PATENT DOCUMENT

Publication Number: US-11748953-B2
Application Number: US-201917057173-A
Country: US
Kind Code: B2

Title: Method and devices for switching between viewing vectors in a synthesized reality setting

Abstract:
In some implementations, a method includes: presenting a synthesized reality (SR) setting associated with predetermined content from a first viewing vector at a first time; presenting a navigation timeline for the predetermined content including a plurality of viewing vector affordances at respective times on the navigation timeline, wherein each viewing vector characterizes how the SR content is displayed at the respective times; detecting an input selecting a respective viewing vector affordance on the navigation timeline associated with a second time within the predetermined content; and, in response to detecting the input selecting the respective viewing vector affordance on the navigation timeline, updating the SR setting in order to present the SR setting from a second viewing vector different from the first viewing vector, wherein the second viewing vector characterizes display of the predetermined content at the second times.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a device including a non-transitory memory and one or more processors coupled with the non-transitory memory:
 presenting a synthesized reality (SR) setting associated with a first portion of predetermined content from a first viewing vector, wherein the first viewing vector characterizes display of the first portion of the predetermined content from a first perspective associated with a first entity within the predetermined content; 
 concurrently presenting a navigation timeline for the predetermined content and a perspective modification affordance associated with the first portion of predetermined content overlaid on the SR setting, wherein the navigation timeline includes a current time indicator and a plurality of viewing vector affordances each associated with a different viewing vector at different times within the predetermined content, and wherein each viewing vector characterizes how SR content is displayed at the different times; 
 detecting a first input selecting the perspective modification affordance associated with the first portion of predetermined content; 
 in response to detecting the first input selecting the perspective modification affordance, presenting a first menu overlaid on the SR setting, wherein the first menu includes a first plurality of selectable perspectives corresponding to different entities associated with the first portion of predetermined content; 
 detecting a second input selecting a respective viewing vector affordance from among the plurality of viewing vector affordances on the navigation timeline associated with a second portion of the predetermined content different from the first portion of the predetermined content, wherein the first portion of the predetermined content corresponds to a first temporal portion of the predetermined content and the second portion of the predetermined content corresponds to a second temporal portion of the predetermined content different from the first temporal portion of the predetermined content; 
 
 in response to detecting the second input selecting the respective viewing vector affordance on the navigation timeline, presenting a second menu including a second plurality of selectable perspectives corresponding to presenting a second menu overlaid on the SR setting, wherein the second menu includes a second plurality of selectable perspectives corresponding to different entities associated with the second portion of the predetermined content, wherein the second plurality of selectable perspectives are different from the first plurality of selectable perspectives; 
 after presenting the second menu overlaid on the SR setting, detecting a third input selecting a respective selectable perspective from among the second plurality of selectable perspectives associated with a second viewing vector; and 
 in response to detecting the third input, updating the SR setting in order to present the SR setting from the second viewing vector different from the first viewing vector, wherein the second viewing vector characterizes display of the second portion of the predetermined content at from a second perspective associated with a second entity within the predetermined content different from the first entity. 
 
     
     
       2. The method of  claim 1 , wherein the first viewing vector is characterized at least in part by a first field-of-view (FOV), a first pose, a first set of SR coordinates within the SR setting, and a first perspective, and wherein the second viewing vector is characterized at least in part by a second FOV, a second pose, a second set of SR coordinates within the SR setting, and a second perspective. 
     
     
       3. The method of  claim 1 , further comprising:
 in response to detecting the second input selecting the respective viewing vector affordance on the navigation timeline, presenting the current time indicator positioned relative to the second portion of the predetermined content. 
 
     
     
       4. The method of  claim 1 , further comprising:
 in response to detecting the second input selecting the respective viewing vector affordance on the navigation timeline, ceasing to display the SR setting from the first viewing vector. 
 
     
     
       5. The method of  claim 1 , wherein the plurality of viewing vector affordances corresponds to predetermined viewing vectors. 
     
     
       6. The method of  claim 1 , wherein the plurality of viewing vector affordances corresponds to viewing vectors determined based on a set of filters. 
     
     
       7. The method of  claim 1 , further comprising:
 presenting a field-of-view (FOV) modification affordance provided to modify a current FOV associated with the second viewing vector based on one or more subsequent inputs. 
 
     
     
       8. The method of  claim 1 , further comprising:
 presenting a pose modification affordance provided to change a current pose associated with the second viewing vector to a subsequent pose based on one or more subsequent inputs. 
 
     
     
       9. The method of  claim 1 , further comprising:
 presenting an SR coordinates modification affordance provided to change a current set of SR coordinates associated with the second viewing vector to a subsequent set of SR coordinates within the SR setting based on one or more subsequent inputs. 
 
     
     
       10. The method of  claim 1 , further comprising:
 while presenting the SR setting from the second viewing vector, presenting a previous perspective affordance for viewing the SR setting from a previous perspective associated with the first viewing vector. 
 
     
     
       11. The method of  claim 1 , wherein the predetermined content corresponds to one of a pre-existing story, movie, TV episode, book, theatrical play, or historical event. 
     
     
       12. The method of  claim 1 , wherein the predetermined content corresponds to emergent content based at least in part on one or more pre-existing source assets. 
     
     
       13. The method of  claim 12 , wherein the one or more pre-existing source assets correspond to at least one of a pre-existing story, movie, TV episode, book, theatrical play, or historical event. 
     
     
       14. The method of  claim 1 , wherein the predetermined content corresponds to conditionally dependent SR content threads. 
     
     
       15. The method of  claim 1 , wherein the first, second, and third inputs corresponds to one of a voice command, a gesture input, a touch input, or a gaze directional input. 
     
     
       16. The method of  claim 1 , further comprising:
 detecting one or more subsequent inputs associated with the navigation timeline; and 
 in response to detecting the one or more subsequent inputs, changing a timescale associated with the navigation timeline for the predetermined content and updating the plurality of viewing vector affordances based on the changed time scale. 
 
     
     
       17. The method of  claim 1 , wherein the device corresponds to a mobile device, and wherein the SR setting is composited with at least a portion of a physical setting and composited content is presented on a display of the mobile device. 
     
     
       18. The method of  claim 1 , wherein the predetermined content corresponds to a plurality of conditionally dependent content threads associated with a related event, and
 wherein the first perspective associated with the first entity corresponds to a first content thread among plurality of conditionally dependent content threads and the second perspective associated with the second entity corresponds to a second content thread among plurality of conditionally dependent content threads different from the first content thread. 
 
     
     
       19. The method of  claim 1 , wherein the first and second entities correspond to different characters within the predetermined content. 
     
     
       20. A device comprising:
 one or more processors; 
 a non-transitory memory; and 
 one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the device to:
 present a synthesized reality (SR) setting associated with a first portion of predetermined content from a first viewing vector, wherein the first viewing vector characterizes display of the first portion of the predetermined content at the first time from a first perspective associated with a first entity within the predetermined content; 
 concurrently present a navigation timeline for the predetermined content and a perspective modification affordance associated with the first portion of predetermined content overlaid on the SR setting, wherein the navigation timeline includes a current time indicator and a plurality of viewing vector affordances each associated with a different viewing vector at different times within the predetermined content, and wherein each viewing vector characterizes how SR content is displayed at the different times; 
 detect a first input selecting the perspective modification affordance associated with the first portion of predetermined content; 
 in response to detecting the first input selecting the perspective modification affordance, present a first menu overlaid on the SR setting, wherein the first menu includes a first plurality of selectable perspectives corresponding to different entities associated with the first portion of predetermined content; 
 detect a second input selecting a respective viewing vector affordance from among the plurality of viewing vector affordances on the navigation timeline associated with a second portion of the predetermined content different from the first portion of the predetermined content, wherein the first portion of the predetermined content corresponds to a first temporal portion of the predetermined content and the second portion of the predetermined content corresponds to a second temporal portion of the predetermined content different from the first temporal portion of the predetermined content; 
 in response to detecting the second input selecting the respective viewing vector affordance on the navigation timeline, present a second menu overlaid on the SR setting, wherein the second menu includes a second plurality of selectable perspectives corresponding to different entities associated with the second portion of the predetermined content, wherein the second plurality of selectable perspectives are different from the first plurality of selectable perspectives; 
 after presenting the second menu overlaid on the SR setting, detect a third input selecting a respective selectable perspective from among the second plurality of selectable perspectives associated with a second viewing vector; and 
 in response to detecting the third input, update the SR setting in order to present the SR setting from a second viewing vector different from the first viewing vector, wherein the second viewing vector characterizes display of the second portion of the predetermined content at from a second perspective associated with a second entity within the predetermined content different from the first entity. 
 
 
     
     
       21. The device of  claim 20 , wherein the predetermined content corresponds to a plurality of conditionally dependent content threads associated with a related event, and
 wherein the first perspective associated with the first entity corresponds to a first content thread among plurality of conditionally dependent content threads and the second perspective associated with the second entity corresponds to a second content thread among plurality of conditionally dependent content threads different from the first content thread. 
 
     
     
       22. The device of  claim 20 , wherein the first and second entities correspond to different characters within the predetermined content. 
     
     
       23. A non-transitory memory storing one or more programs, which, when executed by one or more processors of a device, cause the device to:
 present a synthesized reality (SR) setting associated with a first portion of predetermined content from a first viewing vector, wherein the first viewing vector characterizes display of the first portion of the predetermined content from a first perspective associated with a first entity within the predetermined content; 
 concurrently present a navigation timeline for the predetermined content and a perspective modification affordance associated with the first portion of predetermined content overlaid on the SR setting, wherein the navigation timeline includes a current time indicator and a plurality of viewing vector affordances each associated with a different viewing vector at different times within the predetermined content, and wherein each viewing vector characterizes how SR content is displayed at the different times; 
 detect a first input selecting the perspective modification affordance associated with the first portion of predetermined content; 
 in response to detecting the first input selecting the perspective modification affordance, present a first menu overlaid on the SR setting, wherein the first menu includes a first plurality of selectable perspectives corresponding to different entities associated with the first portion of predetermined content; 
 detect a second input selecting a respective viewing vector affordance from among the plurality of viewing vector affordances on the navigation timeline associated with a second portion of the predetermined content different from the first portion of the predetermined content, wherein the first portion of the predetermined content corresponds to a first temporal portion of the predetermined content and the second portion of the predetermined content corresponds to a second temporal portion of the predetermined content different from the first temporal portion of the predetermined content; 
 in response to detecting the second input selecting the respective viewing vector affordance on the navigation timeline, present a second menu overlaid on the SR setting, wherein the second menu includes a second plurality of selectable perspectives corresponding to different entities associated with the second portion of the predetermined content, wherein the second plurality of selectable perspectives are different from the first plurality of selectable perspectives; 
 after presenting the second menu overlaid on the SR setting, detect a third input selecting a respective selectable perspective from among the second plurality of selectable perspectives associated with a second viewing vector; and 
 in response to detecting the third input, update the SR setting in order to present the SR setting from the second viewing vector different from the first viewing vector, wherein the second viewing vector characterizes display of the second portion of the predetermined content at from a second perspective associated with a second entity within the predetermined content different from the first entity. 
 
     
     
       24. The non-transitory memory of  claim 22 , wherein the predetermined content corresponds to a plurality of conditionally dependent content threads associated with a related event, and
 wherein the first perspective associated with the first entity corresponds to a first content thread among plurality of conditionally dependent content threads and the second perspective associated with the second entity corresponds to a second content thread among plurality of conditionally dependent content threads different from the first content thread. 
 
     
     
       25. The non-transitory memory of  claim 23 , wherein the first and second entities correspond to different characters within the predetermined content.

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to presenting synthesized reality (SR) settings and, more specially, to switching between viewing vectors in an SR setting. 
     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.  1 A and  1 B  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.  3 A  is a block diagram of an example objective-effectuator engine in accordance with some implementations. 
         FIG.  3 B  is a block diagram of an example neural network in accordance with some implementations. 
         FIG.  4    is a flowchart representation of a method of presenting objective-effectuators in accordance with some implementations. 
         FIG.  5    is a block diagram of an electronic device in accordance with some implementations. 
         FIG.  6 A  is a block diagram of conditionally dependent synthesized reality (SR) content threads in accordance with some implementations. 
         FIGS.  6 B and  6 C  illustrate timelines associated with objective-effectuator (OE) encapsulations in accordance with some implementations. 
         FIGS.  7 A- 7 C  illustrate example SR settings in accordance with some implementations. 
         FIGS.  8 A- 8 C  are block diagrams of emergent content architectures in accordance with some implementations. 
         FIGS.  9 A- 9 F  illustrate a plurality of example SR settings from different viewing vectors in accordance with some implementations. 
         FIG.  10    is a flowchart representation of a method of changing viewing vectors within an SR setting 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 changing viewing vectors within a synthesized reality (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: presenting an SR setting associated with predetermined content from a first viewing vector at a first time, wherein the first viewing vector characterizes display of the predetermined content at the first time; presenting a navigation timeline for the predetermined content including a current time indicator positioned at the first time and a plurality of viewing vector affordances each associated with a different viewing vector at respective times on the navigation timeline, wherein each viewing vector characterizes how the SR content is displayed at the respective times; detecting an input selecting a respective viewing vector affordance on the navigation timeline associated with a second time within the predetermined content; and, in response to detecting the input selecting the respective viewing vector affordance on the navigation timeline, updating the SR setting in order to present the SR setting from a second viewing vector different from the first viewing vector, wherein the second viewing vector characterizes display of the predetermined content at the second time. 
     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. 
     While experiencing a synthesized reality (SR) setting including predetermined content spanning a duration of time (e.g., for a narrative or story arc, a movie, historical event, fictional story, etc.), a user is able to move within the SR setting and select different perspectives of the predetermined content (e.g., an omniscient point-of-view (POV), a general&#39;s POV, infantry unit POV, individual soldier&#39;s POV, or the like within a historical battle) from which to experience portions of the predetermined content. However, if a user elects to temporally jump between disparate and discontinuous portions of the predetermined content, the SR characteristics characterizing a viewing vector (e.g., field-of-view (FOV), pose/rotational coordinates, translational coordinates in SR, perspective, and/or the like) associated with the first time may be poorly matched to the predetermined content at a second time. 
     As such, in various implementations, while presenting the SR setting using a first viewing vector, a navigation timeline is displayed (e.g., overlaid on the SR setting, presented as a part of a separate controls interface, or the like) for the predetermined content with a plurality of viewing vector affordances at a corresponding plurality of times on the navigation timeline, wherein each viewing vector is characterized by a FOV, a pose, a set of SR coordinates, a perspective, and/or the like. In some implementations, the viewing vectors associated with the plurality of viewing vector affordances are pre-selected by a director, historian, or the like. In some implementations, the viewing vectors associated with the plurality of viewing vector affordances are selected based on one or more filters determined based on the user (e.g., viewing vectors associated with machinery for a mechanic filter, viewing vectors associated with naval exercises for a naval trainee, viewing vectors associated with weather for a meteorologist, etc.). 
       FIG.  1 A  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.  1 A , 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.  1 A , 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.  1 A , 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.  1 A , 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.  1 A , 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.  8 A- 8 C . 
       FIG.  1 B  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.  1 B , 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.  1 A . In some implementations, the HMD  104  performs substantially the same operations as the electronic device  103  shown in  FIG.  1 A . 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.  1 A ). For example, in some implementations, the electronic device  103  shown in  FIG.  1 A  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.  8 A- 8 C . 
       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.  1 A and  1 B . 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.  3 A  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.  1 A and  1 B ). 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.  3 A , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  1 A and  1 B , 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.  3 B  is a block diagram of the neural network  310  in accordance with some implementations. In the example of  FIG.  3 B , 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.  3 B , 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.  3 B , 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.  3 B , 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.  4    is a flowchart representation of a method  400  of generating and presenting objective-effectuators in a synthesized reality (SR) setting. 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.  1 A , and/or the HMD  104  shown in  FIG.  1 B ). 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.  1 A and  1 B ). In some implementations, the objective-effectuator is characterized by a set of predefined objectives (e.g., the possible objectives  360  shown in  FIG.  3 A ) 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 - 3 B ). 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. 
       FIG.  5    is a block diagram of an electronic device  500  (e.g., enabled with one or more components of the controller  102  and/or the electronic device  103  shown in  FIG.  1 A  or the HMD  104  shown in  FIG.  1 B ) 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 electronic device  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 - 3 B ) 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.  8 A- 8 C . 
     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.  6 A  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.  6 A , 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.  6 A , 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.  6 A , 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, a node 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.  6 B  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.  6 B , 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.  6 B , 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.  6 B , 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.  6 B , 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) apparat from the OE  682 . 
     As shown in  FIG.  6 B , 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.  6 C  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.  6 B . 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.  6 C  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.  7 A- 7 C  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.  7 A  illustrates an example SR setting  700   a . As shown in  FIG.  7 A , 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.  7 A , 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.  7 A , 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.  7 A , 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.  7 A  is arbitrary and may be changed in various other implementations. 
       FIGS.  7 B and  7 C  illustrate an example SR setting  700   b . As shown in  FIGS.  7 B and  7 C , 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.  7 B and  7 C , 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.  7 B , 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.  7 B , 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.  7 B  is arbitrary and may be changed in various other implementations. 
     As shown in  FIG.  7 B , 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.  7 C , 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.  7 C , 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.  7 C , 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.  8 A  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.  6 A ). 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.  8 B  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.  6 A ). 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.  8 C  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   ). 
       FIGS.  9 A- 9 F  illustrate a plurality of SR settings from different viewing vectors 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. According to some implementations, the plurality of SR settings shown in  FIGS.  9 A- 9 F  correspond to predetermined content (e.g., screenplay, theatrical play, movie, TV show, historical event, fictional story, live event, or the like). According to some implementations, the plurality of SR settings shown in  FIGS.  9 A- 9 F  correspond to emergent content that is based at least in part on source assets (e.g., initial or seed content), such as a movie, TV episode, video game, historical event, or the like, as described with reference to  FIGS.  8 A and  8 B . According to some implementations, the plurality of SR settings shown in  FIGS.  9 A- 9 F  correspond to the conditionally dependent SR content threads  600  described in  FIG.  6 A . 
       FIG.  9 A  illustrates an example SR setting  900  presented from a viewing vector  902  at time  904 . As shown in  FIG.  9 A , the SR setting  900  corresponds to a first SR setting (e.g., a driving setting) presented by the electronic device  103  or the HMD  104 , including a vehicle  928 . According to some implementations, the viewing vector  902  is characterized by a perspective  906 , a field-of-view (FOV)  908 , a pose  910 , and a set of coordinates  912 . In  FIG.  9 A , the perspective  906  corresponds to a vehicle  928  within the first SR setting, the FOV  908  corresponds to a size, shape, focal length, etc. of a viewing field associated with the vehicle  928 , the pose  910  corresponds to rotational coordinates associated with the FOV  908 , and the set of coordinates  912  corresponds to the translational coordinates that characterize a location associated with the FOV  908  within the first SR setting. 
     In  FIG.  9 A , the SR setting  900  includes a navigation timeline  920  with a current time indicator  922  and a plurality of viewing vector affordances  924 A,  924 B,  924 C,  924 D,  924 E, and  924 F (sometimes referred to collectively herein as the viewing vector affordances  924 ). According to some implementations, each of the viewing vector affordances  924  is associated with a different viewing vector at respective times on the navigation timeline  920 . In some implementations, each of the viewing vector affordances is associated with an OE that corresponds to a story node within the conditionally dependent SR content threads  600 . In some implementations, each of the viewing vector affordances is associated with an OE encapsulation that corresponds to a story node within the conditionally dependent SR content threads  600 . 
     For example, the electronic device  103  or the HMD  104  detects a first input selecting the viewing vector affordance  924 B at time  904  such as a voice command, gesture input, touch input, gaze direction input, or the like from a user of the electronic device  103  or the HMD  104  via one or more input devices (e.g., microphone, gaze tracker, body pose estimator, touch-sensitive surface, buttons, etc.). Continuing with this example, in response to detecting the first input selecting the viewing vector affordance  924 B,  FIG.  9 B  illustrates an example SR setting  930 A presented from a viewing vector  932 A at time  934  associated with the viewing vector affordance  924 B. 
     As shown in  FIG.  9 B , the SR setting  930 A corresponds to a second SR setting (e.g., a construction setting) presented by the electronic device  103  or the HMD  104 , including a plurality of workers  944 A,  944 B, and  944 C (sometimes collectively referred to as the workers  944 ) and the hands of the user represented as a worker  946  holding an SR item  948  within the second SR setting. In some implementations, the hands of the user are mapped to SR hands of an SR avatar holding the SR item  948  associated with the worker  946 . In some implementations, the hands of the user correspond to video pass-through or optical see-through of his/her hands holding the SR item  948  associated with the worker  946 . 
     According to some implementations, the viewing vector  932 A is characterized by a perspective  936 A, an FOV  938 A, a pose  940 A, and a set of coordinates  942 A. In  FIG.  9 B , the perspective  936 A corresponds to worker within the second SR setting, the FOV  938 A corresponds to a size, shape, focal length, right-side up or upside-down, etc. of a viewing field associated with the worker  946 , the pose  910  corresponds to rotational coordinates associated with the FOV  938 A (e.g., title, angle, and pitch), and the set of coordinates  942 A corresponds to the location associated with the FOV  938 A. 
     For example, the electronic device  103  or the HMD  104  detects a second input associated with modifying the pose  940 A (e.g., rotational coordinates) associated with the FOV  938 A at time  934  such as a voice command, a gesture input, touch input, gaze direction input, or the like from the user of the electronic device  103  or the HMD  104 . For example, the second input corresponds to an interaction with a pose modification affordance (not shown in  FIG.  9 B ). 
     In some implementations, the SR setting  930 A includes an FOV modification affordance (or set of controls) provided to modify the FOV associated with the viewing vector  932 A. In some implementations, the SR setting  930 A includes a pose modification affordance (or set of controls) provided to change the pose (e.g., rotational coordinates) associated with the viewing vector  932 A. In some implementations, the SR setting  930 A includes a coordinates modification affordance (or set of controls) provided to change the coordinates (e.g., translational coordinates) associated with the viewing vector  932 A. In some implementations, the SR setting  930 A includes a perspective modification affordance (or set of controls) provided to change the perspective (e.g., from the worker  946  to the worker  944 A or to a foreman&#39;s perspective associated with a center of gravity of the workers  944  and the worker  946 ). In some implementations, the SR setting  930 A includes a previous perspective affordance provided to revert to a previous perspective (e.g., the perspective  906  in  FIG.  9 A ) at the current time (e.g., time  934 ). In some implementations, the SR setting  930 A includes a set of playback controls (e.g., play, pause, reverse, fast-forward, etc.). 
     Continuing with the example above, in response to detecting the second input associated with changing the pose  940 A,  FIG.  9 C  illustrates an example SR setting  930 B presented from a viewing vector  932 A′ at time  934 +1. The time  934 +1 in  FIG.  9 C  and the time  934  in  FIG.  9 B  are separated by a delta time period  935 A (e.g., X seconds). As shown in  FIG.  9 C , the SR setting  930 B corresponds to the second SR setting presented by the electronic device  103  or the HMD  104 , including a construction item  949 , the plurality of workers  944 , and the hands of the user represented as the worker  946  holding the SR item  948  within the second SR setting. 
     According to some implementations, the viewing vector  932 A′ is characterized by the perspective  936 A, the FOV  938 A, a pose  940 A′ (e.g., the changed pose relative to  FIG.  9 B ), and the set of coordinates  942 A. In  FIG.  9 C , the viewing vector  932 A′ is still associated with the perspective  936 A (e.g., the worker  946 ), the FOV  938 A, and the set of coordinates  942 A, but the viewing vector  932 A′ is associated with the changed pose  940 A′. As one example, the head or body of the worker  946  has been rotated counter-clockwise. 
     For example, the electronic device  103  or the HMD  104  detects a third input associated with changing the perspective  936 A at time  934 +1 such as a voice command, a gesture input, touch input, gaze direction input, or the like from the user of the electronic device  103  or the HMD  104 , for example, interacting with a perspective modification affordance (not shown in  FIG.  9 C ). Continuing with this example, in response to detecting the third input associated with changing the perspective  936 A,  FIG.  9 D  illustrates an example SR setting  930 C presented from a viewing vector  932 B at time  934 +2. The time  934 +2 in  FIG.  9 D  and the time  934  in  FIG.  9 B  are separated by a delta time period  935 B (e.g., Y seconds), wherein the delta time period  935 B is greater than the delta time period  935 A. As shown in  FIG.  9 D , the SR setting  930 C corresponds to the second SR setting presented by the electronic device  103  or the HMD  104 , including a construction item  949 , the plurality of workers  944 , and the worker  946  holding the SR item  948  within the second SR setting. 
     According to some implementations, the viewing vector  932 B is characterized by a perspective  936 B, an FOV  938 B, a pose  940 B, and a set of coordinates  942 B. In  FIG.  9 D , the viewing vector  932 B is associated with perspective  936 B (e.g., an omniscient view of the second SR setting) as opposed to the perspective  936 A in  FIGS.  9 B and  9 C  (e.g., the view of the worker  946  within the second SR setting). 
     For example, the electronic device  103  or the HMD  104  detects a fourth input selecting the viewing vector affordance  924 E at time  934 +2 such as a voice command, gesture input, touch input, gaze direction input, or the like from the user of the electronic device  103  or the HMD  104 . Continuing with this example, in response to detecting the fourth input selecting the viewing vector affordance  924 E,  FIG.  9 E  illustrates the example SR setting  930 D presented from a viewing vector  932 B at time  934 +3, including a viewing vector menu  950  with a plurality of selectable viewing vector affordance options  952 A and  952 B for the time associated with the viewing vector affordance  924 E. The time  934 +3 in  FIG.  9 E  and the time  934  in  FIG.  9 B  are separated by a delta time period  935 C (e.g., Z seconds), wherein the delta time period  935 C is greater than delta time periods  935 A and  935 B. In some implementations, each of the selectable viewing vector affordance options  952 A and  952 B within the viewing vector menu  950  is associated with a different OE that corresponds to a story node within the conditionally dependent SR content threads  600 . In some implementations, each of the selectable viewing vector affordance options  952 A and  952 B within the viewing vector menu  950  is associated with a different OE encapsulation that corresponds to a story node within the conditionally dependent SR content threads  600 . 
     For example, the electronic device  103  or the HMD  104  detects a fifth input selecting the viewing vector affordance option  952 A at time  934 +3 such as a voice command, gesture input, touch input, gaze direction input, or the like from the user of the electronic device  103  or the HMD  104 . Continuing with this example, in response to detecting the fifth input selecting the viewing vector affordance option  952 A,  FIG.  9 F  illustrates an example SR setting  970  presented from a viewing vector  972  at time  974 . 
     As shown in  FIG.  9 F , the SR setting  970  corresponds to a third SR setting (e.g., a parade or procession) presented by the electronic device  103  or the HMD  104 , including a plurality of buildings  984 A,  984 B,  984 C, and  984 D (sometimes collectively referred to as the buildings  984 ) and a plurality of vehicles  986  within the third SR setting. According to some implementations, the viewing vector  972  is characterized by a perspective  976 , an FOV  978 , a pose  980 , and a set of coordinates  982 . In  FIG.  9 F , the perspective  976  corresponds to a view associated with a commander of the plurality of vehicles  986 , the FOV  978  corresponds to a size, shape, focal length, right-side up or upside-down, etc. of a viewing field associated with the commander&#39;s view, the pose  980  corresponds to rotational coordinates associated with the FOV  978  (e.g., title, angle, and pitch), and the set of coordinates  982  corresponds to the location associated with the FOV  978 . 
       FIG.  10    is flowchart representation of a method of changing viewing vectors within an 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.  1 A , and/or the HMD  104  shown in  FIG.  1 B ). 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  1010 , the method  1000  includes presenting a synthesized reality (SR) setting associated with predetermined content from a first viewing vector at a first time, wherein the first viewing vector characterizes display of the predetermined content at the first time. For example, in  FIG.  9 A , the electronic device  103  or the HMD  104  presents the SR setting  900  from a viewing vector  902  at time  904 , where the SR setting  900  corresponds to a first SR setting (e.g., a driving setting). 
     For example, the predetermined content corresponds to a screenplay, theatrical play, movie, TV show, historical event, fictional story, live event, or the like. In some implementations, the predetermined content is replaced with emergent content that is based at least in part on source assets (e.g., initial or seed content), such as a movie, TV episode, video game, historical event, or the like, as described with reference to  FIGS.  8 A and  8 B . In some implementations, the predetermined content is replaced with the conditionally dependent SR content threads  600  described in  FIG.  6 A . 
     In some implementations, the plurality of viewing vector affordances corresponds to predetermined viewing vectors. For example, the predetermined viewing vectors correspond to times and perspectives associated with the most important events in the predetermined content based on crowd-sourced information (e.g., frequency or volume of favorites, likes, social media impressions, etc.). In another example, the predetermined viewing vectors correspond to times and perspectives associated with the most important events in the predetermined content as manually selected by a content creator, historian, director, or the like. 
     In some implementations, the plurality of viewing vector affordances corresponds to viewing vectors determined based on a set of filters. For example, the set of filters are determined based on a user profile associated with the user, a battery of questions answered by the user, or the like. As one example, a plurality of viewing vectors associated with machinery within the predetermined content are selected based on activation of a mechanic filter. As another example, a plurality of viewing vectors associated with naval exercises within the predetermined content are selected based on activation of a naval trainee filter. As yet another example, a plurality of viewing vectors associated with weather phenomena within the predetermined content based on activation of a meteorology filter. 
     In some implementations, as represented by block  1010   a , the first viewing vector is characterized at least in part by a first field-of-view (FOV), a first pose, a first set of SR coordinates within the SR setting. For example, in  FIG.  9 A , the electronic device  103  or the HMD  104  presents the SR setting  900  from the viewing vector  902  characterized by the perspective  906 , the FOV  908 , the pose  910 , and the set of coordinates  912 . 
     In some implementations, the FOV corresponds to a parameter defining the shape, size, focal length, and/or the like of the viewing field. In some implementations, the pose corresponds to a set of rotational coordinates associated with the FOV (e.g., pitch, roll, and yaw). In some implementations, the pose indicates whether the angle of the FOV and whether the FOV is right-side up or upside-down. In some implementations, the set of SR coordinates corresponds to a set of translational coordinates within the SR setting (e.g., global coordinates relative to the SR setting or environment). In some implementations, the perspective corresponds to a unit, point-of-view (POV), or objective-effectuator (OE) associated with the predetermined content such as an omniscient POV, a general&#39;s POV, infantry unit POV, individual soldier&#39;s POV, or the like within a historical battle. 
     In some implementations, the first viewing vector is associated with a respective viewing vector affordance. In some implementations, the first viewing vector is not associated with one of the plurality of viewing vector affordances. For example, the user changed viewing perspectives several times prior to the first time. For example, in  FIG.  9 A , the viewing vector  902  is not associated with one of the plurality of viewing vector affordances  924 . 
     As represented by block  1012 , the method  1000  includes presenting a navigation timeline for the predetermined content including a current time indicator positioned at the first time and a plurality of viewing vector affordances each associated with a different viewing vector at respective times on the navigation timeline, wherein each viewing vector characterizes how the SR content is displayed at the respective times. In one example, the navigation timeline is overlaid on the SR setting. In another example, the navigation timeline is presented as a part of a separate controls interface. For example, in  FIG.  9 A , the electronic device  103  or the HMD  104  presents the SR setting  900 , including a navigation timeline  920  with a current time indicator  922  and a plurality of viewing vector affordances  924 . In some implementations, each of the viewing vector affordances is associated with an OE that corresponds to a story node within the conditionally dependent SR content threads  600 . In some implementations, each of the viewing vector affordances is associated with an OE encapsulation that corresponds to a story node within the conditionally dependent SR content threads  600 . 
     As represented by block  1014 , the method  1000  includes detecting an input selecting a respective viewing vector affordance on the navigation timeline associated with a second time within the predetermined content. In some implementations, the input corresponds to a voice command, gesture input, touch input, gaze direction, or the like. For example, with reference to  FIG.  9 A , the electronic device  103  or the HMD  104  detects a first input selecting the viewing vector affordance  924 B at time  904  such as a voice command, gesture input, touch input, gaze direction input, or the like from a user of the electronic device  103  or the HMD  104  via one or more input devices (e.g., microphone, gaze tracker, body pose estimator, touch-sensitive surface, buttons, etc.). 
     In some implementations, the input corresponds to a first input type, and, when the user input corresponds to a second input type, a menu of viewing vector options for the time is presented. In some implementations, the menu is ranked according to the most popular viewing vectors based on crowd-sourced information. In some implementations, the menu is sorted according to a predetermined order. For example, with respect to the sequence in  FIGS.  9 A and  9 B , the first input selecting the viewing vector affordance  924 B corresponds to a first input type (e.g., short press, single tap, first voice command, etc.). Thus, continuing with the example sequence in  FIGS.  9 A and  9 B , detection of the first input associated with the first input type at time  904  causes the electronic device  103  or the HMD  104  to transition from presenting the SR setting  900  in  FIG.  9 A  from the viewing vector  902  to the SR setting  930 A from the viewing vector  932 A in  FIG.  9 B . In contrast, with reference to the sequence in  FIGS.  9 D and  9 E , the fourth input selecting the viewing vector affordance  924 E corresponds to a second input type (e.g., long press, double tap, second voice command, etc.). Thus, continuing with the example sequence in  FIGS.  9 D and  9 E , detection of the fourth input associated with the second input type at time  934 +3 causes the electronic device  103  or the HMD  104  to present the viewing vector menu  950  with a plurality of selectable viewing vector affordance options  952 A and  952 B for the time associated with the viewing vector affordance  924 E. 
     As represented by block  1016 , the method  1000  includes, in response to detecting the input selecting the respective viewing vector affordance on the navigation timeline, updating the SR setting in order to present the SR setting from a second viewing vector different from the first viewing vector, wherein the second viewing vector characterizes display of the predetermined content at the second time. For example, in response to detecting the first input selecting the viewing vector affordance  924 B at time  904  in  FIG.  9 A ,  FIG.  9 B  illustrates the electronic device  103  or the HMD  104  presenting an example SR setting  930 A from a viewing vector  932 A at time  934  associated with the viewing vector affordance  924 B. 
     In some implementations, as represented by block  1016   a , the second viewing vector is characterized at least in part by a second FOV, a second pose, a second set of SR coordinates within the SR setting, and a second perspective. For example, in  FIG.  9 B , the electronic device  103  or the HMD  104  presents the SR setting  930 A from the viewing vector  932 A characterized by the perspective  936 A, the FOV  938 A, the pose  940 A, and the SR coordinates  942 A. 
     In some implementations, in response to detecting the input selecting the respective viewing vector affordance on the navigation timeline, the device presents the current time indicator positioned at the second time. For example, in response to detecting the first input selecting the viewing vector affordance  924 B at time  904  in  FIG.  9 A , the electronic device  103  or the HMD  104  moves the current time indicator  922  moves to a position proximate to the viewing vector affordance  924 B in  FIG.  9 B . 
     In some implementations, in response to detecting the input selecting the respective viewing vector affordance on the timeline, the device ceases to display the SR setting from the first viewing vector. For example, in response to detecting the first input selecting the viewing vector affordance  924 B at time  904  in  FIG.  9 A , the electronic device  103  or the HMD  104  ceases to display the SR setting ( 900  from the viewing vector  902  and instead displays the SR setting  930 A from the viewing vector  932 A in  FIG.  9 B . 
     In some implementations, the device further presents a field-of-view (FOV) modification affordance provided to modify a current FOV associated with the second viewing vector based on one or more subsequent user inputs. In some implementations, changing the FOV corresponds to increasing or decreasing the viewing filed (e.g., increase the viewing field from 30 degrees to 180 degrees). In some implementations, changing the FOV corresponds to changing the shape of the viewing field (e.g., changing from elliptical to rectangular). In some implementations, changing the FOV corresponds to changing the focal length of the viewing field. In some implementations, changing the FOV corresponds to changing a zoom level of the viewing field. 
     In some implementations, the device further presents a pose modification affordance provided to change a current pose associated with the second viewing vector to a subsequent pose based on one or more subsequent inputs. In some implementations, changing the pose corresponds to changing the roll, pitch, or yaw of the current FOV of the current perspective such as changing the head tilt or pitch of a unit or OE. For example, changing the pose corresponds to changing the FOV from a soldier&#39;s eyes to the FOV from their back. As another example, changing the pose corresponds to changing the FOV from the rear of a vehicle to the FOV from the front a vehicle. For example, with reference to the sequence in  FIGS.  9 B and  9 C , in response to detecting the second input associated with changing the pose  940 A in  FIG.  9 B , the electronic device  103  or the HMD  104  presents the SR setting  930 B presented from a viewing vector  932 A′ at time  934 +1  FIG.  9 C . Continuing with this example sequence, the viewing vector  932 A′ is characterized by the perspective  936 A, the FOV  938 A, a pose  940 A′ (e.g., the changed pose relative to  FIG.  9 B ), and the set of coordinates  942 A. In  FIG.  9 C , the viewing vector  932 A′ is still associated with the perspective  936 A (e.g., the worker  946 ), the FOV  938 A, and the set of coordinates  942 A, but the viewing vector  932 A′ is associated with the changed pose  940 A′. As one example, the head or body of the worker  946  has been rotated counter-clockwise. 
     In some implementations, the device further presents an SR coordinates modification affordance provided to change a current set of SR coordinates associated with the second viewing vector to a subsequent set of SR coordinates within the SR setting based on one or more subsequent inputs. In some implementations, changing the set of SR coordinates corresponds to ambulating, driving, or otherwise moving the current perspective (e.g., the current unit or OE). In some implementations, changing the set of SR coordinates corresponds to changing from a first-person view of the current perspective (e.g., the current unit or OE) to a third-person view. 
     In some implementations, the device further presents a perspective modification affordance provided to change from a current second perspective associated with the second viewing vector to a subsequent perspective based on a menu of options. In some implementations, changing the current perspective (e.g., unit or OE) corresponds to switching from an omniscient point-of-view (POV) to a specific unit/OE&#39;s POV. For example, with reference to the sequence in  FIGS.  9 C and  9 D , in response to detecting the third input associated with changing the perspective  936 A in  FIG.  9 C , the electronic device  103  or the HMD  104  presents the SR setting  930 C presented from a viewing vector  932 B at time  934 +2 in  FIG.  9 D . Continuing with this example sequence, the viewing vector  932 B is characterized by a perspective  936 B, an FOV  938 B, a pose  940 B, and a set of coordinates  942 B. In  FIG.  9 D , the viewing vector  932 B is associated with perspective  936 B (e.g., an omniscient view of the second SR setting) as opposed to the perspective  936 A in  FIGS.  9 B and  9 C  (e.g., the view of the worker  946  within the second SR setting). 
     In some implementations, while presenting the predetermined content from the second viewing vector, the device further presents a previous perspective affordance, for viewing the SR setting from a previous perspective associated with the first viewing vector at the second time. For example, in response to detecting an input associated with the previous perspective affordance in  FIG.  9 B , the electronic device  103  or the HMD  104  transitions from presenting the SR setting from a current viewing vector associated with the perspective  936 A at time  934  to presenting the SR setting from a subsequent vector associated with the perspective  906  at a next time period. 
     In some implementations, the device detects one or more subsequent inputs associated with the time timescale. In response to detecting the one or more subsequent inputs, the device changes a time scale associated with the navigation timeline for the narrative and updating the plurality of viewing vector affordances based on the changed time scale. For example, the one or more subsequent inputs corresponds to pinch-in or pinch out gestures. As another example, the one or more subsequent inputs corresponds to voice commands associated with zooming in or out from the current timescale associated with navigation timeline. 
     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: 20190528
Publication Date: 20230905
Grant Date: 20230905
Priority Date: 20180601
Inventors: Richter, Ian M.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06T19/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T15/005", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T19/006", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/006", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/08", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/04815", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04855", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/044", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T15/005", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T15/20", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 67180861