Microsoft Technology Licensing, LLC

Google Inc.

Vignesh Sachidanandam

Leonardo Giusti

Devon James O'Reilley Stern

Ned Bearer Friend

Brandon Charles Barbello

Tyler Reed Kugler

Ivan Poupyrev

Patrick M Amihood

Alok Chandel

Johan Prag

Selim Flavio Cinek

John David Jacobs

Lucas Dupin Moreira Costa

Artur Tsurkan

Matthew Jay Kotler

Erez Kikin Gil

Hiroshi Tsukahara

Mark E Pearson

Peter Loren Engrav

Nathaniel M Myhre

Abel Seleshi Mengistu

Jung Ook Hong

Yogesh Madhukarrao Joshi

Aniruddha Prabhakar Kulkarni

William Henry Gates, Iii

Jaime Lien

Cory J Cirrincione

Eiji Hayashi

Andrew T Hockman

Gary Caldwell

Scott M Stiles

Doreen Nelson Grieb

Gautam Kedia

Alex Savchenko

Lauren Bedal

Devon Stern

Christopher Hugh Pratley

Erez Kikin-Gil

Patrick Malatack

Michael I Borysenko

Joe Downing

Zachary Adam Kahn

Josh Meisels

Trevor Neil Lacey

Catherine Pham

Zachary Kahn

Devon James O'Reilly Stern

This document describes techniques and systems for authentication management through IMU and radar. The techniques and systems use inertial sensor data from an inertial measurement unit (IMU) and/or radar data to manage authentication for a computing device. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for computing-device authentication.

Authentication management through IMU and radar

This document describes techniques and systems for radar-based gesture-recognition with context-sensitive gating and other context-sensitive controls. Sensor data from a proximity sensor and/or a movement sensor produces a context of a user equipment. The techniques and systems enable the user equipment to recognize contexts when a radar system can be unreliable and should not be used for gesture-recognition, enabling the user equipment to automatically disable or 'gate' the output from the radar system according to context. The user equipment prevents the radar system from transitioning to a high-power state to perform gesture-recognition in contexts where radar data detected by the radar system is likely due to unintentional input. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Context-sensitive control of radar-based gesture-recognition

This document describes techniques and systems that enable input methods for mobile devices. A radar field enables an electronic device to accurately determine that a part of a user is within a gesture zone around the device. Further, the device can determine whether an application configured to receive input through radar-based gestures (radar gestures) is operating on the device. Using these techniques, the device can present a feedback indicator on a display when the user's hand is within a gesture zone around the device. The feedback indicator alerts the user that the user's hand is close enough to the device to make specific radar gestures. This allows the device to provide the user with feedback, which can educate the user about the device's capabilities and allow the user to take advantage of additional functionality and features provided by the availability of the radar gestures.

Radar gesture input methods for mobile devices

Technologies are described related to communication interfaces for wearable devices. User experience with wearable devices may be enhanced through tailored views for communications, calendar items, actions associated with those, where the views and presentations may be dynamically selected and adjusted based on context, user, location, and device capabilities. Smart notifications and user-friendly note taking functionality may be enabled also based on context, user, location, and device capabilities. Other scenarios may be unlocked based on proximity and/or sensor data.

Communication interface for wearable devices

Systems and techniques are described for robust radar-based gesture-recognition. A radar system detects radar-based gestures on behalf of application subscribers. A state machine transitions between multiple states based on inertial sensor data. A no-gating state enables the radar system to output radar-based gestures to application subscribers. The state machine also includes a soft-gating state that prevents the radar system from outputting the radar-based gestures to the application subscribers. A hard-gating state prevents the radar system from detecting radar-based gestures altogether. The techniques and systems enable the radar system to determine when not to perform gesture-recognition, enabling user equipment to automatically reconfigure the radar system to meet user demand. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Robust radar-based gesture-recognition by user equipment

This document describes techniques and systems that enable radar-based authentication status feedback. A radar field is used to enable an electronic device to account for the user's distal physical cues to determine and maintain an awareness of the user's location and movements around the device. This awareness allows the device to anticipate some of the user's intended interactions and provide functionality in a timely and seamless manner, such as preparing an authentication system to authenticate the user before the user touches or speaks to the device. These features also allow the device to provide visual feedback that can help the user understand that the device is aware of the user's location and movements. In some cases, the feedback is provided using visual elements presented on a display.

Radar-based authentication status feedback

This document describes techniques and systems that enable a mobile device-based radar system for applying different power modes to a multi-mode interface. The techniques and systems include a user device having a radar system, and an interaction manager. The radar system generates a radar field, provides radar data, and operates at one of various different radar-power states. The user device analyzes the radar data to detect a presence or movement of a user within the radar field. Responsive to the detection, the radar system changes from a first radar-power state to a second radar-power state. Based on this change, the interaction manager selects a power mode, for a multi-mode interface, that corresponds to the second radar-power state, and applies the selected power mode to the multi-mode interface to provide a corresponding display via a display device.

Mobile device-based radar system for applying different power modes to a multi-mode interface

This document describes techniques and systems that enable a visual indicator for paused radar gestures. The techniques and systems use a radar field to enable an electronic device to accurately determine radar gestures, or other movement, by a user. Further, the electronic device can determine certain conditions that can make it difficult for the electronic device to properly determine the user's radar gestures. When the device includes an application that can be controlled using radar gestures (a radar-gesture application), and the conditions are present, the device can enter a gesture-paused mode. When the device enters this mode, the techniques provide a gesture-paused feedback element on a display, which lets the user know that there is at least one radar-gesture application available or running on the electronic device but that radar gestures cannot presently be used to control the application.

Visual indicator for paused radar gestures

This document describes techniques and systems that enable a mobile device-based radar system () for providing a multi-mode interface (). A radar field () is used to enable a user device () to accurately determine a presence or threshold movement of a user near the user device. The user device provides a multi-mode interface having at least first and second modes and providing a black display or a low-luminosity display in the first mode. The user device detects, based on radar data and during the first mode, a presence or threshold movement by the user relative to the user device and responsively changes the multi-mode interface from the first mode to the second mode. Responsive to the change to the second mode, the user device provides visual feedback corresponding to the implicit interaction by adjusting one or more display parameters of the black display or the low-luminosity display.

Mobile device-based radar system for providing a multi-mode interface

An artificial intelligence ('AI') based system is disclosed for associating low-level user content, such as documents, email messages, and calendar invites, with high-level user activities using topics as an abstraction. The associations can enable a computing system to provide, among other things, activity-specific views that present a specific selection of low-level user content that is most relevant to a user at a particular point in time. The activity-specific views present the right information to users at the right time based on a context of a user and a user's past activities.

AI-driven human-computer interface for associating low-level content with high-level activities using topics as an abstraction

This document describes techniques and systems for radar-based gesture-recognition with context-sensitive gating and other context-sensitive controls. Sensor data from a proximity sensor () and/or a movement sensor () produces a context of a user equipment (). The techniques and systems enable the user equipment () to recognize contexts when a radar system () can be unreliable and should not be used for gesture-recognition, enabling the user equipment () to automatically disable or 'gate' the output from the radar system () according to context. The user equipment () prevents the radar system () from transitioning to a high-power state () to perform gesture-recognition in contexts where radar data detected by the radar system () is likely due to unintentional input. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

An artificial intelligence ('AI')-based system is disclosed for utilizing a schema to auto-generate an application for a specific context. An AI engine selects an activity schema associated with an activity. The schema identifies data sources for obtaining activity-specific content for the activity and can be selected based upon topics associated with the activity. The AI engine also selects a view definition that defines an arrangement of an activity-specific user interface ('UI') for presenting relevant activity-specific content obtained from the data sources identified by the schema. An application is then generated using the schema and the view definition. The application can generate the activity-specific UI for presenting the relevant activity-specific content.

AI-synthesized application for presenting activity-specific UI of activity-specific content

Systems and techniques are described for robust radar-based gesture-recognition. A radar system () detects radar-based gestures on behalf of application subscribers. A state machine () transitions between multiple states based on inertial sensor data. A no-gating state () enables the radar system () to output radar-based gestures to application subscribers. The state machine () also includes a soft-gating state () that prevents the radar system () from outputting the radar-based gestures to the application subscribers. A hard-gating state () prevents the radar system () from detecting radar-based gestures altogether. The techniques and systems enable the radar system () to determine when not to perform gesture-recognition, enabling user equipment () to automatically reconfigure the radar system () to meet user demand. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

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Mercer Island, WA, United States of America

Vignesh Sachidanandam