Using health monitor data to detect macro and micro habits with a behavioral model

A method for communicating activity-related notifications to a user, the method comprising:

• receiving, at a remote computer system from a wireless communication transceiver module of a mobile computing device, a record of activity events of a particular activity type performed by the user over a period of time, the record specifying a time of each activity event in the record of activity events, wherein receiving the record of activity events comprises collecting location data sampled at a location sensor of the mobile computing device comprising the location sensor, a display, and the wireless communication transceiver module, wherein the location data comprises a location coincident performance of each activity event by the user;
• selecting, at the remote computer system, a first time-based filter from a set of time-based filters for the record;
• identifying, at the remote computer system, a cluster of activity events in the record of activity events filtered according to the first time-based filter, wherein identifying the cluster of activity events in the record of activity events comprises:
• obtaining a set of computer-implemented rules defining activity event subgrouping as a function of activity event start time, activity event duration, and coincident location, wherein the set of computer-implemented rules are operable to improve the remote computing system in relation to behavioral pattern prediction, and
• selecting a subgroup of activity events according to the set of computer-implemented rules and corresponding to:
• composite start times and durations falling within a core composite distance threshold of a composite start time and a duration of at least one other activity event within the subgroup of activity events, and
• locations, from the location data sampled at the location sensor, within a threshold distance of a location corresponding to at least one other activity event within the subgroup of activity events;
• and
• predicting, at the remote computer system, representation of a behavioral pattern of the user in the subgroup based on identification of the cluster;
• identifying, at the remote computer system, an early bound on times of activity events of the particular activity type from the cluster of activity events;
• identifying, at the remote computer system, a late bound on times of activity events of the particular activity type from the cluster of activity events;
• extrapolating, from the cluster, a quantitative datum corresponding to the particular activity type and the first time-based filter;
• at a first time succeeding the period of time, presenting a form of the quantitative datum through the display of the mobile computing device associated with the user, the first time within a threshold time of the early bound on a day fulfilling the first time-based filter;
• and
• at a second time succeeding the period of time, in response to an absence of a detected activity of the particular activity type between the early bound and the late bound on a day fulfilling the first time-based filter, presenting to the user a prompt to provide feedback for the absence of a detected activity of the particular activity type, the second time within a threshold time of the late bound on a day fulfilling the first time-based filter.