Patent Publication Number: US-2022222898-A1

Title: Intermediary emergent content

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/267,985 filed on Feb. 11, 2021, which is a national phase entry of PCT application number PCT/US19/52857 filed on Sep. 25, 2019, which claims priority to U.S. patent application No. 62/737,768, filed on Sep. 27, 2018, which are hereby incorporated by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to intermediary emergent content. 
     BACKGROUND 
     Some devices are capable of generating and presenting synthesized reality (SR) settings. Some SR settings include virtual settings that are simulated replacements of physical settings. Some SR settings include augmented settings that are modified versions of physical settings. Some devices that present SR settings include mobile communication devices such as smartphones, head-mountable displays (HMDs), eyeglasses, heads-up displays (HUDs), and optical projection systems. Most previously available devices that present SR settings are ineffective at presenting representations of certain objects. For example, some previously available devices that present SR 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. 
         FIG. 1  is a block diagram of an example system for generating intermediary emergent content items in accordance with some implementations. 
         FIGS. 2A-2F  are diagrams illustrating an example intermediary emergent content item in accordance with some implementations. 
         FIG. 3A  is a block diagram of an example system for training an objective-effectuator engine in accordance with some implementations. 
         FIG. 3B  is a block diagram of a system for generating an intermediary emergent content item in accordance with some implementations. 
         FIG. 4A  is a block diagram of an example neural network being trained to generate intermediary emergent content in accordance with some implementations. 
         FIG. 4B  is a block diagram of an example neural network that generated intermediary emergent content in accordance with some implementations. 
         FIGS. 5A-5L  are diagrams of an example user interface for generating intermediary emergent content in accordance with some implementations. 
         FIGS. 6A-6D  are flowchart representations of a method of generating intermediary emergent content in accordance with some implementations. 
         FIGS. 7A-7B  are flowchart representations of a method of training an objective-effectuator engine in accordance with some implementations. 
         FIGS. 8A-8C  are flowchart representations of a method of generating intermediary emergent content item in accordance with some implementations. 
         FIG. 9  is a block diagram of a server system that generated intermediary emergent content in accordance with some implementations. 
         FIG. 10  is a diagram of an example operating environment in accordance with some implementations. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods for synthesizing intermediary emergent content. 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 obtaining an end state of a first content item spanning a first time duration. In some implementations, the end state of the first content item indicates a first state of an SR agent at the end of the first time duration. In some implementations, the method includes obtaining an initial state of a second content item spanning a second time duration subsequent the first time duration. In some implementations, the initial state of the second content item indicates a second state of the SR agent at the beginning of the second time duration. In some implementations, the method includes synthesizing an intermediary emergent content item spanning over an intermediary time duration that is between the end of the first time duration and the beginning of the second time duration. In some implementations, synthesizing the intermediary emergent content item includes generating a set of bounded objectives for the SR agent by providing the end state of the first content item and the initial state of the second content item to an emergent content engine. In some implementations, the set of bounded objectives are bounded by the end state of the first content item and the initial state of the second content item. In some implementations, synthesizing the intermediary emergent content item includes generating a set of actions for the SR agent by providing the set of bounded objectives to an SR agent engine. In some implementations, the first action in the set of actions matches an action of the SR agent at the end of the first time duration and the last action in the set of actions matches an action of the SR agent at the beginning of the second time duration. In some implementations, synthesizing the intermediary emergent content item includes rendering the intermediary content item for display. 
     Various implementations disclosed herein include devices, systems, and methods for training an SR agent engine. 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 extracting, from a content item, a set of actions performed by an action-performing element in the content item. In some implementations, the method includes determining, by semantic analysis, a set of objectives for an SR agent based on the set of actions. In some implementations, an SR representation of the SR agent corresponds to the action-performing element. In some implementations, the method includes training, based on the set of objectives, an SR agent engine that generates actions for the SR agent. In some implementations, the training is complete when actions generated by the SR agent engine are within an acceptability threshold of the set of actions extracted from the content item. 
     Various implementations disclosed herein include devices, systems, and methods for synthesizing intermediary emergent content. 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 displaying, on the display, a user interface that includes a first representation of a first content item spanning a first time duration and a second representation of a second content item spanning a second time duration. In some implementations, the method includes obtaining, via the input device, a user input corresponding to a request to generate an intermediary emergent content item spanning over an intermediary time duration that is between the end of the first time duration and the beginning of the second time duration. In some implementations, the method includes in response to obtaining the user input, displaying, on the display, a representation of the intermediary emergent content item between the first representation of the first content item and the second representation of the second content item. In some implementations, the intermediary emergent content item is synthesized after the user input is obtained. 
     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. 
     The present disclosure provides methods, systems, and/or devices for generating intermediary emergent content. The intermediary emergent content spans over an intermediary time duration that is between a first time duration that corresponds to a first content item and a second time duration that corresponds to a second content item. An emergent content engine synthesizes the intermediary emergent content based on an end state of the first content item and an initial state of the second content item. The intermediary emergent content allows a user to view how a plot progresses between the first content item and the second content item. 
     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, a 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 a SR setting also may be made responsive to representations of movement (e.g., audio instructions). 
     An individual may interact with and/or sense a 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). A 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 embodiment, 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. 
       FIG. 1  is a block diagram of an example system  100  that synthesizes intermediary emergent content in accordance with some implementations. In various implementations, the system  100  synthesizes intermediary emergent content that spans a time duration that is between time durations corresponding to existing content. Briefly, in various implementations, the system  100  extracts actions from existing content, analyzes the actions to learn objectives, and utilizes the extracted actions and/or the learned objectives to synthesize the intermediary emergent content. To that end, in various implementations, the system  100  includes an emergent content engine  110 , objective-effectuator engines  120 - 1 , . . . ,  120 - n , an objective-effectuator engine trainer  130 , and a plot template datastore  160 . 
     In various implementations, an objective-effectuator represents a behavioral model of an action-performing element. In some implementations, an objective-effectuator models the behavior of an action-performing element. In some implementations, an objective-effectuator performs actions that are within a degree of similarity to actions that the action-performing element performs. In some implementations, an objective-effectuator models a character from fictional material such as a movie, a video game, a comic, and/or a novel. In some implementations, an objective-effectuator models an equipment (e.g., machinery such as a plane, a tank, a robot, a car, etc.). In some implementations, an objective-effectuator models a tangible object from fictional material or from the real-world (e.g., from a physical setting). In various implementations, an objective-effectuator is referred to as an SR agent, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  are referred to as SR agent engines, and the objective-effectuator engine trainer  130  is referred to as an SR agent engine trainer. 
     In various implementations, an objective-effectuator effectuates an action in order to advance (e.g., complete or satisfy) an objective. In some implementations, an objective-effectuator is associated with a particular objective, and the objective-effectuator effectuates actions that improve the likelihood of advancing that particular objective. Referring to  FIG. 1 , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate actions  122 - 1 , . . . ,  122 - n  for corresponding objective-effectuators. In some implementations, the emergent content engine  110  provides objectives  112  to the objective-effectuator engines  120 - 1 , . . . ,  120 - n . The objective-effectuator engines  120 - 1 , . . . ,  120 - n  utilize the objectives  112  to generate the actions  122 - 1 , . . . ,  122 - n.    
     In various implementations, the objective-effectuator engine trainer  130  (“trainer  130 ”, hereinafter for the sake of brevity) trains the objective-effectuator engines  120 - 1 , . . . ,  120 - n . In the example of  FIG. 1 , the trainer  130  trains the objective-effectuator engines  120 - 1 , . . . ,  120 - n  based on a first content item  140  and a second content item  150 . As illustrated in  FIG. 1 , the first content item  140  spans a first time duration T 1 , and the second content item  150  spans a second time duration T 3 .  FIG. 1  illustrates an intermediary duration T 2  between the first time duration T 1  and the second time duration T 3 . There is no existing content that spans the intermediary duration T 2 . After the trainer  130  utilizes the first content item  140  and the second content item  150  to train the objective-effectuator engines  120 - 1 , . . . ,  120 - n , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate an intermediary emergent content item that spans the intermediary duration T 2 . 
     In some implementations, the trainer  130  obtains actions that are extracted from existing content. In the example of  FIG. 1 , the trainer  130  obtains a first set of actions  142  that are extracted from the first content item  140 , and a second set of actions  152  that are extracted from the second content item  150 . In some implementations, the first set of actions  142  includes actions that action-performing elements (e.g., characters, equipment, etc.) perform in the first content item  140  to advance a plot/storyline of the first content item  140 . In some implementations, the second set of actions  152  includes actions that action-performing elements perform in the second content item  150  to advance a plot/storyline of the second content item  150 . In some implementations, a set of actions are extracted from a content item by performing scene analysis on the content item in order to identify the action-performing elements and the actions that the action-performing elements perform. In some implementations, the trainer  130  determines parameters  132  for the objective-effectuator engines  120 - 1 , . . . ,  120 - n  based on the extracted actions. In some implementations, the parameters  132  include neural network parameters. 
     In some implementations, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  obtain objectives that are associated with existing content. In some implementations, the objectives associated with existing content are determined based on (e.g., derived from) the actions that are extracted from the existing content. In the example of  FIG. 1 , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  obtain a first set of learned objectives  144  that are determined based on the first set of actions  142 . The objective-effectuator engines  120 - 1 , . . . ,  120 - n  obtain a second set of learned objectives  154  that are determined based on the second set of actions  152 . In some implementations, learned objectives are determined by performing semantic analysis on the extracted actions. In the example of  FIG. 1 , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  obtain the first set of learned objectives  144  and the second set of learned objectives  154  via an aggregator  170 . In some implementations, the aggregator  170  aggregates (e.g., packages) the first set of learned objectives  144  and the second set of learned objectives  154 . In some implementations, the aggregator  170  provides the sets of learned objectives  144  and  154  to a selector  172  that forwards the sets of learned objectives  144  and  154  to the objective-effectuator engines  120 - 1 , . . . ,  120 - n  during a training phase. 
     During the training phase of the objective-effectuator engines  120 - 1 , . . . ,  120 - n , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate the actions  122 - 1 , . . . ,  122 - n  based on learned objectives (e.g., the sets of learned objectives  144  and  154 ). The trainer  130  compares the generated actions  122 - 1 , . . . ,  122 - n  with the extracted actions. If the generated actions  122 - 1 , . . . ,  122 - n  are within a degree of similarity to the extracted actions, then the trainer  130  determines that the training of the objective-effectuator engines  120 - 1 , . . . ,  120 - n  is complete. If the generated actions  122 - 1 , . . . ,  122 - n  are not within a degree of similarity to the extracted actions, then the trainer  130  adjusts the parameters  132  based on a difference between the generated actions  122 - 1 , . . . ,  122 - n  and the extracted actions. 
     During the training phase of the objective-effectuator engines  120 - 1 , . . . ,  120 - n , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate actions  122 - 1 , . . . ,  122 - n  for the first time duration T 1 . The trainer  130  compares the actions  122 - 1 , . . . ,  122 - n  generated for the first time duration T 1  with the first set of extracted actions  142  (“extracted actions  142 ”, hereinafter for the sake of brevity). In some implementations, if the actions  122 - 1 , . . . ,  122 - n  generated for the first time duration T 1  match (e.g., are within a degree of similarity to) the extracted actions  142 , then the trainer  130  determines that the training of the objective-effectuator engines  120 - 1 , . . . ,  120 - n  is complete. In some implementations, if the actions  122 - 1 , . . . ,  122 - n  generated for the first time duration T 1  match the extracted actions  142 , then the trainer  130  determines whether the objective-effectuator engines  120 - 1 , . . . ,  120 - n  are able to generate actions  122 - 1 , . . . ,  122 - n  for the second time duration T 3  that match the second set of extracted actions  152  (“the extracted actions  152 ”, hereinafter for the sake of brevity). In some implementations, the trainer  130  continues adjusting the parameters  132  until the actions  122 - 1 , . . . ,  122 - n  generated for the first time duration T 1  match the extracted actions  142  from the first content item  140 . 
     During the training phase of the objective-effectuator engines  120 - 1 , . . . ,  120 - n , the objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate actions  122 - 1 , . . . ,  122 - n  for the second time duration T 3 . The trainer  130  compares the actions  122 - 1 , . . . ,  122 - n  generated for the second time duration T 3  with the extracted actions  152 . In some implementations, if the actions  122 - 1 , . . . ,  122 - n  generated for the second time duration T 3  match (e.g., are within a degree of similarity to) the extraction actions  152 , then the trainer  130  determines that the training of the objective-effectuator engines  120 - 1 , . . . ,  120 - n  is complete. In some implementations, the trainer  130  continues adjusting the parameters  132  until the actions  122 - 1 , . . . ,  122 - n  generated for the second time duration T 3  match the extracted actions  152  from the second content item  150 . 
     In some implementations, after determining that the training of the objective-effectuator engines  120 - 1 , . . . ,  120 - n  is complete, the trainer  130  instructs the selector  172  to stop forwarding the learned objectives  144  and  154  to the objective-effectuator engines  120 - 1 , . . . ,  120 - n.    
     During the training phase, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  provide the generated actions  122 - 1 , . . . ,  122 - n  to the emergent content engine  110 , so that the emergent content engine  110  can utilize the actions  122 - 1 , . . . ,  122 - n  as training data. During the training phase, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  provide the generated actions  122 - 1 , . . . ,  122 - n  to themselves, so that the parameters  132  of the objective-effectuator engines  120 - 1 , . . . ,  120 - n  can be adjusted based on the generated actions  122 - 1 , . . . ,  122 - n.    
     In the production phase, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate actions  122 - 1 , . . . ,  122 - n  that collectively form an intermediary emergent content item that spans the intermediary duration T 2 . In some implementations, the emergent content engine  110  generates objectives  112  (e.g., a set of bounded objectives) based on an end state of the first content item  140  and an initial state of the second content item  150 . The emergent content engine  110  provides the objectives  112  to the objective-effectuator engines  120 - 1 , . . . ,  120 - n  via the selector  172 . In the production phase, the selector  172  forwards the objectives  112  to the objective-effectuator engines  120 - 1 , . . . ,  120 - n  instead of forwarding the learned objectives  144  and  154 . 
     The objective-effectuator engines  120 - 1 , . . . ,  120 - n  utilize the objectives  112  provided by the emergent content engine  110  to generate actions  122 - 1 , . . . ,  122 - n  for the intermediary duration T 2 . The actions  122 - 1 , . . . ,  122 - n  for the intermediary duration T 2  collectively form the intermediary emergent content item that spans the intermediary duration T 2 . In some implementations, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  provide the actions  122 - 1 , . . . ,  122 - n  for the intermediary duration T 2  to a rendering and display pipeline, so that the intermediary emergent content item can be presented to a user. 
     In some implementations, the plot template datastore  160  stores various plot templates  162 . In some implementations, each plot template  162  corresponds to a type of plot (e.g., a type of storyline). In some implementations, the plot templates  162  include a plot template for a mystery plot. In some implementations, the plot templates  162  include a plot template for a disaster plot. In some implementations, the plot templates  162  include a plot template for a comedy plot. In some implementations, the emergent content engine  110  selects a plot template  162  from the plot template datastore  160 . In some implementations, the objectives  112  are a function of the plot template  162  that the emergent content engine  110  selects from the plot template datastore  160 . In some implementations, the objectives  112  advance a plot corresponding with the plot template  162  that the emergent content engine  110  selects. 
     In some implementations, the emergent content engine  110  selects one of the plot templates  162  from the plot template datastore  160  based on the end state of the first content item  140  and/or the initial state of the second content item  150 . In some implementations, the emergent content engine  110  selects one of the plot templates  162  based on the learned objectives  144  and/or  154 . In some implementations, the learned objectives  144  and/or  154  indicate a pattern that matches one of the plot templates  162 . In such implementations, the emergent content engine  110  selects the plot template  162  that most closely matches the learned objectives  144  and/or  154 . In some implementations, the emergent content engine  110  selects one of the plot templates  162  based on a user input. For example, in some implementations, the user input specifies which of the plot templates  162  is to be used for the intermediary emergent content item. 
       FIG. 2A  is a diagram that illustrates an end state  146  of the first content item  140  and an initial state  156  of the second content item  150 . In various implementations, the first content item  140  has various states that correspond to different times t 1 , 0 , . . . , t 1 , n  within the first time duration T 1 . The end state  146  of the first content item  140  corresponds to time t 1 , n . In various implementations, the second content item  150  has various states that correspond to different times t 3 , 0 , . . . , t 3 , n  within the second time duration T 3 . The initial state  156  of the second content item  150  corresponds to time t 3 , n.    
     In some implementations, the end state  146  of the first content item  140  indicates how the first content item  140  ends. In some implementations, the end state  146  of the first content item  140  indicates various action-performing elements that are present at time t 1 , n . In some implementations, the end state  146  of the first content item  140  indicates locations of the action-performing elements, actions that the action-performing elements are performing at time t 1 , n , a geographical location where the last scene of the first content item  140  takes place, and/or environmental conditions within the last scene of the first content item  140 . In the example of  FIG. 2A , the end state  146  of the first content item  140  includes a boy action-performing element  202 , a girl action-performing element  204 , a robot action-performing element  206 , and a drone action-performing element  208 . 
     In some implementations, the initial state  156  of the second content item  150  indicates how the second content item  150  starts. In some implementations, the initial state  156  of the second content item  150  indicates various action-performing elements that are present at time t 3 , 0 . In some implementations, the initial state  156  of the second content item  150  indicates locations of the action-performing elements, actions that the action-performing elements are performing at time t 3 , 0 , a geographical location where the first scene of the second content item  150  takes place, and/or environmental conditions within the first scene of the second content item  150 . In the example of  FIG. 2A , the initial state  156  of the second content item  150  includes the boy action-performing element  202  and the robot action-performing element  206 . 
       FIG. 2B  illustrates a plot template  162   a  that is selected from the plot templates  162  shown in  FIG. 1 . In the example of  FIG. 2B , the plot template  162   a  includes a first interim objective  210  at time t 2 , 1 , a second interim objective  212  at time t 2 , 7 , a third interim objective  214  at time t 2 , 10 , a fourth interim objective  216  at time t 2 , 15 , and a fifth interim objective  218  at time t 2 , 16 . The plot template  162   a  indicates a relationship between the various interim objectives  210 - 218 . The relationship between the interim objectives  210 - 218  indicates the plot for the intermediary emergent content item that spans the intermediary duration T 2 . For example, the relationship between the interim objectives  210 - 218  indicates whether the plot is a mystery plot, a disaster plot, a suspense plot, a comedy plot, etc. 
       FIG. 2C  illustrates an example intermediary emergent content item  220  at its initial state  220   a  (at time t 2 , 0 ). The intermediary emergent content item  220  includes an SR representation of a boy objective-effectuator  222  (“boy objective-effectuator  222 ”, hereinafter for the sake of brevity), an SR representation of a girl objective-effectuator  224  (“girl objective-effectuator  224 ”, hereinafter for the sake of brevity), an SR representation of a robot objective-effectuator  226  (“robot objective-effectuator  226 ”, hereinafter for the sake of brevity), and an SR representation of a drone objective-effectuator  228  (“drone objective-effectuator  228 ”, hereinafter for the sake of brevity). In the example of  FIG. 2C , the boy objective-effectuator  222  models the behavior of the boy action-performing element  202 . The girl objective-effectuator  224  models the behavior of the girl action-performing element  204 . The robot objective-effectuator  226  models the behavior of the robot action-performing element  206 . The drone objective-effectuator  228  models the behavior of the drone action-performing element  208 . 
     As illustrated in  FIG. 2C , in some implementations, the initial state  220   a  of the intermediary emergent content item  220  matches (e.g., is identical to) the end state  146  of the first content item  140 . As such, in some implementations, the state of the intermediary emergent content item  220  at time t 2 , 0  is a replica of the end state  146  of the first content item  140 . To that end, the position and actions of the boy objective-effectuator  222  at time t 2 , 0  are the same as the position and actions of the boy action-performing  202  at time t 1 , n . The position and actions of the girl objective-effectuator  224  at time t 2 , 0  are the same as the position and actions of the girl action-performing element  204  at time t 1 , n . The position and actions of the robot objective-effectuator  226  at time t 2 , 0  are the same as the position and actions of the robot action-performing element  206  at time t 1 , n . The position and actions of the drone objective-effectuator  228  at time t 2 , 0  are the same as the position and actions of the drone action-performing element  208  at time t 1 , n.    
       FIGS. 2D and 2E  illustrate intermediate states  220   b  and  220   c , respectively, of the intermediary emergent content item  220 . As illustrated in  FIG. 2D , the intermediate state  220   b  of the intermediary emergent content item  220  corresponds to time t 2 , 7 . In the intermediate state  220   b , the boy objective-effectuator  222  and the girl objective-effectuator  224  are performing actions that are different from the actions that the boy objective-effectuator  222  and the girl objective-effectuator  224  performed at the initial state  220   a  of the intermediary emergent content item  220 . For example, as illustrated in  FIG. 2D , the girl objective-effectuator  224  has turned around and the boy objective-effectuator  222  has raised its arm. 
     As illustrated in  FIG. 2E , the intermediate state  220   c  of the intermediary emergent content item  220  corresponds to time t 2 , 15 . In the intermediate state  220   c , the boy objective-effectuator  222  and the girl objective-effectuator  224  are performing actions that are different from the actions that the boy objective-effectuator  222  and the girl objective-effectuator  224  performed at the initial state  220   a  and the intermediate state  220   b  of the intermediary emergent content item  220 . For example, as illustrated in  FIG. 2E , the girl objective-effectuator  224  and the drone objective-effectuator  228  are about to exit from the scene, and the robot objective-effectuator  226  is moving towards the boy objective-effectuator  222 . 
       FIG. 2F  illustrates an end state  220   d  of the intermediary emergent content item  220  at time t 2 , n . As illustrated in  FIG. 2F , in some implementations, the end state  220   d  of the intermediary emergent content item  220  matches (e.g., is identical to) the initial state  156  of the second content item  150 . As such, in some implementations, the state of the intermediary emergent content item  220  at time t 2 , n  is a replica of the initial state  156  of the second content item  150 . To that end, the position and actions of the boy objective-effectuator  222  at time t 2 , n  are the same as the position and actions of the boy action-performing element  202  at time t 3 , 0 . The position and actions of the robot objective-effectuator  226  at time t 2 , n  are the same as the position and actions of the robot action-performing element  206  at time t 3 , n.    
       FIG. 3A  illustrates an example system  100   a  for training an objective-effectuator engine  120 . In some implementations, the system  100   a  includes the objective-effectuator engine  120 , the trainer  130 , an action extractor  174  and an objective determiner  176 . 
     In some implementations, the action extractor  174  obtains the first content item  140 , and extracts actions  142  from the first content item  140 . In some implementations, the action extractor  174  performs scene analysis to identify the extracted actions  142  that are being performed in the first content item  140 . Although  FIG. 3A  shows a single content item, in some implementations, the action extractor  174  obtains multiple content items (e.g., a series of content items, for example, an entire season with numerous episodes). In some implementations, the action extractor  174  provides the extracted actions  142  to the trainer  130  and/or the objective determiner  176 . 
     In some implementations, the objective determiner  176  determines the first set of objectives  144  based on the extracted actions  142 . In some implementations, the objective determiner  176  derives the first set of objectives  144  from the extracted actions  142 . In some implementations, the objective determiner  176  learns the first set of objectives  144  by analyzing the extracted actions  142 . As such, in some implementations, the first set of objectives  144  are referred to as learned objectives or derived objectives. In some implementations, the objective determiner  176  includes a semantic analyzer that performs semantic analysis on the extracted actions  142  to determine the first set of objectives  144 . For example, in some implementations, the objective determiner  176  performs semantic analysis on text that corresponds to dialogs that are spoken by the action-performing elements in the first content item  140 . 
     In some implementations, the objective determiner  176  provides the first set of objectives  144  to the objective-effectuator engine  120 . In some implementations, the objective-effectuator engine  120  generates actions  122  based on the first set of objectives  144 . For example, in some implementations, the objective-effectuator engine  120  generates actions  122  that advance (e.g., complete or satisfy) the first set of objectives  144 . In some implementations, at least during the training phase, the objective-effectuator engine  120  provides the generated actions  122  to the trainer  130 . 
     In some implementations, the trainer  130  includes an action comparator  134  and an objective-effectuator engine parameter determiner  136  (“parameter determiner  136 ”, hereinafter for the sake of brevity). In some implementations, the parameter determiner  136  determines the parameters  132  based on the extracted actions  142 . In some implementations, the action comparator  134  compares the generated actions  122  with the extracted actions  142 . If the action comparator  134  determines that the generated actions  122  match the extracted actions  142 , then the trainer  130  determines that the training of the objective-effectuator engine  120  is complete. If the action comparator  134  determines that the generated actions  122  do not match the extracted actions  142 , then the parameter determiner  136  adjusts the parameters  132 . In some implementations, the parameter determiner  136  adjusts the parameters  132  based on a difference between the generated actions  122  and the extracted actions  142 . In some implementations, the adjustment to the parameters  132  is a function of (e.g., directly proportional to) the difference between the generated actions  122  and the extracted actions  142 . 
       FIG. 3B  illustrates an example system  100   b  for synthesizing intermediary emergent content items. In some implementations, the system  100   b  includes a state obtainer  178  and an intermediary emergent content synthesizer  300 . In the example of  FIG. 3B , the intermediary emergent content synthesizer  300  includes the emergent content engine  110 , the objective-effectuator engines  120 - 1 , . . . ,  120 - n , and the plot template datastore  160 . 
     In some implementations, the state obtainer  178  obtains the end state  146  of the first content item  140  and the initial state  156  of the second content item  150 . In some implementations, the state obtainer  178  obtains the first content item  140 . In such implementations, the state obtainer  178  analyzes the first content item  140  to determine the end state  146  of the first content item  140 . For example, in some implementations, the state obtainer  178  performs scene analysis on the first content item  140  to identify the action-performing elements that are in the first content item  140 , and the locations and actions of the action-performing elements at the end of the first content item. In some implementations, the state obtainer  178  provides the end state  146  of the first content item  140  to the intermediary emergent content synthesizer  300 . 
     In some implementations, the state obtainer  178  obtains the second content item  150 . In such implementations, the state obtainer  178  analyzes the second content item  150  to determine the initial state  156  of the second content item  150 . For example, in some implementations, the state obtainer  178  performs scene analysis on the second content item  150  to identify the action-performing elements that are in the second content item  150 , and the locations and actions of the action-performing elements at the beginning of the second content item. In some implementations, the state obtainer  178  provides the initial state  156  of the second content item  150  to the intermediary emergent content synthesizer  300 . 
     In some implementations, the intermediary emergent content synthesizer  300  utilizes the end state  146  of the first content item  140  and the initial state  156  of the second content item  150  to synthesize an intermediary emergent content item  310 . The intermediary emergent content item  310  spans the intermediary duration T 2  that is between the first time duration T 1  corresponding to the first content item  140  and the second time duration T 2  corresponding to the second content item  150 . 
     In some implementations, the emergent content engine  110  determines a set of bounded objectives (e.g., the objectives  112 ) based on the end state  146  of the first content item  140  and the initial state  156  of the second content item  150 . In some implementations, the emergent content engine  110  selects a plot template  162  from the plot template datastore  160 . In such implementations, the objectives  112  are a function of the selected plot template  162 . In some implementations, the objectives  112  are bounded by a first set of objectives (e.g., the first set of objectives  144  shown in  FIG. 1 ) associated with the end state  146  of the first content item  140  and a second set of objectives (e.g., the second set of objectives  154  shown in  FIG. 1 ) associated with the initial state  156  of the second content item  150 . 
     In some implementations, the objective-effectuator engines  120 - 1 , . . . ,  120 - n  obtain the objectives  112  from the emergent content engine  1120 . The objective-effectuator engines  120 - 1 , . . . ,  120 - n  generate respective actions  122 - 1 , . . . ,  122 - n  based on the objectives  112 . In some implementations, the actions  122 - 1 , . . . ,  122 - n  collectively form the intermediary emergent content item  310 . 
       FIG. 4A  is a block diagram of a system  400  in accordance with some implementations. In some implementations, the system  400  includes a neural network system  410  (“neural network  410 ”, hereinafter for the sake of brevity) and the trainer  130 . In some implementations, the neural network  410  implements an objective-effectuator engine (e.g., the objective-effectuator engine  120  shown in  FIG. 3A ). In some implementations, the trainer  130  provides neural network parameters  432  (e.g., neural network weights, for example, the parameters  132  shown in  FIG. 3A ) to the neural network  410 . 
     In some implementations, the neural network  410  includes a long short-term memory (LSTM) recurrent neural network (RNN). In the example of  FIG. 4A , the neural network  410  includes an input layer  420 , a first hidden layer  422 , a second hidden layer  424 , a classification layer  426 , and an action selection module  428 . While the neural network  410  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  420  receives various inputs. In some implementations, the input layer  420  obtains (e.g., receives) a set of objectives that are derived from a set of extracted actions. In the example of  FIG. 4A , the input layer  420  receives the first set of learned objectives  144  that were derived from the extracted actions  142  of the first content item  140  shown in  FIG. 3A . In some implementations, the neural network  410  includes a feature extraction module (not shown) that generates a feature stream (e.g., a feature vector) based on the first set of learned objectives  144 . In such implementations, the feature extraction module provides the feature stream to the input layer  420 . As such, in some implementations, the input layer  420  receives a feature stream that is a function of the learned objectives  144 . In various implementations, the input layer  420  includes a number of LSTM logic units  420   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 units  420   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  422  includes a number of LSTM logic units  422   a . In some implementations, the number of LSTM logic units  422   a  ranges between approximately 10-500. Those of ordinary skill in the art will appreciate that, in such implementations, the number of LSTM logic units per layer is orders of magnitude smaller than previously known approaches (being of the order of O(10 1 )-O(10 2 )), which allows such implementations to be embedded in highly resource-constrained devices. As illustrated in the example of  FIG. 4A , the first hidden layer  422  receives its inputs from the input layer  420 . 
     In some implementations, the second hidden layer  424  includes a number of LSTM logic units  424   a . In some implementations, the number of LSTM logic units  424   a  is the same as or similar to the number of LSTM logic units  420   a  in the input layer  420  or the number of LSTM logic units  422   a  in the first hidden layer  422 . As illustrated in the example of  FIG. 4A , the second hidden layer  424  receives its inputs from the first hidden layer  422 . Additionally or alternatively, in some implementations, the second hidden layer  424  receives its inputs from the input layer  420 . 
     In some implementations, the classification layer  426  includes a number of LSTM logic units  426   a . In some implementations, the number of LSTM logic units  426   a  is the same as or similar to the number of LSTM logic units  420   a  in the input layer  420 , the number of LSTM logic units  422   a  in the first hidden layer  422  or the number of LSTM logic units  424   a  in the second hidden layer  424 . In some implementations, the classification layer  426  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 a number of possible actions. In some implementations, each output includes a probability or a confidence measure of the corresponding action matching the extracted actions  142 . 
     In some implementations, the action selection module  428  generates the actions  122  by selecting the top N action candidates provided by the classification layer  426 . In some implementations, the top N action candidates are likely to match the extracted actions  142 . In some implementations, the action selection module  428  provides the generated actions  122  to the trainer  130 , so that the trainer  130  can compare the generated actions  122  with the extracted actions  142 . 
     In some implementations, the trainer  130  (e.g., the action comparator  134 ) compares the generated actions  122  with the extracted actions  142 . If the generated actions  122  match the extracted actions  142 , then the trainer  130  determines that the neural network  410  has been trained. If the generated actions  122  do not match the extracted actions  142 , then the trainer  130  (e.g., the parameter determiner  136 ) adjusts the neural network parameters  432 . In some implementations, the trainer  130  (e.g., the parameter determiner  136 ) iteratively adjusts the neural network parameters  432  until the generated actions  122  match the extracted actions  142 . In some implementations, the generated actions  122  match the extracted actions  142  if the generated actions  122  are within a degree of similarity to the extracted actions  142 . 
     In some implementations, the neural network  410  is trained using a single content item (e.g., the first content item  140  shown in  FIG. 3A ). In some implementations, the neural network  410  is trained using multiple content items (e.g., the first content item  140  and the second content item  150  shown in  FIG. 3B ). In some implementations, the neural network  410  is trained using a series of content items (e.g., an entire season of a show with numerous episodes). In some implementations, the neural network  410  is trained using multiple series of content items (e.g., multiple seasons of a show, where each season has numerous episodes). 
     Referring to  FIG. 4B , the neural network  410  generates the intermediary emergent content item  310  based on one or more inputs. In some implementations, the neural network  410  generates the intermediary emergent content item  310  based on a set of bounded objectives (e.g., based on the set of bounded objectives  112  shown in  FIG. 3B ). As discussed herein, in some implementations, the bounded objectives are derived from an end state of a first content item and an initial state of a second content item. For example, as shown in  FIG. 3B , the objectives  112  are derived from the end state  146  of the first content item  140  and the initial state  156  of the second content item  150 . 
     In some implementations, the neural network  410  utilizes a plot template  162  to generate the intermediary emergent content item  310 . In some implementations, the actions  122  generated by the neural network  410  are a function of the plot template  162   a . For example, if the plot template  162   a  is a comedy plot template, then the actions  122  generated by the neural network  410  satisfy the comedy plot template. 
     In some implementations, the neural network  410  utilizes scene information  440  (e.g., environmental information regarding an SR setting) to generate the intermediary emergent content item  310 . In some implementations, the scene information  440  indicates a boundary for the scene (e.g., a boundary for the SR setting). In such implementations, the actions  122  that form the intermediary emergent content item  310  are performed within the boundary of the scene. In some implementations, the scene information  440  indicates environmental information regarding the scene. In such implementations, the actions  122  that form the intermediary emergent content item  310  are generated based on the environment of the scene. 
     In some implementations, the neural network  410  utilizes information regarding instantiated equipment/characters  442  to generate the intermediary emergent content item  310 . For example, in some implementations, the actions  122  that form the intermediary emergent content item  310  include interacting with the instantiated equipment/characters  442 . 
     In some implementations, the neural network  410  utilizes user-specified constraints  444  to generate the intermediary emergent content item  310 . In some implementations, the actions  122  that form the intermediary emergent content item  310  satisfy the user-specified constraints  444 . For example, in some implementations, the user-specified constraints  444  specify a location where the intermediary emergent content item  310  is to take place. In such implementations, the actions  122  that form the intermediary emergent content item  310  take place at the location specified in the user-specified constraints  444 . In some implementations, the user-specified constraints  444  specify specific equipment/characters that are to be included in the intermediary emergent content item  310 . In such implementations, the actions  122  that form the intermediary emergent content item  310  are associated with the equipment/characters indicated in the user-specified constraints  444 . 
       FIG. 5A  is a diagram of an example user interface  500  in accordance with some implementations. The user interface  500  displays information regarding a content series (e.g., a show). In the example of  FIG. 5A , the user interface  500  includes a show name  502 , a season number  504 , a rating  506 , a first episode representation  510 , a second episode representation  512 , a third episode representation  514 , a fourth episode representation  516 , and play affordances  520  for each episode. 
       FIG. 5B  illustrates a user input  530   a  that corresponds to a request to create an intermediary emergent content item (e.g., gap content, for example, a gap episode). In the example of  FIG. 5B , detecting the user input  530   a  includes detecting contacts that are moving the first episode representation  510  and the second episode representation  512  away from each other. In some implementations, the user input  530   a  includes a zoom gesture that zooms between the first episode representation  510  and the second episode representation  512 . In some implementations, the user input  530   a  corresponds to a request to create an intermediary emergent content item that spans an intermediary time duration between the first episode and the second episode of the show. 
       FIG. 5C  illustrates a prompt  540  that includes a standard generation affordance  542  and a customized generation affordance  544 . In some implementations, the user interface  500  displays the prompt  540  in response to receiving the user input  530   a . In some implementations, a user selection of the standard generation affordance  542  corresponds to a request to generate the intermediary emergent content item with default settings. In some implementations, a user selection of the customized generation affordance  544  corresponds to a request to generate the intermediary emergent content item with customized settings. 
       FIG. 5D  illustrates a user input  530   b  that selects the standard generation affordance  542 . In some implementations, the user input  530   b  corresponds to a request to generate the intermediary emergent content item with default settings. 
       FIG. 5E  illustrates a gap content representation  511  that represents an intermediary emergent content item (e.g., gap content) that spans an intermediary time duration between the first episode and the second episode. As shown in  FIG. 5E , the gap content representation  511  is associated with a play affordance  520 . A user selection of the play affordance  520  triggers playback of the gap content. 
       FIG. 5F  illustrates a user input  530   c  that selects the customized generation affordance  544 . In some implementations, the user input  530   c  corresponds to a request to generate the gap content based on customized settings (e.g., instead of or in addition to default settings). 
       FIG. 5G  illustrates an example customization screen  550  that allows a user to customize the generation of the gap content. In the example of  FIG. 5G , the customization screen  550  includes plot affordances  552 , location affordances  554 , action-performing element affordances  556 , time affordances  558 , and a generation affordance  560 . 
     The plot affordances  552  allow a user to select a plot template for the gap content. For example, in some implementations, the plot affordances  552  allow the user to select one of the plot templates  162  shown in  FIG. 1 . 
     The location affordances  554  allow a user to select a location for the gap content. In some implementations, the location affordances  554  allow the user to select the location where the first episode ended. In some implementations, the location affordances  554  allow the user to select the location where the second episode begins. In some implementations, the location affordances  554  allow the user to specify a location that is different from the locations of the first and second episodes. 
     The action-performing element affordances  556  allow a user to select action-performing elements for the gap content. In some implementations, the action-performing element affordances  556  allow the user to select action-performing elements from the first episode. In some implementations, the action-performing element affordances  556  allow the user to select action-performing elements from the second episode. In some implementations, the action-performing element affordances  556  allow the user to select other action-performing elements that were not included present in the first and second episodes. 
     The time affordances  558  allow a user to select a time duration for the gap content. In some implementations, the time affordances allow the user to specify a time duration for the gap content that is different from suggested time durations. 
     The generation affordance  560  allows a user to generate the gap content based on the selections of the plot affordances  552 , the location affordances  554 , the action-performing element affordances  556  and the time affordances  558 . 
     Referring to  FIG. 5H , in some implementations, some of the plot affordances  552  are not selectable. In the example of  FIG. 5H , the rescue plot affordance is not selectable. In some implementations, certain plot affordances are not selectable based on the type of plots that the first and second episodes are associated with. For example, if the first and second episodes are associated with a comedy plot, then the rescue plot affordance is not selectable for the gap content. 
     In some implementations, some of the location affordances  554  are not selectable. In the example of  FIG. 5H , the location where the first episode ended is not available for the gap content (e.g., because the location was damaged/destroyed at the end of the first episode). 
     In some implementations, some of the action-performing element affordances are not selectable. For example, if a particular action-performing element died during the first episode, then the corresponding action-performing element affordance is not selectable because that particular action-performing element is no longer available for the gap content. 
     Referring to  FIG. 5I , in some implementations, the gap content representation  511  is associated with a modification affordance  570  and a sharing affordance  580 . The modification affordance  570  allows a user to modify the gap content. The sharing affordance  580  allows a user to share the gap content.  FIG. 5I  illustrates a user input  530   d  selecting the modification affordance  570 . 
       FIG. 5J  illustrates a modification screen  572  that is displayed in response to the user input  530   d  selecting the modification affordance  570 . In some implementations, the modification screen  572  includes the plot affordances  552 , the location affordances  554 , the action-performing element affordances  556 , and the time affordances  558 . As such, the modification screen  572  allows the user to change the plot template, the location, the action-performing elements, and/or the time duration for the gap content. 
       FIG. 5K  illustrates a user input  530   e  selecting the sharing affordance  580 . In some implementations, the user input  530   e  corresponds to a request to share the gap content with another user. 
       FIG. 5L  illustrates a share sheet  590  that is displayed in response to the user input  530   e . In some implementations, the share sheet  590  includes a local sharing affordance  592 , a messaging affordance  594 , a mail affordance  596  and a publish affordance  598 . In some implementations, the local sharing affordance  592  allows the user to share the gap content with nearby devices. The messaging affordance  594  allows the user to send the gap content via a message (e.g., an instant message). The mail affordance  596  allows the user to send the gap content via e-mail. The publish affordance  598  allows the user to publish the gap content (e.g., on a content store) and obtain a credit for publishing the gap content. 
       FIG. 6A  is a flowchart representation of a method  600  of generating an intermediary emergent content item. In various implementations, the method  600  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the device  900  shown in  FIG. 9 ). In some implementations, the method  600  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  600  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  600  includes obtaining an end state of a first content item, obtaining an initial state of a second content item, and synthesizing an intermediary emergent content item based on the end state of the first content item and the initial state of the second content item. 
     As represented by block  610 , in some implementations, the method  600  includes obtaining an end state of a first content item spanning a first time duration. For example, as illustrated in  FIG. 3B , the method  600  includes obtaining the end state  146  of the first content item  140  spanning the first time duration T 1 . In some implementations, the end state of the first content item indicates a first state of an objective-effectuator at the end of the first time duration. For example, as illustrated in  FIG. 2B , the end state  146  of the first content item  140  indicates a first state of the boy objective-effectuator  222 . 
     As represented by block  620 , in some implementations, the method  600  includes obtaining an initial state of a second content item spanning a second time duration. For example, as illustrated in  FIG. 3B , the method  600  includes obtaining the initial state  156  of the second content item  150  spanning the second time duration T 2 . In some implementations, the initial state of the second content item indicates a second state of the objective-effectuator at the beginning of the second time duration. For example, as illustrated in  FIG. 2F , the initial state  156  of the second content item  150  indicates a second state of the boy objective-effectuator  222 . 
     As represented by block  630 , in some implementations, the method  600  includes synthesizing an intermediary emergent content item spanning over an intermediary time duration that is between the end of the first time duration and the beginning of the second time duration. For example, as illustrated in  FIG. 3B , the intermediary emergent content item  310  spans the intermediary duration T 2  that is between the first time duration T 1  and the second time duration T 3 . 
     As represented by block  632 , in some implementations, the method  600  includes generating a set of bounded objectives for the objective-effectuator by providing the end state of the first content item and the initial state of the second content item to an emergent content engine. For example, generating the set of bounded objectives  112  shown in  FIG. 3B  by providing the end state  146  of the first content item  140  and the initial state  156  of the second content item  150  to the emergent content engine  110 . In some implementations, the set of bounded objectives are bounded by the end state of the first content item and the initial state of the second content item. For example, the objectives  112  shown in  FIG. 3B  are bounded by the end state  146  of the first content item  140  and the initial state  156  of the second content item  150 . 
     As represented by block  634 , in some implementations, the method  600  includes generating a set of actions for the objective-effectuator by providing the set of bounded objectives to an objective-effectuator engine. For example, generating the set of actions  122 - 1  shown in  FIG. 3B  for an objective-effectuator (e.g., the boy objective-effectuator  222  shown in  FIG. 2C ) by providing the set of bounded objectives  112  to the objective-effectuator engine  120 - 1 . 
     As represented by block  636 , in some implementations, the method  600  includes rendering the intermediary emergent content item for display. For example, as shown in  FIG. 1 , the generated actions  122 - 1 , . . . ,  122 - n  that form the intermediary emergent content item are sent to a rendering and display pipeline. 
     In various implementations, synthesizing the intermediary emergent content item allows the user to view new content that was not originally created by the entity that created the first content item and the second content item. As such, synthesizing the intermediary emergent content item provides the user with an option to watch additional content thereby enhancing user experience and increasing the operability of the device. 
     Referring to  FIG. 6B , as represented by block  630   a , in some implementations, the initial state of the intermediary emergent content item is within a degree of similarity to the end state of the first content item. For example, as illustrated in  FIG. 2C , the initial state  220   a  of the intermediary emergent content item  220  matches (e.g., is identical to) the end state  146  of the first content item  140 . In various implementations, the initial state of the intermediary emergent content item being within a degree of similarity to the end state of the first content item provides continuity between the intermediary emergent content item and the end state of the first content item thereby making the intermediary emergent content item appear more realistic. 
     As represented by block  630   b , in some implementations, the end state of the intermediary emergent content item is within a degree of similarity to the initial state of the second content item. For example, as illustrated in  FIG. 2F , the end state  220   d  of the intermediary emergent content item  220  matches (e.g., is identical to) the initial state  156  of the second content item  150 . In various implementations, the end state of the intermediary emergent content item being within a degree of similarity to the initial state of the second content item provides continuity between the intermediary emergent content item and the initial state of the second content item thereby making the intermediary emergent content item appear more realistic. 
     As represented by block  630   c , in some implementations, a third state of the objective-effectuator at the beginning of the intermediary time duration is within a degree of similarity to the first state of the objective-effectuator at the end of the first time duration. For example, as illustrated in  FIG. 2C , a state of the boy objective-effectuator  222  at time t 2 , 0  matches (e.g., is identical to) a state of the boy action-performing element  202  at time t 1 , n.    
     As represented by block  630   d , in some implementations, a fourth state of the objective-effectuator at the end of the intermediary time duration is within a degree of similarity to the second state of the objective-effectuator at the beginning of the second time duration. For example, as illustrated in  FIG. 2F , a state of the boy objective-effectuator  222  at time t 2 , n  matches a state of the boy action-performing element  202  at time t 3 , 0 . 
     As represented by block  630   e , in some implementations, the set of actions indicate a transition of the objective-effectuator from the first state at the end of the first time duration to the second state at the beginning of the second time duration. For example,  FIGS. 2C-2F  indicate a transition of the boy objective-effectuator  222  from its state at the end of the first time duration T 1  to its state at the beginning of the second time duration T 3 . 
     As represented by block  630   f , in some implementations, the objective-effectuator is absent in the first content item and present in the second content item, and the set of actions correspond to an entrance of the objective-effectuator into the second content item. 
     As represented by block  630   g , in some implementations, the objective-effectuator is present in the first content item and absent in the second content item, and the set of actions correspond to a departure of the objective-effectuator from the first content item. For example,  FIGS. 2C-2F  illustrate a departure of the girl objective-effectuator  224 . 
     As represented by block  640 , in some implementations, the end state of the first content item indicates scene information characterizing a first scene included within the first content item and the initial state of the second content item indicates scene information characterizing a second scene included within the second content item. 
     As represented by block  642 , in some implementations, synthesizing the intermediary emergent content item includes synthesizing a third scene based on the scene information characterizing the first scene and the scene information characterizing the second scene. 
     As represented by block  644 , in some implementations, the first scene corresponds to a first geographical location, the second scene corresponds to a second geographical location, and the third scene corresponds to a third geographical location that is on a route that spans between the first geographical location and the second geographical location. 
     As represented by block  610   a , in some implementations, the method  600  includes performing scene analysis on the first content item in order to identify the objective-effectuator and determine the first state of the objective-effectuator. In various implementations, performing scene analysis reduces the need for a user to manually specify the end state of the first content item thereby reducing the number of user interactions with the device and improving battery life. 
     As represented by block  620   a , in some implementations, the method  600  includes performing scene analysis on the second content item in order to identify the objective-effectuator and determine the second state of the objective-effectuator. In various implementations, performing scene analysis reduces the need for a user to manually specify the initial state of the second content item thereby reducing the number of user interactions with the device and improving battery life. 
     Referring to  FIG. 6D , as represented by block  650 , in some implementations, the method  600  includes selecting a plot template from a plurality of plot templates, and synthesizing the intermediary emergent content item based on the plot template. For example, as illustrated in  FIG. 1 , the emergent content engine  110  selects a plot template  162  from the plot template datastore  160 , and the emergent content engine  110  utilizes the selected plot template  162  to generate the objectives  112 . In various implementations, synthesizing the intermediary emergent content item based on a plot template makes the intermediary emergent content item appear as realistic as the first and second content items thereby enhancing user experience and improving the operability of the device. 
     As represented by block  652 , in some implementations, the plot template is selected based on the end state of the first content item and the initial state of the second content item. For example, in some implementations, the method  600  includes selecting the same plot template that is used by the first content item and the second content item. 
     As represented by block  654 , in some implementations, selecting the plot template includes obtaining a user selection of the plot template (e.g., via the plot affordances  552  shown in  FIG. 5G ). In various implementations, allowing the user to select the plot template gives the user control over the plot/storyline of the intermediary emergent content item thereby enhancing user experience and improving the operability of the device. 
     As represented by block  656 , in some implementations, the method  600  includes providing the plot template to the emergent content engine in order to allow the emergent content engine to generate the set of bounded objectives based on the plot template. For example, as illustrated in  FIG. 2B , the interim objectives  210 - 218  are generated based on the plot template  162   a.    
       FIG. 7A  is a flowchart representation of a method  700  of training an objective-effectuator engine to generate actions that correspond to an intermediary emergent content item. In various implementations, the method  700  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the device  900  shown in  FIG. 9 ). In some implementations, the method  700  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  700  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  700  includes extracting a set of actions performed by an action-performing element, determining a set of objectives for an objective-effectuator based on the set of actions, and training an objective-effectuator engine that generates actions for the objective-effectuator. 
     As represented by block  710 , in some implementations, the method  700  includes extracting, from a content item, a set of actions performed by an action-performing element in the content item. For example, as illustrated in  FIG. 3A , the action extractor  174  extracts the first set of actions  142  from the first content item  140 . 
     As represented by block  720 , in some implementations, the method  700  includes determining, by semantic analysis, a set of objectives for an objective-effectuator based on the set of actions. For example, as illustrated in  FIG. 3A , the objective determiner  176  utilizes semantic analysis to derive the first set of learned objectives  144  from the extracted actions  142 . In some implementations, a synthesized reality (SR) representation of the objective-effectuator corresponds to the action-performing element. 
     As represented by block  730 , in some implementations, the method  700  includes training, based on the set of objectives, an objective-effectuator engine that generates actions for the objective-effectuator. For example, as illustrated in  FIG. 3A , the trainer  130  trains the objective-effectuator engine  120  based on the first set of learned objectives  144 . In some implementations, the training is complete when actions generated by the objective-effectuator engine are within an acceptability threshold of the set of actions extracted from the content item. For example, as illustrated in  FIG. 3A , the training of the objective-effectuator engine  120  is complete when the generated actions  122  are within an acceptability threshold of the extracted actions  142 . In some implementations, the training is complete when the actions generated by the objective-effectuator engine are within a degree of similarity to (e.g., identical to) the set of actions extracted from the content item. 
     In various implementations, training the objective-effectuator engine allows the objective-effectuator engine to generate actions that correspond to an intermediary emergent content item thereby providing the user with more content to watch. Providing the user more content to watch enhances the user experience and improves the operability of the device. Enabling the objective-effectuator engine to generate actions that corresponds to an intermediary emergent content item is less resource-intensive than a content creator curating content. Hence, training the objective-effectuator engine tends to conserve computing resources. 
     As represented by block  710   a , in some implementations, the action-performing element performs actions that advance a plot in the content item. In some implementations, the action-performing element is a character or an equipment (e.g., the boy action-performing element  202  shown in  FIG. 2A ). 
     As represented by block  710   b , in some implementations, the method  700  includes performing scene analysis on the content item in order to identify the action-performing element and extract the set of actions that the action-performing element performs in the content item. For example, as shown in  FIG. 3A , the action extractor  174  performs scene analysis on the first content item  140  and extracts the first set of actions  142  from the first content item  140 . 
     As represented by block  720   a , in some implementations, the SR representation includes an augmented reality (AR) representation. In some implementations, the SR representation includes a virtual reality (VR) representation. In some implementations, the SR representation includes a mixed reality (MR) representation. 
     As represented by block  730   a , in some implementations, the method  700  includes determining that the training is complete when actions generated by the objective-effectuator engine are within a degree of similarity to the actions extracted from the content item. For example, as shown in  FIG. 3A , the training of the objective-effectuator engine  120  is complete when the trainer  130  determines that the generated actions  122  match the extracted actions  142 . 
     As represented by block  730   b , in some implementations, the method  700  includes determining values of one or more parameters of the objective-effectuator engine. For example, as shown in  FIG. 3A , the trainer  130  determines the parameters  132  for the objective-effectuator engine  120 . 
     As represented by block  730   c , in some implementations, the method  700  includes comparing the actions generated by the objective-effectuator engine with the set of actions extracted from the content item, and adjusting the values of the one or more parameters based on the comparison. For example, as shown in  FIG. 3A , the action comparator  134  compares the generated actions  122  with the extracted actions  142 , and the parameter determiner  136  adjusts the parameters  132  based on a difference between the generated actions  122  and the extracted actions  142 . 
     As represented by block  730   d , in some implementations, an amount of adjustment to the values of the one or more parameters is a function of a degree of dissimilarity between the actions generated by the objective-effectuator and the set of actions extracted from the content item. For example, as shown in  FIG. 3A , the parameter determiner  136  adjusts the parameters  132  based on the difference between the generated actions  122  and the extracted actions  142 . 
     Referring to  FIG. 7B , as represented by block  740 , in some implementations, the method  700  includes extracting, from another content item, another set of actions that the objective-effectuator performs in the other content item. In some implementations, the method  700  includes determining another set of objectives based on the other set of actions. In some implementations, the method  700  includes further training the objective-effectuator engine based on the other set of objectives. 
     As represented by block  740   a , in some implementations, the method  700  includes determining that the training is complete when the objective-effectuator engine generates a third set of actions that are within a degree of similarity to the first set of actions and a fourth set of actions that are within a degree of similarity to the second set of actions. 
     In various implementations, utilizing multiple content items (e.g., multiple episodes or an entire season of a show) to train an objective-effectuator engine results more realistic intermediary emergent content items thereby enhancing user experience and improving the operability of the device that generates the intermediary emergent content items. 
     As represented by block  750 , in some implementations, the method  700  includes training, based on the set of actions, an emergent content engine that generates objectives for the objective-effectuator. For example, training the emergent content engine  110  shown in  FIG. 1 . 
     As represented by block  750   a , in some implementations, the method  700  includes determining that the training of the emergent content engine is complete when the emergent content engine generates objectives that match the first set of objectives determined based on the first set of objectives. For example, as shown in  FIG. 1 , the training of the emergent content engine  110  is complete when the objectives  112  generated by the emergent content engine  110  match the first set of learned objectives  144  and/or the second set of learned objectives  154 . 
     As represented by block  760 , in some implementations, the method  700  includes extracting another set of actions that another action-performing element performs in the content item, determining another set of objectives based on the other set of actions, and training, based on the other set of objectives, another objective-effectuator engine that generates actions for another objective-effectuator that corresponds to the other action-performing element. 
     As represented by block  770 , in some implementations, the method  700  includes determining a plot template that corresponds with the content item, and providing the plot template to the objective-effectuator engine during the training. For example, as shown in  FIG. 1 , the emergent content engine  110  selects one of the plot templates  162  from the plot template datastore  160 . 
     As represented by block  770   a , in some implementations, the method  700  includes selecting the plot template from a plurality of plot templates based on the set of objectives from the objective-effectuator and/or based on the set of actions performed by the action-performing element in the content item. Selecting the plot template based on the set of objectives and/or the set of actions makes the intermediary emergent content item appear more realistic thereby enhancing user experience and improving the effectiveness of the device that is synthesizing the intermediary emergent content item. 
       FIG. 8A  is a flowchart representation of a method  800  of generating an intermediary emergent content item in accordance with some implementations. In various implementations, the method  800  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the device  900  shown in  FIG. 9 ). In some implementations, the method  800  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  800  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  800  includes displaying a user interface that includes representations of content items, obtaining a user input corresponding to a request to generate an intermediary emergent content item, and displaying a representation of the intermediary emergent content item. 
     As represented by block  810 , in some implementations, the method  800  includes displaying, on the display, a user interface that includes a first representation of a first content item spanning a first time duration and a second representation of a second content item spanning a second time duration. For example, as shown in  FIG. 5A , the user interface  500  includes the first episode representation  510  and the second episode representation  512 . 
     As represented by block  820 , in some implementations, the method  800  includes obtaining, via the input device, a user input corresponding to a request to generate an intermediary emergent content item spanning over an intermediary time duration that is between the end of the first time duration and the beginning of the second time duration. For example, as shown in  FIG. 5B , the user input  530   a  corresponds to a request create gap content that spans an intermediary time duration that is between the first episode and the second episode. 
     As represented by block  830 , in some implementations, the method  800  includes in response to obtaining the user input, displaying, on the display, a representation of the intermediary emergent content item between the first representation of the first content item and the second representation of the second content item. In some implementations, the intermediary emergent content item is synthesized after the user input is obtained. For example, as shown in  FIG. 5E , the gap content representation  511  is displayed between the first episode representation  510  and the second episode representation  512 . 
     Referring to  FIG. 8B , as represented by block  840 , in some implementations, in response to obtaining the user input, the method  800  includes displaying a prompt that includes a first affordance that corresponds to generating a standard version of the intermediary emergent content item and a second affordance that corresponds to generating a customized version of the intermediary emergent content item. For example, as shown in  FIG. 5D , the prompt  540  includes a standard generation affordance  542  and a customized generation affordance  544 . Displaying the prompt allows the user to generate a standard version of the intermediary emergent content item or a customized version of the intermediary emergent content item thereby providing more device functionality. 
     As represented by block  842 , in some implementations, the method  800  includes detecting a selection of the first affordance. In some implementations, in response to detecting the selection of the first affordance corresponding to the standard version, the method  800  includes synthesizing the standard version of the intermediary emergent content item without obtaining additional user inputs. For example, as shown in  FIGS. 5D-5E , in response to receiving the user input  530   b , the device generates the gap content and displays the gap content representation  511 . 
     As represented by block  844 , in some implementations, the method  800  includes detecting a selection of the second affordance. In some implementations, in response to detecting the selection of the second affordance corresponding to the customized version, the method  800  includes displaying, on the display, a customization screen that allows customization of the intermediary emergent content items. For example, as shown in  FIGS. 5F-5G , in response to the user input  530   c , the device displays the customization screen  550 . 
     As represented by block  846 , in some implementations, the customization screen includes a plurality of plot affordances that correspond to respective plot templates for the intermediary emergent content item. For example, as shown in  FIG. 5G , the customization screen  550  includes the plot affordances  552 . 
     As represented by block  846   a , in some implementations, one or more of the plot affordances are not selectable based on an end state of the first content item and an initial state of the second content item. For example, as shown in  FIG. 5H , some of the plot affordances  552  are not selectable. 
     As represented by block  848 , in some implementations, the customization screen includes a plurality of location affordances that correspond to respective locations for the intermediary emergent content item. For example, as shown in  FIG. 5G , the customization screen  550  includes the location affordances  554 . 
     As represented by block  848   a , in some implementations, one of the plurality of location affordances corresponds to an end state of the first content item. For example, as shown in  FIG. 5G , one of the location affordances  554  allows the user to select a location for the gap content that corresponds to the location where the first episode ends. 
     As represented by block  848   b , in some implementations, one of the plurality of location affordances corresponds to an initial state of the second content item. For example, as shown in  FIG. 5G , one of the location affordances  554  allows the user to select a location for the gap content that corresponds to the location where the second episode begins. 
     As represented by block  848   c , in some implementations, one of the plurality of location affordances includes an input field that accepts a location for the intermediary emergent content item. For example, as shown in  FIG. 5G , one of the location affordances  554  includes an input field that allows the user to specify a location that is different from the location where the first episode ends and the location where the second episode begins. 
     As represented by block  850 , in some implementations, the customization screen includes plurality of affordances that correspond to action-performing elements that can be included in the intermediary emergent content item. For example, as shown in  FIG. 5G , the customization screen  550  includes various action-performing element affordances  556 . 
     As represented by block  850   a , in some implementations, one of the plurality of affordances corresponds to an action-performing element from the first content item. For example, as shown in  FIG. 5G , some of the action-performing element affordances  556  correspond to action-performing elements from the first episode. 
     As represented by block  850   b , in some implementations, one of the plurality of affordances corresponds to an action-performing element from the second content item. For example, as shown in  FIG. 5G , some of the action-performing element affordances  556  correspond to action-performing elements from the second episode. 
     As represented by block  850   c , in some implementations, one of the plurality of affordances corresponds to an action-performing element that was not present in the first content item and the second content item. For example, as shown in  FIG. 5G , some of the action-performing element affordances  556  correspond to action-performing elements that are neither present in the first episode nor in the second episode. 
     Referring to  FIG. 8C , as represented by block  860 , in some implementations, the customization screen includes a plurality of time affordances that correspond to respective time durations for the intermediary emergent content item. For example, as shown in  FIG. 5G , the customization screen  550  includes the time affordances  558 . 
     As represented by block  870 , in some implementations, the representation of the intermediary emergent content item is associated with a share affordance that allows sharing the intermediary emergent content item with other devices. For example, as shown in  FIG. 5I , the gap content representation  511  is associated with the sharing affordance  580 . 
     As represented by block  880 , in some implementations, the representation of the intermediary emergent content item is associated with a modify affordance that allows modifying the intermediary emergent content item. For example, as shown in  FIG. 5I , the gap content representation  511  is associated with the modification affordance  570 . 
     As represented by block  880   a , in some implementations, the method  800  includes detecting a selection of the modify affordance. In response to detecting the selection of the modify affordance, the method  800  includes displaying a modification screen that allows modification of a plot template, a location, action-performing elements and a time duration associated with the intermediary emergent content item. For example, as shown in  FIGS. 5I-5J , in response to receiving the user input  530   d , the modification screen  572  is displayed. 
       FIG. 9  is a block diagram of a device  900  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 device  900  includes one or more processing units (CPUs)  901 , a network interface  902 , a programming interface  903 , a memory  904 , and one or more communication buses  905  for interconnecting these and various other components. 
     In some implementations, the network interface  902  is provided to, among other uses, establish and maintain a metadata tunnel between a cloud hosted network management system and at least one private network including one or more compliant devices. In some implementations, the one or more communication buses  905  include circuitry that interconnects and controls communications between system components. The memory  904  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  904  optionally includes one or more storage devices remotely located from the one or more CPUs  901 . The memory  904  comprises a non-transitory computer readable storage medium. 
     In some implementations, the memory  904  or the non-transitory computer readable storage medium of the memory  904  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  906 , the emergent content engine  110 , the objective-effectuator engines  120 - 1 , . . . ,  120 - n , the plot template datastore  160  including the plot templates  162 , and the objective-effectuator trainer  130 . 
     Referring to  FIG. 10 , an example operating environment  1000  includes a controller  102  and a head-mountable device (HMD)  104 . In the example of  FIG. 10 , the HMD  104 , being worn by a user  10 , presents (e.g., displays) an SR setting according to various implementations. In the example of  FIG. 10 , the SR setting corresponds to (e.g., displays) the intermediary content item  220 . In some implementations, the HMD  104  includes an integrated display (e.g., a built-in display) that displays the SR setting. In some implementations, the HMD  104  includes a head-mountable enclosure. In various implementations, the head-mountable enclosure includes an attachment region to which another device with a display can be attached. For example, in some implementations, an electronic device can be attached to the head-mountable enclosure. In various implementations, the head-mountable enclosure is shaped to form a receptacle for receiving another device that includes a display (e.g., the electronic device). For example, in some implementations, the electronic device slides/snaps into or otherwise attaches to the head-mountable enclosure. In some implementations, the display of the device attached to the head-mountable enclosure presents (e.g., displays) the SR setting (e.g., the intermediary content item  220 ). In various implementations, examples of the electronic device include smartphones, tablets, media players, laptops, etc. In some implementations, the controller  102  and/or the HMD  104  include the emergent content engine  110  that generates the intermediary content item  220 . 
     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.