Abstract: Systems and methods are provided that automatically process message input and provide action responses according to the processing results. The automatic action response system may leverage at least one machine-learning algorithm that is trained using a dataset. The provided action responses may comprise of default action responses and/or intelligent action responses that are based at least in part on prior conversational data between a user and a sender. Some intelligent action responses may include text-based replies, which eliminate the need for a user to type a reply on a device screen, thereby saving previous time, conserving device battery life, and preserving the integrity of the device hardware. A portion of a message may be highlighted manually by a user or automatically by the action response system to initiate the automatic action response system. In this way, a more efficient and productive user experience across various devices and applications is achieved.

Abstract: Systems and methods are provided that automatically process message input and provide action responses according to the processing results. The automatic action response system may leverage at least one machine-learning algorithm that is trained using a dataset. The provided action responses may comprise of default action responses and/or intelligent action responses that are based at least in part on prior conversational data between a user and a sender. Some intelligent action responses may include text-based replies, which eliminate the need for a user to type a reply on a device screen, thereby saving previous time, conserving device battery life, and preserving the integrity of the device hardware. A portion of a message may be highlighted manually by a user or automatically by the action response system to initiate the automatic action response system. In this way, a more efficient and productive user experience across various devices and applications is achieved.

Abstract: Systems and methods are provided that automatically process message input and provide action responses according to the processing results. The automatic action response system may leverage at least one machine-learning algorithm that is trained using a dataset. The provided action responses may comprise of default action responses and/or intelligent action responses that are based at least in part on prior conversational data between a user and a sender. Some intelligent action responses may include text-based replies, which eliminate the need for a user to type a reply on a device screen, thereby saving previous time, conserving device battery life, and preserving the integrity of the device hardware. A portion of a message may be highlighted manually by a user or automatically by the action response system to initiate the automatic action response system. In this way, a more efficient and productive user experience across various devices and applications is achieved.

Abstract: A status system suitable for monitoring and maintaining status for shared resources is provided. The status system obtains status for a shared resource from a plurality of users of the shared resource, the status indicating an observed status of the shared resource received via a device of each of the plurality of users. Based on the obtained status from the plurality of users, the status system determines a current status for the shared resource. A central database in a central data storage is updated with the current status for the shared resource. In response to a request for the status of the shared resource, the status system retrieves the current status of the shared resource and causes display of the current status in a user interface.

Abstract: Systems, methods and apparatus for categorizing electronic content. In one example, the system, method, and apparatus include receiving electronic content items; analyzing textual data and metadata associated with the electronic content items; generating a project workspace based on information associated with one selected from a group consisting of a user of the computing device, the electronic content items, textual data and metadata associated with the electronic content items; categorizing the electronic content items into the project workspace based on intrinsic data and extrinsic data associated with the user; and displaying the project workspace and the electronic content items associated with the project workspace.

Abstract: Systems and methods are provided that automatically process message input and provide action responses according to the processing results. The automatic action response system may leverage at least one machine-learning algorithm that is trained using a dataset. The provided action responses may comprise of default action responses and/or intelligent action responses that are based at least in part on prior conversational data between a user and a sender. Some intelligent action responses may include text-based replies, which eliminate the need for a user to type a reply on a device screen, thereby saving previous time, conserving device battery life, and preserving the integrity of the device hardware. A portion of a message may be highlighted manually by a user or automatically by the action response system to initiate the automatic action response system. In this way, a more efficient and productive user experience across various devices and applications is achieved.

Abstract: A meeting service with meeting time and location optimization is described. In some examples, organizer and attendee behaviors may be analyzed and used as contextual factors in computing ranking scores for potential meeting times. To optimize a meeting for the attendees (including the organizer), potential meeting times and locations may be ranked based on weighted coefficients for the factors that are used in ranking such as attendee schedules, preferences, meeting type, location attributes, attendee preferences for locations, and extraneous factors such as events and other happenings that may impact availability or access of attendees as well as meeting locations.

Abstract: Variety of approaches to provide a meeting time picker with automated suggestions are described. A calendar application initiates operations to provide a meeting time picker upon detecting an intent of a meeting organizer to organize a meeting. The intent lists a meeting invitee. Availability information associated with the meeting invitee is retrieved from a calendar provider. A first layer, a second layer, and third layer of timeslots are generated for a presentation. The first layer includes a meeting organizer schedule. The second layer includes an aggregate schedule of the meeting invitee. The third layer includes a suggested schedule of a meeting time. The first layer, the second layer, and the third layer are presented in a scheduling grid to the meeting organizer.

Abstract: Variety of approaches to provide time polling are described. A calendar application initiates operations to provide meeting time polling upon processing an availability information associated with a meeting organizer and/or a meeting invitee to provide suggested timeslot(s). Selected timeslot(s) are received as a selection from the suggested timeslot(s) by the meeting organizer. The selected timeslots are transmitted to the meeting invitee for prompting the meeting invitee for a vote. Next, voted timeslots are received as a selection from the selected timeslot(s) by the meeting invite. The voted timeslot(s) are provided to the meeting organizer for prompting the meeting organizer to finalize the meeting.