Patent Description:
Augmented reality (AR), mixed reality (MR), virtual reality (VR), and cross reality (XR) may allow users to interact with an immersive environment having artificial elements such that the user may feel a part of that environment. For example, VR systems may display stereoscopic scenes to users in order to create an illusion of depth, and a computer may adjust the scene content in real-time to provide the illusion of the user moving within the scene. When the user views images through a VR system, the user may thus feel as if they are moving within the scenes from a first-person point of view. Similarly, MR systems may combine computer generated virtual content with real-world images or a real-world view to augment a user's view of the world, or alternatively combines virtual representations of real-world objects with views of a three-dimensional virtual world. The simulated environments of virtual reality and/or the mixed environments of mixed reality may thus provide an interactive user experience for multiple applications.

<CIT> discloses a system for offloading augmented reality processing to a remote computing system.

This disclosure includes references to "one embodiment" or "an embodiment. " The appearances of the phrases "in one embodiment" or "in an embodiment" do not necessarily refer to the same embodiment. Particular features, structures, or characteristics may be combined in any suitable manner consistent with this disclosure. The invention is defined in the appended claims and the scope of protection is determined by these appended claims.

Within this disclosure, different entities (which may variously be referred to as "units," "circuits," other components, etc.) may be described or claimed as "configured" to perform one or more tasks or operations. This formulation-[entity] configured to [perform one or more tasks]-is used herein to refer to structure (i.e., something physical, such as an electronic circuit). More specifically, this formulation is used to indicate that this structure is arranged to perform the one or more tasks during operation. A structure can be said to be "configured to" perform some task even if the structure is not currently being operated. A "display system configured to display three-dimensional content to a user" is intended to cover, for example, a liquid crystal display (LCD) performing this function during operation, even if the LCD in question is not currently being used (e.g., a power supply is not connected to it). Thus, an entity described or recited as "configured to" perform some task refers to something physical, such as a device, circuit, memory storing program instructions executable to implement the task, etc. This phrase is not used herein to refer to something intangible. Thus, the "configured to" construct is not used herein to refer to a software entity such as an application programming interface (API).

The term "configured to" is not intended to mean "configurable to. " An unprogrammed FPGA, for example, would not be considered to be "configured to" perform some specific function, although it may be "configurable to" perform that function and may be "configured to" perform the function after programming.

As used herein, the terms "first," "second," etc. are used as labels for nouns that they precede, and do not imply any type of ordering (e.g., spatial, temporal, logical, etc.) unless specifically stated. For example, in a processor having eight processing cores, the terms "first" and "second" processing cores can be used to refer to any two of the eight processing cores. In other words, the "first" and "second" processing cores are not limited to processing cores <NUM> and <NUM>, for example.

As used herein, the term "based on" is used to describe one or more factors that affect a determination. This term does not foreclose the possibility that additional factors may affect a determination. That is, a determination may be solely based on specified factors or based on the specified factors as well as other, unspecified factors. Consider the phrase "determine A based on B. " This phrase specifies that B is a factor used to determine A or that affects the determination of A. This phrase does not foreclose that the determination of A may also be based on some other factor, such as C. This phrase is also intended to cover an embodiment in which A is determined based solely on B. As used herein, the phrase "based on" is thus synonymous with the phrase "based at least in part on.

As used herein, a physical environment refers to a physical world that people can sense and/or interact with without aid of electronic systems. Physical environments, such as a physical park, include physical articles, such as physical trees, physical buildings, and physical people. People can directly sense and/or interact with the physical environment, such as through sight, touch, hearing, taste, and smell.

As used herein, a computer generated reality (CGR) environment refers to a wholly or partially simulated environment that people sense and/or interact with via an electronic system. In CGR, a subset of a person's physical motions, or representations thereof, are tracked, and, in response, one or more characteristics of one or more virtual objects simulated in the CGR environment are adjusted in a manner that comports with at least one law of physics. For example, a CGR system may detect a person's head turning and, in response, adjust graphical content and an acoustic field presented to the person in a manner similar to how such views and sounds would change in a physical environment. In some situations (e.g., for accessibility reasons), adjustments to characteristic(s) of virtual object(s) in a CGR environment may be made in response to representations of physical motions (e.g., vocal commands).

A person may sense and/or interact with a CGR object using any one of their senses, including sight, sound, touch, taste, and smell. For example, a person may sense and/or interact with audio objects that create 3D or spatial audio environment that provides the perception of point audio sources in 3D space. In another example, audio objects may enable audio transparency, which selectively incorporates ambient sounds from the physical environment with or without computer generated audio. In some CGR environments, a person may sense and/or interact only with audio objects.

Examples of CGR include virtual reality and mixed reality.

As used herein, a virtual reality (VR) environment refers to a simulated environment that is designed to be based entirely on computer generated sensory inputs for one or more senses. A VR environment comprises a plurality of virtual objects with which a person may sense and/or interact. For example, computer generated imagery of trees, buildings, and avatars representing people are examples of virtual objects. A person may sense and/or interact with virtual objects in the VR environment through a simulation of the person's presence within the computer generated environment, and/or through a simulation of a subset of the person's physical movements within the computer generated environment.

As used herein, a mixed reality (MR) environment refers to a simulated environment that is designed to incorporate sensory inputs from the physical environment, or a representation thereof, in addition to including computer generated sensory inputs (e.g., virtual objects). On a virtuality continuum, a mixed reality environment is anywhere between, but not including, a wholly physical environment at one end and virtual reality environment at the other end.

In some MR environments, computer generated sensory inputs may respond to changes in sensory inputs from the physical environment. Also, some electronic systems for presenting an MR environment may track location and/or orientation with respect to the physical environment to enable virtual objects to interact with real objects (that is, physical articles from the physical environment or representations thereof). For example, a system may account for movements so that a virtual tree appears stationery with respect to the physical ground.

Examples of mixed realities include augmented reality and augmented virtuality.

As used herein, an augmented reality (AR) environment refers to a simulated environment in which one or more virtual objects are superimposed over a physical environment, or a representation thereof. For example, an electronic system for presenting an AR environment may have a transparent or translucent display through which a person may directly view the physical environment. The system may be configured to present virtual objects on the transparent or translucent display, so that a person, using the system, perceives the virtual objects superimposed over the physical environment. Alternatively, a system may have an opaque display and one or more imaging sensors that capture images or video of the physical environment, which are representations of the physical environment. The system composites the images or video with virtual objects, and presents the composition on the opaque display. A person, using the system, indirectly views the physical environment by way of the images or video of the physical environment, and perceives the virtual objects superimposed over the physical environment. As used herein, a video of the physical environment shown on an opaque display is called "pass-through video," meaning a system uses one or more image sensor(s) to capture images of the physical environment, and uses those images in presenting the AR environment on the opaque display. Further alternatively, a system may have a projection system that projects virtual objects into the physical environment, for example, as a hologram or on a physical surface, so that a person, using the system, perceives the virtual objects superimposed over the physical environment.

An augmented reality environment also refers to a simulated environment in which a representation of a physical environment is transformed by computer generated sensory information. For example, in providing pass-through video, a system may transform one or more sensor images to impose a select perspective (e.g., viewpoint) different than the perspective captured by the imaging sensors. As another example, a representation of a physical environment may be transformed by graphically modifying (e.g., enlarging) portions thereof, such that the modified portion may be representative but not photorealistic versions of the originally captured images. As a further example, a representation of a physical environment may be transformed by graphically eliminating or obfuscating portions thereof.

An augmented virtuality (AV) environment refers to a simulated environment in which a virtual or computer generated environment incorporates one or more sensory inputs from the physical environment. The sensory inputs may be representations of one or more characteristics of the physical environment. For example, an AV park may have virtual trees and virtual buildings, but people with faces photorealistically reproduced from images taken of physical people. As another example, a virtual object may adopt a shape or color of a physical article imaged by one or more imaging sensors. As a further example, a virtual object may adopt shadows consistent with the position of the sun in the physical environment.

Delivering a great CGR experience (such as an AR, MR, VR, or XR experience) can entail using a considerable amount of hardware and software resources to provide dynamic and vibrant content. The resources available to provide such content, however, operate within limited constraints. For example, a display device may have limited processing ability, operate using a battery supply, and have a network connection with limited bandwidth. Management of these resources can be particularly important for CGR systems as issues, such as jitter and latency, can quickly ruin an experience. For example, it may be difficult for two users to interact within one another if there is a significant delay between events occurring at one user's display device and events occurring at another user's display device.

The present disclosure describes embodiments in which a display device attempts to discover computing devices available to assist the display device and offloads tasks to these computing devices to expand the amount of available computing resources for delivering content. As will be described in greater detail below, in various embodiments, a display device may collect information identifying abilities of the one or more compute devices to assist the display device. For example, the display device may determine that a user has a nearby tablet and laptop that are not currently being used and both have graphics processing units (GPUs). Based on this discovery, the display device may evaluate a set of tasks associated with the content being displayed and may offload one or more tasks to the discovered devices. In various embodiments, the display device may continue to collect compute ability information from available computing devices as operating conditions may change over time. For example, if the display device is communicating wirelessly with a tablet and a user operating the display device walks out of the room, the display device may detect this change and redistribute tasks accordingly. In evaluating what tasks to offload, the display device may consider many factors pertaining to compute resources, energy budgets, quality of service, network bandwidth, security, etc. in an effort to meet various objectives pertaining to, for example, precision, accuracy, fidelity, processing time, power consumption, privacy considerations, etc. Dynamically discovering compute resources and redistributing tasks in real time based on these factors can allow a much richer experience for a user than if the user were confined to the limited resources of the display device and, for example, a desktop computer connected to the display device.

Turning now to <FIG>, a block diagram of distribution system <NUM> is depicted. In the illustrated embodiment, distribution system <NUM> includes a display device <NUM>, which includes world sensors <NUM>, user sensors <NUM>, and a distribution engine <NUM>. As shown, system <NUM> may further include one or more compute nodes 140A-F. In some embodiments, system <NUM> may be implemented differently than shown. For example, multiple display devices <NUM> may be used, more (or fewer) compute nodes <NUM> may be used, etc..

Display device <NUM>, in various embodiments, is a computing device configured to display content to a user such as a three-dimensional view <NUM> as well as, in some embodiments, provide audio content <NUM>. In the illustrated embodiment, display device is depicted as phone; however, display device may be any suitable device such as a tablet, television, laptop, workstation, etc. In some embodiments, display device <NUM> is a head-mounted display (HMD) configured to be worn on the head and to display content to a user. For example, display device <NUM> may be a headset, helmet, goggles, glasses, a phone inserted into an enclosure, etc. worn by a user. As will be described below with respect to <FIG>, display device <NUM> may include a near-eye display system that displays left and right images on screens in front of the user eyes to present 3D view <NUM> to a user. In other embodiments, device <NUM> may include projection-based systems, vehicle windshields having integrated display capability, windows having integrated display capability, displays formed as lenses designed to be placed on a person's eyes (e.g., similar to contact lenses), headphones/earphones, speaker arrays, input systems (e.g., wearable or handheld controllers with or without haptic feedback), etc. Display device <NUM> may be used to provide any of various user experiences to a user. In various embodiments, these experiences may leverage AR, MR, VR, or XR environments. For example, display device <NUM> may provide collaboration and creation experiences, which may allow users to work together creating content in an AR environment. Display device <NUM> may provide co-presence experiences in which multiple users may personally connect in a MR environment. As used herein, the term "co-presence" refers to a shared CGR experience in which two people can interact with one another using their respective devices. Display device <NUM> may provide gaming experiences in which a user performs activities in a VR environment. In various embodiments, display device <NUM> may provide other non-CGR experiences. For example, a user may operate display device <NUM> to stream a media content such as music or movie, which may be displayed in three or two dimensions. To facilitate delivery of these various experiences, display device <NUM> may employ the use of world sensors <NUM> and user sensors <NUM>.

World sensors <NUM>, in various embodiments, are sensors configured to collect various information about the environment in which a user operates display device <NUM>. In some embodiments, world sensors <NUM> may include one or more visible-light cameras that capture video information of the user's environment. This information may, for example, be used to provide a virtual view of the real environment, detect objects and surfaces in the environment, provide depth information for objects and surfaces in the real environment, provide position (e.g., location and orientation) and motion (e.g., direction and velocity) information for the user in the real environment, etc. In some embodiments, display device <NUM> may include left and right cameras located on a front surface of the display device <NUM> at positions that, in embodiments in which display device <NUM> is an HMD, are substantially in front of each of the user's eyes. In other embodiments, more or fewer cameras may be used in display device <NUM> and may be positioned at other locations. In some embodiments, world sensors <NUM> may include one or more world mapping sensors (e.g., infrared (IR) sensors with an IR illumination source, or Light Detection and Ranging (LIDAR) emitters and receivers/detectors) that, for example, capture depth or range information for objects and surfaces in the user's environment. This range information may, for example, be used in conjunction with frames captured by cameras to detect and recognize objects and surfaces in the real-world environment, and to determine locations, distances, and velocities of the objects and surfaces with respect to the user's current position and motion. The range information may also be used in positioning virtual representations of real-world objects to be composited into a virtual environment at correct depths. In some embodiments, the range information may be used in detecting the possibility of collisions with real-world objects and surfaces to redirect a user's walking. In some embodiments, world sensors <NUM> may include one or more light sensors (e.g., on the front and top of display device <NUM>) that capture lighting information (e.g., direction, color, and intensity) in the user's physical environment. This information, for example, may be used to alter the brightness and/or the color of the display system in display device <NUM>.

User sensors <NUM>, in various embodiments, are sensors configured to collect various information about a user operating display device <NUM>. In some embodiments in which display device <NUM> is an HMD, user sensors <NUM> may include one or more head pose sensors (e.g., IR or RGB cameras) that may capture information about the position and/or motion of the user and/or the user's head. The information collected by head pose sensors may, for example, be used in determining how to render and display views of the virtual environment and content within the views. For example, different views of the environment may be rendered based at least in part on the position of the user's head, whether the user is currently walking through the environment, and so on. As another example, the augmented position and/or motion information may be used to composite virtual content into the scene in a fixed position relative to the background view of the environment. In some embodiments there may be two head pose sensors located on a front or top surface of the display device <NUM>; however, in other embodiments, more (or fewer) head-pose sensors may be used and may be positioned at other locations. In some embodiments, user sensors <NUM> may include one or more eye tracking sensors (e.g., IR cameras with an IR illumination source) that may be used to track position and movement of the user's eyes. In some embodiments, the information collected by the eye tracking sensors may be used to adjust the rendering of images to be displayed, and/or to adjust the display of the images by the display system of the display device <NUM>, based on the direction and angle at which the user's eyes are looking. In some embodiments, the information collected by the eye tracking sensors may be used to match direction of the eyes of an avatar of the user to the direction of the user's eyes. In some embodiments, brightness of the displayed images may be modulated based on the user's pupil dilation as determined by the eye tracking sensors. In some embodiments, user sensors <NUM> may include one or more eyebrow sensors (e.g., IR cameras with IR illumination) that track expressions of the user's eyebrows/forehead. In some embodiments, user sensors <NUM> may include one or more lower jaw tracking sensors (e.g., IR cameras with IR illumination) that track expressions of the user's mouth/jaw. For example, in some embodiments, expressions of the brow, mouth, jaw, and eyes captured by sensors <NUM> may be used to simulate expressions on an avatar of the user in a co-presence experience and/or to selectively render and composite virtual content for viewing by the user based at least in part on the user's reactions to the content displayed by display device <NUM>. In some embodiments, user sensors <NUM> may include one or more hand sensors (e.g., IR cameras with IR illumination) that track position, movement, and gestures of the user's hands, fingers, and/or arms. For example, in some embodiments, detected position, movement, and gestures of the user's hands, fingers, and/or arms may be used to simulate movement of the hands, fingers, and/or arms of an avatar of the user in a co-presence experience. As another example, the user's detected hand and finger gestures may be used to determine interactions of the user with virtual content in a virtual space, including but not limited to gestures that manipulate virtual objects, gestures that interact with virtual user interface elements displayed in the virtual space, etc..

In various embodiments, display device <NUM> includes one or network interfaces for establishing a network connection with compute nodes <NUM>. The network connection may be established using any suitable network communication protocol including wireless protocols such as Wi-Fi®, Bluetooth®, Long-Term Evolution ™, etc. or wired protocols such as Ethernet, Fibre Channel, Universal Serial Bus™ (USB), etc. In some embodiments, the connection may be implemented according to a proprietary wireless communications technology (e.g., <NUM> gigahertz (GHz) wireless technology) that provides a highly directional wireless link between the display device <NUM> and one or more of compute nodes <NUM>. In some embodiments, display device <NUM> is configured to select between different available network interfaces based on connectivity of the interfaces as well as the particular user experience being delivered by display device <NUM>. For example, if a particular user experience requires a high amount of bandwidth, display device <NUM> may select a radio supporting the proprietary wireless technology when communicating wirelessly with high performance compute 140E. If, however, a user is merely streaming a movie from laptop 140B, Wi-Fi® may be sufficient and selected by display device <NUM>. In some embodiments, display device <NUM> may use compression to communicate over the network connection in instances, for example, in which bandwidth is limited.

Compute nodes <NUM>, in various embodiments, are nodes available to assist in producing content used by display device <NUM> such as facilitating the rendering of 3D view <NUM>. Compute nodes <NUM> may be or may include any type of computing system or computing device. As shown in <FIG>, compute nodes <NUM> may in general may be classified into primary, second, and tertiary compute meshes <NUM>. In the illustrated embodiment, primary compute mesh 142A includes compute nodes <NUM> belonging to a user of display device <NUM>. These compute nodes <NUM> may provide less compute ability than compute nodes <NUM> in other meshes <NUM>, but may be readily available to the user of display device <NUM>. For example, a user operating display device <NUM> at home may be able to leverage the compute ability of his or her phone, watch 140A, laptop 140B, and/or tablet 140C, which may be in the same room or a nearby room. Other examples of such compute nodes <NUM> may include wireless speakers, set-top boxes, game consoles, game systems, internet of things (IoT) devices, home network devices, and so on. In the illustrated embodiment, secondary compute mesh 142B includes nearby compute nodes <NUM>, which may provide greater compute ability at greater costs and, in some instances, may be shared by multiple display devices <NUM>. For example, a user operating display device <NUM> may enter a retail store having a workstation 140D and/or high-performance compute (HPC) device 140E and may be able to receive assistance for such a node <NUM> in order to interact with store products in an AR environment. In the illustrated embodiment, tertiary compute mesh 142C includes high-performance compute nodes <NUM> available to a user though cloud-based services. For example, server cluster 140F may be based at a server farm remote from display device <NUM> and may implement one or more services for display devices <NUM> such as rendering three-dimensional content, streaming media, storing rendered content, etc. In such an embodiment, compute nodes <NUM> may also include logical compute nodes such as virtual machines, containers, etc., which may be provided by server cluster 140F.

Accordingly, compute nodes <NUM> may vary substantially in their abilities to assist display device <NUM>. Some compute nodes <NUM>, such as watch 140A, may have limited processing ability and be power restricted such being limited to a one-watt battery power supply while other nodes, such as server cluster 140F, may have almost unlimited processing ability and few power restrictions such as being capable of delivering multiple kilowatts of compute. In various embodiments, compute nodes <NUM> may vary in their abilities to perform particular tasks. For example, workstation 140D may execute specialized software such as a VR application capable of providing specialized content. HPC 140E may include specified hardware such as multiple high-performance central processing units (CPUs), graphics processing units (GPUs), image signal processors (ISPs), circuitry supporting neural network engines, secure hardware (e.g., secure element, hardware security module, secure processor, etc.), etc. In some embodiments, compute nodes <NUM> may vary in their abilities to perform operations securely. For example, tablet 140C may include a secure element configured to securely store and operate on confidential data while workstation 140D may be untrusted and accessible over an unencrypted wireless network connection. In various embodiments, compute nodes <NUM> may be dynamic in their abilities to assist display device <NUM>. For example, display device <NUM> may lose connectivity with tablet 140C when a user operating display device <NUM> walks into another room. Initially being idle, laptop 140B may provide some assistance to display device <NUM>, but provide less or no assistance after someone else begins using laptop 140B for some other purpose.

Distribution engine <NUM>, in various embodiments, is executable to discover compute nodes <NUM> and determine whether to offload tasks <NUM> to the discovered compute nodes <NUM>. In the illustrated embodiment, distribution engine <NUM> make this determination based on compute ability information <NUM> and the particular tasks <NUM> being offloaded. Compute ability information <NUM> may refer generally to any suitable information usable by engine <NUM> to assess whether tasks <NUM> should (or should not) be offloaded to particular compute nodes <NUM>. As will be described in greater detail below with respect to <FIG>, compute ability information <NUM> may include information about resource utilization, power constraints of a compute node <NUM>, particular hardware or software present at compute nodes <NUM>, the abilities to perform specialized tasks <NUM>, etc. Since the abilities of compute nodes <NUM> may change over time, in some embodiments, distribution engine <NUM> may continually receive compute ability information <NUM> in real time while display device <NUM> is displaying content. If a particular compute node <NUM>, for example, declines to accept a task <NUM> or leaves meshes <NUM>, distribution engine <NUM> may determine to dynamically redistribute tasks <NUM> among the compute nodes <NUM> and display device <NUM>.

Distribution engine <NUM> may evaluate any of various tasks <NUM> for potential offloading. These tasks <NUM> may pertain to the rendering of content being displayed on display device <NUM> such as performing mesh assembly, shading, texturing, transformations, lighting, clipping, rasterization, etc. These tasks <NUM> may also pertain to the rendering in that they affect what is displayed. For example, as will be discussed below with <FIG>, display device <NUM> may deliver an AR experience that uses an object classifier to identify a particular object captured in video frames collected by a camera sensor <NUM>. Rather than implement the classifier fully at display device <NUM>, distribution engine <NUM> may offload one or more tasks <NUM> pertaining the classifier to one or more compute nodes <NUM>. Display device <NUM> may then indicate the results of the object classification in 3D view <NUM>. Tasks <NUM> may also pertain to other content being provided by display device <NUM> such as audio or tactile content being provided to a user. For example, as will be discussed below with <FIG>, one or more tasks related to voice recognition may be offloaded to compute nodes <NUM>. Tasks <NUM> may also pertain to other operations such as storing rendered content for subsequent retrieval by the same display device <NUM> or other devices such as a friend's phone. Accordingly, tasks <NUM> performed in the distribution system <NUM> may be consumed by algorithms/components that produce visual elements (feeding the display), aural elements (e.g. room acoustics) and interaction (e.g. gestures, speech) to meet experience goals. As will be discussed below with respect to <FIG>, engine <NUM> may evaluate compute ability information <NUM> in conjunction with a graph structure defining a set of tasks to be performed, the interdependencies of the tasks, and their respective constraints (e.g., perceptual latencies and thresholds for visual, audio and interaction elements of the experience) as well as one or more user-specific quality of service (QoS) parameters. In various embodiments, engine <NUM> supplies this information to a cost function that attempts to minimize, for example, power consumption and latency while ensuring that the best user experience is delivered. In some embodiments, distribution engine <NUM> may also handle collecting results from performance of tasks <NUM> by nodes <NUM> and routing the results to the appropriate consuming hardware and/or software in display device <NUM>.

Although depicted within display device <NUM>, distribution engine <NUM> may reside elsewhere and, in some embodiments, in multiple locations. For example, a first instance of distribution engine <NUM> may reside at display device <NUM> and a second instance of distribution engine <NUM> may reside at laptop 140B. In such an embodiment, the distribution engine <NUM> at laptop 140B may collect instances of compute ability information <NUM> from one or more other compute nodes <NUM>, such as tablet 140C as shown in <FIG>, and provide a set of tasks <NUM> offloaded from display device <NUM> to the other compute nodes <NUM>. In some embodiments, the distribution engine <NUM> at laptop 140B may forward the received compute ability information <NUM> (or combine it with the compute ability information <NUM> sent by laptop 140B) on to the distribution engine <NUM> at display device <NUM>, which may determine what to distribute to the other compute nodes <NUM>. In some embodiments, the distribution engine <NUM> at laptop 140B may, instead, make the determination locally as to what should be offloaded to the other nodes <NUM>.

Turning now to <FIG>, a block diagram of a distribution engine <NUM> is depicted. In the illustrated embodiment, distribution engine <NUM> includes a discovery engine <NUM>, graph selector <NUM>, personalization engine <NUM>, constraint analyzer <NUM>, and a task issuer <NUM>. In other embodiments, engine <NUM> may be implemented differently than shown.

Discovery engine <NUM>, in various embodiments, handles discovery of available compute nodes <NUM> though exchanging discovery information <NUM>. Discovery engine <NUM> may use suitable techniques for discovering compute nodes <NUM>. For example, engine <NUM> may employ a protocol such as simple service discovery protocol (SSDP), Wi-Fi® Aware, zero-configuration networking (zeroconf), etc. As will be described with <FIG>, engine <NUM> may send out a broadcast request to compute nodes <NUM> and/or receive broadcasted notifications from compute nodes <NUM>. In some embodiments, discovery engine <NUM> also handles collection of compute ability information <NUM> received from computes nodes <NUM>. In the illustrated embodiment, engine <NUM> aggregates this information <NUM> into dynamic constraint vectors <NUM>, which it provides to constraint analyzer <NUM>. As will also be discussed with <FIG>, constraint vectors <NUM> may include multiple factors that pertaining to compute nodes' <NUM> compute ability and are dynamically updated as the state of available compute nodes <NUM> changes.

Graph selector <NUM>, in various embodiments, identifies a set of tasks <NUM> for performing a user-requested experience and determines a corresponding task graph <NUM> for use by constraint analyzer <NUM>. As noted above, display device <NUM> may support providing multiple different types of user experiences to a user. When a user requests a particular experience (e.g., a co-presence experience between two users), selector <NUM> may receive a corresponding indication <NUM> of the request and identify the appropriate set of tasks <NUM> to facilitate that experience. In doing so, selector <NUM> may determine one or more task graphs <NUM>. As will be described below with respect to <FIG> and <FIG>, in various embodiments, task graphs <NUM> are graph data structures that includes multiple, interdependent graph nodes, each defining a set of constraints for performing a respective one of the set of tasks <NUM>. In some embodiments, selector <NUM> may dynamically assemble task graphs <NUM> based on a requested experience indication <NUM> and one or more contextual factors about the experience. In some embodiments, however, selector <NUM> may select one or more already created, static task graphs <NUM>.

Personalization engine <NUM>, in various embodiments, produces user-specific QoS parameters <NUM> pertaining to a particular user's preference or tolerance for a particular quality of service. When a user operates a display device to enjoy a CGR experience, a user may have specific tolerances for factors such as latency, jitter, resolution, frame rate, etc. before the experience becomes unenjoyable. For example, if a user is trying to navigate a three-dimensional space in a VR game, the user may be become dizzy and disoriented if the movement through the space is jittery. Also, one user's tolerance for these factors may vary from another. To ensure that a given user has an enjoyed experience, distribution engine <NUM> (or some other element of display device <NUM>) may collect user-specific parameters <NUM> pertaining to a user's preference or tolerance to these user-specific factors. For example, engine <NUM> may determine, for a given an experience, a minimum frame rate for displaying three-dimensional content, a minimum latency for displaying the three-dimensional content, and a minimum resolution for displaying the three-dimensional content. If engine <NUM> is unable to distribute a particular set of tasks <NUM> in a manner that satisfies these requirements, engine <NUM> may indicate that the experience cannot currently be provided or evaluate a different set of tasks <NUM> to ensure that parameters <NUM> can be satisfied. In some embodiments, parameters <NUM> may be determined by prompting a user for input. For example, display device <NUM> may present content associated with a particular QoS and ask if it is acceptable to a user. In other embodiments, parameters <NUM> may be determined as a user experiences a particular QoS and based on sensors <NUM> and <NUM>. For example, sensors <NUM> and/or <NUM> may provide various information indicating that a user is experiencing discomfort, and engine <NUM> may adjust the QoS of the experience to account for this detected discomfort.

Constraint analyzer <NUM>, in various embodiments, determines how tasks <NUM> should be distributed among display device <NUM> and compute nodes <NUM> based on dynamic constraint vectors <NUM>, task graphs <NUM>, and QoS parameters <NUM>. Accordingly, analyzer <NUM> may analyze the particular compute abilities of nodes <NUM> identified in vectors <NUM> and match those abilities to constraints in task graphs <NUM> while ensuring that QoS parameters <NUM> are met. In some embodiments, this matching may include determining multiple different distribution plans <NUM> for distributing tasks <NUM> among display device <NUM> and compute nodes <NUM> and calculating a cost function <NUM> for each different distribution plans <NUM>. In various embodiments, cost function <NUM> is a function (or collection of functions) that determines a particular cost for a given distribution plan <NUM>. The cost of a given plan <NUM> may be based on any of various factors such as total power consumption for implementing a plan <NUM>, latency for implementing the plan <NUM>, quality of service, etc. Based on the calculated cost functions of the different plans <NUM>, analyzer <NUM> may select a particular distribution <NUM> determined to have the least costs (or the highest cost under some threshold amount).

Task issuer <NUM>, in various embodiments, facilitates implementation of the distribution plan <NUM> selected by constraint analyzer <NUM>. Accordingly, issuer <NUM> may examine distribution plan <NUM> to determine that a particular task <NUM> has been assigned to a particular node <NUM> and contact that node <NUM> to request that it perform that assigned task <NUM>. In some embodiments, issuer <NUM> also handles collecting the appropriate data to perform an assigned task <NUM> and conveying the data to the node <NUM>. For example, if a given task <NUM> relies on information from a world sensor <NUM> and/or user sensor <NUM> (e.g., images collected by an externally facing camera sensor <NUM>), issuer <NUM> may assemble this information from the sensor <NUM> or <NUM> and communicate this information over a network connection to the compute node <NUM> assigned the task <NUM>.

Turning now to <FIG>, a block diagram of distribution engine <NUM> is depicted. In the illustrated embodiment, discovery engine <NUM> includes a recruiter <NUM> and collector <NUM>. In some embodiments, discovery engine <NUM> may be implemented differently than shown.

Recruiter <NUM>, in various embodiments, handles discovering and obtaining assistance from compute nodes <NUM>. Although recruiter <NUM> may use any suitable technique as mentioned above, in the illustrated embodiment, recruiter <NUM> sends a discovery broadcast <NUM> soliciting assistance from any available compute nodes <NUM> and identifies compute nodes <NUM> based on their responses. As used herein, the term "broadcast" is to be interpreted in accordance with its established meaning and includes a communication directed to more than one recipient. For example, if communication over a network connection is using IPv4, recruiter <NUM> may send a discovery broadcast <NUM> to a broadcast address having a host portion consisting of all ones. In various embodiments, discovery broadcast <NUM> may be conveyed across a local area network accessible to display device <NUM> in order to identify other nodes <NUM> a part of the network. In some embodiments, recruiter <NUM> may receive broadcasted notifications <NUM> from compute nodes <NUM>. That is, rather responding to any solicitation of recruiter <NUM>, a compute node <NUM> may send a notification <NUM> indicating that it is available to assist any display device <NUM> that happens to need assistance. In some embodiments, recruiter <NUM> receives additional information about available compute nodes <NUM> such as user information <NUM>. In various embodiments, compute nodes <NUM> may provide information <NUM> about a user (or users) of a compute node <NUM> so that recruiter <NUM> can determine whether a compute node is a part of primary mesh 142A discussed above. In such an embodiment, distribution engine <NUM> may confirm that display device <NUM> shares the same user as a given compute node <NUM> (or is using a friend's or family member's compute node <NUM>) before attempting to distribute tasks <NUM> to that node <NUM>. For example, in some embodiments, compute nodes <NUM> belonging to primary mesh 142A may indicate that they share a common family account, which may be associated with some service. In response to receive information <NUM>, engine <NUM> may determine that display device <NUM> also is associated with the family account in order to identify the compute nodes <NUM> as being part of primary mesh 142A. In some embodiments, recruiter <NUM> may also send a request soliciting assistance from server cluster 140F, which may implement a cloud-based service for rendering three-dimensional content as well as providing other services as noted above. In some embodiments, after discovering nodes <NUM>, discovery engine <NUM> may begin receiving computing ability information <NUM>.

Collector <NUM>, in various embodiments, is executable to compile dynamic constraint vectors <NUM> and convey them to constraint analyzer <NUM>. In some embodiments, a constraint vector <NUM> may include information about a single node <NUM>; in other embodiments, a vector <NUM> may be multi-dimensional and include information <NUM> from multiple nodes <NUM>. As shown, a given vector <NUM> may include one or more past entries 300A pertaining to previous compute ability information <NUM> as well as the current real-time information <NUM> in an entry 300B. In some embodiments, collector <NUM> may also analyze current and past information <NUM> to predict future abilities of compute nodes <NUM> to facilitate assisting display device <NUM> as shown in entry 300C. For example, collector <NUM> may employ a learning algorithm that evaluates past and present information <NUM> over time. In the illustrated embodiment, a dynamic constraint vector <NUM> includes processor capabilities <NUM>, memory capabilities <NUM>, power budget <NUM>, network capabilities <NUM>, security capabilities <NUM>, specific task affinities <NUM>, and task latencies <NUM>. In other embodiments, vector <NUM> may include more (or less) elements than <NUM>-<NUM>; aspects described below with respect to one element may also be applicable to others.

Processor capabilities <NUM>, in various embodiments, identify processor information of a given compute node <NUM>. Capabilities <NUM> may, for example, identify the number of processors, types of processors, operating frequencies, etc. In some embodiments, capabilities <NUM> may identify the processor utilization of a compute node <NUM>. For example, capabilities <NUM> may identify that a processor is at <NUM>% utilization. In another embodiment, capabilities <NUM> may express an amount that a given compute node <NUM> is willing to allocate to display device <NUM>. For example, capabilities <NUM> may identify that a given compute node is willing to allocate <NUM>% of its processor utilization.

Memory capabilities <NUM>, in various embodiments, identify memory information of a given compute node <NUM>. Capabilities <NUM> may, for example, identify the types of memories and their storage capacities. In some embodiments, capabilities <NUM> may also identify a current utilization of space. For example, capabilities <NUM> may identify that a compute node <NUM> is able to store a particular size of data.

Power budget <NUM>, in various embodiments, identifies constraints pertaining to the power consumption of a compute node. For example, in instances when a compute node <NUM> is using a battery supply, power budget <NUM> may identify the current charge level of the battery and its total capacity. In instances when a compute node <NUM> has a plugged-in power supply, power budget <NUM> may identify the plugged-in aspect along with the wattage being delivered. In some embodiments, power budget <NUM> may indicate thermal information for a compute node <NUM>. Accordingly, if a given node <NUM> is operating well below its thermal constraints, it may be able to accommodate a greater number of tasks <NUM>. If, however, a given node <NUM> is reaching its thermal constraints, tasks <NUM> may need to be redistributed among other nodes <NUM> and display device <NUM>.

Network capabilities <NUM>, in various embodiments, include information about a compute node's <NUM> network interfaces. For example, capabilities <NUM> may identify the types of network interfaces supported by a given compute node <NUM> such as Wi-Fi®, Bluetooth®, etc. Capabilities <NUM> may also indicate the network bandwidth available via the network interfaces, which may be dynamic based on communication channel conditions. Capabilities <NUM> may also identify the network latencies for communicating with display device <NUM>. For example, capabilities <NUM> may indicate that an Internet Control Message Protocol (ICMP) echo request takes <NUM> to receive a response.

Security capabilities <NUM>, in various embodiments, include information about a compute node's <NUM> ability to perform tasks <NUM> in a secure manner. As noted above, sensors <NUM> and <NUM> may collect sensitive information, which may need to be protected to ensure a user's privacy. For example, in supplying an MR experience, a camera sensor <NUM> may collect images of a user's surroundings. In various embodiments, distribution engine <NUM> may verify security capabilities <NUM> before offloading a task <NUM> that includes processing the images (or some other form of sensitive information). In some embodiments, capacities <NUM> may identify a node's <NUM> ability to process information securely by identifying the presence of particular hardware such as a secure element, biometric authentication sensor, hardware secure module (HSM), secure processor, secure execution environment, etc. In some embodiments, capabilities <NUM> may provide a signed certificate from a manufacturer of a compute node <NUM> attesting the secure capabilities of a compute node <NUM>. In some embodiments, the certificate may also attest to other capabilities of a given node <NUM> such as the presence of particular (as discussed with task affinities <NUM>), an ability to perform a biometric authentication, whether the device includes confidential data of a user, etc. In some embodiments, capabilities <NUM> may identify whether a secure network connection exists due to the use of encryption or a dedicated physical connection. In some embodiments, capabilities <NUM> may identify whether a compute node <NUM> includes a biometric sensor and is configured to perform a biometric authentication of a user.

Specific task affinities <NUM>, in various embodiments, include information about a compute node's <NUM> ability to handle particular tasks <NUM>. Accordingly, affinities <NUM> may identify the presence of particular hardware and/or software for performing particular tasks <NUM>. For example, affinities <NUM> may identify that a given node <NUM> has a GPU and thus is perhaps more suited for performing three-dimensional rendering tasks <NUM>. As another example, affinities <NUM> may identify that a given node <NUM> has a secure element having a user's payment credentials and thus can assist in performing a payment transaction for the user. As yet another example, affinities <NUM> may identify that a given node <NUM> supports a neural network engine supporting one or more tasks such as object classification discussed below.

Task Latencies <NUM>, in various embodiments, include information about how long a compute node may take to handle a given task <NUM>. For example, latencies <NUM> may identify that a particular task <NUM> is expected to <NUM> based on previous instances in which the compute node <NUM> performed the task <NUM> and the current utilizations of the node's <NUM> resources. In some embodiment, latencies <NUM> may include network connectivity information discussed above with network capabilities <NUM> such as a latency of a network connection. In such an embodiment, distribution engine <NUM> may determine, for example, to not offload a given task <NUM> if the time taken to offload and perform a task <NUM> as indicated by task latencies <NUM> exceeds some threshold.

Turning now to <FIG>, a block diagram of a task graph 222A is depicted. As noted above and shown in <FIG>, in various embodiments, a task graph <NUM> is a graph data structure having multiple nodes <NUM> corresponding to a set of tasks <NUM> being considered for offloading. In the illustrated embodiment, task graph 222A is an example of a task graph <NUM> for a set of tasks 154A-C performed to classify on object present in one or more video frames <NUM> from a camera sensor <NUM>. For example, a user operating display device <NUM> may have walked into a store selling a product. When a user looks at the product with display device <NUM>, display device <NUM> may attempt to classify the object and present AR content about the product being sold. As shown, task graph 222A includes a graph node 400A for an object-detection task 154A in which an object is detected in video frames <NUM> and a bounding box is placed around the object for subsequent analysis. Task graph 222A then includes a graph node 400B for an image-crop task 154B in which content external to the bounding box is removed from frames <NUM> to produce cropped frames <NUM>. Lastly, task graph 222A includes a graph node 400C for an object-classification task 154C in which the cropped frames <NUM> are analyzed to identify the classification <NUM> of the object in the cropped frames <NUM>-e.g., that the user is looking at a pair of shoes.

As shown, each graph node <NUM> may define a corresponding set of task constraints <NUM> for its respective task <NUM>. In the illustrated embodiment, task constraints <NUM> includes a type <NUM>, desired task latency <NUM>, energy profile <NUM>, desired network connection <NUM>, security requirement <NUM>, desired compute capabilities <NUM>, and task chaining <NUM>. In some embodiments, more (or less) constraints <NUM> may be defined for a given node <NUM>. Also, constraints defined for one graph node <NUM> may be different from those defined in another graph node <NUM>.

Type <NUM>, in various embodiments, identifies a type of task <NUM> associated with a particular node <NUM>. For example, node 400A may indicate its type <NUM> is object detection while node 400B may indicate its type <NUM> is image cropping.

Desired task latency <NUM>, in various embodiments, identifies a maximum permissible latency for performing a given task <NUM>. For example, a latency <NUM> specified in node 400C may indicate that the object-classification task <NUM> should be completed within <NUM>. Accordingly, if task latencies <NUM> in vectors <NUM> indicate that a given compute node <NUM> cannot satisfy this latency <NUM>, analyzer <NUM> may preclude the compute node <NUM> from being considered as a candidate for offloading object-classification task 154C.

Energy profile <NUM>, in various embodiments, indicates an expected energy consumption for performing a given task <NUM>. For example, the profile <NUM> for node 400A may indicate that object detection is a lesser energy-intensive task <NUM> while the profile <NUM> for node 400C may indicate that object classification is a higher energy-intensive task <NUM>. Thus, analyzer <NUM> may assign task 154A to a more power-restricted compute node <NUM> or display device <NUM> while assigning task 154C to a less power-restricted node <NUM> as indicated, for example, by power budget <NUM> in a vector <NUM>.

Desired network connection <NUM>, in various embodiments, indicates desired characteristics for a network connection associated with a given task <NUM>. These characteristics may be a type of network connection (e.g., Wi-Fi®, Bluetooth®, etc.), a desired bandwidth for a connection, and/or a desired latency for a network connection. For example, a task <NUM> requiring a high bandwidth (e.g., streaming media content to display device <NUM>) may indicate a desire for a higher bandwidth connection. Accordingly, analyzer <NUM> may attempt to match characteristics identified in desired network connection <NUM> with those identified in network capabilities <NUM> for compute nodes <NUM>.

Security requirement <NUM>, in various embodiments, indicates a requirement to perform a given task <NUM> in a secure manner. For example, given the potential for video frames <NUM> to include sensitive content, each of nodes 400A-C may specify a requirement <NUM> for tasks 154A-C to performed in a secure manner. Accordingly, analyzer <NUM> may assign tasks 154A-C to compute nodes <NUM> based on security capabilities <NUM> in vectors <NUM>. Other examples of sensitive content may include keychain data, passwords, credit card information, biometric data, user preferences, other forms of personal information. Accordingly, if a particular task <NUM> is being performed using such information, a security requirement <NUM> may be set to ensure, for example, that any node <NUM> handling this information is able to protect using some form of secure hardware such as a secure element, hardware secure module (HSM), secure processor, etc. In various embodiments, security requirement <NUM> may be important with assigning tasks <NUM> to a given node <NUM> and may be continually evaluated by engine <NUM> as the set of available nodes <NUM> change. For example, if a first node <NUM> is handling a task <NUM> having a security requirement <NUM> and that node <NUM> becomes unavailable, display device <NUM> may determine to discontinue a particular experience if another node <NUM> cannot be found that can satisfy the requirement <NUM>.

Desired compute capabilities <NUM>, in various embodiments, indicates a desire for a compute node <NUM> to have particular hardware and/or software handle an offloaded task <NUM>. For example, node 400C may specify hardware (or software) implementing a neural network classifier operable to perform the object-classification task 154C. In some instances, capabilities <NUM> may include more a general specification (e.g., for general purpose hardware implementing a neural network) or may include a more specific specification (e.g., special-purpose hardware designed specifically to implement a convolution neural network (CNN) for object classification). Accordingly, analyzer <NUM> may evaluate desired compute capabilities <NUM> against specific task affinities <NUM> specified in vectors <NUM>.

Task chaining <NUM>, in various embodiments, indicates that two or more tasks <NUM> should be grouped together when they are assigned to display device <NUM> or a compute node <NUM>. For example, although not show in <FIG>, the task chaining <NUM> for node 400A may indicate that task 154A is supposed to performed at the same compute node <NUM> as task 154B. Thus, analyzer <NUM> may be restricted from assigning tasks 154A and tasks 154B to different nodes <NUM>. As will be discussed below with <FIG>, in some embodiments, data for chained-together tasks <NUM> may be collocated in memory to improve the efficiency of accessing the data and/or its security when performing the tasks <NUM>.

As noted above, after evaluating task graphs <NUM> in conjunction with dynamic constraint vectors <NUM> and user-specific QoS parameters <NUM>, constraint analyzer <NUM> may determine a distribution plan <NUM> for offloading tasks <NUM>. In some embodiments, the distribution plan <NUM> may be recorded in nodes <NUM>. For example, analyzer <NUM> may indicate in node 400A that task 154A has been assigned to display device <NUM>, indicate in node 400B that task 154B has been assigned to watch 140A, and indicate in node 400C that task 154C has been assigned to HPC 140E. In other embodiments, plan <NUM> may indicated differently-and, in some embodiments, provided separately from task graph 222A.

Turning now to <FIG>, a block diagram of another task graph 222B is depicted. As noted above, distribution engine <NUM> may evaluate tasks <NUM> that pertain to content other than the visual content being presented on display device <NUM>. For example, in the illustrated embodiment, task graph 222B pertains to a set of tasks <NUM> for performing audio classification, which may be used in voice recognition. As shown, task graph 222B includes a graph node 400D for an audio-detection task 154D in which a recorded audio stream <NUM> is analyzed for a voice to place a bounding box <NUM> around the voice. Task graph 222B further includes an audio-cropping task 154E in which the recorded audio <NUM> is cropped based on the bounding box <NUM>. Task graph 222B then includes a node 400F for an audio-classification task 154F in which the voice in the cropped audio <NUM> is classified and an indication <NUM> of the classification is presented-e.g., that a user is asking about the current weather today. Similar to task graph 222A, analyzer <NUM> may analyze task constraints <NUM> defined by nodes 400D-400F in conjunction with vectors <NUM> and parameters <NUM> in order to determine a distribution plan <NUM>. For example, as shown in <FIG>, analyzer <NUM> has selected a plan <NUM> that assigns audio-detection task 154D to display device <NUM>, audio-cropping task 154E to watch 140A, and audio-classification task 154F to server cluster 140F. A result of the audio classification may then be presented, for example, via an audio system of display device <NUM> such as announcing the current weather.

Turning now to <FIG>, a block diagram of a larger task graph <NUM> is depicted. In various embodiments, task graph <NUM> may be substantially larger than a few nodes <NUM>-even larger, in some embodiments, than the number of nodes <NUM> depicted in <FIG>. In the illustrated embodiment, nodes <NUM> have been distributed among display device <NUM>, watch 140A, HPC 140E, and server cluster 140F as indicated by the different shades of gray. As shown, graph <NUM> may begin with a root node <NUM>, which may be selected based on the particular experience requested by the user, and conclude with multiple terminal nodes <NUM> providing outputs to multiple systems such as another display device <NUM>, a display system of display device <NUM>, an audio system of display device <NUM>, etc. In some embodiments, task graph <NUM> may be implemented differently than shown-e.g., graph <NUM> may include more branches of nodes <NUM>, edges of nodes <NUM> may connect to previous nodes <NUM> in a manner that forms loops, etc..

In various embodiments, task graph <NUM> may include nodes <NUM> that receive inputs from various sources. Accordingly, in the illustrated embodiment, HPC 140E may store cached content <NUM> that was previously generated and usable to facilitate a subsequent CGR experience. For example, in a museum exhibit depicting a city map having rendered buildings overlaying the map, HPC 140E may cache content <NUM> generated beforehand to expedite future renderings of the map. In an example discussed below with respect to <FIG>, a user may store previously generated content <NUM> to share it with another device on which the content <NUM> can be redisplayed.

As mentioned above and shown in <FIG>, task graph <NUM> may also include one or more instances of chained tasks <NUM> performed at the same compute node <NUM>. For example, in the illustrated embodiments, chained tasks <NUM> have both been assigned to watch 140A. In some embodiments, chained tasks <NUM> may be changed based on task chaining parameters <NUM> specified in a group of nodes <NUM> as discussed above. In some embodiments, distribution engine <NUM> may determine that a group of tasks <NUM> should be chained because they can be more efficiently performed, performed more quickly, consume less power, reduce network traffic, etc. when performed at the same compute node <NUM>.

Turning now to <FIG>, a block diagram of components within display device <NUM> and a compute node <NUM> is depicted. In the illustrated embodiment, display device <NUM> includes a display system <NUM>, controller <NUM>, memory <NUM>, secure element <NUM>, and a network interface <NUM> in addition to world sensors <NUM> and user sensors <NUM> discussed above. As shown, a given compute node <NUM> includes a controller <NUM>, memory <NUM>, and network interface <NUM>. In some embodiments, display device <NUM> and compute nodes <NUM> may be implemented differently than shown. For example, display device <NUM> and/or compute node <NUM> may include multiple network interfaces <NUM>, display device <NUM> may not include a secure element <NUM>, compute node <NUM> may include a secure element <NUM>, etc. In some embodiments, display device <NUM> and/or compute node <NUM> may include one or more speakers for presenting audio content <NUM>.

Display system <NUM>, in various embodiments, is configured to display rendered frames to a user. Display <NUM> may implement any of various types of display technologies. For example, as discussed above, display system <NUM> may include near-eye displays that present left and right images to create the effect of three-dimensional view <NUM>. In some embodiments, near-eye displays may use digital light processing (DLP), liquid crystal display (LCD), liquid crystal on silicon (LCoS), or light-emitting diode (LED). As another example, display system <NUM> may include a direct retinal projector that scans frames including left and right images, pixel by pixel, directly to the user's eyes via a reflective surface (e.g., reflective eyeglass lenses). To create a three-dimensional effect in view <NUM>, objects at different depths or distances in the two images are shifted left or right as a function of the triangulation of distance, with nearer objects shifted more than more distant objects. Display system <NUM> may support any medium such as an optical waveguide, a hologram medium, an optical combiner, an optical reflector, or any combination thereof. In some embodiments, display system <NUM> may be the transparent or translucent and be configured to become opaque selectively.

Controller <NUM>, in various embodiments, includes circuity configured to facilitate operation of display device <NUM>. Accordingly, controller <NUM> may include one or more processors configured to execute program instructions, such as distribution engine <NUM>, to cause display device <NUM> to perform various operations described herein. These processors may be CPUs configured to implement any suitable instruction set architecture, and may be configured to execute instructions defined in that instruction set architecture. For example, in various embodiments controller <NUM> may include general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as ARM, x86, PowerPC, SPARC, RISC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of the processors may commonly, but not necessarily, implement the same ISA. Controller <NUM> may employ any microarchitecture, including scalar, superscalar, pipelined, superpipelined, out of order, in order, speculative, non-speculative, etc., or combinations thereof. Controller <NUM> may include circuitry to implement microcoding techniques. Controller <NUM> may include one or more levels of caches, which may employ any size and any configuration (set associative, direct mapped, etc.). In some embodiments, controller <NUM> may include at least GPU, which may include any suitable graphics processing circuitry. Generally, a GPU may be configured to render objects to be displayed into a frame buffer (e.g., one that includes pixel data for an entire frame). A GPU may include one or more graphics processors that may execute graphics software to perform a part or all of the graphics operation, or hardware acceleration of certain graphics operations. In some embodiments, controller <NUM> may include one or more other components for processing and rendering video and/or images, for example image signal processors (ISPs), coder/decoders (codecs), etc. In some embodiments, controller <NUM> may be implemented as a system on a chip (SOC).

Memory <NUM>, in various embodiments, is a non-transitory computer readable medium configured to store data and program instructions executed by processors in controller <NUM> such as distribution engine <NUM>. Memory <NUM> may include any type of volatile memory, such as dynamic random-access memory (DRAM), synchronous DRAM (SDRAM), double data rate (DDR, DDR2, DDR3, etc.) SDRAM (including mobile versions of the SDRAMs such as mDDR3, etc., or low power versions of the SDRAMs such as LPDDR2, etc.), RAMBUS DRAM (RDRAM), static RAM (SRAM), etc. Memory <NUM> may also be any type of non-volatile memory such as NAND flash memory, NOR flash memory, nano RAM (NRAM), magneto-resistive RAM (MRAM), phase change RAM (PRAM), Racetrack memory, Memristor memory, etc. In some embodiments, one or more memory devices may be coupled onto a circuit board to form memory modules such as single inline memory modules (SIMMs), dual inline memory modules (DIMMs), etc. Alternatively, the devices may be mounted with an integrated circuit implementing system in a chip-on-chip configuration, a package-on-package configuration, or a multi-chip module configuration.

In some embodiments, data pertaining to tasks <NUM> may be stored in memory <NUM> based on the particular tasks <NUM>. As noted above, a set of tasks <NUM> may be chained together to be performed by the same compute node <NUM> or display device <NUM>. In such an embodiment, data for the set of tasks <NUM> may be located together in order to expedite access. For example, the data may be collocated in the same physical storage, the same memory pages, a contiguous block of memory addresses, etc. In some embodiments, tasks <NUM> associated with secure operations may be encrypted and/or stored in a portion of memory <NUM> having restricted access. For example, this portion of memory <NUM> may be protected using encryption provided by secure element <NUM>.

Secure element (SE) <NUM>, in various embodiments, is a secure circuit configured perform various secure operations for display device <NUM>. As used herein, the term "secure circuit" refers to a circuit that protects an isolated, internal resource from being directly accessed by an external circuit such as controller <NUM>. This internal resource may be memory that stores sensitive data such as personal information (e.g., biometric information, credit card information, etc.), encryptions keys, random number generator seeds, etc. This internal resource may also be circuitry that performs services/operations associated with sensitive data such as encryption, decryption, generation of digital signatures, etc. For example, SE <NUM> may maintain one or more cryptographic keys that are used to encrypt data stored in memory <NUM> in order to improve the security of display device <NUM>. As another example, secure element <NUM> may also maintain one or more cryptographic keys to establish secure connections, authenticate display device <NUM> or a user of display device <NUM>, etc. As yet another example, SE <NUM> may maintain biometric data of a user and be configured to perform a biometric authentication by comparing the maintained biometric data with biometric data collected by one or more of user sensors <NUM>. As used herein, "biometric data" refers to data that uniquely identifies the user among other humans (at least to a high degree of accuracy) based on the user's physical or behavioral characteristics such as fingerprint data, voice-recognition data, facial data, iris-scanning data, etc..

Network interface <NUM>, in various embodiments, includes one or more interfaces configured to communicate with external entities such as compute nodes <NUM>. As noted above, network interface <NUM> may support any suitable wireless technology such as Wi-Fi®, Bluetooth®, Long-Term Evolution ™, etc. or any suitable wired technology such as Ethernet, Fibre Channel, Universal Serial Bus™ (USB) etc. In some embodiments, interface <NUM> may implement a proprietary wireless communications technology (e.g., <NUM> gigahertz (GHz) wireless technology) that provides a highly directional wireless connection between the display device <NUM> and one or more of compute nodes <NUM>.

Controller <NUM>, in various embodiments, includes circuity configured to facilitate operation of display device <NUM>. Controller <NUM> may implement any of the functionality described above with respect to controller <NUM>. For example, controller <NUM> may include one or more processors configured to execute program instructions to cause compute node <NUM> to perform various operations described herein such as processing code <NUM> to process offloaded tasks <NUM>.

Memory <NUM>, in various embodiments, is configured to store data and program instructions executed by processors in controller <NUM>. Memory <NUM> may include any suitable volatile memory and/or non-volatile memory such as those noted above with memory <NUM>. Memory <NUM> may be implemented in any suitable configuration such as those noted above with memory <NUM>.

Network interface <NUM>, in various embodiments, includes one or more interfaces configured to communicate with external entities such as display device <NUM> as well as other compute nodes <NUM>. Network interface <NUM> may also implement any of suitable technology such as those noted above with respect to network interface <NUM>.

Turning now to <FIG>, a diagram of on-device processing 600A is depicted. In the illustrated embodiment, on-device processing 600A is an example in which display device <NUM> is unable to use available compute nodes <NUM> to assist in presenting a 3D view 102A. In this particular example, a user is participating in a co-presence experience in which the user is viewing some buildings of a city skyline with one or more other users represented using respective avatars 602A. Because display device <NUM> is limited to its local compute ability, avatars 602A may be depicted as only heads and fewer buildings may be rendered in view 102A.

Turning now to <FIG>, a diagram of compute-node processing 600B is depicted. In the illustrated embodiment, compute-node processing 600B is an example in which display device <NUM> is able to leverage the compute ability of other compute nodes <NUM>. In this example, a user may be participating in a similar co-presence experience as discussed above, but display device <NUM> discovers a nearby watch 140A and workstation 140D and offloads tasks <NUM> to them. Now, 3D view 102B is rendered in more detail such as including more buildings. Avatars 602B of other participants now have bodies in addition to their heads.

Turning now to <FIG>, a diagram of shared-node processing 600C is depicted. As noted above, in some instances, two or more display devices <NUM> may share a compute node <NUM>. In the illustrated embodiment, shared-node processing 600C is an example of display devices 100A and 100B sharing an HPC 140E, but using separate watches 140A1 and 140A2. For example, both users may be in a museum hosting an MR exhibit in which users view some buildings. To facilitate users with display devices 100A and 100B, the museum may operate an HPC 140E, which display devices 100A and 100B detect when the users enter the exhibit. The HPC 140E may allow display devices <NUM> to provide more vibrant content than if display devices <NUM> only used their respective watches 140A. In such an example, HPC 140E may provide first compute ability information <NUM> to display device 100A and second compute ability information <NUM> to display device 100B in order to perform one or more tasks offloaded from display device 100A while performing one or more tasks offloaded from the display device 100B. As more display devices <NUM> discover HPC 140E, it may be become more restricted in its compute abilities and indicate this restriction in subsequent communications of compute ability information <NUM>. As such, display devices <NUM> may redistribute more tasks <NUM> to their respective watches 140A. Or alternatively, the user operating display device 100A may walk away from HPC 140E such that the network connection to HPC 140E degrades to the point it can no longer be used, and display device 100A may dynamically redistribute tasks <NUM> among itself and watch 140A.

Turning now to <FIG>, a diagram of stored processing 600D is depicted. As noted above, a compute node <NUM> may assist display device <NUM> by storing content for display device <NUM>. In some embodiments, this content may be used to facilitate subsequent content rendering on display device <NUM>. In some embodiments, this content may be used to facilitate presenting content on other devices, which may include other display devices <NUM>. For example, in the illustrated embodiment, a user operating display device <NUM> may be viewing displayed content <NUM> including a three-dimensional mixed reality (MR) environment that includes a collection of buildings rendered on a surface. In some instances, a user may want to share this displayed content <NUM> for replay on another device such as a friend's phone <NUM>. In response to receiving such a request, display device <NUM> may request that a compute node <NUM>, such as server cluster 140F, store displayed content <NUM>. In some embodiments, server cluster 140F may then receive a request for displayed content <NUM> from the phone <NUM> and provide content <NUM> to phone <NUM> for presentation on a display of phone <NUM>.

In some embodiments, content <NUM> displayed on display device <NUM> and phone <NUM> is rendered based on data provided by one or more sensors <NUM> and/or <NUM> in the display device. For example, the data may include data collected by a sensor in the display device configured to measure an orientation of device <NUM> such as a pose of a user's head in an embodiment in which devices <NUM> is an HMD. Accordingly, as a user operating display device <NUM> changes the orientation of device <NUM> such as changing his or her head, displayed content on both display device <NUM> and phone <NUM> may be adjusted to reflect the changing view in front of display device <NUM>. As another example, the data may include data collected by an externally facing camera in the display device configured to capture video frames of the environment in which the display device is operated. Accordingly, real-world content included the frames may also be included the content <NUM> displayed on both display device <NUM> and phone <NUM>. In various embodiments, server cluster 140F may also receive one or more tasks <NUM> offloaded from display device <NUM> to facilitate rendering content for display device <NUM> in addition to storing displayed content <NUM>. In some embodiments, receiving the one or more tasks may include receiving data collected by one or more sensors <NUM> and/or <NUM> and using the received data to perform the one or more offloaded tasks <NUM>.

Turning now to <FIG>, a flow diagram of a method <NUM> is depicted. Method <NUM> is one embodiment of a method that may be performed by a display device such as display device <NUM> or other examples of devices noted above. In many instances, performance of method <NUM> (or method <NUM>-<NUM> discussed below) can significantly improve the user experience by expanding the compute available to deliver content to the display device such as AR, MR, VR, or XR content.

In step <NUM>, the display device discovers, via a network interface (e.g., network interface <NUM>), one or more compute nodes (e.g., compute nodes <NUM>) operable to facilitate rendering three-dimensional content displayed on a display system (e.g., display system <NUM>) of the display device. In such an embodiment, the discovering includes receiving information (e.g., compute ability information <NUM>) identifying abilities of the one or more compute nodes to facilitate the rendering. In some embodiments, the display device receives, while the display system is displaying the three-dimensional content, real-time information identifying current abilities of the one or more compute nodes to facilitate the rendering. In various embodiments, the real-time information includes one or more power constraints (e.g., power budget <NUM>) of a compute node facilitating the rendering. In some embodiments, the one or more power constraints (e.g., power budget <NUM>) includes a constraint associated with a battery supplying power to the compute node, a constraint associated with a processor utilization of the compute node, or a thermal constraint of the compute node. In various embodiments, the real-time information includes one or more latency constraints (e.g., network capabilities <NUM> and task latencies <NUM>) of a compute node facilitating the rendering. In some embodiments, the one or more latency constraints include a latency of a network connection between the compute node and the display device, a bandwidth of the network connection, or a time value identifying an expected time for performing a distributed task at the compute node.

In various embodiments, the discovering includes sending, via the network interface, a request (e.g., discovery engine <NUM>) soliciting assistance of compute nodes for facilitating the rendering and identifies the one or more compute nodes based on responses received from the one or more compute nodes. In some embodiments, identifying the one or more compute nodes includes determining whether the one or more compute nodes share a common user with the display device (e.g., are a part of primary mesh 142A). In some embodiments, the sending includes broadcasting the request (e.g., discovery broadcast <NUM>) across a local area network accessible via the network interface. In some embodiments, the discovering includes sending, via the network interface, a request soliciting assistance from a computer cluster (e.g., server cluster 140F) implementing a cloud-based service for rendering three-dimensional content and disturbing one or more of the set of tasks to the computer cluster.

In step <NUM>, the display device evaluates, based on the received information, a set of tasks (e.g., tasks <NUM>) to identify one or more of the tasks to offload to the one or more compute nodes for facilitating the rendering. In various embodiments, the display device determines a plurality of different distribution plans (e.g., distribution plans <NUM>) for distributing the tasks among the display device and the one or more compute nodes, calculates, based on the received information, a cost function (e.g., cost function <NUM>) for each of the plurality of different distribution plans, and selects, based on the calculated cost functions, one of the plurality of distribution plans for the distributing.

In various embodiments, the display device receives, from the user of the display device, a request to perform a particular operation including displaying the three-dimensional content and, based on the particular operation, determines a graph data structure that includes a plurality of graph nodes, each of the plurality of graph nodes defining a set of constraints for performing a respective one of the set of tasks. In such an embodiment, the evaluating of the set of tasks includes analyzing the graph data structure to determine a distribution plan for the distributing. In some embodiments, one of the plurality of graph nodes specifies a constraint (e.g., desired compute capabilities <NUM>) for using particular hardware to perform one of the set of tasks, and the evaluating includes identifying a compute node having the particular hardware for performing the task. In some embodiment, the particular hardware is a graphics processing unit (GPU). In some embodiments, the display device includes a camera configured to capture images of an environment in which the user operates the display device, the task is classification of an object (e.g., object classification 154C) present in the images, and the particular hardware is hardware implementing a neural network classifier operable to classify the object. In some embodiments, the display device includes a camera configured to capture images of an environment in which the user operates the display device, one of the plurality of graph nodes specifies a constraint (e.g., security requirement <NUM>) for performing a task using the images in a secure manner, and the evaluating includes identifying a compute node operable to perform the task in the secure manner. In some embodiments, the identifying of the compute node includes determining that a network connection between the display device and the compute node is encrypted.

In various embodiments, the display device collects one or more user-specific parameters (e.g., parameters <NUM>) pertaining to the user's tolerance for rendering the three-dimensional content in accordance with a particular quality of service, and the evaluating of the set of tasks is based on the collected one or more user-specific parameters. In some embodiments, the one or more user-specific parameters includes a minimum frame rate for displaying the three-dimensional content, a minimum latency for displaying the three-dimensional content, or a minimum resolution for displaying the three-dimensional content.

In step <NUM>, the display device distributes, via the network interface, the identified one or more tasks to the one or more compute nodes for processing by the one or more compute nodes. In some embodiments, step <NUM> includes the display device dynamically identifying, based on the real-time information, ones of the tasks for offloading and redistributing the dynamically identified tasks among the display device and the one or more compute nodes. In some embodiments, the display device analyzes the received real-time information to predict future abilities (e.g., predicted entry 300C) of the one or more compute nodes to facilitate the rendering and, based on the predicted future abilities, redistributes the dynamically identified tasks among the display device and the one or more compute nodes.

Turning now to <FIG>, a flow diagram of a method <NUM> is depicted. Method <NUM> is one embodiment of a method that may be performed by a computing device, such as display device <NUM> or one of compute nodes <NUM>, executing program instructions such as those of distribution engine <NUM>.

In step <NUM>, the computing device receives compute information (e.g., compute ability information <NUM>) identifying abilities of one or more compute nodes to facilitate rendering three-dimensional content (e.g., 3D view <NUM>) displayed on a display device. In some embodiments, the compute information is being continuously received while the three-dimensional content is being displayed on the display device, and the compute information includes (e.g., processor capabilities <NUM>, memory capabilities <NUM>, or network capabilities <NUM>) utilizations for one or more hardware resources included the one or more compute nodes. In some embodiments, prior to receiving the compute information, the computing device discovers the one or more compute nodes by sending a broadcast (e.g., discovery broadcast <NUM>) asking for assistance in rendering the three-dimensional content.

In step <NUM>, the computing device determines, based on the compute information, whether to offload one or more tasks (e.g., tasks <NUM>) associated with the rendering of the three-dimensional content. In some embodiments, the computing device calculates a cost function (e.g., cost function <NUM>) for a plurality of different distribution plans (e.g., distribution plan <NUM>) for distributing the one or more tasks among the one or more compute nodes and, based on the calculating, selects one of the plurality of distribution plans determined to have a lowest power consumption. In some embodiments, the computing device receives, from a user of the display device, an indication (e.g., requested experience indication <NUM>) of a desired experience to be provided to the user and, based on the indication, determines a graph data structure (e.g., task graph <NUM>) having a plurality of graph nodes corresponding to a set of tasks for providing the experience, and the determining whether to offload the one or more tasks includes evaluating parameters (e.g., task constraints <NUM>) specified in the plurality of graph nodes. In some embodiments, one of the plurality of graph nodes identifies a particular task (e.g., type <NUM>) to be performed and identifies particular latency (e.g., desired task latency <NUM>) for performing the task, and the determining whether to offload the one or more tasks includes determining whether a compute node can satisfy the particular latency. In some embodiments, the computing device evaluates a user's interaction with the three-dimensional content to determine a user-specific tolerance (e.g., user-specific QoS parameters) to a latency associated with the rendering and determines whether to offload the one or more tasks based on the determined user-specific tolerance to the latency.

In step <NUM>, the computing device offloads the one or more tasks to the one or more compute nodes to cause the one or more compute nodes to perform the one or more offloaded tasks. In some embodiments, the computing device receives, from a camera attached to the display device, images (e.g., video frames <NUM>) collected from an environment in which the display device is operated and offloads, to a compute node, a task that includes using content of the collected images to produce mixed reality content displayed on the display device.

Turning now to <FIG>, a flow diagram of a method <NUM> is depicted. Method <NUM> is one embodiment of a method that may be performed by a computing device implementing a compute node such as compute node <NUM>.

In step <NUM>, the computing device provides compute information (e.g., compute ability information <NUM>) identifying an ability of the computing device to facilitate rendering three-dimensional content (e.g., 3D view <NUM>) displayed on a display device (e.g., display device <NUM>). In various embodiments, the computing device continuously provides the compute information while the computing device is performing the one or more tasks. In some embodiments, the compute information includes a value (e.g., power budget <NUM>) indicating a current level of a battery supplying power to the computing device. In some embodiments, the compute information includes latency information (e.g., task latencies <NUM>) usable to determine an expected time for the computing device to perform an offloaded task. In some embodiments, the computing device receives a request (e.g., a discovery broadcast <NUM>) to assist in rendering the three-dimensional content and, in response to the request, provides information (e.g., user information <NUM>) about a user of the computing device, the information about the user being usable to determine whether the display device is being used by the same user.

In step <NUM>, the computing device receives one or more tasks (e.g., tasks <NUM>) offloaded from the display device based on the provided compute information. In some embodiments, step <NUM> includes the computing device receiving image information (e.g., video frames <NUM>, bounding box <NUM>, or cropped frame <NUM>) collected from a camera (e.g., camera sensor <NUM>) embedded in the display device.

In step <NUM>, the computing device performs the one or more tasks to facilitate the rendering of the three-dimensional content. In some embodiments, step <NUM> includes the computing device processing the received image information to produce content to be mixed with the three-dimensional content to present a mixed reality environment on the display device.

In step <NUM>, the computing device provides results from performing the tasks.

In various embodiments, method <NUM> further includes the computing device receiving compute information identifying an ability of one or more other computing devices to facilitate rendering the three-dimensional content displayed on a display device and providing a set of tasks offloaded from the display device to the one or more other computing devices. In some embodiments, the computing device provides the received compute information to another computing device configured to determine whether to offload the set of tasks to the one or more other computing devices. In some embodiments, the computing device determines, based on the received compute information, whether to offload the set of tasks to the one or more other computing devices. In some embodiments, the computing device provides first compute information identifying an ability of the computing device to facilitate rendering three-dimensional content displayed on a first display device (e.g., display device 100A in <FIG>), provides second compute information identifying an ability of the computing device to facilitate rendering three-dimensional content displayed on a second display device (e.g., display device 100B), and performs one or more tasks offloaded from the first display device while performing one or more tasks offloaded from the second display device.

Turning now to <FIG>, a flow diagram of method <NUM> is depicted. Method <NUM> is one embodiment of a method performed by a computing system, such as system <NUM> or one of compute nodes <NUM>, to facilitate sharing content of a display device on other devices.

Method <NUM> begins in step <NUM> with the computing system storing three-dimensional content (e.g., displayed content <NUM>) rendered for a display device (e.g., display device <NUM>). In various embodiments, the three-dimensional content is rendered based on data provided by one or more sensors (e.g., world sensors <NUM> or user sensors <NUM>) in the display device. In some embodiments, the three-dimensional content includes mixed reality (MR) content rendered based on an environment in which the display device is operated by a user. In some embodiments, the data includes data collected by a sensor in the display device configured to measure a pose of a user's head. In some embodiments, the data includes data collected by an externally facing camera in the display device configured to capture video frames of the environment in which the display device is operated. In step <NUM>, the computing system receives a request for the three-dimensional content from a computing device (e.g., phone <NUM>) other than the display device. In step <NUM>, the computing system provides the three-dimensional content to the computing device for presentation on a display of the computing device. In various embodiments, method <NUM> further includes the computing system receiving one or more tasks (e.g., tasks <NUM>) offloaded from the display device to facilitate rendering of the three-dimensional content. In some embodiments, receiving the one or more tasks includes receiving data collected by the one or more sensors and the computing system using the received data to perform the one or more offloaded tasks to facilitate the rendering.

Turning now to <FIG>, a block diagram of a capabilities exchange <NUM> is depicted. As discussed above, compute nodes <NUM> may provide compute ability information <NUM> to distribution engine <NUM> in order to facilitate determining what tasks <NUM> should be offloaded. In some embodiments, in order to ensure that this information <NUM> is accurate, some of this information may be included in a signed attestation provided by a compute node <NUM>. Accordingly, in the illustrated embodiment, a compute node <NUM> (such as tablet 140C) may contact a trusted certificate authority <NUM> to obtain a signed certificate <NUM> attesting to its capabilities and present the certificate <NUM> to distribution engine <NUM>.

Trusted certificate authority (CA) <NUM>, in various embodiments, is a trusted computing system configured to issue signed certificates <NUM>. In some embodiments, CA <NUM> may be operated by a manufacturer of display device <NUM> and/or a compute node <NUM>; however, in other embodiments, CA <NUM> may be operated by some other trusted entity. In various embodiments, a compute node <NUM> may obtain a certificate <NUM> by generating a public-key pair having a public key 814A and a corresponding private key 814B and issuing a certificate signing request (CSR) to CA <NUM>. In some embodiments, the CSR is further signed by a trusted key maintained by a compute node <NUM> in order to establish trust with CA <NUM>. Such a trusted key, for example, may be stored in a compute node <NUM> during its manufacturing. In some embodiments, this trusted key may be unique to a given compute node <NUM> (or, in another embodiment, unique to a particular generation of devices being of the same type-i.e., devices of the same type and generation may store the same key). Once the CSR can be successfully verified, CA <NUM> may issue a corresponding certificate <NUM>, which may be signed using a trusted private key maintained by CA <NUM>.

Certificate <NUM> may include any suitable information usable by distribution engine <NUM> such as one or more of parameters <NUM>-<NUM> discussed above. For example, certificate <NUM> may specify that a compute node <NUM> includes secure hardware (e.g., an SE, HSM, secure processor, etc.) as a security capability <NUM>. As another example, certificate <NUM> may specify a task affinity <NUM> for performing neural-network related tasks <NUM> as the compute node <NUM> may include specialized hardware implementing a neural network engine. In some embodiments, certificate <NUM> may include manufacturer information attesting to a compute node <NUM> being a genuine device such as identifying the name of the manufacturer and confirming that the authenticity of the compute node <NUM> has been verified. Certificate <NUM> may also include public key 814A, a digital signature generated using private key 814B, and the digital signature of CA <NUM> mentioned above. In some embodiments, certificate <NUM> may be X. <NUM> compliant; however, in other embodiments, certificate <NUM> may be implemented using some other form of signed attestation.

Once certificate <NUM> has been received, distribution engine <NUM> may verify certificate <NUM> to ensure that its authenticity. This may include verifying the signature of CA <NUM> to ensure the integrity of certificate <NUM>'s content. In some embodiments, distribution engine <NUM> may further authenticate a compute node <NUM> by issuing a challenge to the compute node <NUM> to perform a cryptographic operation using private key 814A of the public-key pair and validating a result (e.g., a digital signature) of the cryptographic operation using public key 814A of the public-key pair. If the verification is successful, distribution engine <NUM> may then attempt to identify tasks <NUM> having task constraints <NUM> matching the capabilities identified in certificate <NUM>. In some embodiments, display device <NUM> may also use public key 814A to establish a secure connection with a compute node <NUM> such as establishing a shared cryptographic key using an Elliptic-Curve Diffie-Hellman (ECDH) exchange.

Turning now to <FIG>, a flow diagram of a method <NUM> is depicted. Method <NUM> is one embodiment of a method that may be performed by a computing device such as display device <NUM> or other examples of devices noted above. In many instances, performance of method <NUM> can improve security of the computing device when interacting with other compute nodes to present a CGR experience.

In step <NUM>, the computing device identifies a plurality of tasks (e.g., tasks <NUM>) to be performed for presenting a computer generated reality (e.g., 3D view <NUM>) to a user. In various embodiments, the plurality of tasks includes tasks that require particular capabilities to be performed. In some embodiments, step <NUM> includes evaluating a graph data structure (e.g., task graph <NUM>) having graph nodes corresponding to the plurality of tasks, the graph nodes specifying criteria (e.g., task constraints <NUM>) for performing the plurality of task. In such an embodiment, the computing device determines, from ones of the graph nodes, that the one or more tasks require the one or more capabilities (e.g., based on desired compute capabilities <NUM>).

In step <NUM>, the computing device receives, from a compute node (e.g., compute nodes <NUM>), a signed attestation (e.g., capabilities certificate <NUM>) specifying that the compute node has one or more of the capabilities. In some embodiments, the signed attestation specifies that the compute node includes secure hardware (e.g. secure element <NUM>) configured to cryptographically isolate data operated on during performance of an offloaded task by the compute node. In some embodiments, the signed attestation specifies that the compute node includes a neural network engine usable to perform an offloaded task. In some embodiments, the signed attestation attests to the compute node being a genuine product of a particular manufacturer. In various embodiments, the signed attestation is issued by a certificate authority (e.g., certificate authority <NUM>) in response to a certificate signing request issued by the compute node for a public-key pair generated by the compute node.

In step <NUM>, in response to a successful verification of the signed attestation, the computing device offloads, to the compute node, one or more of the plurality of tasks determined to require the one or more capabilities specified in the signed attestation. In some embodiments, the computing device verifies the signed attestation by issuing a challenge to the compute node to perform a cryptographic operation using a private key (e.g., private key 814B) of the public-key pair and validating a result of the cryptographic operation using a public key (e.g., public key 814A) of the public-key pair.

Turning now to <FIG>, a block diagram of personalization engine <NUM> is depicted. As mentioned above, personalization engine <NUM> may produce user-specific QoS parameters <NUM> pertaining to a particular user's preference or tolerance for a particular quality of service. In the illustrated embodiment, engine <NUM> includes one or more likelihood estimators <NUM>, a signal encoder <NUM>, and a personal cache <NUM>. In other embodiments, engine <NUM> may be implemented differently than shown.

Likelihood estimators <NUM>, in various embodiments, analyze signals and condition-specific features relevant to the user's experience (e.g., to preserve object shape, enhance audio, smoothing, filtering, compression, etc.). In the illustrated embodiment, estimator <NUM> receives sensor streams <NUM>, system constraints <NUM>, and context cues <NUM>. Sensor streams <NUM> may contain raw multi-modal sensor data (e.g., from cameras, inertial measurement units (IMUs), audio sensors, or other ones of world sensors <NUM> and user sensors <NUM>) and computed metadata (e.g., pertaining to statistical properties of signals). System constraints <NUM> may contain constrains pertaining to power, compute, latency, or various other constraints discussed above. Context cues <NUM> may provide hints about saliency and attributes that may be more relevant such as user context (e.g., content preference, security, privacy, emotional state, health related, audio volume), perceptual tolerance thresholds (e.g. sensing discomfort), safety (e.g., warnings to avoid hazards), etc. Context cues <NUM> may also include information about specific locations/zones where display device <NUM> may be providing particular experiences (e.g., in a store, museum, etc.)-thus, personalization engine <NUM> may customize/personalize QoS parameters <NUM> based on delivering curated experiences in specific locations/zones. In the illustrated embodiment, estimators <NUM> output probability maps <NUM> to signal encoder <NUM>.

Signal encoder <NUM>, in various embodiments, uses probability maps <NUM> and dynamic QoS estimates <NUM> to generate user-specific parameters <NUM>. QoS estimates <NUM> may be based on location and network conditions-or other conditions. In various embodiments, parameters <NUM> may be output as QoS vector values that can be applied to satisfy overall system constraints (e.g., pertaining location, power, latency, bandwidth, fidelity, etc.).

Personal cache <NUM>, in various embodiments, stores various parameter information, which may be previously determined by likelihood estimator <NUM> and signal encoder <NUM> and analyzed in subsequent determinations. In the illustrated embodiment, these parameters include previously determined probability maps <NUM> and previously determined user-specific QoS parameters <NUM>, which may be combined with other stages (e.g. estimation, training, inference, adaptation). In various embodiments, personal cache <NUM> is implemented in a manner that preserves the privacy of stored information as this information may include user-related information.

Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even where only a single embodiment is described with respect to a particular feature. Examples of features provided in the disclosure are intended to be illustrative rather than restrictive unless stated otherwise. The above description is intended to cover such alternatives, modifications, and equivalents as would be apparent to a person skilled in the art having the benefit of this disclosure.

Claim 1:
A display device (<NUM>), comprising:
a display system (<NUM>) configured to display three-dimensional content to a user;
a network interface (<NUM>);
one or more processors (<NUM>); and
memory (<NUM>) having program instructions stored therein that are executable by the one or more processors (<NUM>) to cause the display device (<NUM>) to perform operations including:
discovering, via the network interface (<NUM>), one or more compute nodes (<NUM>) operable to facilitate rendering the three-dimensional content, wherein the discovering includes receiving information (<NUM>,<NUM>) identifying abilities of the one or more compute nodes (<NUM>) to facilitate the rendering;
receiving, from the user of the display device (<NUM>), a request to perform a particular operation including displaying the three-dimensional content;
based on the particular operation, determining a graph data structure that includes a plurality of graph nodes, wherein each of the plurality of graph nodes defines a set of constraints for performing a respective one of the set of tasks;
based on the received information, evaluating a set of tasks to identify one or more of the tasks to offload to the one or more compute nodes (<NUM>) for facilitating the rendering, wherein the evaluating of the set of tasks includes analyzing the graph data structure to determine a distribution plan for the distributing; and
distributing, via the network interface (<NUM>), the identified one or more tasks to the one or more compute nodes (<NUM>) for processing by the one or more compute nodes (<NUM>).