is significant (as a percentage of the audience — more than x% of the people identified in the same radius and time frame) there is a clear indicator that the particular situation (people arrangement+point in time+ location) is requiring mobile devices in silent mode. Assuming that this behavior follows particular patterns — like specific days of the week, months, time-slots within the day, size of the audience, time-frame length, etc. — the system can safely identify this location and time arrangement as ‘sensitive to noise’.

This way non-compliant users (part of the audience) automatically receive notifications to silent their mobiles; or, in a more intrusive scenario their mobile automatically switches to silent mode
On exit from the radius or the time-frame, each mobile device returns to its previous state.

This would also allow to automatically and dynamically flag particular places as noise sensitive and reflect this to maps and place references. It could be implemented as a feature embedded at the operating system level of mobile devices (across popular Operating Systems) storing anonymous location and phone state data into a centralized cloud-based data store.
#improveDigitalLife #improveSocialBehavior