Detecting and modeling temporal computer activity patterns
First Claim
1. A method, comprising operations of:
- a) accessing a database of activity data based on presence information of an individual at a location and gathered over time;
b) for a selected weekday, computing probabilities over time that said individual is present at said location during said selected weekday;
c) based on said probabilities, determining estimated features of inactivity that correspond to said selected weekday;
d) searching said database for a plurality of feature instances corresponding to a first feature of said estimated features of inactivity;
e) computing probability distributions of characteristics of said plurality of feature instances and storing, in computer memory, said probability distributions along with an identifier of said first feature as an activity rhythm model of said first feature, wherein said characteristics of said plurality of feature instances comprise a start-time, and end-time, and a duration and wherein further said probability distributions of said activity rhythm model of said first feature comprise a start-time distribution, an end-time distribution, a duration distribution, and a frequency of occurrence metric; and
f) displaying a view in a graphical user interface in a time management application, wherein the view depends on the activity rhythm model, wherein each operation of the method is executed by a microprocessor.
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Accused Products
Abstract
A method is described with which to detect and model a person'"'"'s temporal activity patterns from a record of the persons computer activity or online presence. The method is both predictive and descriptive of temporal features and is constructed with a minimal amount of beforehand knowledge. Activity related data is accumulated from a mechanism that is involved in the activity of a person. Significant inactivity features are identified within the activity data. These inactivity features are characterized so as to project the temporal activity of the person. Real-time activity of the person is then detected and inactivity periods are checked for likelihood of belonging to a previously characterized significant feature. The resulting information is formatted and made available to individuals having a need for the information.
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Citations
24 Claims
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1. A method, comprising operations of:
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a) accessing a database of activity data based on presence information of an individual at a location and gathered over time; b) for a selected weekday, computing probabilities over time that said individual is present at said location during said selected weekday; c) based on said probabilities, determining estimated features of inactivity that correspond to said selected weekday; d) searching said database for a plurality of feature instances corresponding to a first feature of said estimated features of inactivity; e) computing probability distributions of characteristics of said plurality of feature instances and storing, in computer memory, said probability distributions along with an identifier of said first feature as an activity rhythm model of said first feature, wherein said characteristics of said plurality of feature instances comprise a start-time, and end-time, and a duration and wherein further said probability distributions of said activity rhythm model of said first feature comprise a start-time distribution, an end-time distribution, a duration distribution, and a frequency of occurrence metric; and f) displaying a view in a graphical user interface in a time management application, wherein the view depends on the activity rhythm model, wherein each operation of the method is executed by a microprocessor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer-readable storage medium having stored thereon a computer program, the computer program comprising a set of instructions which when executed by a computer cause the computer to perform the operations of:
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a) accessing a database of activity data based on presence information of an individual at a location, wherein said database comprises records of daily activity data organized by weekday; b) examining records of said database corresponding to a selected weekday to compute probabilities over time that said individual is active at said location during said selected weekday; c) based on said probabilities, determining features of inactivity that correspond to said selected weekday; d) searching said database to identify a plurality of records that contain periods of inactivity that are similar to a first feature of said features of inactivity; e) computing probability distributions of characteristics of said periods of inactivity identified in said d) and storing, in said memory, said probability distributions along with an identifier of said first feature as an activity rhythm model of said first feature, wherein said characteristics of said periods of inactivity comprise a start-time, and end-time, and a duration and wherein further said probability distributions of said activity rhythm model of said first feature comprise a start-time distribution, an end-time distribution, and a duration distribution; and f) displaying a view in a graphical user interface in a time management application, wherein the view depends on the activity rhythm model. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification