Abstract: Various techniques for performing contextual event scheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This trained machine learning model is operated by the event scheduling service determine a proposed time and proposed scheduling parameters based on the contextual information, to identify a proposed event time and event scheduling parameters based on the model, the data indicating a user state, or external data. Further examples to evaluate user activity and identify schedule characteristics based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Abstract: Disclosed methods, systems, and storage media provide state-based time/task management interfaces. A computer device may determine various user states and user intents, and generate an instance of a graphical user interface (GUI) comprising objects and semantic time anchors. Each object may correspond to a user intent and each semantic time anchor may be associated with a user state. The computer device may obtain a first input comprising a selection of an object and obtain a second input comprising a selection of a semantic time anchor. The computer device may generate another instance of the GUI to indicate an association of the selected object with the selected semantic time anchor. The computer device may generate a notification to indicate a user intent of the selected object upon occurrence of a state that corresponds with the selected semantic time anchor. Other embodiments may be described and/or claimed.

Abstract: Various techniques for performing contextual event scheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This trained machine learning model is operated by the event scheduling service determine a proposed time and proposed scheduling parameters based on the contextual information, to identify a proposed event time and event scheduling parameters based on the model, the data indicating a user state, or external data. Further examples to evaluate user activity and identify schedule characteristics based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Abstract: Various techniques for performing contextual event rescheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This model is operated by the event scheduling service determine a contextual action option for rescheduling an electronic communication event at a proposed time with proposed scheduling parameters. The model may identify the proposed time and event scheduling parameters, from data indicating a user state, or external data, in addition to a semantic text option (such as “Call Back After Meeting”) corresponding to the proposed time and event scheduling parameters. Further examples to evaluate user activity and identify reschedule options based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Abstract: Various systems and methods for providing a meeting attendance tracking system are provided herein. A meeting attendance tracking system includes an indoor positioning service coupled to a plurality of radio transmitters, and operable to determine a location of a user device, the user device associated with a user, the location of the user device corresponding with a location of the user; a database interface to: access a database of scheduled events for the user; and determine a current event from the database of scheduled events, the current event corresponding with a current time and date; and a scheduler coupled to the indoor positioning service and the database interface, to: determine that the location of the user device does not correspond with a location of the current event; determine whether the user is attending a conflicting event; and provide a notification to the user or a meeting organizer regarding the current event.

Abstract: Systems, apparatuses and methods may provide for identifying a plurality of user intents and identifying user state data. Additionally, a time sorted list of intents may be generated based on the plurality of user intents and the user state data, wherein the time sorted list of intents defines a user route with respect to a particular day. In one example, the sorted list of intents is dynamically updated in response to a change in the user state data, a change in the plurality of user intents and/or a conflict between two or more of the plurality of user intents.