Microsoft Technology Licensing, LLC

Bernd Ingo Plontsch

Omar Shaya

Ryen William White

Robert Alexander Sim

Elnaz Nouri

Manjot Singh Pal

Florian Peninon

Dominic Davies

Thomas Bogner

Basak Haznedaroglu

Julius Schäper

Shahar Prish

Shrey Nitin Shah

Chad Erik Fowler

Benjamen Ljudmilov Mateev

Viktoryia Akulich

Julius Raphael Melchior Schaper

Timothy Alexander Yates

Antonio José Consuegra Berruezo

Tae Hun Kim

Iurii Kondratiuk

Klorida Miraj

Selina Wang

The present disclosure relates to systems and methods for an interactive, intelligent hub built around the completion of a task. This hub brings together resources, information, suggested steps, and other automated assistance to facilitate the completion of the task. AI-based assistance may indicate which steps can be completed by automated processes, and dispatch those processes, or suggest resources to assist in the completion of other steps. The hub displays the current status of the task, and lives until the completion of the task, or abandonment by the user.

Semi-autonomous intelligent task hub

The unique attributes of handheld devices and how they are used—particularly multi-screen devices—are leveraged to define rules for automatically triggering workflows. By monitoring signals from various device sensors, the device can anticipate a user's intention to perform an action, such as capturing a quick thought. A workflow for performing the action (or actions) may be automatically triggered based on rules for evaluating the sensor signals. By anticipating the user's intentions, the device can automatically perform many of the underlying actions behind the scenes, thereby minimizing the actions performed by the user and improving the user experience. In this way, cumbersome, multi-step user inputs and interactions are avoided by anticipating user intentions and automatically triggering workflows.

Contextual workflow triggering on devices

A system for assisting users in making progress towards the completion of a task by recommending actions is provided. The techniques disclosed herein enable a system to receive user input signals, such as a voice input, a text input or any other type of input comprising phrases indicating a task. The system can then automatically generate recommendations for directing the user to complete the task. The techniques disclosed herein can also identify a contextually-relevant application that is most suitable for completing the identified task. For instance, a user may have several messaging applications. Based on a context of the user's input, and other historical data utilized by one or more machine learning engines, a system can recommend an appropriate application, or provide a ranking of the applications, suitable for completing an identified task. The techniques disclosed herein also employ machine learning engines to improve accuracy of the recommendations over time.

Contextually-aware recommendations for assisting users with task completion

An enhanced task management feature can provide enhanced task management capabilities including making the entering of new tasks in an electronic application easier for the user, such as having the user see suggestions of where to put the new task and, in some cases, providing suggestions for details associated with the task. The enhanced task management feature can receive task input associated with a task item from an application; use the task input and at least a user-specific model to identify at least one likely task list name for the task item; and provide a suggested task list name from the at least one likely task list name to the application for display. The enhanced task management feature can receive from user input a task list name and the task item and the task item can then be assigned to a task list associated with the task list name.

Enhanced task management feature for electronic applications

Presenting and manipulating task items includes creating a task item within a productivity application in association with a content item created with the productivity application and assigning the task item to one or more users. When the task item has been assigned, the task item is displayed to each user when each users opens the productivity application, or when the productivity application is already open to the user. User activity is then monitored in relation to the content item that is associated with the task item such that metadata for the user activity is captured. Based on the captured metadata, the users are alerted to the user activity that has occurred on the content item.

Presenting and manipulating task items

In non-limiting examples of the present disclosure, systems, methods and devices for assisting with task completion are provided. A natural language input may be received. A natural language processing engine may be applied to the natural language input. A primary task associated with the natural language input may be identified. A plurality of subtasks for completing the primary task may be identified from the natural language input. A determination may be made from the natural language input that the primary task or one of the plurality of subtasks is more important than other tasks. The primary task and the plurality of subtasks may be added to a list of tasks in a task completion application. An indication of importance may be associated in the task completion application in association with the task or subtask that is determined to be more important.

Natural language processing and machine learning for personalized tasks experience

Display screen with icon

Display screen with graphical user interface

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Berlin, Germany

Bernd Ingo Plontsch