PATENT DOCUMENT

Publication Number: US-11238661-B2
Application Number: US-201916965139-A
Country: US
Kind Code: B2

Title: Method and devices for presenting and manipulating conditionally dependent synthesized reality content threads

Abstract:
In one implementation, a method includes: instantiating a first objective-effectuator (OE) associated with first attributes and a second OE associated with second attributes into a synthesized reality (SR) setting, wherein the first OE is encapsulated within the second OE; providing a first objective to the first OE based on the first and second attributes; providing a second objective to the second OE based on the second attributes, wherein the first and second objectives are associated with a time period between a first and second temporal points; generating a first set of actions for the first OE based on the first objective and a second set of actions for the second OE based on the second objective; and rendering for display the SR setting for the time period including the first set of actions performed by the first OE and the second set of actions performed by the second OE.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a computing system including non-transitory memory and one or more processors, wherein the computing system is communicatively coupled to a display device and one or more input devices:
 instantiating a first objective-effectuator (OE) associated with a first set of attributes and a second OE associated with a second set of attributes into a synthesized reality (SR) setting, wherein the first OE is encapsulated within the second OE, wherein the first set of attributes includes first contextual information associated with a current state of the first OE, and wherein the second set of attributes includes second contextual information associated with a current state of the second OE; 
 generating a first objective for the first OE based on the first set of attributes for the first OE and the second set of attributes for the second OE, wherein the first objective is consistent with the first and second contextual information; 
 generating a second objective for the second OE based on the second set of attributes for the second OE, wherein the second objective is different from the first objective, and wherein the first and second objectives are associated with a first time period between a first temporal point and a second temporal point; 
 generating a first set of actions associated with the first time period for the first OE based on the first objective; 
 generating a second set of actions associated with the first time period for the second OE based on the second objective; and 
 rendering for display the SR setting including the first set of actions performed by the first OE and the second set of actions performed by the second OE. 
 
 
     
     
       2. The method of  claim 1 , further comprising:
 instantiating a third OE associated with a third set of attributes into the SR setting for a second time period, wherein the first OE is encapsulated within the second and third OEs; and 
 updating the first objective for first OE for the second time period based on the first and second sets of attributes and also the third set of attributes associated with the third OE. 
 
     
     
       3. The method of  claim 1 , further comprising:
 removing the second OE from the SR setting for a second time period; and 
 updating the first objective for first OE for the second time period based on the first set of attributes. 
 
     
     
       4. The method of  claim 1 , further comprising:
 instantiating a third OE associated with a third set of attributes and a fourth OE associated with a fourth set of attributes into the SR setting, wherein the third OE is encapsulated within the fourth OE. 
 
     
     
       5. The method of  claim 4 , wherein the first and second OEs are associated with a first OE encapsulation, and wherein the third and fourth OEs are associated with a second OE encapsulation. 
     
     
       6. The method of  claim 5 , wherein at least one OE is included in both the first and second OE encapsulations. 
     
     
       7. The method of  claim 1 , wherein the SR setting is associated with an event, and wherein the first and second objectives are synthesized based on source assets associated with the event. 
     
     
       8. The method of  claim 7 , further comprising:
 extracting a set of actions from source assets associated with the event, wherein the first and second objectives are derived from the set of actions, wherein the first and second objectives are consistent with the set of actions. 
 
     
     
       9. The method of  claim 1 , further comprising:
 receiving a user input removing a respective OE from the SR setting for a second time period; and 
 in response to receiving the user input:
 removing the respective OE from the SR setting; and 
 continuing rendering the SR setting for display for the second time period. 
 
 
     
     
       10. The method of  claim 1 , further comprising:
 receiving a user input adding a respective OE to the SR setting for a second time period; and 
 in response to receiving the user input:
 adding the respective OE from the SR setting; and 
 continuing rendering the SR setting for display for the second time period. 
 
 
     
     
       11. The method of  claim 1 , wherein generating the second set of actions includes generating the second set of actions associated with the first time period for the second OE based on a self-preservation objective instead of the second objective if a predetermined criterion is satisfied. 
     
     
       12. The method of  claim 1 , further comprising:
 receiving a user input selecting a respective OE within the SR setting; and 
 in response to receiving the user input, rendering for display the SR setting through the perspective of the respective OE. 
 
     
     
       13. The method of  claim 1 , further comprising:
 obtaining contextual information characterizing the SR setting. 
 
     
     
       14. The method of  claim 13 , wherein the contextual information includes information associated with OEs and OE encapsulations instantiated within the SR setting. 
     
     
       15. The method of  claim 13 , wherein the contextual information includes information associated with user-specified information associated with the SR setting. 
     
     
       16. The method of  claim 1 , further comprising:
 setting virtual environmental conditions for the SR setting. 
 
     
     
       17. The method of  claim 16 , wherein the virtual environmental conditions are set based on source assets characterizing a scene. 
     
     
       18. The method of  claim 16 , further comprising:
 receiving a user input modifying the virtual environmental conditions for the SR setting for a second time period; and 
 in response to receiving the user input, modifying the virtual environmental conditions for the SR setting based on the user input for the second time period. 
 
     
     
       19. A computing system comprising:
 one or more processors; 
 a non-transitory memory; 
 an interface for communicating with a display device and one or more input devices; and 
 one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the computing system to:
 instantiate a first objective-effectuator (OE) associated with a first set of attributes and a second OE associated with a second set of attributes into a synthesized reality (SR) setting, wherein the first OE is encapsulated within the second OE, wherein the first set of attributes includes first contextual information associated with a current state of the first OE, and wherein the second set of attributes includes second contextual information associated with a current state of the second OE; 
 generate a first objective for the first OE based on the first set of attributes for the first OE and the second set of attributes for the second OE, wherein the first objective is consistent with the first and second contextual information; 
 generate a second objective for the second OE based on the second set of attributes for the second OE, wherein the second objective is different from the first objective, and wherein the first and second objectives are associated with a first time period between a first temporal point and a second temporal point; 
 generate a first set of actions associated with the first time period for the first OE based on the first objective; 
 generate a second set of actions associated with the first time period for the second OE based on the second objective; and 
 render for display the SR setting including the first set of actions performed by the first OE and the second set of actions performed by the second OE. 
 
 
     
     
       20. A non-transitory memory storing one or more programs, which, when executed by one or more processors of a computing system with an interface for communicating with a display device and one or more input devices, cause the computing system to:
 instantiate a first objective-effectuator (OE) associated with a first set of attributes and a second OE associated with a second set of attributes into a synthesized reality (SR) setting, wherein the first OE is encapsulated within the second OE, wherein the first set of attributes includes first contextual information associated with a current state of the first OE, and wherein the second set of attributes includes second contextual information associated with a current state of the second OE; 
 generate a first objective for the first OE based on the first set of attributes for the first OE and the second set of attributes for the second OE, wherein the first objective is consistent with the first and second contextual information;
 generate a second objective for the second OE based on the second set of attributes for the second OE, wherein the second objective is different from the first objective, and wherein the first and second objectives are associated with a first time period between a first temporal point and a second temporal point; 
 
 generate a first set of actions associated with the first time period for the first OE based on the first objective; 
 generate a second set of actions associated with the first time period for the second OE based on the second objective; and 
 render for display the SR setting including the first set of actions performed by the first OE and the second set of actions performed by the second OE. 
 
     
     
       21. The method of  claim 1 , wherein the first attributes for the first OE includes a first set of possible action for performance by the first OE, wherein the second attributes for the second OE includes a second set of possible action for performance by the second OE, and wherein the first objective is also consistent with the first and second sets of possible actions. 
     
     
       22. The method of  claim 1 , wherein at least some of the first and second sets of attributes are extracted from source material associated with the first and second OEs.

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to objective-effectuators (OEs) within synthesized reality (SR) settings and, more specifically, to presenting and manipulating OEs within conditionally dependent SR content threads. 
     BACKGROUND 
     Some devices are capable of generating and presenting synthesized reality settings. Some synthesized reality settings include virtual settings that are simulated replacements of physical settings. Some synthesized reality settings include augmented settings that are modified versions of physical settings. Some devices that present synthesized reality settings include mobile communication devices such as smartphones, head-mountable displays (HMDs), eyeglasses, heads-up displays (HUDs), head-mountable enclosures, and optical projection systems. Most previously available devices that present synthesized reality setting are ineffective at presenting representations of certain objects. For example, some previously available devices that present synthesized reality settings are unsuitable for presenting representations of objects that are associated with an action. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIGS. 1A and 1B  are diagrams of example operating environments in accordance with some implementations. 
         FIG. 2  is a block diagram of an example system in accordance with some implementations. 
         FIG. 3A  is a block diagram of an example emergent content engine in accordance with some implementations. 
         FIG. 3B  is a block diagram of an example neural network in accordance with some implementations. 
         FIGS. 4A-4E  are flowchart representations of a method of generating content for SR settings in accordance with some implementations. 
         FIG. 5  is a block diagram of a server system enabled with various components of the emergent content engine in accordance with some implementations. 
         FIG. 6A  is a block diagram of conditionally dependent synthesized reality (SR) content threads in accordance with some implementations. 
         FIGS. 6B and 6C  illustrate timelines associated with objective-effectuator (OE) encapsulations in accordance with some implementations. 
         FIGS. 7A-7C  illustrate example SR presentation scenarios in accordance with some implementations. 
         FIGS. 8A-8C  are block diagrams of emergent content architectures in accordance with some implementations. 
         FIG. 9  is a flowchart representation of a method of instantiating an OE encapsulation within an SR setting in accordance with some implementations. 
         FIG. 10  is a flowchart representation of a method of initializing and generating content for an OE encapsulation within an SR setting in accordance with some implementations. 
         FIG. 11  is a flowchart representation of a method of initializing and generating content for an OE within an SR setting in accordance with some implementations. 
         FIG. 12  is a flowchart representation of a method of selecting a point-of-view within an SR setting (e.g., a conditionally dependent SR content threads environment) in accordance with some implementations. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods for instantiating an OE encapsulation within an SR setting. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes: instantiating a first objective-effectuator (OE) associated with a first set of attributes and a second OE associated with a second set of attributes into a synthesized reality (SR) setting, wherein the first OE is encapsulated within the second OE; providing a first objective to the first OE based on the first and second sets of attributes; providing a second objective to the second OE based on the second set of attributes, wherein the first and second objectives are associated with a first time period between a first temporal point and a second temporal point; generating a first set of actions associated with the first time period for the first OE based on the first objective; generating a second set of actions associated with the first time period for the second OE based on the second objective; and rendering for display the SR setting including the first set of actions performed by the first OE and the second set of actions performed by the second OE. 
     Various implementations disclosed herein include devices, systems, and methods for initializing and generating content for an OE encapsulation within an SR setting. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes: synthesizing an initial set of objectives for an objective-effectuator (OE) encapsulation based on a set of actions extracted from source assets associated with an event, wherein the OE encapsulation includes a first OE encapsulated within a second OE, and wherein the initial set of objectives includes a first objective for the first OE that is consistent with a second objective for the second OE; instantiating the OE encapsulation into a virtual, wherein the OE encapsulation is characterized by the initial set of objectives and a set of visual rendering attributes; generating updated objectives for the OE encapsulation based on a function of the initial set of objectives, contextual information associated with the event, and the set of actions; and modifying the OE encapsulation based on the updated set of objectives. 
     Various implementations disclosed herein include devices, systems, and methods for initializing and generating content for an OE within an SR setting. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes: synthesizing an initial set of objectives for a first objective-effectuator (OE) based on a set of actions extracted from source assets associated with an event; instantiating the first OE into an SR setting, wherein the first OE is characterized by the initial set of objectives and a set of visual rendering attributes; generating updated objectives for the first OE based on a function of the initial set of objectives, contextual information associated with the event, and the set of actions; and modifying the first OE based on the updated set of objectives. 
     Various implementations disclosed herein include devices, systems, and methods for selecting a point-of-view within an SR setting (e.g., a conditionally dependent SR content threads environment). In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes: presenting a first SR view of an event that includes SR content associated with the event, wherein the SR content includes a plurality of related layers of SR content that perform actions associated with the event; detecting selection of a respective layer among the plurality of related layers of SR content associated with the event; and presenting a second SR view of the event that includes the respective layer of SR content in response to the selection of the respective layer, wherein the second SR view corresponds to a point-of-view of the respective layer. 
     In accordance with some implementations, a device includes one or more processors, a non-transitory memory, and one or more programs. In some implementations, the one or more programs are stored in the non-transitory memory and are executed by the one or more processors. In some implementations, the one or more programs include instructions for performing or causing performance of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions that, when executed by one or more processors of a device, cause the device to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes one or more processors, a non-transitory memory, and means for performing or causing performance of any of the methods described herein. 
     DESCRIPTION 
     Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects and/or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein. 
     A physical setting refers to a world that individuals can sense and/or with which individuals can interact without assistance of electronic systems. Physical settings (e.g., a physical forest) include physical elements (e.g., physical trees, physical structures, and physical animals). Individuals can directly interact with and/or sense the physical setting, such as through touch, sight, smell, hearing, and taste. 
     In contrast, a synthesized reality (SR) setting refers to an entirely or partly computer-created setting that individuals can sense and/or with which individuals can interact via an electronic system. In SR, a subset of an individual&#39;s movements is monitored, and, responsive thereto, one or more attributes of one or more virtual objects in the SR setting is changed in a manner that conforms with one or more physical laws. For example, an SR system may detect an individual walking a few paces forward and, responsive thereto, adjust graphics and audio presented to the individual in a manner similar to how such scenery and sounds would change in a physical setting. Modifications to attribute(s) of virtual object(s) in an SR setting also may be made responsive to representations of movement (e.g., audio instructions). 
     An individual may interact with and/or sense an SR object using any one of his senses, including touch, smell, sight, taste, and sound. For example, an individual may interact with and/or sense aural objects that create a multi-dimensional (e.g., three dimensional) or spatial aural setting, and/or enable aural transparency. Multi-dimensional or spatial aural settings provide an individual with a perception of discrete aural sources in multi-dimensional space. Aural transparency selectively incorporates sounds from the physical setting, either with or without computer-created audio. In some SR settings, an individual may interact with and/or sense only aural objects. 
     One example of SR is virtual reality (VR). A VR setting refers to a simulated setting that is designed only to include computer-created sensory inputs for at least one of the senses. A VR setting includes multiple virtual objects with which an individual may interact and/or sense. An individual may interact and/or sense virtual objects in the VR setting through a simulation of a subset of the individual&#39;s actions within the computer-created setting, and/or through a simulation of the individual or his presence within the computer-created setting. 
     Another example of SR is mixed reality (MR). An MR setting refers to a simulated setting that is designed to integrate computer-created sensory inputs (e.g., virtual objects) with sensory inputs from the physical setting, or a representation thereof. On a reality spectrum, a mixed reality setting is between, and does not include, a VR setting at one end and an entirely physical setting at the other end. 
     In some MR settings, computer-created sensory inputs may adapt to changes in sensory inputs from the physical setting. Also, some electronic systems for presenting MR settings may monitor orientation and/or location with respect to the physical setting to enable interaction between virtual objects and real objects (which are physical elements from the physical setting or representations thereof). For example, a system may monitor movements so that a virtual plant appears stationery with respect to a physical building. 
     One example of mixed reality is augmented reality (AR). An AR setting refers to a simulated setting in which at least one virtual object is superimposed over a physical setting, or a representation thereof. For example, an electronic system may have an opaque display and at least one imaging sensor for capturing images or video of the physical setting, which are representations of the physical setting. The system combines the images or video with virtual objects, and displays the combination on the opaque display. An individual, using the system, views the physical setting indirectly via the images or video of the physical setting, and observes the virtual objects superimposed over the physical setting. When a system uses image sensor(s) to capture images of the physical setting, and presents the AR setting on the opaque display using those images, the displayed images are called a video pass-through. Alternatively, an electronic system for displaying an AR setting may have a transparent or semi-transparent display through which an individual may view the physical setting directly. The system may display virtual objects on the transparent or semi-transparent display, so that an individual, using the system, observes the virtual objects superimposed over the physical setting. In another example, a system may comprise a projection system that projects virtual objects into the physical setting. The virtual objects may be projected, for example, on a physical surface or as a holograph, so that an individual, using the system, observes the virtual objects superimposed over the physical setting. 
     An augmented reality setting also may refer to a simulated setting in which a representation of a physical setting is altered by computer-created sensory information. For example, a portion of a representation of a physical setting may be graphically altered (e.g., enlarged), such that the altered portion may still be representative of but not a faithfully-reproduced version of the originally captured image(s). As another example, in providing video pass-through, a system may alter at least one of the sensor images to impose a particular viewpoint different than the viewpoint captured by the image sensor(s). As an additional example, a representation of a physical setting may be altered by graphically obscuring or excluding portions thereof. 
     Another example of mixed reality is augmented virtuality (AV). An AV setting refers to a simulated setting in which a computer-created or virtual setting incorporates at least one sensory input from the physical setting. The sensory input(s) from the physical setting may be representations of at least one characteristic of the physical setting. For example, a virtual object may assume a color of a physical element captured by imaging sensor(s). In another example, a virtual object may exhibit characteristics consistent with actual weather conditions in the physical setting, as identified via imaging, weather-related sensors, and/or online weather data. In yet another example, an augmented reality forest may have virtual trees and structures, but the animals may have features that are accurately reproduced from images taken of physical animals. 
     Many electronic systems enable an individual to interact with and/or sense various SR settings. One example includes head mounted systems. A head mounted system may have an opaque display and speaker(s). Alternatively, a head mounted system may be designed to receive an external display (e.g., a smartphone). The head mounted system may have imaging sensor(s) and/or microphones for taking images/video and/or capturing audio of the physical setting, respectively. A head mounted system also may have a transparent or semi-transparent display. The transparent or semi-transparent display may incorporate a substrate through which light representative of images is directed to an individual&#39;s eyes. The display may incorporate LEDs, OLEDs, a digital light projector, a laser scanning light source, liquid crystal on silicon, or any combination of these technologies. The substrate through which the light is transmitted may be a light waveguide, optical combiner, optical reflector, holographic substrate, or any combination of these substrates. In one implementation, the transparent or semi-transparent display may transition selectively between an opaque state and a transparent or semi-transparent state. In another example, the electronic system may be a projection-based system. A projection-based system may use retinal projection to project images onto an individual&#39;s retina. Alternatively, a projection system also may project virtual objects into a physical setting (e.g., onto a physical surface or as a holograph). Other examples of SR systems include heads up displays, automotive windshields with the ability to display graphics, windows with the ability to display graphics, lenses with the ability to display graphics, headphones or earphones, speaker arrangements, input mechanisms (e.g., controllers having or not having haptic feedback), tablets, smartphones, and desktop or laptop computers. 
     The present disclosure provides methods, systems, and/or devices for presenting and manipulating SR settings. An emergent content engine generates objectives for objective-effectuators, and provides the objectives to corresponding objective-effectuator engines so that the objective-effectuator engines can generate actions that satisfy the objectives. The objectives generated by the emergent content engine indicate plots or story lines for which the objective-effectuator engines generate actions. Generating objectives enables presentation of dynamic objective-effectuators that perform actions as opposed to presenting static objective-effectuators, thereby enhancing the user experience and improving the functionality of the device presenting the SR setting. 
       FIG. 1A  is a block diagram of an example operating environment  100 A in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  100 A includes a controller  102  and an electronic device  103 . In the example of  FIG. 1A , the electronic device  103  is being held by a user  10 . In some implementations, the electronic device  103  includes a smartphone, a tablet, a laptop, or the like. 
     As illustrated in  FIG. 1A , the electronic device  103  presents a synthesized reality setting  106 . In some implementations, the synthesized reality setting  106  is generated by the controller  102  and/or the electronic device  103 . In some implementations, the synthesized reality setting  106  includes a virtual setting that is a simulated replacement of a physical setting. In other words, in some implementations, the synthesized reality setting  106  is simulated by the controller  102  and/or the electronic device  103 . In such implementations, the synthesized reality setting  106  is different from the physical setting where the electronic device  103  is located. In some implementations, the synthesized reality setting  106  includes an augmented setting that is a modified version of a physical setting. For example, in some implementations, the controller  102  and/or the electronic device  103  modify (e.g., augment) the physical setting where the electronic device  103  is located in order to generate the synthesized reality setting  106 . In some implementations, the controller  102  and/or the electronic device  103  generate the synthesized reality setting  106  by simulating a replica of the physical setting where the electronic device  103  is located. In some implementations, the controller  102  and/or the electronic device  103  generate the synthesized reality setting  106  by removing and/or adding items from the simulated replica of the physical setting where the electronic device  103  is located. 
     In some implementations, the synthesized reality setting  106  includes various SR representations of objective-effectuators, such as a boy action figure representation  108   a , a girl action figure representation  108   b , a robot representation  108   c , and a drone representation  108   d . In some implementations, the objective-effectuators represent characters from fictional materials, such as movies, video games, comics, and novels. For example, the boy action figure representation  108   a  represents a ‘boy action figure’ character from a fictional comic, and the girl action figure representation  108   b  represents a ‘girl action figure’ character from a fictional video game. In some implementations, the synthesized reality setting  106  includes objective-effectuators that represent characters from different fictional materials (e.g., from different movies/games/comics/novels). In various implementations, the objective-effectuators represent things (e.g., tangible objects). For example, in some implementations, the objective-effectuators represent equipment (e.g., machinery such as planes, tanks, robots, cars, etc.). In the example of  FIG. 1A , the robot representation  108   c  represents a robot and the drone representation  108   d  represents a drone. In some implementations, the objective-effectuators represent things (e.g., equipment) from fictional materials. In some implementations, the objective-effectuators represent things from a physical setting, including things located inside and/or outside of the synthesized reality setting  106 . 
     In various implementations, the objective-effectuators perform one or more actions in order to effectuate (e.g., complete/satisfy/achieve) one or more objectives. In some implementations, the objective-effectuators perform a sequence of actions. In some implementations, the controller  102  and/or the electronic device  103  determine the actions that the objective-effectuators are to perform. In some implementations, the actions of the objective-effectuators are within a degree of similarity to actions that the corresponding characters/things perform in the fictional material. In the example of  FIG. 1A , the girl action figure representation  108   b  is performing the action of flying (e.g., because the corresponding ‘girl action figure’ character is capable of flying, and/or the ‘girl action figure’ character frequently flies in the fictional materials). In the example of  FIG. 1A , the drone representation  108   d  is performing the action of hovering (e.g., because drones in physical settings are capable of hovering). In some implementations, the controller  102  and/or the electronic device  103  obtain the actions for the objective-effectuators. For example, in some implementations, the controller  102  and/or the electronic device  103  receive the actions for the objective-effectuators from a remote server that determines (e.g., selects) the actions. 
     In various implementations, an objective-effectuator performs an action in order to satisfy (e.g., complete or achieve) an objective. In some implementations, an objective-effectuator is associated with a particular objective, and the objective-effectuator performs actions that improve the likelihood of satisfying that particular objective. In some implementations, SR representations of the objective-effectuators are referred to as object representations, for example, because the SR representations of the objective-effectuators represent various objects (e.g., real objects, or fictional objects). In some implementations, an objective-effectuator representing a character is referred to as a character objective-effectuator. In some implementations, a character objective-effectuator performs actions to effectuate a character objective. In some implementations, an objective-effectuator representing an equipment is referred to as an equipment objective-effectuator. In some implementations, an equipment objective-effectuator performs actions to effectuate an equipment objective. In some implementations, an objective-effectuator representing an environment is referred to as an environmental objective-effectuator. In some implementations, an environmental objective-effectuator performs environmental actions to effectuate an environmental objective. 
     In some implementations, the synthesized reality setting  106  is generated based on an input from the user  10 . For example, in some implementations, the electronic device  103  receives an input indicating a terrain for the synthesized reality setting  106 . In such implementations, the controller  102  and/or the electronic device  103  configure the synthesized reality setting  106  such that the synthesized reality setting  106  includes the terrain indicated via the input. In some implementations, the input indicates environmental conditions for the synthesized reality setting  106 . In such implementations, the controller  102  and/or the electronic device  103  configure the synthesized reality setting  106  to have the environmental conditions indicated by the input. In some implementations, the environmental conditions include one or more of temperature, humidity, pressure, visibility, ambient light level, ambient sound level, time of day (e.g., morning, afternoon, evening, or night), and precipitation (e.g., overcast, rain, or snow). 
     In some implementations, the actions for the objective-effectuators are determined (e.g., generated) based on an input from the user  10 . For example, in some implementations, the electronic device  103  receives an input indicating placement of the SR representations of the objective-effectuators. In such implementations, the controller  102  and/or the electronic device  103  position the SR representations of the objective-effectuators in accordance with the placement indicated by the input. In some implementations, the input indicates specific actions that the objective-effectuators are permitted to perform. In such implementations, the controller  102  and/or the electronic device  103  select the actions for the objective-effectuator from the specific actions indicated by the input. In some implementations, the controller  102  and/or the electronic device  103  forgo actions that are not among the specific actions indicated by the input. In some implementations, the controller  102  and/or the electronic device  103  include at least a portion of the emergent content architectures in  FIGS. 8A-8C . 
       FIG. 1B  is a block diagram of an example operating environment  100 B in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  100 B includes the controller  102  and a head-mountable device (HMD)  104 . In the example of  FIG. 1B , the HMD  104  is worn by the user  10 . In various implementations, the HMD  104  operates in substantially the same manner as the electronic device  103  shown in  FIG. 1A . In some implementations, the HMD  104  performs substantially the same operations as the electronic device  103  shown in  FIG. 1A . In some implementations, the HMD  104  includes a head-mountable enclosure. In some implementations, the head-mountable enclosure is shaped to form a receptacle for receiving an electronic device with a display (e.g., the electronic device  103  shown in  FIG. 1A ). For example, in some implementations, the electronic device  103  shown in  FIG. 1A  can be slid into the HMD  104 . In some implementations, the HMD  104  includes an integrated display for presenting a synthesized reality experience to the user  10 . In some implementations, the controller  102  and/or the HMD  104  include at least a portion of the emergent content architectures in  FIGS. 8A-8C . 
       FIG. 2  is a block diagram of an example system  200  that generates objectives for various objective-effectuators in an SR setting. For example, the system  200  generates objectives for the boy action figure representation  108   a , the girl action figure representation  108   b , the robot representation  108   c , and/or the drone representation  108   d  shown in  FIGS. 1A and 1B . In the example of  FIG. 2 , the system  200  includes a boy action figure character engine  208   a , a girl action figure character engine  208   b , a robot equipment engine  208   c , and a drone equipment engine  208   d  that generate actions  210  for the boy action figure representation  108   a , the girl action figure representation  108   b , the robot representation  108   c , and the drone representation  108   d , respectively. In some implementations, the system  200  also includes an environmental engine  208   e , an emergent content engine  250 , and a display engine  260 . 
     In various implementations, the emergent content engine  250  generates respective objectives  254  for objective-effectuators that are in the SR setting and/or for the environment of the SR setting. In the example of  FIG. 2 , the emergent content engine  250  generates boy action figure objectives  254   a  for the boy action figure representation  108   a , girl action figure objectives  254   b  for the girl action figure representation  108   b , robot objectives  254   c  for the robot representation  208   c , drone objectives  254   d  for the drone representation  108   d , and/or environmental objectives  254   e  (e.g., environmental conditions) for the environment of the SR setting  106 . As illustrated in  FIG. 2 , the emergent content engine  250  provides the objectives  254  to corresponding character/equipment/environmental engines. In the example of  FIG. 2 , the emergent content engine  250  provides the boy action figure objectives  254   a  to the boy action figure character engine  208   a , the girl action figure objectives  254   b  to the girl action figure character engine  208   b , the robot objectives  254   c  to the robot equipment engine  208   c , the drone objectives  254   d  to the drone equipment engine  208   d , and the environmental objectives  254   e  to the environmental engine  208   e.    
     In various implementations, the emergent content engine  250  generates the objectives  254  based on a function of possible objectives  252  (e.g., a set of predefined objectives), contextual information  258  characterizing the SR setting, and actions  210  provided by the character/equipment/environmental engines. For example, in some implementations, the emergent content engine  250  generates the objectives  254  by selecting the objectives  254  from the possible objectives  252  based on the contextual information  258  and/or the actions  210 . In some implementations, the possible objectives  252  are stored in a datastore. In some implementations, the possible objectives  252  are obtained from corresponding fictional source material (e.g., by scraping video games, movies, novels, and/or comics). For example, in some implementations, the possible objectives  252  for the girl action figure representation  108   b  include saving lives, rescuing pets, fighting crime, etc. 
     In some implementations, the emergent content engine  250  generates the objectives  254  based on the actions  210  provided by the character/equipment/environmental engines. In some implementations, the emergent content engine  250  generates the objectives  254  such that, given the actions  210 , a probability of completing the objectives  254  satisfies a threshold (e.g., the probability is greater than the threshold, for example, the probability is greater than 80%). In some implementations, the emergent content engine  250  generates objectives  254  that have a high likelihood of being completed with the actions  210 . 
     In some implementations, the emergent content engine  250  ranks the possible objectives  252  based on the actions  210 . In some implementations, a rank for a particular possible objective  252  indicates the likelihood of completing that particular possible objective  252  given the actions  210 . In such implementations, the emergent content engine  250  generates the objective  254  by selecting the highest N ranking possible objectives  252 , where N is a predefined integer (e.g., 1, 3, 5, 10, etc.). 
     In some implementations, the emergent content engine  250  establishes initial/end states  256  for the SR setting based on the objectives  254 . In some implementations, the initial/end states  256  indicate placements (e.g., locations) of various character/equipment representations within the SR setting. In some implementations, the SR setting is associated with a time duration (e.g., a few seconds, minutes, hours, or days). For example, the SR setting is scheduled to last for the time duration. In such implementations, the initial/end states  256  indicate placements of various character/equipment representations at/towards the beginning and/or at/towards the end of the time duration. In some implementations, the initial/end states  256  indicate environmental conditions for the SR setting at/towards the beginning/end of the time duration associated with the SR setting. 
     In some implementations, the emergent content engine  250  provides the objectives  254  to the display engine  260  in addition to the character/equipment/environmental engines. In some implementations, the display engine  260  determines whether the actions  210  provided by the character/equipment/environmental engines are consistent with the objectives  254  provided by the emergent content engine  250 . For example, the display engine  260  determines whether the actions  210  satisfy objectives  254 . In other words, in some implementations, the display engine  260  determines whether the actions  210  improve the likelihood of completing/achieving the objectives  254 . In some implementations, if the actions  210  satisfy the objectives  254 , then the display engine  260  modifies the SR setting in accordance with the actions  210 . In some implementations, if the actions  210  do not satisfy the objectives  254 , then the display engine  260  forgoes modifying the SR setting in accordance with the actions  210 . 
       FIG. 3A  is a block diagram of an example emergent content engine  300  in accordance with some implementations. In some implementations, the emergent content engine  300  implements the emergent content engine  250  shown in  FIG. 2 . In various implementations, the emergent content engine  300  generates the objectives  254  for various objective-effectuators that are instantiated in an SR setting (e.g., character/equipment representations such as the boy action figure representation  108   a , the girl action figure representation  108   b , the robot representation  108   c , and/or the drone representation  108   d  shown in  FIGS. 1A and 1B ). In some implementations, at least some of the objectives  254  are for an environmental engine (e.g., the environmental engine  208   e  shown in  FIG. 2 ) that affects an environment of the SR setting. 
     In various implementations, the emergent content engine  300  includes a neural network system  310  (“neural network  310 ”, hereinafter for the sake of brevity), a neural network training system  330  (“a training module  330 ”, hereinafter for the sake of brevity) that trains (e.g., configures) the neural network  310 , and a scraper  350  that provides possible objectives  360  to the neural network  310 . In various implementations, the neural network  310  generates the objectives  254  (e.g., the objectives  254   a  for the boy action figure representation  108   a , the objectives  254   b  for the girl action figure representation  108   b , the objectives  254   c  for the robot representation  108   c , the objectives  254   d  for the drone representation  108   d , and/or the environmental objectives  254   e  shown in  FIG. 2 ). 
     In some implementations, the neural network  310  includes a long short-term memory (LSTM) recurrent neural network (RNN). In various implementations, the neural network  310  generates the objectives  254  based on a function of the possible objectives  360 . For example, in some implementations, the neural network  310  generates the objectives  254  by selecting a portion of the possible objectives  360 . In some implementations, the neural network  310  generates the objectives  254  such that the objectives  254  are within a degree of similarity to the possible objectives  360 . 
     In various implementations, the neural network  310  generates the objectives  254  based on the contextual information  258  characterizing the SR setting. As illustrated in  FIG. 3A , in some implementations, the contextual information  258  indicates instantiated equipment representations  340 , instantiated character representations  342 , user-specified scene/environment information  344 , and/or actions  210  from objective-effectuator engines. 
     In some implementations, the neural network  310  generates the objectives  254  based on the instantiated equipment representations  340 . In some implementations, the instantiated equipment representations  340  refer to equipment representations that are located in the SR setting. For example, referring to  FIGS. 1A and 1B , the instantiated equipment representations  340  include the robot representation  108   c  and the drone representation  108   d  in the SR setting  106 . In some implementations, the objectives  254  include interacting with one or more of the instantiated equipment representations  340 . For example, referring to  FIGS. 1A and 1B , in some implementations, one of the objectives  254   a  for the boy action figure representation  108   a  includes destroying the robot representation  108   c , and one of the objectives  254   b  for the girl action figure representation  108   b  includes protecting the robot representation  108   c.    
     In some implementations, the neural network  310  generates the objectives  254  for each character representation based on the instantiated equipment representations  340 . For example, referring to  FIGS. 1A and 1B , if the SR setting  106  includes the robot representation  108   c , then one of the objectives  254   a  for the boy action figure representation  108   a  includes destroying the robot representation  108   c . However, if the SR setting  106  does not include the robot representation  108   c , then the objective  254   a  for the boy action figure representation  108   a  includes maintaining peace within the SR setting  106 . 
     In some implementations, the neural network  310  generates objectives  254  for each equipment representation based on the other equipment representations that are instantiated in the SR setting. For example, referring to  FIGS. 1A and 1B , if the SR setting  106  includes the robot representation  108   c , then one of the objectives  254   d  for the drone representation  108   d  includes protecting the robot representation  108   c . However, if the SR setting  106  does not include the robot representation  108   c , then the objective  254   d  for the drone representation  108   d  includes hovering at the center of the SR setting  106 . 
     In some implementations, the neural network  310  generates the objectives  254  based on the instantiated character representations  342 . In some implementations, the instantiated character representations  342  refer to character representations that are located in the SR setting. For example, referring to  FIGS. 1A and 1B , the instantiated character representations  342  include the boy action figure representation  108   a  and the girl action figure representation  108   b  in the SR setting  106 . In some implementations, the objectives  254  include interacting with one or more of the instantiated character representations  342 . For example, referring to  FIGS. 1A and 1B , in some implementations, one of the objectives  254   d  for the drone representation  108   d  includes following the girl action figure representation  108   b . Similarly, in some implementations, one of the objectives  254   c  for the robot representation  108   c  include avoiding the boy action figure representation  108   a.    
     In some implementations, the neural network  310  generates the objectives  254  for each character representation based on the other character representations that are instantiated in the SR setting. For example, referring to  FIGS. 1A and 1B , if the SR setting  106  includes the boy action figure representation  108   a , then one of the objectives  254   b  for the girl action figure representation  108   b  includes catching the boy action figure representation  108   a . However, if the SR setting  106  does not include the boy action figure representation  108   a , then the objective  254   b  for the girl action figure representation  108   b  includes flying around the SR setting  106 . 
     In some implementations, the neural network  310  generates objectives  254  for each equipment representation based on the character representations that are instantiated in the SR setting. For example, referring to  FIGS. 1A and 1B , if the SR setting  106  includes the girl action figure representation  108   b , then one of the objectives  254   d  for the drone representation  108   d  includes following the girl action figure representation  108   b . However, if the SR setting  106  does not include the girl action figure representation  108   b , then the objective  254   d  for the drone representation  108   d  includes hovering at the center of the SR setting  106 . 
     In some implementations, the neural network  310  generates the objectives  254  based on the user-specified scene/environment information  344 . In some implementations, the user-specified scene/environment information  344  indicates boundaries of the SR setting. In such implementations, the neural network  310  generates the objectives  254  such that the objectives  254  can be satisfied (e.g., achieved) within the boundaries of the SR setting. In some implementations, the neural network  310  generates the objectives  254  by selecting a portion of the possible objectives  252  that are better suited for the environment indicated by the user-specified scene/environment information  344 . For example, the neural network  310  sets one of the objectives  254   d  for the drone representation  108   d  to hover over the boy action figure representation  108   a  when the user-specified scene/environment information  344  indicates that the skies within the SR setting are clear. In some implementations, the neural network  310  forgoes selecting a portion of the possible objectives  252  that are not suitable for the environment indicated by the user-specified scene/environment information  344 . For example, the neural network  310  forgoes the hovering objective for the drone representation  108   d  when the user-specified scene/environment information  344  indicates high winds within the SR setting. 
     In some implementations, the neural network  310  generates the objectives  254  based on the actions  210  provided by various objective-effectuator engines. In some implementations, the neural network  310  generates the objectives  254  such that the objectives  254  can be satisfied (e.g., achieved) given the actions  210  provided by the objective-effectuator engines. In some implementations, the neural network  310  evaluates the possible objectives  360  with respect to the actions  210 . In such implementations, the neural network  310  generates the objectives  360  by selecting the possible objectives  360  that can be satisfied by the actions  210  and forgoing selecting the possible objectives  360  that cannot be satisfied by the actions  210 . 
     In various implementations, the training module  330  trains the neural network  310 . In some implementations, the training module  330  provides neural network (NN) parameters  312  to the neural network  310 . In some implementations, the neural network  310  includes model(s) of neurons, and the neural network parameters  312  represent weights for the model(s). In some implementations, the training module  330  generates (e.g., initializes or initiates) the neural network parameters  312 , and refines (e.g., adjusts) the neural network parameters  312  based on the objectives  254  generated by the neural network  310 . 
     In some implementations, the training module  330  includes a reward function  332  that utilizes reinforcement learning to train the neural network  310 . In some implementations, the reward function  332  assigns a positive reward to objectives  254  that are desirable, and a negative reward to objectives  254  that are undesirable. In some implementations, during a training phase, the training module  330  compares the objectives  254  with verification data that includes verified objectives. In such implementations, if the objectives  254  are within a degree of similarity to the verified objectives, then the training module  330  stops training the neural network  310 . However, if the objectives  254  are not within the degree of similarity to the verified objectives, then the training module  330  continues to train the neural network  310 . In various implementations, the training module  330  updates the neural network parameters  312  during/after the training. 
     In various implementations, the scraper  350  scrapes content  352  to identify the possible objectives  360 . In some implementations, the content  352  includes movies, video games, comics, novels, and fan-created content such as blogs and commentary. In some implementations, the scraper  350  utilizes various methods, systems and/or, devices associated with content scraping to scrape the content  352 . For example, in some implementations, the scraper  350  utilizes one or more of text pattern matching, HTML (Hyper Text Markup Language) parsing, DOM (Document Object Model) parsing, image processing and audio analysis to scrape the content  352  and identify the possible objectives  360 . 
     In some implementations, an objective-effectuator is associated with a type of representation  362 , and the neural network  310  generates the objectives  254  based on the type of representation  362  associated with the objective-effectuator. In some implementations, the type of representation  362  indicates physical characteristics of the objective-effectuator (e.g., color, material type, texture, etc.). In such implementations, the neural network  310  generates the objectives  254  based on the physical characteristics of the objective-effectuator. In some implementations, the type of representation  362  indicates behavioral characteristics of the objective-effectuator (e.g., aggressiveness, friendliness, etc.). In such implementations, the neural network  310  generates the objectives  254  based on the behavioral characteristics of the objective-effectuator. For example, the neural network  310  generates an objective of being destructive for the boy action figure representation  108   a  in response to the behavioral characteristics including aggressiveness. In some implementations, the type of representation  362  indicates functional and/or performance characteristics of the objective-effectuator (e.g., strength, speed, flexibility, etc.). In such implementations, the neural network  310  generates the objectives  254  based on the functional characteristics of the objective-effectuator. For example, the neural network  310  generates an objective of always moving for the girl action figure representation  108   b  in response to the behavioral characteristics including speed. In some implementations, the type of representation  362  is determined based on a user input. In some implementations, the type of representation  362  is determined based on a combination of rules. 
     In some implementations, the neural network  310  generates the objectives  254  based on specified objectives  364 . In some implementations, the specified objectives  364  are provided by an entity that controls (e.g., owns or created) the fictional material from where the character/equipment originated. For example, in some implementations, the specified objectives  364  are provided by a movie producer, a video game creator, a novelist, etc. In some implementations, the possible objectives  360  include the specified objectives  364 . As such, in some implementations, the neural network  310  generates the objectives  254  by selecting a portion of the specified objectives  364 . 
     In some implementations, the possible objectives  360  for an objective-effectuator are limited by a limiter  370 . In some implementations, the limiter  370  restricts the neural network  310  from selecting a portion of the possible objectives  360 . In some implementations, the limiter  370  is controlled by the entity that owns (e.g., controls) the fictional material from where the character/equipment originated. For example, in some implementations, the limiter  370  is controlled by a movie producer, a video game creator, a novelist, etc. In some implementations, the limiter  370  and the neural network  310  are controlled/operated by different entities. In some implementations, the limiter  370  restricts the neural network  310  from generating objectives that breach a criterion defined by the entity that controls the fictional material. 
       FIG. 3B  is a block diagram of the neural network  310  in accordance with some implementations. In the example of  FIG. 3B , the neural network  310  includes an input layer  320 , a first hidden layer  322 , a second hidden layer  324 , a classification layer  326 , and an objective selection module  328 . While the neural network  310  includes two hidden layers as an example, those of ordinary skill in the art will appreciate from the present disclosure that one or more additional hidden layers are also present in various implementations. Adding additional hidden layers adds to the computational complexity and memory demands, but may improve performance for some applications. 
     In various implementations, the input layer  320  receives various inputs. In some implementations, the input layer  320  receives the contextual information  258  as input. In the example of  FIG. 3B , the input layer  320  receives inputs indicating the instantiated equipment  340 , the instantiated characters  342 , the user-specified scene/environment information  344 , and the actions  210  from the objective-effectuator engines. In some implementations, the neural network  310  includes a feature extraction module (not shown) that generates a feature stream (e.g., a feature vector) based on the instantiated equipment  340 , the instantiated characters  342 , the user-specified scene/environment information  344 , and/or the actions  210 . In such implementations, the feature extraction module provides the feature stream to the input layer  320 . As such, in some implementations, the input layer  320  receives a feature stream that is a function of the instantiated equipment  340 , the instantiated characters  342 , the user-specified scene/environment information  344 , and the actions  210 . In various implementations, the input layer  320  includes a number of LSTM logic elements  320   a , which are also referred to as neurons or models of neurons by those of ordinary skill in the art. In some such implementations, an input matrix from the features to the LSTM logic elements  320   a  includes rectangular matrices. The size of this matrix is a function of the number of features included in the feature stream. 
     In some implementations, the first hidden layer  322  includes a number of LSTM logic elements  322   a . As illustrated in the example of  FIG. 3B , the first hidden layer  322  receives its inputs from the input layer  320 . 
     In some implementations, the second hidden layer  324  includes a number of LSTM logic elements  324   a . In some implementations, the number of LSTM logic elements  324   a  is the same as or similar to the number of LSTM logic elements  320   a  in the input layer  320  or the number of LSTM logic elements  322   a  in the first hidden layer  322 . As illustrated in the example of  FIG. 3B , the second hidden layer  324  receives its inputs from the first hidden layer  322 . Additionally or alternatively, in some implementations, the second hidden layer  324  receives its inputs from the input layer  320 . 
     In some implementations, the classification layer  326  includes a number of LSTM logic elements  326   a . In some implementations, the number of LSTM logic elements  326   a  is the same as or similar to the number of LSTM logic elements  320   a  in the input layer  320 , the number of LSTM logic elements  322   a  in the first hidden layer  322  or the number of LSTM logic elements  324   a  in the second hidden layer  324 . In some implementations, the classification layer  326  includes an implementation of a multinomial logistic function (e.g., a soft-max function) that produces a number of outputs that is approximately equal to the number of possible actions  360 . In some implementations, each output includes a probability or a confidence measure of the corresponding objective being satisfied by the actions  210 . In some implementations, the outputs do not include objectives that have been excluded by operation of the limiter  370 . 
     In some implementations, the objective selection module  328  generates the objectives  254  by selecting the top N objective candidates provided by the classification layer  326 . In some implementations, the top N objective candidates are likely to be satisfied by the actions  210 . In some implementations, the objective selection module  328  provides the objectives  254  to a rendering and display pipeline (e.g., the display engine  260  shown in  FIG. 2 ). In some implementations, the objective selection module  328  provides the objectives  254  to one or more objective-effectuator engines (e.g., the boy action figure character engine  208   a , the girl action figure character engine  208   b , the robot equipment engine  208   c , the drone equipment engine  208   d , and/or the environmental engine  208   e  shown in  FIG. 2 ). 
       FIG. 4A  is a flowchart representation of a method  400  of generating content for SR settings. In various implementations, the method  400  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the controller  102 , the electronic device  103  shown in  FIG. 1A , and/or the HMD  104  shown in  FIG. 1B ). In some implementations, the method  400  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  400  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). Briefly, in some implementations, the method  400  includes instantiating an objective-effectuator into an SR setting, obtaining contextual information for the SR setting, generating an objective for the objective-effectuator, setting environmental conditions for the SR setting, establishing initial conditions for the objective-effectuator based on the objective, and modifying the objective-effectuator based on the objective. 
     As represented by block  410 , in various implementations, the method  400  includes instantiating an objective-effectuator into an SR setting (e.g., instantiating the boy action figure representation  108   a , the girl action figure representation  108   b , the robot representation  108   c , and/or the drone representation  108   d  into the SR setting  106  shown in  FIGS. 1A and 1B ). In some implementations, the objective-effectuator is characterized by a set of predefined objectives (e.g., the possible objectives  360  shown in  FIG. 3A ) and a set of visual rendering attributes. 
     As represented by block  420 , in various implementations, the method  400  includes obtaining contextual information characterizing the SR setting (e.g., the contextual information  258  shown in  FIGS. 2-3B ). In some implementations, the method  400  includes receiving the contextual information (e.g., from a user). 
     As represented by block  430 , in various implementations, the method  400  includes generating an objective for the objective-effectuator based on a function of the set of predefined objectives, the contextual information, and a set of predefined actions for the objective-effectuator. For example, referring to  FIG. 2 , the method  400  includes generating the objectives  254  based on the possible objectives  252 , the contextual information  258 , and the actions  210 . 
     As represented by block  440 , in various implementations, the method  400  includes setting environmental conditions for the SR setting based on the objective for the objective-effectuator. For example, referring to  FIG. 2 , the method  400  includes generating the environmental objectives  254   e  (e.g., the environmental conditions). 
     As represented by block  450 , in various implementations, the method  400  includes establishing initial conditions and a current set of actions for the objective-effectuator based on the objective for the objective-effectuator. For example, referring to  FIG. 2 , the method  400  include establishing the initial/end states  256  for various objective-effectuators (e.g., character representations, equipment representations and/or the environment). 
     As represented by block  460 , in various implementations, the method  400  includes modifying the objective-effectuator based on the objective. For example, referring to  FIG. 2 , in some implementations, the method  400  includes providing the objectives  254  to the display engine  260  and/or to one or more objective-effectuator engines. 
     Referring to  FIG. 4B , as represented by block  410   a , in various implementations, the method  400  includes obtaining a set of predefined objectives (e.g., the possible objectives  360  shown in  FIG. 3A ) from source material (e.g., the content  352  shown in  FIG. 3A , for example, movies, books, video games, comics, and/or novels). As represented by block  410   b , in various implementations, the method  400  includes scraping the source material for the set of predefined objectives. 
     As represented by block  410   c , in some implementations, the method  400  includes determining the set of predefined objectives based on a type of representation (e.g., the type of representation  362  shown in  FIG. 3A ). As represented by block  410   d , in some implementations, the method  400  includes determining the set of predefined objectives based on user-specified configuration (e.g., the type of representation  362  shown in  FIG. 3A  is determined based on a user input). 
     As represented by block  410   e , in some implementations, the method  400  includes determining the predefined objectives based on a limit specified by an object owner. For example, referring to  FIG. 3A , in some implementations, the method  400  includes limiting the possible objectives  360  selectable by the neural network  310  by operation of the limiter  370 . 
     As represented by block  410   f , in some implementations, the SR setting (e.g., the SR setting  106  shown in  FIGS. 1A and 1B ) include an SR setting that is a simulated replacement of a real-world scene. 
     As represented by block  410   g , in some implementations, the SR setting (e.g., the SR setting  106  shown in  FIGS. 1A and 1B ) includes an augmented scene that is a modified version of a real-world scene. 
     As represented by block  410   h , in some implementations, the objective-effectuator is a representation of a character (e.g., the boy action figure representation  108   a  and/or the girl action figure representation  108   b  shown in  FIGS. 1A and 1B ) from one or more of a movie, a video game, a comic, a novel, or the like. 
     As represented by block  410   i , in some implementations, the objective-effectuator is a representation of an equipment (e.g., the robot representation  108   c  and/or the drone representation  108   d  shown in  FIGS. 1A and 1B ) from one or more of a movie, a video game, a comic, a novel, or the like. 
     As represented by block  410   j , in some implementations, the method  400  includes obtaining a set of visual rendering attributes from an image. For example, in some implementations, the method  400  includes capturing an image and extracting the visual rendering attributes from the image (e.g., by utilizing devices, methods, and/or systems associated with image processing). 
     Referring to  FIG. 4C , as represented by block  420   a , in various implementations, the contextual information indicates whether objective-effectuators have been instantiated in the SR setting. As represented by block  420   b , in some implementations, the contextual information indicates which character representations have been instantiated in the SR setting (e.g., the contextual information includes the instantiated characters representation  342  shown in  FIGS. 3A-3B ). As represented by block  420   c , in some implementations, the contextual information indicates equipment representations that have been instantiated in the SR setting (e.g., the contextual information includes the instantiated equipment representations  340  shown in  FIGS. 3A-3B ). 
     As represented by block  420   d , in various implementations, the contextual information includes user-specified scene information (e.g., user-specified scene/environment information  344  shown in  FIGS. 3A-3B ). As represented by block  420   e , in various implementations, the contextual information indicates a terrain (e.g., a landscape, for example, natural artifacts such as mountains, rivers, etc.) of the SR setting. As represented by block  420   f , in various implementations, the contextual information indicates environmental conditions within the SR setting (e.g., the user-specified scene/environmental information  344  shown in  FIGS. 3A-3B ). 
     As represented by block  420   g , in some implementations, the contextual information includes a mesh map of a real-world scene (e.g., a detailed representation of the real-world scene where the device is located). In some implementations, the mesh map indicates positions and/or dimensions of real-world artifacts that are located at the real-world scene. 
     Referring to  FIG. 4D , as represented by block  430   a , in some implementations, the method  400  includes utilizing a neural network (e.g., the neural network  310  shown in  FIGS. 3A-3B ) to generate the objectives. As represented by block  430   b , in some implementations, the neural network generates the objectives based on a set of neural network parameters (e.g., the neural network parameters  312  shown in  FIG. 3A ). As represented by block  430   c , in some implementations, the method  400  includes adjusting the neural network parameters based on the objectives generated by the neural network (e.g., adjusting the neural network parameters  312  based on the objectives  254  shown in  FIG. 3A ). 
     As represented by block  430   d , in some implementations, the method  400  includes determining neural network parameters based on a reward function (e.g., the reward function  332  shown in  FIG. 3A ) that assigns a positive reward to desirable objectives and a negative reward to undesirable objectives. As represented by block  430   e , in some implementations, the method  400  includes configuring (e.g., training) the neural network based on reinforcement learning. As represented by block  430   f , in some implementations, the method  400  includes training the neural network based on content scraped (e.g., by the scraper  350  shown in  FIG. 3A ) from videos such as movies, books such as novels and comics, and video games. 
     As represented by block  430   g , in some implementations, the method  400  includes generating a first objective if a second objective-effectuator is instantiated in the SR setting. As represented by block  430   h , in some implementations, the method  400  includes generating a second objective if a third objective-effectuator is instantiated in the SR setting. More generally, in various implementations, the method  400  includes generating different objectives for an objective-effectuator based on the other objective-effectuators that are present in the SR setting. 
     As represented by block  430   i , in some implementations, the method  400  includes selecting an objective if, given a set of actions, the likelihood of the objective being satisfied is greater than a threshold. As represented by block  430   j , in some implementations, the method  400  includes forgoing selecting an objective if, given the set of actions, the likelihood of the objective being satisfied is less than the threshold. 
     Referring to  FIG. 4E , as represented by block  440   a , in some implementations, the method  400  includes setting one or more of a temperature value, a humidity value, a pressure value and a precipitation value within the SR setting. In some implementations, the method  400  includes making it rain or snow in the SR setting. As represented by block  440   b , in some implementations, the method  400  includes setting one or more of an ambient sound level value (e.g., in decibels) and an ambient lighting level value (e.g., in lumens) for the SR setting. As represented by block  440   c , in some implementations, the method  400  includes setting states of celestial bodies within the SR setting (e.g., setting a sunrise or a sunset, setting a full moon or a partial moon, etc.). 
     As represented by block  450   a , in some implementations, the method  400  includes establishing initial/end positions of objective-effectuators. In some implementations, the SR setting is associated with a time duration. In such implementations, the method  400  includes setting initial positions that the objective-effectuators occupy at or near the beginning of the time duration, and/or setting end positions that the objective-effectuators occupy at or near the end of the time duration. 
     As represented by block  450   b , in some implementations, the method  400  includes establishing initial/end actions for objective-effectuators. In some implementations, the SR setting is associated with a time duration. In such implementations, the method  400  includes establishing initial actions that the objective-effectuators perform at or near the beginning of the time duration, and/or establishing end actions that the objective-effectuators perform at or near the end of the time duration. 
     As represented by block  460   a , in some implementations, the method  400  includes providing the objectives to a rendering and display pipeline (e.g., the display engine  260  shown in  FIG. 2 ). As represented by block  460   b , in some implementations, the method  400  includes modifying the objective-effectuator such that the objective-effectuator can be seen as performing actions that satisfy the objectives. 
       FIG. 5  is a block diagram of a server system  500  enabled with one or more components of a device (e.g., the controller  102 , the electronic device  103  shown in  FIG. 1A , and/or the HMD  104  shown in  FIG. 1B ) in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the server system  500  includes one or more processing units (CPUs)  501 , a network interface  502 , a programming interface  503 , a memory  504 , and one or more communication buses  505  for interconnecting these and various other components. 
     In some implementations, the network interface  502  is provided to communicate with one or more local devices (e.g., via near-field communication or a local network) and/or one or more remote devices (e.g., WiFi, Ethernet, etc.). In some implementations, the one or more communication buses  505  include circuitry that interconnects and controls communications between system components. The memory  504  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM or other random-access solid-state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  504  optionally includes one or more storage devices remotely located from the one or more CPUs  501 . The memory  504  comprises a non-transitory computer readable storage medium. 
     In some implementations, the memory  504  or the non-transitory computer readable storage medium of the memory  504  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  506 , the neural network  310 , the training module  330 , the scraper  350 , and the possible objectives  360 . As described herein, the neural network  310  is associated with the neural network parameters  312 . As described herein, the training module  330  includes a reward function  332  that trains (e.g., configures) the neural network  310  (e.g., by determining the neural network parameters  312 ). As described herein, the neural network  310  determines objectives (e.g., the objectives  254  shown in  FIGS. 2-3B ) for objective-effectuators in an SR setting and/or for the environment of the SR setting. In some implementations, the memory  504  include at least a portion of the emergent content architectures in  FIGS. 8A-8C . 
     In some implementations, the electronic device  500  optionally includes one or more input devices such as an eye tracker, touch-sensitive surface, keypad or keyboard, accelerometer, gyroscope, inertial measurement unit (IMU), grip sensor, one or more microphones, one or more buttons, one or more interior-facing image sensors, one or more exterior-facing image sensors, one or more depth sensors, one or more physiological sensors (e.g., heartbeat sensor, glucose level detector, etc.), one or more environmental sensors (e.g., barometer, humidity sensor, thermometer, ambient light detector, etc.), and/or the like. In some implementations, the electronic device  500  optionally includes one or more output/feedback devices such as a haptics engine, skin shear engine, one or more displays, one or more speakers, and/or the like. 
       FIG. 6A  is a block diagram of conditionally dependent SR content threads  600  in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, the conditionally dependent SR content threads  600  correspond to a mesh of related content threads (e.g., stories, narratives, etc.) associated with a movie, TV episode, novel, theatrical play, or event such as an athletic event, ritual, coronation, inauguration, concert, opera, theatrical performance, battle, or other large-scale occurrence. 
     According to some implementations, the conditionally dependent SR content threads  600  includes a plurality of content threads (sometime also referred to herein as “stories” or “story nodes” for the sake of brevity) that are linked together in a tree, mesh, or web of inter-related stories. In some implementations, each of the plurality of stories within the conditionally dependent SR content threads  600  corresponds to a particular point-of-view of the event. In some implementations, each of the plurality of stories within the conditionally dependent SR content threads  600  is based on source assets/materials including, for example, plans for the event such as battle plans or an order of battle, ground truth for the event such as the course and outcomes of the battle, historical accounts and books, movies, video games, novels, and/or the like. 
     According to some implementations, the root of the conditionally dependent SR content threads  600  includes a super-macro story  610 , which is, in turn, associated with one or more macro stories  620   a ,  620   b , . . . ,  620   n  (sometimes collectively referred to herein as macro stories  620 ). As shown in  FIG. 6A , in some implementations, each of the macro stories  620  is associated with one or more sub-macro stories. For example, the macro story  620   a  is associated with sub-macro stories  632   a ,  632   b , . . . ,  632   n  (sometimes collectively referred to herein as sub-macro stories  632 ), and the macro story  620   n  is associated with sub-macro stories  634   a ,  634   b , . . . ,  634   n  (sometimes collectively referred to herein as sub-macro stories  634 ). 
     As shown in  FIG. 6A , in some implementations, each of the sub-macro stories is associated with one or more micro stories. For example, the sub-macro story  634   a  is associated with micro stories  640   a , . . . ,  640   n  (sometimes collectively referred to herein as micro stories  640 ). In some implementations, each of the micro stories  640  is associated with one or more sub-micro stories and, in turn, each of the sub-micro stories is associated with one or more super-micro stories. As shown in  FIG. 6 , for example, the micro story  640   a  is associated with a sub-micro story  650 , and the sub-micro story  650  is associated with a super-micro story  660 . 
     As one example, the super-macro story  610  corresponds to the overall story associated with a particular battle of a war in the historical records. Continuing with this example, the macro stories  620  correspond to various military branches for the countries involved in a particular battle. As such, in this example, the macro story  620   n  corresponds to a naval military branch. Continuing with this example, the sub-macro stories  634  correspond to individual ships. As such, in this example, the sub-macro story  634   a  corresponds to a particular transport ship. 
     As shown in  FIG. 6A , there is a relationship  635  between the sub-macro story  632   n  corresponding to a squadron of aircraft and the sub-macro story  634   a . In this example, the squadron of aircraft are providing air support and defense for the particular transport ship (among other transport ships). 
     Continuing with this example, the micro stories  640  correspond to a plurality of amphibious landing craft being transported by the particular transport ship. Continuing with this example, the sub-micro story  650  corresponds to a platoon of infantry or marines assigned to respective amphibious landing craft among the plurality of amphibious landing craft being transported by the particular transport ship. Continuing with this example, the super-micro story  660  corresponds to a particular infantryman or marine assigned to the respective amphibious landing craft. 
     In some implementations, a user is presented SR content associated with an omniscient third-person view of the super-macro story  610  (e.g., a particular battle). The user may switch to another point-of-view within the conditionally dependent SR content threads  600  in order to view SR content associated with the selected point-of-view within the conditionally dependent SR content threads  600  such as the perspective of the naval military branch associated with macro story  620   n , the perspective of the particular transport ship associated with the sub-macro story  634   a , the perspective of the respective amphibious landing craft  640   a , the perspective of the platoon of infantry or marines associated with sub-micro story  650 , or the perspective the particular infantryman or marine assigned to the respective amphibious landing craft associated with the super-micro story  660 . 
     According to some implementations, a node of the conditionally dependent SR content threads  600  may be removed by the user in order to view a simulation of the event that excludes the particular node and associated child nodes. According to some implementations, anode may be added to the conditionally dependent SR content threads  600  by the user in order to view a simulation of the event that includes the particular node and associated child nodes. 
       FIG. 6B  is a block diagram of a timeline  675  associated with an objective-effectuator (OE) encapsulation in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, as shown in  FIG. 6B , the timeline  675  illustrates the state of an OE encapsulation from the point-of-view of a lowest-level OE  684  at a plurality of time periods or temporal points T 0 , T 1 , T 2 , and T 3 . 
     In some implementations, an OE encapsulation includes a plurality of conditional, related, correlated, associated, or dependent OEs that are encapsulated or nested based on contextual information. In some implementations, each OE corresponds to a character within a synthesized reality (SR) setting. As one example, an OE encapsulation corresponds to a set of related characters or entities such as a transport ship including N amphibious landing crafts each with M marines. In this example, the lowest-level OE is an individual marine that is encapsulated/nested within his company, which, in turn, is encapsulated/nested within the amphibious land craft. Continuing with this example, the amphibious land craft is encapsulated/nested within the transport ship. 
     In some implementations, when the first OE is encapsulated within the second OE, the first OE is associated with the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is correlated with the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is related to the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is dependent on the second OE. In other words, the first OE is provided objectives and performs actions that are consistent with the context and attributes of the second OE because the first OE is encapsulated within the second OE. 
     As shown in  FIG. 6B , at time T 0 , the OE encapsulation  685   a  includes an OE  684  encapsulated/nested within an OE  682 , which, in turn, is encapsulated/nested within an OE  680 . For example, at time T 0 , while approaching a beach to make an amphibious landing, the OE encapsulation  685   a  includes the OE  684  (e.g., an individual marine) encapsulated/nested within the OE  682  (e.g., a company of marines), which, in turn, is encapsulated/nested within an OE  680  (e.g., an amphibious landing craft). 
     In some implementations, when a first OE is encapsulated/nested within a second OE, the first OE is provided an objective based on the context of the second OE within which it is encapsulated and, in turn, the first OE performs actions that are consistent with that context. As an example, a marine on a transport ship is not given a scouting objective to perform scouting patrols or a fire suppression objective to mortar a target while on the transport ship. In some implementations, the lower-level OE performs actions consistent with higher-level OEs in its encapsulation. In some implementations, one or more other lower-level OEs are encapsulated within the first OE. In some implementations, the second OE is encapsulated within one or more other higher-level OEs. 
     In some implementations, the system determines a set of OE encapsulations for each time period of the event. As such, an OE encapsulation may change over the course of the event such as OEs (layers) being stripped away or added. As one example, a particular battle encapsulation starts as transport ship→amphibious assault vehicle→company of marines→individual marine while in transport (T 0 ). Continuing with this example, the OE encapsulation changes to amphibious assault vehicle→company of marines→individual marine while approaching the beach (T 1 ). Continuing with this example, the OE encapsulation changes again to company of marines→individual marine while storming the beach (T 2 ). Continuing with this example, the OE encapsulation changes again when the marines may find and enter a vehicle after reaching the beach (T 3 ) which changes the encapsulation to vehicle→subset of company of marines→individual marine. 
     As shown in  FIG. 6B , at time T 1 , the OE encapsulation  685   b  includes the OE  684  encapsulated/nested within the OE  682 . For example, at time T 1 , after landing on a beach, the OE encapsulation  685   b  includes the OE  684  (e.g., the individual marine) encapsulated/nested within the OE  682  (e.g., the company of marines). 
     As shown in  FIG. 6B , at time T 2 , the OE encapsulation  685   c  includes the OE  684 . For example, at time T 2 , after landing on a beach and completing a mission, the OE encapsulation  685   c  includes the OE  684  (e.g., the individual marine) apparatus from the OE  682 . 
     As shown in  FIG. 6B , at time T 3 , the OE encapsulation  685   d  includes the OE  684  encapsulated/nested within the OE  686 . For example, at time T 3 , after landing on a beach and reaching a rendezvous point, the OE encapsulation  685   d  includes the OE  684  (e.g., the individual marine) encapsulated/nested within the OE  686  (e.g., an evacuation helicopter or truck). 
       FIG. 6C  is a block diagram of a timeline  695  associated with an objective-effectuator (OE) encapsulation in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, the timeline  695  is related to the timeline  675  in  FIG. 6B . The time  695  illustrates the parallel functioning of the OEs even after the OE encapsulation changes over the time periods or temporal points T 0 , T 1 , T 2 , and T 3 . In other words,  FIG. 6C  illustrates the OEs  680  and  682  that are stripped away from the OE encapsulation  685   a  and  685   b  over time continuing to function in parallel with the OE  684 . 
       FIGS. 7A-7C  illustrate example SR settings  700   a  and  700   b  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. 
       FIG. 7A  illustrates an example SR setting  700   a . As shown in  FIG. 7A , the physical setting  706  includes a table  702 . In this example, a portion of the physical setting  706  is within the field-of-view  704  of the electronic device  103 , where the field-of-view  704  is associated with an external facing image sensor of the electronic device  103  (e.g., a tablet or mobile phone). In other words, the user is looking at the table  702  from a side or perspective orientation through the electronic device  103 . As such, the portion of the physical setting  706 , including the table  702 , is displayed on the display  705  of the electronic device  103  (e.g., a live video stream or video pass-through of the physical setting  706 ). As shown in  FIG. 7A , the electronic device  103  displays an SR setting  106  on the display  705  that includes SR content superimposed on or composited with the table  702  and the wall. 
     As shown in  FIG. 7A , for example, the SR content (e.g., associated with a particular battle) overlaid or superimposed on the table  702  includes a first SR content layer  710   a  (e.g., an underwater layer) with SR content elements  712  (e.g., sunken ships, sunken amphibious landing craft, underwater mines, and/or the like), a second AR content layer  710   b  (e.g., a water surface layer) with SR content elements  714  (e.g., amphibious landing craft with platoons of infantrymen or marines, transport ships, and/or the like), and a third SR content layer  710   c  (e.g., an aerial layer) with SR content elements  716  (e.g., aircraft, dirigibles, shells, and/or the like). As shown in  FIG. 7A , for example, the SR content  720  overlaid on the wall includes SR content elements  722  (e.g., defensive position, stationary guns, and/or the like on land). One of ordinary skill in the art will appreciate that the number, structure, dimensions, and placement of the SR content layers and associated SR content elements in  FIG. 7A  is arbitrary and may be changed in various other implementations. 
       FIGS. 7B and 7C  illustrate an example SR setting  700   b . As shown in  FIGS. 7B and 7C , as one example, the user  10  wears the HMD  104  his/her head (e.g., AR-enabled glasses) with optical see-through of the physical setting  706  (e.g., the user&#39;s living room). As shown in  FIGS. 7B and 7C , as another example, the user  10  wears the HMD  104  on his/her head (e.g., an SR-enabled headset) with video pass-through of the physical setting  706  (e.g., the user&#39;s living room). 
     As shown in  FIG. 7B , in state  725  (e.g., associated with time period T 1 ), the HMD  104  superimposes or overlays SR content on the table  702  and the walls of the physical setting  706 . In this example, with reference to state  725 , the SR content (e.g., associated with a particular battle) overlaid on the table  702  includes a first SR content layer  710   a  (e.g., an underwater layer) with SR content elements  712  (e.g., sunken ships, sunken amphibious landing craft, underwater mines, and/or the like), a second SR content layer  710   b  (e.g., a water surface layer) with SR content elements  714  (e.g., amphibious landing craft with platoons of infantrymen or marines, transport ships, and/or the like), and a third SR content layer  710   c  (e.g., an aerial layer) with SR content elements  716  (e.g., aircraft, dirigibles, shells, and/or the like). As shown in  FIG. 7B , with reference to state  725 , the SR content elements  720  overlaid on the front wall includes SR content elements  722  (e.g., defensive position, stationary guns, and/or the like on land), and the SR content elements  730  overlaid on the side wall includes peripheral or environment details. One of ordinary skill in the art will appreciate that the number, structure, dimensions, and placement of the SR content layers and associated SR content elements in  FIG. 7B  is arbitrary and may be changed in various other implementations. 
     As shown in  FIG. 7B , state  725  corresponds to a first SR view of the event such as an omniscient third-person view of the overall event (e.g., a particular battle). In response to receiving an input from the user  10  (e.g., a voice command, gesture, or the like) selecting the third AR content layer  710   c  (e.g., an aerial layer), the HMD  104  presents a second SR view of the event associated with the third SR content layer  710   c  (e.g., an aerial layer). 
     As shown in  FIG. 7C , in state  735  (e.g., associated with time period T 2 ), the HMD  104  updates the SR content superimposed or overlaid on the table  702  in response to the selectin of the third AR content layer  710   c . As shown in  FIG. 7C , in state  735 , the HMD  104  superimposes or overlays SR content on the table  702  that corresponds to the third SR content layer  710   c . In this example, with reference to state  735 , the SR content (e.g., associated with a particular battle) overlaid on the table  702  includes the third SR content layer  710   c  (e.g., an aerial layer) with SR content elements  716  (e.g., aircraft, dirigibles, shells, and/or the like). For example, in  FIG. 7C , the third SR content layer  710   c  corresponds to a center of gravity of a squadron of aircraft on approach to attack the defensive positions on within a battle site. As another example, in state  735 , the user  10  may be presented the point-of-view of a particular pilot among the squadron of aircraft on approach to attack the defensive positions on a battle site (not shown). 
       FIG. 8A  is a block diagram of an emergent content architecture  800  in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, an objective-effectuator (OE) encapsulation of related (e.g., encapsulated/nested) OE engines  810   a ,  810   b ,  810   c , and  810   d  (sometimes collectively referred to herein as the OE engines  810 ) (e.g., similar to character engines  208   a - e  in  FIG. 2 ) perform actions based on objectives derived from the objective generator  807 . 
     As one example, the OE encapsulation includes a top-level OE engine  810   a  associated with a transport ship, a second-level OE engine  810   b  associated with an amphibious landing craft being transported by the transport ship, a third-level OE engine  810   c  associated a platoon of infantrymen or marine assigned to the amphibious landing craft, and a bottom-level OE engine  810   d  associated with a particular infantryman or marine. As such, the various OE engines within the OE encapsulation are related in some manner as they correspond to connected nodes within a conditionally dependent SR content threads associated with the event (e.g., as described with reference to the story nodes in  FIG. 6A ). According to some implementations, the emergent content architecture  800  is structured to produce concurrent actions for the various OE engines within the OE encapsulation for consistent content. In various implementations, one of ordinary skill in the art will appreciate that emergent content architecture  800  may include an arbitrary number of OE encapsulations. In some implementations, a same OE engine may be shared between two or more OE encapsulations due to the related nature of OEs to OE encapsulations. 
     According to some implementations, the objective generator  807  (e.g., a neural network or other AI construct) produces objectives for each OE per time period based on a bank of predetermined objectives, previous objectives, source assets, and/or other information (e.g., similar to the emergent content engine  250  in  FIG. 2 ). According to some implementations, the encapsulation manager  825  determines the OE encapsulation (e.g., the nesting or layering of related OEs). For example, the encapsulation manager  825  determines the OE encapsulation based on the connections between the story nodes within conditionally dependent SR content threads associated with the event. In some implementations, the encapsulation manager  825  modifies the OE encapsulation over time (e.g., adding or removing layers of OEs) based on the objectives, source assets, and/or other information. 
     In some implementations, the demultiplexer  845  routes the objectives on a per OE basis to their respective OE engines  810 . In some implementations, the OE engines  810  perform actions based on their objectives. According to some implementations, the actions for a time period are captured and provided to a display pipeline  855  for rendering and display in order to present the SR content associated with the event to a user (e.g., an SR reconstruction or simulation of a battle or other event based on the source assets then emergent content). According to some implementations, the actions for a time period are captured and provided to the OE engines  810  as training feedback  865 . According to some implementations, the actions are provided to the objective generator  807  which in turn produces updated objective for each OE per time period (e.g., as described above with reference to the emergent content engine  250  in  FIG. 2 ). 
       FIG. 8B  is a block diagram of an emergent content architecture  850  in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, an objective-effectuator (OE) encapsulation of related (e.g., encapsulated/nested) OE engines  810   a ,  810   b ,  810   c , and  810   d  (sometimes collectively referred to herein as the OE engines  810 ) (e.g., similar to character engines  208   a - e  in  FIG. 2 ) perform actions based on initial objectives derived from source assets  805  at time T 0  and based on updated objectives derived from the emergent content engine  250  at times T 1  and on. 
     According to some implementations, the encapsulation manager  825  determines the OE encapsulation (e.g., the nesting or layering of related OEs). For example, the encapsulation manager  825  determines the OE encapsulation based on the connections between the story nodes within a conditionally dependent SR content threads associated with the event. In some implementations, the encapsulation manager  825  modifies the OE encapsulation over time (e.g., adding or removing layers of OEs) based on the objectives, source assets, and/or other information. 
     As one example, the OE encapsulation includes a top-level OE engine  810   a  associated with a transport ship, a second-level OE engine  810   b  associated with an amphibious landing craft being transported by the transport ship, a third-level OE engine  810   c  associated a platoon of infantrymen or marine assigned to the amphibious landing craft, and a bottom-level OE engine  810   d  associated with a particular infantryman or marine. As such, the various OE engines within the OE encapsulation are related in some manner as they correspond to connected nodes within a conditionally dependent SR content threads associated with the event (e.g., as described with reference to the story nodes in  FIG. 6A ). According to some implementations, the emergent content architecture  850  is structured to produce concurrent actions for the various OE engines within the OE encapsulation for consistent emergent content. In various implementations, one of ordinary skill in the art will appreciate that emergent content architecture  850  may include an arbitrary number of OE encapsulations. In some implementations, a same OE engine may be shared between two or more OE encapsulations due to the related nature of OEs to OE encapsulations. 
     According to some implementations, the initial objectives are produced by the operations of a scraper engine  820  and an objective generator  830  based on source assets  805  associated with an event (e.g., plans  810   a , ground truth  810   b , and personal accounts or the like  810   c  for the event). In some implementations, the scraper engine  820  performs parsing and understanding operations on the source assets  805  in order to produce extracted actions for each OE (e.g., characters identified in the source assets  805 ) per time period. For example, in some implementations, the scraper engine  820  extracts the actions from the source assets  805 . Thereafter, in some implementations, the objective generator  830  (e.g., a neural network or other AI construct) produces initial objectives for each OE per time period. 
     According to some implementations, a multiplexer  835  enables one of the initial objectives or the updated objectives as inputs to the OE encapsulation. In some implementations, the demultiplexer  845  routes the objectives on a per OE basis to their respective OE engines  810 . In some implementations, the OE engines  810  perform actions based on their objectives. According to some implementations, the actions for a time period are captured and provided to a display pipeline  855  for rendering and display in order to present the SR content associated with the event to a user (e.g., an SR reconstruction or simulation of a battle or other event based on the source assets then emergent content). According to some implementations, the actions for a time period are captured and provided to the emergent content engine  250  and the OE engines  810  as training feedback  865 . According to some implementations, the actions are provided to the emergent content engine  250  which in turn produces updated objective for each OE per time period (e.g., as described above with reference to the emergent content engine  250  in  FIG. 2 ). 
       FIG. 8C  is a block diagram of an emergent content architecture  875  in accordance with some implementations. While certain specific features are illustrated, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations, OE engines  860   a ,  860   b ,  860   c , and  860   d  (sometimes collectively referred to herein as the OE engines  860 ) (e.g., similar to character engines  208   a - e  in  FIG. 2 ) perform actions based on initial objectives derived from source assets  805  at time T 0  and based on updated objectives derived from the emergent content engine  250  at times T 1  and on. 
     According to some implementations, the initial objectives are produced by the operations of a scraper engine  820  and an objective generator  830  based on source assets  805  (e.g., a movie, TV episode, audio book, novel, magazine article, etc.). In some implementations, the scraper engine  820  performs parsing and understanding operations on the source assets  805  in order to produce extracted actions for each OE (e.g., characters identified in the source assets  805 ) per time period. For example, in some implementations, the scraper engine  820  extracts the actions from the source assets  805 . Thereafter, in some implementations, the objective generator  830  (e.g., a neural network or other AI construct) produces initial objectives for each OE per time period. 
     According to some implementations, the multiplexer  835  enables one of the initial objectives or the updated objectives as inputs to the OE engines  860 . In some implementations, the demultiplexer  845  routes the objectives on a per OE basis to their respective OE engines  860 . In some implementations, the OE engines  860  perform actions based on their objectives. According to some implementations, the actions for a time period are captured and provided to a display pipeline  855  for rendering and display in order to present the SR content associated with the event to a user (e.g., an SR reconstruction or simulation of a battle or other event based on the source assets then emergent content). According to some implementations, the actions for a time period are captured and provided to the emergent content engine  250  and the OE engines  860  as training feedback  865 . According to some implementations, the actions are provided to the emergent content engine  250  which in turn produces updated objective for each OE per time period (e.g., as described above with reference to the emergent content engine  250  in  FIG. 2 ). 
       FIG. 9  is a flowchart representation of a method  900  of instantiating an objective-effectuator (OE) encapsulation within an SR setting in accordance with some implementations. In various implementations, the method  900  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the controller  102 , the electronic device  103  shown in  FIG. 1A , and/or the HMD  104  shown in  FIG. 1B ). In some implementations, the method  900  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  900  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     The method  900 , for example, generates objectives for OEs in an OE encapsulation, which result in related plots in an SR setting that are consistent with the context of the encapsulation (e.g., while on the transport ship, a marine performs actions consistent with the transport ship context). As one example, the OE encapsulation associated with an event (e.g., a particular battle) corresponds to navy→transport ship→amphibious assault vehicle→company of marines→individual marine. 
     As represented by block  9 - 1 , the method  900  includes instantiating a first OE associated with a first set of attributes (e.g., visual rendering attributes, possible actions, contextual information, etc.) and a second OE associated with a second set of attributes into an SR setting, wherein the first OE is encapsulated within the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is associated with the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is correlated with the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is related to the second OE. In some implementations, when the first OE is encapsulated within the second OE, the first OE is dependent on the second OE. In other words, the first OE is provided objectives and performs actions that are consistent with the context and attributes of the second OE because the first OE is encapsulated within the second OE. In some implementations, the lower-level OE performs actions consistent with higher-level OEs in which it is encapsulated. In some implementations, the one or more other lower-level OEs are encapsulated within the first OE. In some implementations, the second OE is encapsulated within one or more other higher-level OEs. 
     In some implementations, the attributes are extracted from source material/assets associated with the event including, for example, plans for the event such as battle plans, ground truth for the event such as the course and outcomes of the battle, historical accounts or memoirs, screenplays, scripts, text or audio books, movies, video games, novels, etc. In some implementations, the system determines a set of OE encapsulations for each time period (e.g., as shown in  FIGS. 6B and 6C ). As such, an OE encapsulation may change over the course of the event such as OEs (layers) being stripped away or added. As one example, a particular battle encapsulation starts as transport ship→amphibious assault vehicle→company of marines→individual marine while in transport (T 0 ). Continuing with this example, the OE encapsulation changes to amphibious assault vehicle→company of marines→individual marine while approaching the beach (T 1 ). Continuing with this example, the OE encapsulation changes again to company of marines→individual marine while storming the beach (T 2 ). Continuing with this example, the OE encapsulation changes again when the marines may find and enter a vehicle after reaching the beach (T 3 ) which changes the encapsulation to vehicle→subset of company of marines→individual marine. 
     As represented by block  9 - 2 , the method  900  includes providing a first objective to the first OE based on the first and second sets of attributes. In some implementations, the first OE is provided an objective based on the context of the second OE within which it is encapsulated and, in turn, the first OE performs actions that are consistent with that context. For example, a marine on a transport ship is not given a scouting objective to perform scouting patrols or a fire suppression objective to mortar a target while on the transport ship. 
     As represented by block  9 - 3 , the method  900  includes providing a second objective to the second OE based on the second set of attributes, wherein the first and second objectives are associated with a first time period between a first temporal point and a second temporal point (e.g., the objectives are valid or active for the particular time period). In some implementations, the objectives correspond to initial objectives synthesized from the source assets. In some implementations, the objectives are updated every time period by the emergent content engine. In some implementations, the OE encapsulations are updated every time period by an encapsulation manager such as adding or removing OEs (layers) from an encapsulation. In one example, the first and second temporal points correspond to start and end times for a scene within source content. As another example, the first and second temporal points correspond to natural break points in an instruction set or the like. As yet another example, the first and second temporal points correspond to start and end states for an event (e.g., the state of troops before and after a battle). Thus, according to some implementations, the first and second temporal points provide book ends or guide posts for the objectives. 
     In some implementations, the method  900  includes synthesizing the first and second objectives based on source assets/materials (e.g., a movie, book, historical account associated with an event or the like). For example, the source assets correspond to plans for the event such as battle plans, ground truth for the event such as the course and outcomes of the battle, historical accounts or memoirs, screenplays, scripts, text or audio books, movies, video games, novels, etc. For example, the source assets also include 3D models of terrain, infrastructure, vehicles, humanoids, animals, etc. associated with the story or event. 
     In some implementations, the method  900  includes extracting a set of actions associated with an event from the source assets, and wherein the first and second objectives are derived from the set of actions (e.g., the set of actions include basic plot points associated with a book, movie, event, or the like such as its start and end situations). In some implementations, the first and second objectives are consistent with the set of actions. For example, if the predefined set of actions does not include killing, then the objective cannot be to kill. For example, if the set of actions includes at least start and end points (e.g., book-ends) for the event, the objective is derived to get a character from the start to the end point. For example, characters cannot perform actions “outside” of intellectual property (IP) or digital rights management (DRM)-limited bounds. 
     In some implementations, the first and second objectives are generated by utilizing a neural network. For example, the neural network generates the first and second objectives based on a set of neural network parameters (e.g., weights). For example, the neural network parameters are determined by a reward function. In some implementations, the first and second objectives are provided to a training module that adjusts parameters of a neural network that generates the objectives. For example, the training module includes a reward function that assigns positive rewards to desirable actions and negative rewards to undesirable actions. For example, the training module utilizes reinforcement learning to configure the neural network. For example, the training module utilizes fan-created content (e.g., blog posts), canon video, novels, books, comics and/or video games to train the neural network. 
     As represented by block  9 - 4 , the method  900  includes generating a first set of actions associated with the first time period for the first OE based on the first objective. As represented by block  9 - 5 , the method  900  includes generating a second set of actions associated with the first time period for the second OE based on the second objective. For example, with reference to  FIG. 8A , the OE engines  810  perform actions based on objectives from the objective generator  807 . 
     As represented by block  9 - 6 , the method  900  includes rendering for display the SR setting including the first set of actions performed by the first OE and the second set of actions performed by the second OE. In some implementations, the SR setting is associated with an event. For example, the event corresponds to an athletic event, a concert, a battle, or another large-scale occurrence. For example, with reference to  FIG. 8A , the display pipeline  855  renders the actions performed by the OEs for the first time period in order to present the SR content to a user. 
     In some implementations, the method  900  includes obtaining contextual information characterizing the SR setting. For example, the contextual information includes all OEs and OE encapsulations instantiated within the SR setting. For example, the contextual information includes user-specified scene/environment info. For example, the contextual information includes instantiated characters and equipment assets. For example, the contextual information includes identities of other characters that are to be instantiated. For example, the contextual information includes mesh maps for objects present in the user&#39;s environment (e.g., a desk). 
     In some implementations, the method  900  includes setting virtual environmental conditions for the SR setting based on the source assets. In some implementations, the virtual environment conditions include one or more of terrain conditions, weather conditions, lighting conditions and environment sounds. In some implementations, the method  900  includes changing the terrain and/or environmental conditions based on user inputs to test different simulations of the SR setting. For example, the terrain and/or weather associated with an event may be changed from its historical parameters to see how the outcome of the event may have changed with the changed terrain and/or weather (e.g., simulated winter weather in place of summer weather for the historical event, or simulated flat terrain replacing sloped rugged terrain for the historical event). 
     In some implementations, the method  900  includes receiving user selection of a specific OE and, in response, displaying SR content associated with the actions performed by the specific OE. For example, the user is able to “look through the eyes” of the selected OE and optionally control at least some aspects of the selected OE such as its movements. 
     In some implementations, the method  900  includes updating the objective for first OE for a next time period based on the first and second sets of attributes and also attributes of a new OE. For example, a third OE layer is added to the OE encapsulation, which further constrains the actions and objectives for the first OE. In some implementations, the new layer is a high-level layer than the first OE which is higher or equal to the second OE. For example, with reference to  FIG. 8A , the encapsulation manager  825  updates the OE encapsulation by adding an additional OE engine within the OE encapsulation. 
     In some implementations, the method  900  includes updating the objective for first OE for a next time period to be a function of the first set of attributes. For example, the second OE layer is removed, opening new actions and objectives for the first OE. For example, with reference to  FIG. 8A , the encapsulation manager  825  updates the OE encapsulation by removing one of the OE engines  810  within the OE encapsulation. 
     In some implementations, the method  900  includes adding/removing OEs to test different simulations within the SR setting. For example, if the transport ship typically has N amphibious landing craft, run a simulation within N−1 or N+1 amphibious landing craft to see how a battle&#39;s macro or micro outcome may change. For example, remove entire OE encapsulations and/or individual OEs within the SR setting. In some implementations, one of the OEs performs actions inconsistent with its objective when a predetermined criterion is satisfied (e.g., a morale criterion, self-preservation criterion, etc.). For example, a solider forgoes performing actions consistent with his/her objective and instead deserts his/her post if the predetermined criterion is satisfied. 
     In some implementations, the method  900  includes instantiating a second OE encapsulation including a third OE and a fourth OE. For example, at least one of the third or fourth OEs is included in both the first and second OE encapsulations. 
       FIG. 10  is a flowchart representation of a method  1000  of initializing and generating content for an objective-effectuator (OE) encapsulation within SR setting in accordance with some implementations. In various implementations, the method  1000  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the controller  102 , the electronic device  103  shown in  FIG. 1A , and/or the HMD  104  shown in  FIG. 1B ). In some implementations, the method  1000  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  1000  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  10 - 1 , the method  1000  includes obtaining source assets associated with an event. For example, the event corresponds to an athletic event, a concert, a battle, or another large-scale occurrence. For example, the source assets correspond to plans for the event such as battle plans, ground truth for the event such as the course and outcomes of the battle, historical accounts and books, movies, video games, novels, etc. For example, the source assets also include to 3D models of terrain, infrastructure, vehicles, humanoids, animals, etc. associated with the story or event. 
     As represented by block  10 - 2 , the method  1000  includes identifying an OE encapsulation based on the source assets, the OE encapsulation including a first OE and a second OE. For example, the OE encapsulation corresponds to a set of related characters or entities such as a transport ship including N amphibious landing crafts each with M marines. In some implementations, the OEs corresponds to characters associated with the event such as humanoids, vehicles, androids, robots, animals, etc. For example, with reference to  FIG. 8B , the encapsulation manager  825  determines the OE encapsulation including the OE engines  810  based on the source assets associated with the event. 
     As represented by block  10 - 3 , the method  1000  includes extracting a set of actions performed by the first and second OEs during the event based on the source assets. For example, the set of actions correspond actions sequences for each OE derived from a screenplay or script for the event. In some implementations, the device synthesizes a screenplay for the event. In some implementations, the device receives a sequence of actions or a set of potential actions for the OEs from a character engine that generates the sequence of actions. In some implementations, the device receives a user input that indicates a sequence of actions. For example, the set of actions includes movement trajectory, operation of weapons or other related equipment, dialogue, etc. for the set of related characters. For example, with reference to  FIG. 8B , the scraper engine  820  extracts a set of actions performed by the OEs during the event. 
     As represented by block  10 - 4 , the method  1000  includes synthesizing an initial set of objectives for the OE encapsulation based on the set of actions extracted from source assets associated with an event, wherein the initial set of objectives includes a first objective for the first OE that is consistent with a second objective for the second OE. For example, with reference to  FIG. 8B , the objective generator  830  synthesizes an initial set of objectives based on the extracted set of actions. 
     For example, the initial set of objectives are such that OEs within the OE encapsulation act in concert and do not conflict—cannot act to break from the related grouping. In some implementations, the initial set of objectives includes an objective for each OE within the OE encapsulation. In some implementations, the initial set of objectives is consistent with the set of actions. For example, if the predefined set of actions does not include killing, then the objective cannot be to kill. For example, if the set of actions includes at least start and end points (e.g., book-ends) for the event, the objective is derived to get a character from the start to the end point. For example, characters cannot perform actions “outside” of intellectual property (IP) or digital rights management (DRM)-limited bounds. 
     In some implementations, synthesizing the initial set of objectives includes utilizing a neural network. For example, a neural network generates the initial set of objectives based on a set of neural network parameters (e.g., weights). For example, the neural network parameters are determined by a reward function 
     In some implementations, the initial set of objectives is provided to a training module that adjusts parameters of a neural network that generates the objective. For example, the training module includes a reward function that assigns positive rewards to desirable actions and negative rewards to undesirable actions. For example, the training module utilizes reinforcement learning to configure the neural network. For example, the training module utilizes fan-created content (e.g., blog posts), canon video, novels, books, comics and/or video games to train the neural network. 
     As represented by block  10 - 5 , the method  1000  includes instantiating (e.g., at time T 0 ) the OE encapsulation into an SR setting (e.g., an SR setting), wherein the OE encapsulation is characterized by the initial set of objectives (e.g., synthesized from the source assets) and a set of visual rendering attributes. For example, with reference to  FIG. 8B , at time T 0 , the emergent content architecture  850  is initialized with initial objectives derived from source assets  805 . 
     In some implementations, the method  1000  includes setting virtual environmental conditions for the SR setting based on the initial set of objectives. For example, the virtual environment conditions include one or more of terrain conditions, weather conditions, lighting conditions, environment sounds, and/or the like. 
     In some implementations, the method  1000  includes obtaining contextual information characterizing the SR setting. For example, the contextual information includes all OEs and OE encapsulations instantiated within the SR setting. For example, the contextual information includes user-specified scene/environment info. For example, the contextual information includes instantiated characters and equipment assets. For example, the contextual information includes identities of other characters that are to be instantiated. For example, the contextual information includes mesh maps for objects present in the user&#39;s environment (e.g., a desk). 
     As represented by block  10 - 6 , the method  1000  includes generating updated objectives for the OE encapsulation based on a function of the initial set of objectives, contextual information associated with the event, and the set of actions. For example, the set of revised objectives cannot conflict due to their encapsulation. In some implementations, generating the updated set of objectives includes utilizing a neural network. For example, parameters of the neural network are provided by a reward function. For example, with reference to  FIG. 8B , the emergent content engine  250  generates an updated set of objectives based at least in part on the previous actions performed by the OE engine  810 . 
     As represented by block  10 - 7 , the method  1000  includes modifying the OE encapsulation based on the updated set of objectives (e.g., at time T 1 ). For example, with reference to  FIG. 8B , at time T 1  and on, the emergent content architecture  850  operates with updated objectives from the emergent content engine  250 . In some implementations, the modified objective is provided to a character engine that generated the OE representation, and ultimately to a rendering and display pipeline in order to output SR content showing the OE performing the sequence of actions within the SR setting, and potentially in combination with one or more other OEs. For example, the OE performs actions that achieve the objective or improve the likelihood of achieving the objective. 
       FIG. 11  is a flowchart representation of a method  1100  of initializing and generating content for an objective-effectuator (OE) within SR setting in accordance with some implementations. In various implementations, the method  1100  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the controller  102 , the electronic device  103  shown in  FIG. 1A , and/or the HMD  104  shown in  FIG. 1B ). In some implementations, the method  1100  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  1100  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  11 - 1 , the method  1100  includes obtaining source assets associated with an event. For example, the event corresponds to an athletic event, a concert, a battle, or another large-scale occurrence. For example, the source assets correspond to plans for the event such as battle plans, ground truth for the event such as the course and outcomes of the battle, historical accounts and books, movies, video games, novels, etc. For example, the source assets also include to 3D models of terrain, infrastructure, vehicles, humanoids, animals, etc. associated with the story or event. 
     As represented by block  11 - 2 , the method  1100  includes extracting a set of actions performed by the first OE during the event based on the source assets. For example, the set of actions correspond actions sequences for each OE derived from a screenplay or script for the event. In some implementations, the device synthesizes a screenplay for the event. In some implementations, the device receives a sequence of actions or a set of potential actions for the OEs from a character engine that generates the sequence of actions. In some implementations, the device receives a user input that indicates a sequence of actions. For example, the set of actions includes movement trajectory, operation of weapons or other related equipment, dialogue, etc. for the OEs. For example, with reference to  FIG. 8C , the scraper engine  820  extracts a set of actions performed by the OEs during the event. 
     As represented by block  11 - 3 , the method  1100  includes synthesizing an initial set of objectives for the first OE based on the set of actions extracted from source assets associated with an event. For example, with reference to  FIG. 8C , the objective generator  830  synthesizes an initial set of objectives based on the extracted set of actions. In some implementations, the initial set of objectives is consistent with the set of actions. For example, if the predefined set of actions does not include killing, then the objective cannot be to kill. For example, if the set of actions includes at least start and end points (e.g., book-ends) for the event, the objective is derived to get a character from the start to the end point. For example, characters cannot perform actions “outside” of intellectual property (IP) or digital rights management (DRM)-limited bounds. 
     In some implementations, synthesizing the initial set of objectives includes utilizing a neural network. For example, a neural network generates the initial set of objectives based on a set of neural network parameters (e.g., weights). For example, the neural network parameters are determined by a reward function 
     In some implementations, the initial set of objectives is provided to a training module that adjusts parameters of a neural network that generates the objective. For example, the training module includes a reward function that assigns positive rewards to desirable actions and negative rewards to undesirable actions. For example, the training module utilizes reinforcement learning to configure the neural network. For example, the training module utilizes fan-created content (e.g., blog posts), canon video, novels, books, comics and/or video games to train the neural network. 
     As represented by block  11 - 4 , the method  1100  includes instantiating (e.g., at time T 0 ) the first OE into an SR setting (e.g., an SR setting), wherein the first OE encapsulation is characterized by the initial set of objectives (e.g., synthesized from the source assets) and a set of visual rendering attributes. As such, with reference to  FIG. 8C , at time T 0 , the OE engines  860  within the emergent content architecture  875  are initialized with initial objectives derived from source assets  805 . 
     In some implementations, the method  1100  includes setting virtual environmental conditions for the SR setting based on the initial set of objectives. For example, the virtual environment conditions include one or more of terrain conditions, weather conditions, lighting conditions, environment sounds, and/or the like. 
     In some implementations, the method  1100  includes obtaining contextual information characterizing the SR setting. For example, the contextual information includes all OEs instantiated within the SR setting. For example, the contextual information includes user-specified scene/environment info. For example, the contextual information includes instantiated characters and equipment assets. For example, the contextual information includes identities of other characters that are to be instantiated. For example, the contextual information includes mesh maps for objects present in the user&#39;s environment (e.g., a desk). 
     As represented by block  11 - 5 , the method  1100  includes generating updated objectives for the first OE based on a function of the initial set of objectives, contextual information associated with the event, and the set of actions. In some implementations, generating the updated set of objectives includes utilizing a neural network. For example, parameters of the neural network are provided by a reward function. For example, with reference to  FIG. 8C , the emergent content engine  250  generates an updated set of objectives based at least in part on the previous actions performed by the OE engine  860 . 
     As represented by block  11 - 6 , the method  1100  includes modifying the first OE based on the updated set of objectives (e.g., at time T 1 ). For example, with reference to  FIG. 8C , at time T 1  and on, the OE engines  860  within the emergent content architecture  875  operate with updated objectives from the emergent content engine  250 . In some implementations, the modified objective is provided to a character engine that generated the OE representation, and ultimately to a rendering and display pipeline in order to output SR content showing the OE performing the sequence of actions within the SR setting, and potentially in combination with one or more other OEs. For example, the OE performs actions that achieve the objective or improve the likelihood of achieving the objective. 
       FIG. 12  is a flowchart representation of a method  1200  of selecting a point-of-view within an SR setting (e.g., a conditionally dependent SR content threads environment) in accordance with some implementations. In various implementations, the method  1200  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the controller  102 , the electronic device  103  shown in FIG.  1 A, and/or the HMD  104  shown in  FIG. 1B ). In some implementations, the method  1200  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  1200  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     The method  1200 , for example, enables a user to select between different points of view within the multi-level conditionally dependent SR content threads such as an omniscient third-person view of an overall battlefield, a squadron view for a group of planes, a captain view for a destroyer, a first-person view for a specific marine within an amphibious landing craft, or the like 
     As represented by block  12 - 1 , the method  1200  includes presenting a first SR view of an event with SR content associated with the event that includes a plurality of inter-related layers of SR content that perform actions associated with an event. For example, the event corresponds to an athletic event, a concert, a battle, or another large-scale occurrence. In some implementations, the SR content is synthesized based on source material/assets associated with the event including, for example, plans for the event such as battle plans, ground truth for the event such as the course and outcomes of the battle, historical accounts or memoirs, screenplays, scripts, text or audio books, movies, video games, novels, etc. 
     In some implementations, as represented by block  12 - 1   a , the method  1200  includes panning around or zooming in/out within the first SR view based on user inputs. As such, the user is able to view the event from a nearly infinite number of perspectives. In some implementations, as represented by block  12 - 1   b , the method  1200  includes adding and/or removing layers and/or SR content elements based on user inputs. As such, the user is able to play-out various “what if” simulations of the event with modified layers and/or changed numbers of elements. 
     In some implementations, SR content includes the environmental conditions (e.g., weather) and/or terrain associated with the event based on source assets associated with the event. In some implementations, the method  1200  includes modifying the environmental conditions and/or terrain associated with the event based on user inputs. As such, the user is able to play-out various “what if” simulations of the event with modified environmental conditions (e.g., weather) and/or terrain. 
     As represented by block  12 - 2 , the method  1200  includes detecting selection of a respective layer among the plurality of inter-related layers of SR content. For example, the selection corresponds to a voice command, a gestural command, selection of an affordance associated with the respective layer, or the like. 
     As represented by block  12 - 3 , the method  1200  includes presenting a second SR view of the event that includes the respective layer of SR content, where the second SR view corresponds to a point-of-view of the respective layer. According to some implementations, the first SR view corresponds to an omniscient third-person view of the event (e.g., a virtual view of a battlefield commander), and the second SR view corresponds to a first-person view of an individual character within the event (e.g., a soldier on the battlefield or a pilot in an aircraft). As one example, state  725  in  FIG. 7B  shows a first SR view of the event (e.g., a particular battle from an overall multi-dimensional view), and state  735  in  FIG. 7C  shows a second SR view of the event e.g., a particular battle from an aerial view). 
     In some implementations, while presenting the second SR view, the method  1200  includes presenting lower level layers in addition to the respective layer. For example, if the respective layer corresponds to a specific transport ship, also show the amphibious landing craft each including a company of marines riding on the transport ship. 
     In some implementations, while presenting the second SR view, the method  1200  includes presenting directly related higher level layers in addition to the respective layer. For example, if the respective layer corresponds to a specific marine, also show the balance of his company and the amphibious landing ship on which the specific marine is riding. 
     In some implementations, while presenting the second SR view, the method  1200  includes excluding presentation of higher-level layers. For example, if the respective layer corresponds to a specific pilot of an aircraft, exclude the other crew on the airplane and the other aircraft in the squadron. 
     In some implementations, while presenting the second SR view, the method  1200  includes excluding presentation of orthogonal equal level layers. For example, if the respective layer corresponds to a specific marine, also show the balance of his company and the amphibious landing ship on which the specific marine is riding but no other amphibious landing craft from the same transport ship. 
     In some implementations, as represented by block  12 - 3   a , the method  1200  includes controlling the SR content element associated with the second SR view based on user inputs. In some implementations, at run-time, the layers continue to playout concurrently whether they are currently presented or not. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
     It will also be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first node could be termed a second node, and, similarly, a second node could be termed a first node, which changing the meaning of the description, so long as all occurrences of the “first node” are renamed consistently and all occurrences of the “second node” are renamed consistently. The first node and the second node are both nodes, but they are not the same node. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Metadata:
Filing Date: 20190215
Publication Date: 20220201
Grant Date: 20220201
Priority Date: 20180219
Inventors: Richter, Ian M.
ROCKWELL, Michael J.
SAINI, AMRITPAL SINGH
SOARES, OLIVIER
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N3/044", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/006", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/044", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 65729427