Abstract: Techniques for managing a user's tasks based on the user's focus level. High focus time slots are identified during which the user has a high level of focus, as well as low focus time slots during which the user has a low level of focus. Tasks for completion by the user are scheduled such that the high focus time slots are allocated to tasks that require a high level of user focus, and the low focus time slots are allocated to tasks that require a low level of user focus. High focus times may be identified based on user indications and/or on biometric data describing the user's physical state. Interruption requests may also be managed based on the user's focus level. Necessary resources may be pre-loaded prior to the high focus time periods, and breaks may be automatically scheduled to maintain the user's high level of focus.

Abstract: Item classification rules are created based on examples selected by a user, such as by selecting a subset of emails, and the rule is used across a larger set of items to obtain automatic classification of similar items according to the rule. Based on an analysis, a candidate classification rule is generated identifying text-based features shared among the items of the subset. The user can review the candidate rule as well as a resultant subset of items generated by the rule, and either accept the candidate rule or make an adjustment to the examples and then perform one or more iterations of the analysis to refine the rule. Adjustments can be made by removing items incorrectly included in a resultant subset and/or adding items incorrectly excluded from a resultant subset, and using the adjusted subset in a next iteration.

Abstract: Techniques for managing a user's tasks based on the user's focus level. High focus time slots are identified during which the user has a high level of focus, as well as low focus time slots during which the user has a low level of focus. Tasks for completion by the user are scheduled such that the high focus time slots are allocated to tasks that require a high level of user focus, and the low focus time slots are allocated to tasks that require a low level of user focus. High focus times may be identified based on user indications and/or on biometric data describing the user's physical state. Interruption requests may also be managed based on the user's focus level. Necessary resources may be pre-loaded prior to the high focus time periods, and breaks may be automatically scheduled to maintain the user's high level of focus.

Abstract: A method of operating a computerized device to indicate importance of messages to a user includes calculating importance scores for the messages based on importance features of the messages, the importance scores calculated as weighted sums of respective feature scores for the messages, and selecting messages for including in a subset of messages based on the importance scores. Message data and respective importance indicators for the subset of messages are displayed to the user, the importance indicators drawn from a set of distinct importance indicators corresponding to the set of importance features. An importance indicator is displayed for a given importance feature of a message when a feature score for the importance feature is above a threshold.

Abstract: A messaging application such as an email application receives an electronic message including request text, and automatically analyzes the electronic message to extract the request text and match it to a matching predefined request. Predefined requests are associated with respective functions of the messaging application generating electronic messaging items. The electronic messaging item associated with the matching request is automatically generated. As an example, a matching request may be a request for a reply containing requested information. The requested information is obtained, and a reply message is automatically generated using a compose or reply function of the messaging application. The reply message can be presented to a user to enable the user to modify the message before it is sent.

Abstract: Item classification rules are created based on examples selected by a user, such as by selecting a subset of emails, and the rule is used across a larger set of items to obtain automatic classification of similar items according to the rule. Based on an analysis, a candidate classification rule is generated identifying text-based features shared among the items of the subset. The user can review the candidate rule as well as a resultant subset of items generated by the rule, and either accept the candidate rule or make an adjustment to the examples and then perform one or more iterations of the analysis to refine the rule. Adjustments can be made by removing items incorrectly included in a resultant subset and/or adding items incorrectly excluded from a resultant subset, and using the adjusted subset in a next iteration.