Quiet day inference for users

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.

Wherefore, what is claimed is: 
     
         1 . A system for inferring whether or not an upcoming day is going to be a quiet day for a user, the system comprising:
 one or more computing devices, said one or more computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices, and a computer program having a plurality of sub-programs executable by said one or more computing devices, wherein the plurality of sub-programs configure said one or more computing devices to:   utilize one or more different user-specific event parameters to compute a user busyness score for the upcoming day, said user-specific event parameters being based in part on a history of events for the user and past behavior of the user;   if the user busyness score for the upcoming day is less than a quiet day threshold, infer that the upcoming day is going to be quiet for the user; and   output a notification to the user to notify the user whether the upcoming day is going to be quiet.   
     
     
         2 . The system of  claim 1 , wherein the plurality of sub-programs further configure said one or more computing devices to present to the user one or more quiet day actions when the upcoming day is going to be quiet. 
     
     
         3 . The system of  claim 1 , wherein said user-specific event parameters comprise one or more of:
 one or more explicit user-specific event parameters; or   one or more inferred user-specific event parameters.   
     
     
         4 . The system of  claim 3 , wherein the one or more explicit user-specific event parameters comprise one or more of:
 a number of meetings scheduled for the user on a specific day of a specific week; or   a cumulative duration of said meetings; or   a number of reminders existing for the user on the specific day of the specific week.   
     
     
         5 . The system of  claim 3 , wherein the one or more inferred user-specific event parameters comprise one or more of:
 a number of pending tasks the user is assigned to complete on a specific day of a specific week; or   a number of commutes made by the user on the specific day of the specific week; or   a cumulative duration of said commutes.   
     
     
         6 . The system of  claim 1 , wherein the upcoming day coincides with a specific day of a week and a sub-program for utilizing the one or more different user-specific event parameters to compute the user busyness score for the upcoming day comprises sub-programs for:
 for each of said user-specific event parameters,   computing a first value for the user-specific event parameter on the upcoming day,   computing a second value for the user-specific event parameter on said specific day of each of a prescribed number of past weeks,   computing an historic weighted average of the user-specific event parameter over the prescribed number of past weeks, and   using the first value for the user-specific event parameter on the upcoming day and the historic weighted average of the user-specific event parameter over the prescribed number of past weeks to compute a parameter-related user busyness score for the user-specific event parameter, and   using a weighted combination of parameter-related user busyness scores that are computed for said user-specific event parameters to compute the user busyness score for the upcoming day.   
     
     
         7 . The system of  claim 6 , wherein the prescribed number of past weeks is nine. 
     
     
         8 . The system of  claim 6 , wherein a weight applied to each user-specific event parameter to compute the historic weighted average is initialized to a value defined by a prescribed function that decreases over the prescribed number of past weeks. 
     
     
         9 . The system of  claim 8 , wherein the prescribed function comprises one of:
 a linear function having a negative slope; or   an exponentially decreasing function.   
     
     
         10 . The system of  claim 6 , wherein a weight applied to each parameter-related user busyness score is initialized to a generic value which is subsequently adjusted over time based upon explicit feedback that is received from the user. 
     
     
         11 . The system of  claim 10 , wherein the generic value is computed as one divided by a total number of said user-specific event parameters that are utilized. 
     
     
         12 . The system of  claim 10 , wherein the generic value is based at least upon one of:
 an analysis of the history of events for the user and the past behavior of the user; or   a study that asks one or more users to, for each user-specific event parameter, rank the user-specific event parameter with regard to how good a busyness indicator the user-specific event parameter is in comparison to other user-specific event parameters.   
     
     
         13 . The system of  claim 1 , wherein the quiet day threshold is dynamically determined based on at least one of the history of events for the user, the past behavior of the user, or past user busyness scores that were computed for the user. 
     
     
         14 . At least one computer-readable storage medium having embodied thereon computer-usable instructions which, when executed by at least one processor, implement a method for inferring whether or not an upcoming day is going to be a quiet day for a user, the method comprising:
 utilizing one or more different user-specific event parameters to compute a user busyness score for the upcoming day, said user-specific event parameters being based in part on a history of events for the user and past behavior of the user;   whenever the user busyness score for the upcoming day is less than a quiet day threshold, inferring that the upcoming day is going to be quiet for the user; and   outputting, via a user device, a notification to the user to notify the user whether the upcoming day is going to be quiet.   
     
     
         15 . The at least one computer-readable storage medium of  claim 14 , the method further comprising presenting to the user one or more quiet day actions when the upcoming day is going to be quiet. 
     
     
         16 . The at least one computer-readable storage medium of  claim 14 , wherein said user-specific event parameters comprise one or more of:
 a number of meetings scheduled for the user on a specific day of a specific week; or   a cumulative duration of said meetings; or   a number of reminders existing for the user on the specific day of the specific week; or   a number of pending tasks the user is assigned to complete on the specific day of the specific week; or   a number of commutes made by the user on the specific day of the specific week; or   a cumulative duration of said commutes.   
     
     
         17 . The at least one computer-readable storage medium of  claim 14 :
 wherein the upcoming day coincides with a specific day of a week; and   wherein utilizing the one or more different user-specific event parameters to compute the user busyness score for the upcoming day comprises:   for each of said user-specific event parameters,   computing a first value for the user-specific event parameter on the upcoming day,   computing a second value for the user-specific event parameter on said specific day of each of a prescribed number of past weeks,   computing an historic weighted average of the user-specific event parameter over the prescribed number of past weeks, and   using the first value for the user-specific event parameter on the upcoming day and the historic weighted average of the user-specific event parameter over the prescribed number of past weeks to compute a parameter-related user busyness score for the user-specific event parameter; and   using a weighted combination of parameter-related user busyness scores that are computed for said user-specific event parameters to compute the user busyness score for the upcoming day.   
     
     
         18 . The at least one computer-readable storage medium of  claim 17 , wherein a weight applied to each user-specific event parameter to compute the historic weighted average is initialized to a value defined by a prescribed function that decreases over the prescribed number of past weeks. 
     
     
         19 . A computer-implemented process for inferring whether or not an upcoming day is going to be a quiet day for a user, the computer-implemented process comprising:
 utilizing one or more different user-specific event parameters to compute a user busyness score for the upcoming day, said user-specific event parameters being based in part on a history of events for the user and past behavior of the user;   whenever the user busyness score for the upcoming day is less than a quiet day threshold, inferring that the upcoming day is going to be quiet for the user; and   outputting a notification to the user to notify the user whether the upcoming day is going to be quiet.   
     
     
         20 . The computer-implemented process of  claim 19 , further comprising presenting to the user one or more quiet day actions when the upcoming day is going to be quiet.