Abstract: An inference is made regarding whether or not an upcoming day is going to be a busy day for a user. One or more different user-specific event parameters are utilized to compute a user busyness score for the upcoming day, where these parameters are based in part on a history of events for the user and their past behavior. Then, whenever the user busyness score for the upcoming day is greater than a busy day threshold, it is inferred that the upcoming day is going to be a busy day for the user. Whenever the user busyness score for the upcoming day is less than a quiet day threshold, it is inferred that the upcoming day is going to be a quiet day for the user.

Abstract: An inference is made regarding whether or not an upcoming day is going to be a busy day for a user. One or more different user-specific event parameters are utilized to compute a user busyness score for the upcoming day, where these parameters are based in part on a history of events for the user and their past behavior. Then, whenever the user busyness score for the upcoming day is greater than a busy day threshold, it is inferred that the upcoming day is going to be a busy day for the user. Whenever the user busyness score for the upcoming day is less than a quiet day threshold, it is inferred that the upcoming day is going to be a quiet day for the user.

Abstract: An inference is made regarding whether or not an upcoming day is going to be a busy day for a user. One or more different user-specific event parameters are utilized to compute a user busyness score for the upcoming day, where these parameters are based in part on a history of events for the user and their past behavior. Then, whenever the user busyness score for the upcoming day is greater than a busy day threshold, it is inferred that the upcoming day is going to be a busy day for the user. Whenever the user busyness score for the upcoming day is less than a quiet day threshold, it is inferred that the upcoming day is going to be a quiet day for the user.

Abstract: In some implementations, sensors provide sensor data reflecting user activity detected by the sensors. An event analyzer generates an impact score for a change to an event associated with a user based on routine-related aspects generated from one or more user routine models associated with the user. The one or more user routine models are trained based at least in part on interaction data comprised of the sensor data. The impact score may be generated by analyzing the event attributes with respect to the routine-related aspects. The impact score is generated based on determining a difference in a level of deviation caused by the change, between one or more event attributes and routine-related aspects and based on comparing a time of the event to a reference time. The impact score can be used to determine which changes to events are important to the user.

Abstract: In some implementations, sensors provide sensor data reflecting user activity detected by the sensors. An event analyzer generates an unusualness score for an event associated with a user based on routine-related aspects generated from one or more user routine models associated with the user. The one or more user routine models are trained based at least in part on interaction data comprised of the sensor data. Event attributes of the event can be received that include a time of the event and attendees of the event. The unusualness score may be generated by analyzing the event attributes with respect to the routine-related aspects. The unusualness score is generated to quantify a level of deviation between the event attributes and the routine-related aspects. Service content can be generated for the user based at least in part on the unusualness score generated for the event.