Microsoft Technology Licensing, LLC

Nirupama Chandrasekaran

Ryen William White

Michael Gamon

Nikrouz Ghotbi

Mark James Encarnacion

Robert Alexander Sim

Elnaz Nouri

Sujay Kumar Jauhar

Vanessa Graham Murdock

Eric J Horvitz

Zhengyou Zhang

Andrew David Wilson

Zicheng Liu

Philip Andrew Chou

Cha Zhang

Dinei A Florencio

Paul Nathan Bennett

Gopal Parupudi

David C James

Rajesh Hegde

Patrick Pantel

Gregg Robert Wygonik

Michael Joseph Healy

Gaurav Raghu Dhawan

Richard Lloyd Hughes

Rajagopalan Badri Narayanan

Prabhdeep Singh

John Leo Ellis

Russell Allen Herring, Jr

Anantha Deepthi Uppala

Khuzaima Iqbal

Prabhu Padhi

Mathieu Etienne Jacques Audouin

Atif Gul

Ingo R A G Bringemeier

Ben Yim

Sean Edward Andrist

Richard L Hughes

Eric Joel Horvitz

Michael Healy

Ingo RAG Bringemeier

Aspects of the present disclosure relate to task template generation and social task discovery. In examples, a task template catalog comprises task templates, which may be automatically generated and/or user-submitted, among other examples. Task templates can be reviewed, shared, and curated within the task template catalog. A user may browse the task catalog or search the task catalog for task templates. Once the user selects a task template, a task is generated based on the task template and added to the user's task list. In some examples, aspects of a task template may be customized. For example, a task may comprise parametric or conditional subtasks, thereby enabling a user to further tailor the task template to his or her needs. Thus, the task catalog provides a starting point from which the user can author a task in a task management application.

Task templates and social task discovery

Systems and methods can be implemented to provide suggestions to a user device to execute user tasks in a variety of applications. In various embodiments, a set of tasks of a user from a number of task sources are identified for possible consolidation. The tasks in the set can be analyzed to identify a location to accomplish the tasks, estimate times to complete the tasks at the identified locations, and compute routes to the locations from a starting location or compute routes with respect to a route to another location of another task of the set of tasks. The identified locations, the estimated times, and the computed routes can be used to determine which tasks of the set of tasks are available for consolidation. One or more suggestions of task consolidations can be generated to or within the user device. Additional systems and methods are disclosed.

Location-based task suggestions

Systems and methods related to engaging with a virtual assistant via ancillary input are provided. Ancillary input may refer to non-verbal, non-tactile input based on eye-gaze data and/or eye-gaze attributes, including but not limited to, facial recognition data, motion or gesture detection, eye-contact data, head-pose or head-position data, and the like. Thus, to initiate and/or maintain interaction with a virtual assistant, a user need not articulate an attention word or words. Rather the user may initiate and/or maintain interaction with a virtual assistant more naturally and may even include the virtual assistant in a human conversation with multiple speakers. The virtual assistant engagement system may utilize at least one machine-learning algorithm to more accurately determine whether a user desires to engage with and/or maintain interaction with a virtual assistant. Various hardware configurations associated with a virtual assistant device may allow for both near-field and/or far-field engagement.

Non-verbal engagement of a virtual assistant

Aspects of the technology described herein provide an efficient user interface that enables users to respond to tasks quickly by providing automated quick task notifications via an audio channel. An audio channel quick task system includes components for recognizing and extracting quick tasks from content (e.g., interpersonal communications, composed content, line of business (LOB) application documents), and for prioritizing and routing the quick tasks to the user via an audio channel at an appropriate and relevant time. The system is enabled to process a user response, determine an action for handling the quick task, and execute the action on behalf of the user (e.g., pass a reply to a requestor, pass an instruction to an application or service, queue the quick task notification, delegate the quick task to another user or bot, forward the quick task to a companion device, or launch an application on a companion device).

Automated quick task notifications via an audio channel

A Personal Virtual Assistant (PVA) system takes existing inputs and processes them to understand the user's personality, intent, etc., through analysis of features such as the user's tone, language use, vocal volume, facial expressions, and context from device use and surroundings. As a result of the processing, the PVA system augments how it responds and interacts with the user. Such augmented responses may include simple things like changing the assistant's tone to complimenting the user's mood, as well as changing her use of language, volume, and the way she shares information, like giving good news when the user might be down, or suppressing bad news when the time is not right for the user to optimally process it.

Empathetic personal virtual digital assistant

A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

Automatic extraction of commitments and requests from communications and content

The subject disclosure is directed towards an immersive conference, in which participants in separate locations are brought together into a common virtual environment (scene), such that they appear to each other to be in a common space, with geometry, appearance, and real-time natural interaction (e.g., gestures) preserved. In one aspect, depth data and video data are processed to place remote participants in the common scene from the first person point of view of a local participant. Sound data may be spatially controlled, and parallax computed to provide a realistic experience. The scene may be augmented with various data, videos and other effects/animations.

Immersive remote conferencing

A system and method that allows for distress or SoS messages from various managed resources to be sent to a reliable location such as a server. The server can gather the distress messages and provide a summary or health status based on the gathered distress messages.

Computing system infrastructure to administer distress messages

A client in a plurality of clients with a same network address or similar common identifier is designated a Peer Distribution Point (PDP) agent for distributing a software application divided into several packages to the plurality of clients. The plurality of clients are part of a client-server hierarchical system comprising a Central SMS site server and several Distributing servers. The Central SMS site server generates policies for the packages that are pushed to a Distribution server assigned to the plurality of clients from where the PDP agent pulls the policies. The Central SMS site server generates the packages that are pushed to the Distributing server after the PDP agent pulls the policies. The packages are pulled by the PDP agent from the Distributing server. The other clients pull the packages from the PDP agent as and when needed.

Peer distribution point feature for system management server

Growing community of inventors

Seattle, WA, United States of America

Nirupama Chandrasekaran