Abstract: Various systems and methods for processing activity data with a knowledge engine to generate actionable insights for a human subject are described. These actionable insights may include identifying a most likely action given a particular state of the human subject, identifying a most likely state in which the human subject performs a particular activity, or identifying anomalies in human activity patterns. In an example, an electronic processing system operates the knowledge engine with operations that: identify patterns of activity using clustering of events, identify meaningful patterns of activity from the patterns of activity based on co-occurrence of characteristics for respective events, rank the identified meaningful patterns of activity based on confidence and support of respective patterns to occur for a human subject, and generate a personalization action (such as an action for a software application) based on the ranked, identified meaningful patterns of activity.

Abstract: Various systems and methods for processing activity data with a knowledge engine to generate actionable insights for a human subject are described. These actionable insights may include identifying a most likely action given a particular state of the human subject, identifying a most likely state in which the human subject performs a particular activity, or identifying anomalies in human activity patterns. In an example, an electronic processing system operates the knowledge engine with operations that: identify patterns of activity using clustering of events, identify meaningful patterns of activity from the patterns of activity based on co-occurrence of characteristics for respective events, rank the identified meaningful patterns of activity based on confidence and support of respective patterns to occur for a human subject, and generate a personalization action (such as an action for a software application) based on the ranked, identified meaningful patterns of activity.

Abstract: An exercise schedule optimizer system includes a user interface to receive data input from an exerciser and provide data output to the exerciser. The system also includes an exercise goal generator to generate an exercise goal based on at least data regarding the exerciser, an exercise schedule proposer to propose one or more exercise schedule proposals based on at least a historical exercise pattern of the exerciser, and an optimal exercise scheduler to determine an optimal exercise schedule based on at least the one or more exercise schedule proposals.

Abstract: An event schedule optimizer system includes a user interface, an invitees extractor, a time optimizer, and a place optimizer. The user interface is to receive data input from an organizer and provide data output to the organizer. The invitees extractor is to generate an intended invitees list including intended invitees for a target event based on the data input from the organizer. The time optimizer is to determine an optimal time period for the target event based on a list of historical events of the organizer. The place optimizer is to determine an optimal place for the target event based on a set of proposed times and the list of historical events of the organizer.