Apple Inc.

Other

Bo Morgan

Mark E Drummond

Siva Chandra Mouli Sivapurapu

Dan Feng

Payal Jotwani

Mu Qiao

Behrooz Mahasseni

Daniel Laszlo Kovacs

Aashi Manglik

Edward S Ahn

Peter Meier

Ian M Richter

Bradley Warren Peebler

Shaun D Budhram

Cameron J Dunn

Michael J Gutensohn

John Christopher Russell

Daniel Ulbricht

Angela Blechschmidt

Michelle Chua

Adam Michael O'Hern

Shem Nguyen

Novaira Masood

Noah Gamboa

Noah Jonathan Gamboa

James Moll

A device obtains a request to display a graphical environment. The graphical environment is associated with a set of saliency values corresponding to respective portions of the graphical environment. A POV for displaying the graphical environment is selected based on the set of saliency values. The graphical environment is displayed from the selected POV on the display.

Displaying an environment from a selected point-of-view

In one implementation, a method of composing a scene content is performed at a device including a display, one or more processors, and non-transitory memory. The method includes generating a definition of a scene based on textual or speech input and a model of a physical environment, wherein the definition includes a constraint that defines a spatial relationship between a virtual asset and an anchor asset that corresponds to one or more physical objects in the physical environment. The method includes generating, based on the definition of the scene and the model of the physical environment, a first instance of the scene that satisfies the constraint with the virtual asset in the spatial relationship with a first one of the one or more physical objects in the physical environment. The method includes presenting, on the display, the first instance of the scene.

Methods and systems for composing and executing a scene

In some implementations, a method includes obtaining an objective for a computer-generated reality (CGR) representation of an objective-effectuator. In some implementations, the objective is associated with a plurality of time frames. In some implementations, the method includes determining a plurality of candidate plans that satisfy the objective. In some implementations, the method includes selecting a first candidate plan of the plurality of candidate plans based on a selection criterion. In some implementations, the method includes effectuating the first candidate plan in order to satisfy the objective. In some implementations, the first candidate plan triggers the CGR representation of the objective-effectuator to perform a series of actions over the plurality of time frames associated with the objective.

Planner for an objective-effectuator

In some implementations, a device includes one or more processors and a non-transitory memory. In some implementations, a method includes obtaining an object that includes a plurality of portions including a first portion and a second portion. In some implementations, the method includes determining a first saliency value for the first portion of the object and a second saliency value for the second portion of the object. In some implementations, the method includes triggering presentation of the first portion of the object in accordance with a first display characteristic that is based on the first saliency value and the second portion of the object in accordance with a second display characteristic that is based on the second saliency value.

Triggering presentation of an object based on saliency

In some implementations, a method includes obtaining, by a virtual intelligent agent (VIA), a perceptual property vector (PPV) for a graphical representation of a physical element. In some implementations, the PPV includes one or more perceptual characteristic values characterizing the graphical representation of the physical element. In some implementations, the method includes instantiating a graphical representation of the VIA in a graphical environment that includes the graphical representation of the physical element and an affordance that is associated with the graphical representation of the physical element. In some implementations, the method includes generating, by the VIA, an action for the graphical representation of the VIA based on the PPV. In some implementations, the method includes displaying a manipulation of the affordance by the graphical representation of the VIA in order to effectuate the action generated by the VIA.

Responding to representations of physical elements

According to various implementations, a method is performed at an electronic device including one or more processors, a non-transitory memory, and a display. The method includes displaying, on the display, a virtual agent that is associated with a first viewing frustum. The first viewing frustum includes a user avatar associated with a user, and the user avatar includes a visual representation of one or more eyes. The method includes, while displaying the virtual agent associated with the first viewing frustum, obtaining eye tracking data that is indicative of eye behavior associated with an eye of the user, updating the visual representation of one or more eyes based on the eye behavior, and directing the virtual agent to perform an action based on the updating and scene information associated with the electronic device.

Directing a virtual agent based on eye behavior of a user

In one implementation, a method of displaying content is performed at a device including a display, one or more processors, and non-transitory memory. The method includes scanning a first physical environment to detect a first physical object in the first physical environment and a second physical object in the first physical environment, wherein the first physical object meets at least one first object criterion and the second physical object meets at least one second object criterion. The method includes displaying, in association with the first physical environment, a virtual object moving along a first path from the first physical object to the second physical object. The method includes scanning a second physical environment to detect a third physical object in the second physical environment and a fourth physical object in the second physical environment, wherein the third physical object meets the at least one first object criterion and the fourth physical object meets the at least one second object criterion. The method includes displaying, in association with the second physical environment, the virtual object moving along a second path from the third physical object to the fourth physical object, wherein the second path is different than the first path.

Various implementations disclosed herein include devices, systems, and methods for training of an action determining component of a computer character. In some implementations, actions are taken by the character in a 3D environment according to an action determining component of the character, where the character is rewarded or penalized for interactions associated with an object/concept in the 3D environment according to an assigned object/concept reward or penalty. In some implementations, the reward or the penalty assigned to the object/concept is modified, and the character is then rewarded or penalized for interactions associated with the object/concept according to the modified reward or the modified penalty. The action determining component of the character is trained using a reinforcement learning technique that accounts for rewards or penalties obtained by virtual character for interactions associated with the object/concept.

Training a character through interactions

In one implementation, a method of achieving an objective using a scene graph includes identifying a set of transition couples of a plurality of transition couples between sets of spatial relationships of the scene graph, wherein each spatial relationship of a respective first set of spatial relationships indicated by each transition couple of the set of transition couples is included in an initial set of spatial relationships or an respective second set of spatial relationships indicated by a previous transition couple and wherein a particular spatial relationship is included in a respective second set of spatial relationships of a last transition couple of the set of transition couples.

Scene graph assisted navigation

In some implementations, a method includes obtaining a semantic construction of a physical environment. In some implementations, the semantic construction of the physical environment includes a representation of a physical element and a semantic label for the physical element. In some implementations, the method includes obtaining a graphical representation of the physical element. In some implementations, the method includes synthesizing a perceptual property vector (PPV) for the graphical representation of the physical element based on the semantic label for the physical element. In some implementations, the PPV includes one or more perceptual characteristic values characterizing the graphical representation of the physical element. In some implementations, the method includes compositing an affordance in association with the graphical representation of the physical element. In some implementations, the affordance allows interaction with the graphical representation of the physical element in accordance with the perceptual characteristic values included in the PPV.

Perceptual property vector for an object

In some implementations, a method includes obtaining environmental data corresponding to a physical environment. In some implementations, the method includes determining, based on the environmental data, a bounding surface of the physical environment. In some implementations, the method includes detecting a physical element located within the physical environment based on the environmental data. In some implementations, the method includes determining a semantic label for the physical element based on at least a portion of the environmental data corresponding to the physical element. In some implementations, the method includes generating a semantic construction of the physical environment based on the environmental data. In some implementations, the semantic construction of the physical environment includes a representation of the bounding surface, a representation of the physical element and the semantic label for the physical element.

Generating a semantic construction of a physical setting

In some implementations, a device includes one or more sensors, one or more processors and a non-transitory memory. In some implementations, a method includes determining that a first portion of a physical environment is associated with a first saliency value and a second portion of the physical environment is associated with a second saliency value that is different from the first saliency value. In some implementations, the method includes obtaining, via the one or more sensors, environmental data corresponding to the physical environment. In some implementations, the method includes generating, based on the environmental data, a model of the physical environment by modeling the first portion with a first set of modeling features that is a function of the first saliency value and modeling the second portion with a second set of modeling features that is a function of the second saliency value.

Modeling a physical environment based on saliency

In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

Computationally efficient model selection

A method includes generating, in coordination with an emergent content engine, a first objective for a first objective-effectuator and a second objective for a second objective-effectuator instantiated in a computer-generated reality (CGR) environment. The first and second objectives are associated with a mutual plan. The method includes generating, based on characteristic values associated with the first and second objective-effectuators a first directive for the first objective-effectuator and a second directive for the second objective-effectuator. The first directive limits actions generated by the first objective-effectuator over a first set of time frames associated with the first objective and the second directive limits actions generated by the second objective-effectuator over a second set of time frames associated with the second objective. The method includes displaying manipulations of CGR representations of the first and second objective-effectuators in the CGR environment in accordance with the first and second directives.

Growing community of inventors

Emerald Hills, CA, United States of America

Bo Morgan