Apple Inc.

Mary A Pyc

Grant H Mulliken

Izzet B Yildiz

Steven A Marchette

Sterling R Crispin

Ronald J Guglielmone, Jr

Mikaela D Estep

Fletcher R Rothkopf

Aaron Mackay Burns

Nathan Gitter

Benjamin Hylak

Pau Sastre Miguel

Brian Pasley

Raphael A Bernier

Christine Godwin

Patrick O Eronini

Srinath Nizampatnam

Eve Ekman

Lauren Cheung

Anura A Patil

Jonathan Hadida

Embodiments are directed to aspects of the extended reality environments that are selected or otherwise modified to account for distracting stimuli. Similarly, one or more metrics that reflect, or are otherwise indicative of, a user's ability to focus on a current or upcoming activity may be used to adjust a user's interaction with the extended reality environment. The extended reality environment can generated by an extended reality system that includes a head-mounted display, a set of sensors and a processor configured to enter a focus mode that reduces distraction in an extended reality environment. While in the focus mode, the process or can receive imaging data of a physical environment around a user using the set of sensors and generate the extended reality environment that includes a reproduction of the first region of the physical environment where an identified object is replaced with the additional content.

Operating modes for reducing distraction in extended reality environments

In one implementation, a method of assisting in the rehearsal of a presentation is performed at a device including a display, one or more processors, and non-transitory memory. The method includes obtaining a difficulty level for a rehearsal of a presentation. The method includes displaying, on the display, one or more slides of the presentation. The method includes displaying, on the display in association with a volumetric environment, one or more virtual objects based on the difficulty level.

Virtual presentation rehearsal

Various implementations disclosed herein include devices, systems, and methods that assess physiological data of a user and a scene understanding of a physical environment to determine a retention state. For example, an example process may include obtaining physiological data in an environment during a first period of time. The process may further include identifying one or more of the objects in the environment based on determining a scene understanding of the environment. The process may further include determining, based on the physiological data and the scene understanding, features associated with interaction events for the one or more objects. The process may further include determining, based on the features, a retention state during the first period of time, the retention state associated with retention of a concept associated with an object of the one or more objects. The process may further include providing feedback based on identifying the retention state.

User feedback based on retention prediction

Some implementations disclosed herein present a computer-generated reality (CGR) environment in which a user participates in an activity, identify a cognitive state of the user (e.g., working, learning, resting, etc.) based on data regarding the user's body (e.g., facial expressions, hand movements, physiological data, etc.), and update the environment with a surrounding environment that promotes a cognitive state of the user for the activity.

Creation of optimal working, learning, and resting environments on electronic devices

Growing community of inventors

Campbell, CA, United States of America

Mary A Pyc