PATENT DOCUMENT

Publication Number: US-11317154-B1
Application Number: US-202117238758-A
Country: US
Kind Code: B1

Title: Adaptive content delivery

Abstract:
A method includes obtaining a user focus indicator value that is associated with a subject. A plurality of filter values for a respective set of media content filters are determined based on the subject and contextual data. The method includes delivering, based on a first combination of the filter values, a first set of media content items associated with a first combination of content delivery mediums. The method includes delivering, based on a second combination of the filter values that is different from the first combination, a second set of media content items that is associated with a second combination of content delivery mediums that is different from the first combination of content delivery mediums. The second set is different from the first set.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a device including a display, a non-transitory memory and one or more processors coupled with the display and the non-transitory memory:
 obtaining a user focus indicator value that is associated with a subject; 
 determining a plurality of filter values for a respective set of media content filters based on the subject and contextual data associated with the device or a user of the device, wherein the contextual data indicates a learning style of the user and the plurality of filter values includes a value for a content engagement level filter that is set based on the learning style of the user; 
 delivering, based on a first combination of the plurality of filter values, a first set of one or more media content items that are associated with a first combination of content delivery mediums, wherein the first set of one or more media content items is selected from a plurality of media content items that provide information about the subject; and 
 delivering, based on a second combination of the plurality of filter values that is different from the first combination of the plurality of filter values, a second set of one or more media content items that are associated with a second combination of content delivery mediums that is different from the first combination of content delivery mediums, wherein the second set of one or more media content items is selected from the plurality of media content items, wherein the second set is different from the first set. 
 
 
     
     
       2. The method of  claim 1 , wherein the first combination of content delivery mediums includes a first combination of visual delivery, audio delivery or tactile delivery, and the second combination of content delivery mediums includes a second combination of visual delivery, audio delivery or tactile delivery. 
     
     
       3. The method of  claim 2 , wherein the visual delivery includes displaying text, a still image or a video. 
     
     
       4. The method of  claim 2 , wherein the audio delivery includes playing an audiobook, a podcast, a song or an audio portion of a video. 
     
     
       5. The method of  claim 2 , wherein the tactile delivery includes generating vibrations or providing tactile feedback. 
     
     
       6. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to visual delivery when the learning style of the user corresponds to a visual learner. 
     
     
       7. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to audio delivery when the learning style of the user corresponds to an auditory learner. 
     
     
       8. The method of  claim 1 , wherein determining the plurality of filter values comprises setting the value of the content engagement level filter to a value that is less than a threshold engagement level when the learning style of the user corresponds to a passive learner. 
     
     
       9. The method of  claim 1 , wherein determining the plurality of filter values comprises setting the value of the content engagement level filter to a value that is greater than a threshold engagement level when the learning style of the user corresponds to an active learner. 
     
     
       10. The method of  claim 1 , wherein the contextual data indicates other devices that are proximate to the device. 
     
     
       11. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to visual delivery when the contextual data indicates that there is a television within a threshold viewing distance of the user. 
     
     
       12. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to tactile delivery when the contextual data indicates that there is a tactile device within a threshold tactile distance of the user. 
     
     
       13. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to audio delivery when the contextual data indicates that there is a speaker within a threshold auditory distance of the user. 
     
     
       14. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter based on a time of day. 
     
     
       15. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter based on a location of the device. 
     
     
       16. The method of  claim 1 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter based on a user preference for a particular content delivery medium. 
     
     
       17. The method of  claim 1 , further comprising converting a first media content item from a first content delivery medium to a second content delivery medium based on the contextual data. 
     
     
       18. A non-transitory memory storing one or more programs, which, when executed by one or more processors of a device, cause the device to:
 obtain a user focus indicator value that is associated with a subject; 
 determine a plurality of filter values for a respective set of media content filters based on the subject and contextual data associated with the device or a user of the device, wherein the contextual data indicates a learning style of the user and the plurality of filter values includes a value for a content engagement level filter that is set based on the learning style of the user; 
 deliver, based on a first combination of the plurality of filter values, a first set of one or more media content items that are associated with a first combination of content delivery mediums, wherein the first set of one or more media content items is selected from a plurality of media content items that provide information about the subject; and 
 deliver, based on a second combination of the plurality of filter values that is different from the first combination of the plurality of filter values, a second set of one or more media content items that are associated with a second combination of content delivery mediums that is different from the first combination of content delivery mediums, wherein the second set of one or more media content items is selected from the plurality of media content items, wherein the second set is different from the first set. 
 
     
     
       19. A device comprising:
 one or more processors; 
 a non-transitory memory; 
 one or more displays; and 
 one or more programs stored in the non-transitory memory, which, when executed by the one or more processors, cause the device to:
 obtain a user focus indicator value that is associated with a subject; 
 determine a plurality of filter values for a respective set of media content filters based on the subject and contextual data associated with the device or a user of the device wherein the contextual data indicates a learning style of the user and the plurality of filter values includes a value for a content engagement level filter that is set based on the learning style of the user; 
 deliver, based on a first combination of the plurality of filter values, a first set of one or more media content items that are associated with a first combination of content delivery mediums, wherein the first set of one or more media content items is selected from a plurality of media content items that provide information about the subject; and 
 deliver, based on a second combination of the plurality of filter values that is different from the first combination of the plurality of filter values, a second set of one or more media content items that are associated with a second combination of content delivery mediums that is different from the first combination of content delivery mediums, wherein the second set of one or more media content items is selected from the plurality of media content items, wherein the second set is different from the first set. 
 
 
     
     
       20. The non-transitory memory of  claim 18 , wherein the first combination of content delivery mediums includes a first combination of visual delivery, audio delivery or tactile delivery, and the second combination of content delivery mediums includes a second combination of visual delivery, audio delivery or tactile delivery. 
     
     
       21. The non-transitory memory of  claim 18 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to visual delivery when the learning style of the user corresponds to a visual learner. 
     
     
       22. The non-transitory memory of  claim 18 , wherein determining the plurality of filter values comprises setting the value of the content engagement level filter to a value that is less than a threshold engagement level when the learning style of the user corresponds to a passive learner. 
     
     
       23. The non-transitory memory of  claim 18 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to visual delivery when the contextual data indicates that there is a television within a threshold viewing distance of the user. 
     
     
       24. The non-transitory memory of  claim 18 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter based on a location of the device. 
     
     
       25. The device of  claim 19 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to audio delivery when the learning style of the user corresponds to an auditory learner. 
     
     
       26. The device of  claim 19 , wherein determining the plurality of filter values comprises setting the value of the content engagement level filter to a value that is greater than a threshold engagement level when the learning style of the user corresponds to an active learner. 
     
     
       27. The device of  claim 19 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter to audio delivery when the contextual data indicates that there is a speaker within a threshold auditory distance of the user. 
     
     
       28. The device of  claim 19 , wherein determining the plurality of filter values comprises setting a value of a content delivery medium filter based on a time of day. 
     
     
       29. The device of  claim 19 , wherein the one or more programs further cause the device to convert a first media content item from a first content delivery medium to a second content delivery medium based on the contextual data.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Patent App. No. 63/032,301, filed on May 29, 2020, which is incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to adaptive content delivery. 
     BACKGROUND 
     Content delivery platforms deliver media content items to users of various devices. Some content delivery platforms store various media content items. A user has to search for a desired media content item and request delivery of the desired media content item upon finding the desired media content item. Searching for the desired media content item can sometimes be resource-intensive and time-consuming. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIGS. 1A-1G  are diagrams of an example operating environment in accordance with some implementations. 
         FIG. 2  is a block diagram of a content delivery platform in accordance with some implementations. 
         FIG. 3  is a flowchart representation of a method of delivering media content items in accordance with some implementations. 
         FIG. 4  is a block diagram of a device that delivers media content items in accordance with some implementations. 
         FIGS. 5A-5F  are diagrams of another example operating environment in accordance with some implementations. 
         FIG. 6  is a block diagram of a content curation engine in accordance with some implementations. 
         FIG. 7  is a flowchart representation of a method of curating content in accordance with some implementations. 
         FIG. 8  is a block diagram of a device that curates content in accordance with some implementations. 
         FIGS. 9A-9F  are diagrams of another example operating environment in accordance with some implementations. 
         FIG. 10  is a block diagram of a content updating engine in accordance with some implementations. 
         FIG. 11  is a flowchart representation of a method of updating a media content item in accordance with some implementations. 
         FIG. 12  is a block diagram of a device that updates a media content item 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 delivering media content items. In various implementations, a device includes a display, a non-transitory memory and one or more processors coupled with the display and the non-transitory memory. In some implementations, a method includes obtaining a user focus indicator value that is associated with a subject. In some implementations, the method includes determining a plurality of filter values for a respective set of media content filters based on the subject and contextual data associated with the device or a user of the device. In some implementations, the method includes delivering, based on a first combination of the plurality of filter values, a first set of one or more media content items that is associated with a first combination of content delivery mediums. In some implementations, the first set of one or more media content items is selected from a plurality of media content items that provide information about the subject. In some implementations, the method includes delivering, based on a second combination of the plurality of filter values that is different from the first combination, a second set of one or more media content items that are associated with a second combination of content delivery mediums that is different from the first combination of content delivery mediums. In some implementations, the second set of one or more media content items are selected from the plurality of media content items. In some implementations, the second set is different from the first set. 
     Various implementations disclosed herein include devices, systems, and methods for curating content that provides information regarding a subject. In various implementations, a device includes a display, a non-transitory memory and one or more processors coupled with the display and the non-transitory memory. In some implementations, a method includes obtaining a user focus indicator value that is associated with a particular subject. In some implementations, the method includes identifying, based on the user focus indicator value, a first media content item that includes information about a first plurality of subjects including a first primary subject that matches the particular subject associated with the user focus indicator value. In some implementations, the first media content item is associated with a first content delivery medium. In some implementations, the method includes identifying a second media content item that includes a second plurality of subjects including a second primary subject. In some implementations, the second plurality of subjects includes the particular subject associated with the user focus indicator value and the second media content item is associated with a second content delivery medium that is different from the first content delivery medium. In some implementations, the method includes synthesizing a third media content item based on a portion of the information included in the first media content item and a portion of the information included in the second media content item. In some implementations, the third media content item is associated with a combination of the first content delivery medium and the second content delivery medium. 
     Various implementations disclosed herein include devices, systems, and methods for updating a media content item. In various implementations, a device includes a display, a non-transitory memory and one or more processors coupled with the display and the non-transitory memory. In some implementations, a method includes receiving a selection request for a first media content item that includes information about a subject. In some implementations, the first media content item is generated by a first media content provider. In some implementations, the method includes identifying a set of media content items that provides information about the subject. In some implementations, at least a portion of the set of media content items is generated by a second media content provider that is different from the first media content provider. In some implementations, the method includes updating the first media content item based on the information provided by the set of media content items in order to generate an updated first media content item. In some implementations, the method includes presenting the updated first media content item. 
     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. 
     Previously available content delivery platforms tend to present content in a manner that results in a uniform user experience. For example, if a user watched a video regarding the American Civil War (“Civil War”, hereinafter for the sake of brevity), then a previously available content delivery platform may recommend similar videos regarding the Civil War. Some previously available content delivery platforms include a recommendation engine that tends to recommend content that is delivered via a particular delivery medium (e.g., a video delivery platform recommends videos) and does not recommend content that is delivered via other delivery mediums (e.g., the video delivery platform does not recommend podcasts, songs or braille). As such, some content delivery platforms fail to recommend content that is tailored to a user&#39;s context. 
     While the uniform user experience provided by previously available content delivery platforms provides predictability, the user experience is sometimes inappropriate. For example, presenting a documentary about the Civil War on a Sunday afternoon when the user has less energy and more receptive to passive learning that requires little or no user engagement may be appropriate. However, presenting the documentary about the Civil War during a Monday morning commute may be inappropriate because it may be unsafe for the user to watch a video while driving. Additionally or alternatively, the user may be more receptive to active learning that requires more user engagement than passive learning or is cognitively more demanding than passive learning (e.g., listening to a discussion of the documentary instead of watching another documentary). As such, sometimes the user is unable to engage with recommended content due to the restrictive recommendations of previously available content delivery platforms. 
     The present disclosure provides methods, systems, and/or devices for selecting media content items based on a number of context variables in order to deliver a user experience that is contextually relevant. A content delivery platform determines respective filter values for media content filters based on the context variables. The media content filters can include a content delivery medium filter, a content engagement level filter, a content complexity filter, a content duration filter and/or a content rating filter. 
     The content delivery platform can determine filter values based on user characteristics such as a learning style of a user and a current state of the user. The learning style can indicate whether the user is a visual learner, an auditory learner, a passive learner or an active learner. The current state of the user can include a health state of the user (e.g., tired vs. energetic), a dietary intake of the user (e.g., recent caloric input) and/or sleep history of the user (e.g., slept 8 hours vs. slept 4 hours). The current state of the user can also indicate whether the user is under stress or sick. The content delivery platform can determine filter values based on devices that are proximate to the user (e.g., select high definition (HD) movie when there is an HDTV near the user). 
     Different filter values result in different media content items being selected thereby resulting in different user experiences for the same subject. Filter values based on a first state of the user (e.g., low energy or lack of alertness) result in the selection of a first media content item. For example, the content delivery platform presents a documentary related to the Civil War when contextual data indicates that the user has a passive learning style, the user is tired, the user recently had an eight hundred calorie meal, and there is a TV near the user. By contrast, filter values based on a second state of the user (e.g., energetic or alert) result in the selection of a second media content item. For example, the content delivery platform presents a Civil War discussion between history scholars when the contextual data indicates that the user has a passive learning style, the user is alert, the user had his/her morning coffee, and there is a speaker near the user. 
     Utilizing contextual data to filter media content items results in the selection and presentation of contextually-relevant media content items thereby enhancing a user experience of the user. Filtering media content items based on contextual data allows a device to present media content items that the user is more likely to engage with thereby enhancing a user experience of the user. Determining filter values based on the contextual data reduces a need for the user to provide user inputs that correspond to manually setting the filter values. Reducing user inputs tends to prolong a battery of a battery-operated device thereby improving an operability of the device. 
       FIG. 1A  is a block diagram of an example operating environment  10  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  10  includes an electronic device  30 , a content delivery platform  100  and a media content datastore  150 . In some implementations, the electronic device  30  includes a handheld computing device that can be held by a user  20 . For example, in some implementations, the electronic device  30  includes a smartphone, a tablet, a media player, a laptop, or the like. In some implementations, the electronic device  30  includes a wearable computing device that can be worn by the user  20 . For example, in some implementations, the electronic device  30  includes a head-mountable device (HMD), an electronic watch or a pair of headphones. 
     Although the content delivery platform  100  is shown as being separate from the electronic device  30 . In some implementations, the content delivery platform  100  is integrated into the electronic device  30 . Although the media content datastore  150  is shown as being separate from the content delivery platform  100 . In some implementations, the media content datastore  150  is integrated into the content delivery platform  100 . In some implementations, the content delivery platform  100  and/or the media content datastore  150  reside at the electronic device  30 . 
     In various implementations, the media content datastore  150  stores various media content items  152 . A media content item  152  (e.g., each media content item  152 ) provides information regarding a subject  154 . For example, a subject  154  of a particular media content item  152  may include the Civil War. In some implementations, a media content item  152  includes information regarding a primary subject and information regarding other secondary subjects. For example, a primary subject of a particular media content item  152  may include the Civil War and a secondary subject of the media content item  152  may include musket. 
     In various implementations, a media content item  152  (e.g., each media content item  152 ) is associated with a content delivery medium  156  (“delivery medium  156 ”, hereinafter for the sake of brevity). In some implementations, the media content item  152  provides information regarding the subject  154  via the delivery medium  156  associated with the media content item  152 . An example of the delivery medium  156  includes visual delivery such as a video, text such as an ebook, a research paper and a blog article, and a graphic such as an image, a pie chart and a bar chart. Another example of the delivery medium  156  includes audio delivery such as a podcast, a song, a radio station and audio portion of a video. Another example of the delivery medium includes tactile delivery such as a set of haptic feedbacks, for example, braille. 
     In various implementations, a media content item  152  (e.g., each media content item  152 ) is associated with a content engagement level  158  (“engagement level  158 ”, hereinafter for the sake of brevity). In some implementations, the engagement level  158  indicates an amount of user interaction that the media content item  152  induces. For example, in some implementations, the engagement level  158  indicates whether or not the media content item  152  requires the user  20  to participate (e.g., by answering questions posed in the media content item  152 ). In some implementations, if the engagement level  158  of the media content item  152  has a value that is below a threshold engagement level (e.g., the value corresponds to low engagement), then an amount of user interaction that the media content item  152  induces is below a threshold user interaction (e.g., the media content item  152  does not require the user  20  to participate). In some implementations, if the engagement level  158  of the media content item  152  has a value that is above the threshold engagement level (e.g., the value corresponds to high engagement), then an amount of user interaction that the media content item  152  induces is above the threshold user interaction (e.g., the media content item  152  requires the user  20  to participate, for example, by answering questions or performing an accompanying activity). 
     In various implementations, a media content item  152  (e.g., each media content item  152 ) is associated with a content complexity  160  (“complexity  160 ”, hereinafter for the sake of brevity). In some implementations, the complexity  160  indicates an amount of cognition associated with the media content item  152  (e.g., an amount of cognition required to comprehend the information provided by the media content item  152 ). In some implementations, the complexity  160  indicates an amount of cognitive brain power that the user  20  has to expend in order to comprehend the media content item  152 . In some implementations, if the complexity  160  of the media content item  152  has a value that is below a threshold complexity (e.g., the value corresponds to low complexity), then an amount of cognitive brain power that the user  20  has to expend to comprehend the media content item  152  is below a threshold cognitive brain power (e.g., the user  20  can likely comprehend the information provided by the media content item  152  with relative ease). In some implementations, if the complexity  160  of the media content item  152  has a value that is above the threshold complexity (e.g., the value corresponds to high complexity), then an amount of cognitive brain power that the user  20  has to expend to comprehend the media content item  152  is above the threshold cognitive brain power (e.g., the user  20  may have some difficulty in comprehending the information provided by the media content item  152 ). 
     In various implementations, a media content item  152  (e.g., each media content item  152 ) is associated with a content duration  162  (“duration  162 ”, hereinafter for the sake of brevity). In some implementations, the duration  162  indicates a time duration of the media content item  152 . For example, if the media content item  152  is a video, then the duration  162  indicates a length of the video. In some implementations, the duration  162  indicates an amount of time that the user  20  may require to comprehend the information provided by the media content item  152 . For example, if the media content item  152  is a graphic (e.g., an image or a chart), then the duration  162  may indicate an amount of time that the user  20  may require to understand the information provided by the graphic. 
     In various implementations, a media content item  152  (e.g., each media content item  152 ) is associated with a content rating  164  (“rating  164 ”, hereinafter for the sake of brevity). In some implementations, the rating  164  indicates an age group that the media content item  152  is suitable for. In some implementations, the rating  164  has a value that indicates that the media content item  152  is suitable for all audiences (e.g., the rating  164  has a value of General (G)). In some implementations, the rating  164  has a value that indicates that portions of the media content item  152  may not be suitable for children (e.g., the rating  164  has a value of Parental Guidance suggested (PG)). In some implementations, the rating  164  has a value that indicates that the media content item  152  contains some adult material (e.g., the rating  164  has a value of Restricted (R)). In some implementations, a value of the rating  164  is set in accordance with the Motion Picture Associated of America (MPAA) rating system. 
     In various implementations, the content delivery platform  100  obtains a user focus indicator value  170  that indicates a subject that the user  20  is interested in learning about. In some implementations, the content delivery platform  100  receives the user focus indicator value  170  from the electronic device  30 . For example, in some implementations, the user  20  specifies the user focus indicator value  170 . In some implementations, the content delivery platform  100  determines the user focus indicator value  170  based on the subject  154  of media content items  152  that the electronic device  30  has previously presented to the user  20  within a threshold amount of time (e.g., within the last 24 hours). 
     In some implementations, the content delivery platform  100  obtains contextual data  174  that is associated with the electronic device  30  or with the user  20  of the electronic device  30 . In some implementations, the content delivery platform  100  receives the contextual data  174  from the electronic device  30 . In some implementations, the contextual data  174  includes sensor data that is captured by sensors of the electronic device  30 . For example, in some implementations, the contextual data  174  includes a location of the electronic device  30 . In some implementations, the contextual data  174  indicates which other devices are proximate to the electronic device  30 . 
     In some implementations, the contextual data  174  indicates user characteristics of the user  20 . For example, in some implementations, the contextual data  174  indicates a learning style of the user  20  (e.g., the contextual data  174  indicates whether the user  20  is a visual learner, an auditory learner, an active learner, a passive learner, etc.). 
     A visual learner generally refers to a person that prefers to learn information regarding a subject by viewing visual content such as images or videos. As such, in various implementations, a visual learner tends to prefer media content items  152  in which the delivery medium  156  corresponds to visual delivery. For example, a visual learner tends to prefer media content items  152  that include images or videos. 
     An auditory learner generally refers to a person that prefers to learn information regarding a subject by listening to auditory content such as podcasts, audiobooks or music. As such, in various implementations, an auditory learner tends to prefer media content items  152  in which the delivery medium  156  corresponds to aural delivery. For example, an auditory learner tends to prefer media content items  152  that include podcasts, audiobooks, music, etc. 
     An active learner generally refers to a person that prefers to learn information regarding a subject by actively participating in an activity that leads to discovery of the information. For example, an active learner may prefer to learn information regarding a subject by answering questions. An active learner may refer to a person that enjoys hands-on learning (e.g., a person that learns by performing a physical activity). In some implementations, an active learner is referred to as a kinesthetic learner. As such, in various implementations, an active learner tends to prefer media content items  152  in which the engagement level  158  is greater than a threshold engagement level (e.g., media content items  152  that require a relatively high level of engagement). 
     A passive learner generally refers to a person that prefers to learn information regarding a subject via passive participation, for example, instead of active participation. For example, a passive learner may prefer to watch or listen to a media content item  152  without having to answer questions or perform a physical activity to discover the information provided by the media content item  152 . A passive learner may refer to a person that does not enjoy hands-on learning. As such, in various implementations, a passive learner tends to prefer media content items  152  in which the engagement level  158  is less than the threshold engagement level (e.g., media content items  152  that require a relatively low level of engagement). 
     In some implementations, the contextual data  174  indicates a current state of the user  20  (e.g., a current health state of the user  20 ). For example, in some implementations, the contextual data  174  indicates an energy level of the user (e.g., whether the user is tired or fresh). In some implementations, the contextual data  174  indicates an alertness level of the user (e.g., whether the user has had his/her morning coffee). In some implementations, the contextual data  174  indicates a drowsiness level of the user (e.g., whether the user is sleepy or wide awake). 
     In various implementations, the content delivery platform  100  determines the current state of the user  20  based on sensor data from a set of one or more sensors in the electronic device  30  (e.g., an image sensor, a heart rate sensor, etc.). In some implementations, the content delivery platform  100  determines the current state of the user  20  by performing facial analysis on a facial image of the user  20  captured by the electronic device  30 . For example, the content delivery platform  100  may determine that the user  20  is sleepy if the user&#39;s eyes are open less than a threshold amount (e.g., the user&#39;s eyes appear droopy). In some implementations, the content delivery platform  100  determines the current state of the user  20  based on a heart rate of the user. For example, the content delivery platform  100  may determine that the user is under stress when an average heart rate of the user  20  is greater than a threshold heart rate. In some implementations, the content delivery platform  100  obtains a user input that indicates the current state of the user  20  (e.g., the user  20  specifies whether the user  20  is tired, energetic, alert, sleepy, etc.). 
     In various implementations, the content delivery platform  100  utilizes a set of one or more media content filters  110  to filter the media content items  152  and generate a set  180  of one or more media content items  152 . The content delivery platform  100  delivers (e.g., transmits) the set  180  to the electronic device  30 . In various implementations, the content delivery platform  100  determines filter values  130  for the media content filters  110  based on the user focus indicator value  170  and the contextual data  174 . 
     In various implementations, the media content filters  110  include a content subject filter  111  (“subject filter  111 ”, hereinafter for the sake of brevity). In some implementations, the content delivery platform  100  determines a filter value  130  for the subject filter  111  based on the user focus indicator value  170 . For example, if the user focus indicator value  170  indicates that the user  20  is interested in learning about the Civil War, then the content delivery platform  100  sets a filter value  130  of the subject filter  111  to the Civil War. 
     In various implementations, the media content filters  110  include a content delivery medium filter  112  (“delivery medium filter  112 ”, hereinafter for the sake of brevity). In some implementations, the content delivery platform  100  determines a filter value  130  for the delivery medium filter  112  based on the contextual data  174 . In some implementations, the content delivery platform  100  set a filter value  130  of the delivery medium filter  112  to visual delivery when the contextual data  174  indicates that the user  20  is a visual learner. In some implementations, the content delivery platform  100  sets a filter value  130  of the delivery medium filter  112  to audio delivery when the contextual data  174  indicates that the user  20  is performing a task such as driving and cannot look at visual content. In some implementations, the content delivery platform  100  set a filter value  130  of the delivery medium filter  112  to tactile delivery when the contextual data  174  indicates that the electronic device  30  is in an accessibility mode. Controlling the delivery medium  156  of the set  180  of media content items  152  based on the contextual data  174  increases a likelihood of the user  20  engaging with the set  180  of media content items  152  thereby enhancing a user experience provided by the electronic device  30 . 
     In various implementations, the media content filters  110  include a content engagement level filter  114  (“engagement level filter  114 ”, hereinafter for the sake of brevity). In some implementations, the content delivery platform  100  determines a filter value  130  for the engagement level filter  114  based on the contextual data  174 . In some implementations, the content delivery platform  100  sets a filter value  130  of the engagement level filter  114  to a value that is below a threshold engagement level (e.g., to a relatively low engagement level) when the contextual data  174  indicates that the user  20  is a passive learner (e.g., when the contextual data  174  indicates that the user  20  does not like to participate while learning). Controlling the engagement level  158  of the set  180  of media content items  152  based on the contextual data  174  increases a likelihood of the set  180  of media content items  152  matching a current state of the user  20  thereby enhancing a user experience provided by the electronic device  30 . 
     In various implementations, the content delivery platform  100  sets a filter value  130  of the engagement level filter  114  based on a current state (e.g., a current health state) of the user  20 . In some implementations, the content delivery platform  100  sets a filter value  130  of the engagement level filter  114  to a value that is above the threshold engagement level (e.g., to a relatively high engagement level) when the contextual data  174  indicates that an energy level of the user  20  is above a threshold energy level (e.g., when the contextual data  174  indicates that the user  20  has sufficient energy to participate, for example, answer questions). In some implementations, the content delivery platform  100  sets the filter value  130  of the engagement level filter  114  to a value that is below the threshold engagement level (e.g., to a relatively low engagement level) when the contextual data  174  indicates that the energy level of the user  20  is below the threshold energy level (e.g., when the contextual data  174  indicates that the user  20  is tired and does not have sufficient energy to participate, for example, answer questions). 
     In various implementations, the media content filters  110  include a content complexity filter  116  (“complexity filter  116 ”, hereinafter for the sake of brevity). In some implementations, the content delivery platform  100  determines a filter value  130  for the complexity filter  116  based on the contextual data  174 . In some implementations, the content delivery platform  100  sets a filter value  130  of the complexity filter  116  to a value that is below a threshold complexity (e.g., to a relatively low complexity) when the contextual data  174  indicates that the user  20  is tired or drowsy. In some implementations, the content delivery platform  100  sets a filter value  130  of the complexity filter  116  to a value that is above the threshold complexity (e.g., to a relatively high complexity) when the contextual data  174  indicates that the user  20  is alert or wide awake. Controlling the complexity  160  of the set  180  of media content items  152  based on the contextual data  174  increases a likelihood of the user  20  comprehending the information provided by the set  180  of media content items  152  thereby enhancing a user experience provided by the electronic device  30 . 
     In various implementations, the media content filters  110  include a content duration filter  118  (“duration filter  118 ”, hereinafter for the sake of brevity). In some implementations, the content delivery platform  100  determines a filter value  130  for the duration filter  118  based on the contextual data  174 . In some implementations, the content delivery platform  100  sets a filter value  130  of the duration filter  118  based on an amount of time between a current time and a start time of an upcoming calendar event. For example, if the upcoming calendar event is in 45 minutes, then the content delivery platform  100  sets the filter value  130  of the duration filter  118  to less than 30 minutes. In some implementations, the content delivery platform  100  sets a filter value  130  of the duration filter  118  based on a current health state of the user  20 . For example, if the user  20  appears drowsy, then the content delivery platform  100  sets the filter value  130  of the duration filter  118  based on an average amount of time that the user  20  takes to fall asleep. For example, if the user  20  falls asleep within 20 minutes on average, then the content delivery platform  100  sets the filter value  130  of the duration filter  118  to a value between 15 minutes and 30 minutes. Controlling the duration  162  of the set  180  of media content items  152  based on the contextual data  174  reduces a likelihood of the electronic device  30  playing the set  180  of media content items  152  when the user  20  is not engaging with the set of 180 of media content items  152  thereby reducing a battery consumption of the electronic device  30  and improving an operability of the electronic device  30 . 
     In various implementations, the media content filters  110  include a content rating filter  120  (“rating filter  120 ”, hereinafter for the sake of brevity). In some implementations, the content delivery platform  100  determines a filter value  130  for the rating filter  120  based on the contextual data  174 . In some implementations, the content delivery platform  100  sets a filter value  130  of the rating filter  120  based on an estimated age of the user  20 . For example, if the estimated age of the user  20  is less than 10 years, then the content delivery platform  100  sets the filter value  130  of the rating filter  120  to a value that corresponds to a rating that is suitable for all audiences (e.g., General (G)). In some implementations, the content delivery platform  100  sets a filter value  130  of the rating filter  120  based on an estimated age of a person within a threshold distance of the user  20 . For example, if the user  20  is near his/her child, then the content delivery platform  100  sets the filter value to a rating that is suitable for kids (e.g., G). Controlling the rating  164  of the set  180  of media content items  152  based on the contextual data  174  increases a likelihood of the set  180  of the media content items  152  being relevant and/or age-appropriate to the user  20  and/or to a person near the user  20 . 
     In various implementations, the content delivery platform  100  generates the set  180  of media content items  152  by retrieving media content items  152  that satisfy the filter values  130  for the media content filters  110 . In some implementations, the content delivery platform  100  filters out media content items  152  that do not satisfy the filter values  130  of the media content filters  110 . As such, the content delivery platform  100  forgoes including media content items  152  that breach the filter values  130  of the media content filters  110  into the set  180 . In various implementations, the content delivery platform  100  delivers (e.g., transmits) the set  180  of the media content items  152  to the electronic device  30 . 
     In the example of  FIG. 1B , the media content datastore  150  includes a first media content item  152 - 1 , a second media content item  152 - 2 , . . . , and a fifteenth media content item  152 - 15 . Each media content item  152  in the media content datastore  150  is associated with a content delivery medium value (“delivery medium value”, hereinafter for the sake of brevity). The first media content item  152 - 1  has a first delivery medium value  156 - 1  indicating that the first media content item  152 - 1  includes audio. For example, the first media content item  152 - 1  may include an audiobook, a podcast or a song. The second media content item  152 - 2  has a second delivery medium value  156 - 2  indicating that the second media content item  152 - 2  includes video content. For example, the second media content item  152 - 2  is a video clip, a documentary or a movie. The third media content item  152 - 3  has a third delivery medium value  156 - 3  that corresponds to an image. For example, the third media content item  152 - 3  includes a picture. The fourth media content item  152 - 4  has a fourth delivery medium value  156 - 4  that corresponds to tactile delivery. For example, the fourth media content item  152 - 4  includes haptic content (e.g., vibrational responses, pneumatic responses or braille-style content). 
     The fifth media content item  152 - 5  has a fifth delivery medium value  156 - 5  that corresponds to video. The sixth media content item  152 - 6  has a sixth delivery medium value  156 - 6  that corresponds to a bar chart. The seventh media content item  152 - 7  has a seventh delivery medium value  156 - 7  indicating that the seventh media content item  152 - 7  is a podcast. The eighth media content item  152 - 8  has an eighth delivery medium value  156 - 8  indicating that the eighth media content item  152 - 8  is a video. The ninth media content item  152 - 9  has a ninth delivery medium value  156 - 9  indicating that the ninth media content item  152 - 9  includes audio content. 
     The tenth media content item  152 - 10  has a tenth delivery medium value  156 - 10  indicating that the tenth media content item  152 - 10  is a video. The eleventh media content item  152 - 11  has an eleventh delivery medium value  156 - 11  indicating that the eleventh media content item  152 - 11  is a video. The twelfth media content item  152 - 12  has a twelfth delivery medium value  156 - 12  indicating that the twelfth media content item  152 - 12  is an image. The thirteenth media content item  152 - 13  has a thirteenth delivery medium value  156 - 13  indicating that the thirteenth media content item  152 - 13  is a song. The fourteenth media content item  152 - 14  has a fourteenth delivery medium value  156 - 14  indicating that the fourteenth media content item  152 - 14  is a video. The fifteenth media content item  152 - 15  has a fifteenth delivery medium value  156 - 15  indicating that the fifteenth media content item  152 - 15  is an ebook. 
     In the example of  FIG. 1B , the contextual data  174  indicates that a learning style  174   a  of the user  20  corresponds to a visual learner. The content delivery platform  100  sets a value of the delivery medium filter  112  to video and graphic based on the learning style  174   a  indicating that the user  20  is a visual learner. Since the user  20  is a visual learner, setting the value of the delivery medium filter  112  to video and graphic filters out audio content such as audiobooks and textual context such as ebooks. The content delivery platform  100  filters out the first media content item  152 - 1  (as indicated by the strike-through) because the first media content item  152 - 1  is audio content and not a video or a graphic. The content delivery platform  100  filters out the fourth media content item  152 - 4  (as indicated by the strike-through) because the fourth media content item  152 - 4  is tactile content and not a video or a graphic. The content delivery platform  100  filters out the seventh media content item  152 - 7  (as indicated by the strike-through) because the seventh media content item  152 - 7  is a podcast and not a video or a graphic. The content delivery platform  100  filters out the ninth media content item  152 - 9  (as indicated by the strike-through) because the ninth media content item  152 - 9  is an audio and not a video or a graphic. The content delivery platform  100  filters out the thirteenth media content item  152 - 13  (as indicated by the strike-through) because the thirteenth media content item  152 - 13  is a song and not a video or a graphic. The content delivery platform  100  filters out the fifteenth media content item  152 - 15  (as indicated by the strike-through) because the fifteenth media content item  152 - 15  is an ebook and not a video or a graphic. In the example of  FIG. 1B , the set  180  of media content items  152  includes the second media content item  152 - 2 , the third media content item  152 - 3 , the fifth media content item  152 - 5 , the sixth media content item  152 - 6 , the eighth media content item  152 - 8 , the tenth media content item  152 - 10 , the eleventh media content item  152 - 11 , the twelfth media content item  152 - 12 , and the fourteenth media content item  152 - 14 . 
     In the example of  FIG. 1C , the contextual data  174  further indicates that an energy level  174   b  of the user  20  is low. In some implementations, the energy level  174   b  includes a value that is below a threshold energy value. In some implementations, the energy level  174   b  includes a biological measurement of the user  20 , for example, a blood glucose measurement of the user  20 . In some implementations, the blood glucose measurement of the user  20  is below a threshold blood glucose measurement. In some implementations, the contextual data  174  indicates a body pose of the user  20 , and the content delivery platform  100  determines the energy level  174   b  based on the body pose. For example, if the body pose of the user  20  indicates that the user  20  is slouching, then the content delivery platform  100  determines that an energy level of the user  20  is below a threshold energy level (e.g., the user  20  has low energy). By contrast, if the body pose of the user  20  indicates that the user  20  is sitting up straight, then the content delivery platform  100  determines that an energy level of the user  20  is greater than the threshold energy level (e.g., the user  20  has high energy). In some implementations, the contextual data  174  includes a set of facial images of the user  20 , and the content delivery platform  100  determines the energy level  174   b  based on the set of facial images. For example, if the facial images indicate that the user  20  is closing his/her eyes more frequently than a threshold rate for closing eyes, then the content delivery platform  100  determines that the user  20  has low energy. 
     In some implementations, each media content item  152  in the media content datastore  150  is associated with an engagement level value. The second media content item  152 - 2  has an engagement level value  158 - 2  indicating that the second media content item  152 - 2  likely requires active participation from the user  20  (e.g., the second media content item  152 - 2  requires the user  20  to answer questions or to think critically). Similar to the second media content item  152 - 2 , the tenth media content item  152 - 10  has an engagement level value  158 - 10  indicating that the tenth media content item  152 - 10  likely requires active participation from the user  20 . 
     The third media content item  152 - 3  has an engagement level value  158 - 3  indicating that the third media content item  152 - 3  is suitable for passive consumption (e.g., the third media content item  152 - 3  does not require active participation from the user  20 , for example, the third media content item  152 - 3  does not require the user  20  to answer questions or to think critically). Similar to the third media content item  152 - 3 , the fifth media content item  152 - 5 , the sixth media content item  152 - 6 , the eighth media content item  152 - 8 , the eleventh media content item  152 - 11 , the twelfth media content item  152 - 12  and the fourteenth media content item  152 - 14  have engagement level values  158 - 3 ,  158 - 5 ,  158 - 6 ,  158 - 8 ,  158 - 11 ,  158 - 12  and  158 - 14 , respectively, indicative of delivering information in a passive manner (e.g., not requiring the user  20  to answer questions or to think critically). 
     Since the energy level  174   b  of the user  20  is low, the content delivery platform  100  sets a value of the engagement level filter  114  to passive content. As such, the content delivery platform  100  filters out the second media content item  152 - 2  and the tenth media content item  152 - 10  (as indicated by the strike-through) because of the requisite active participation. In the example of  FIG. 1C , the set  180  of media content items  152  includes the third media content item  152 - 3 , the fifth media content item  152 - 5 , the sixth media content item  152 - 6 , the eighth media content item  152 - 8 , the eleventh media content item  152 - 11 , the twelfth media content item  152 - 12  and the fourteenth media content item  152 - 14 . 
     In the example of  FIG. 1D , the contextual data  174  further indicates that an alertness level  174   c  of the user  20  is medium (e.g., because, based on sleep tracking data and dietary intake data, the user  20  woke up and had a coffee recently). In some implementations, the alertness level  174   c  includes a value that is between two threshold alertness values. In some implementations, the alertness level  174   c  includes a biological measurement of the user  20 , for example, a global oscillatory power of the user  20  and/or a brain wave being exhibited by the user  20 . In some implementations, the contextual data  174  indicates a body pose of the user  20 , and the content delivery platform  100  determines the alertness level  174   c  based on the body pose. For example, if the body pose of the user  20  indicates that the user  20  is slouching, then the content delivery platform  100  determines that the user  20  is at an alertness level that is between two threshold alertness levels (e.g., a medium alertness level). By contrast, if the body pose of the user  20  indicates that the user  20  is sitting up straight, then the content delivery platform  100  determines that the user  20  is at an alertness level that is greater than a threshold alertness level (e.g., a high alertness level). In some implementations, the contextual data  174  includes a set of facial images of the user  20 , and the content delivery platform  100  determines the alertness level  174   c  based on the set of facial images. For example, if the facial images indicate that the user  20  is closing his/her eyes more frequently than a threshold rate for closing eyes, then the content delivery platform  100  determines that the user  20  is at an alertness level that is below a threshold alertness level (e.g., a low alertness level). 
     In some implementations, each media content item  152  in the media content datastore  150  is associated with a complexity value. The third media content item  152 - 3  has a complexity value  160 - 3  indicating that the third media content item  152 - 3  includes information that is of low complexity (e.g., given the alertness level  174   c  of the user  20 , the user  20  can likely understand the information provided by the third media content item  152 - 3  with relative ease). Similar to the third media content item  152 - 3 , the eleventh media content item  152 - 11  has a complexity value  160 - 11  indicating that the eleventh media content item  152 - 11  includes information that is of low complexity. In some implementations, the complexity values  160 - 3  and  160 - 11  are low because the complexity values  160 - 3  and  160 - 11  are below a threshold complexity value. 
     The fifth media content item  152 - 5  has a complexity value  160 - 5  indicating that the fifth media content item  152 - 5  includes information that is of medium complexity (e.g., given the alertness level  174   c  of the user  20 , the user  20  can likely understand the information provided by the fifth media content item  152 - 5  with some effort). Similar to the fifth media content item  152 - 5 , the eighth media content item  152 - 8  and the twelfth media content item  15 - 12  have complexity values  160 - 8  and  160 - 12 , respectively, indicative of medium complexity information. 
     The sixth media content item  152 - 6  and the fourteenth media content item  152 - 14  have complexity values  160 - 6  and  160 - 14 , respectively, indicative of high complexity information. Given the alertness level  174   c  of the user  20 , the user  20  will likely have great difficulty in understanding the information provided by the sixth media content item  152 - 6  and the fourteenth media content item  152 - 14 . As a result, the content delivery platform  100  filters out the sixth media content item  152 - 6  and the fourteenth media content item  152 - 14  (as indicated by the strike-through). In the example of  FIG. 1D , the set  180  of media content items  152  includes the third media content item  152 - 3 , the fifth media content item  152 - 5 , the eighth media content item  152 - 8 , the eleventh media content item  152 - 11  and the twelfth media content item  152 - 12 . 
     In the example of  FIG. 1E , the contextual data  174  further indicates that a drowsiness level  174   d  of the user  20  is high (e.g., because, based on a set of facial images of the user  20 , eyelids of the user  20  are closed more than a threshold amount of time or the user  20  is yawning more than a threshold number of times). In some implementations, the drowsiness level  174   d  is low because a value of the drowsiness level  174   d  is greater than a threshold drowsiness value. In some implementations, the drowsiness level  174   d  includes a physiological measurement of the user  20 , for example, position of eyelids. 
     In some implementations, each media content item  152  in the media content datastore  150  is associated with a duration value indicating a time length of the media content item  152 . The fifth media content item  152 - 5  has a duration value  162 - 5  of one hour. The eighth media content item  152 - 8  has a duration value  162 - 8  of seven minutes. The eleventh media content item  152 - 11  has a duration value  162 - 11  of eighteen minutes. The third media content item  152 - 3  and the twelfth media content item  152 - 12  have duration values  162 - 3  and  162 - 12 , respectively, that are null because the third media content item  152 - 3  and the twelfth media content item  152 - 12  are images. 
     In the example of  FIG. 1E , the content delivery platform  100  determines that, since the user  20  is very drowsy, the user  20  will likely not be able to watch a media content item  152  that is longer than a threshold amount of time (e.g., thirty minutes). As such, the content delivery platform  100  filters out the fifth media content item  152 - 5  (as indicated by the strike-through). In some implementations, the content delivery platform  100  determines the threshold amount of time based on an estimated amount of time that the user  20  will take to fall asleep (e.g., based on prior sleep patterns and the drowsiness level  174   d ). In the example of  FIG. 1E , the set  180  of media content items  152  includes the third media content item  152 - 3 , the eighth media content item  152 - 8 , the eleventh media content item  152 - 11  and the twelfth media content item  152 - 12 . 
     In the example of  FIG. 1F , the contextual data  174  further indicates that an estimated age  174   e  of the user  20  and/or of a person near the user  20 . In the example of  FIG. 1F , the estimate age  174   e  is between five and eight years. In some implementations, there are multiple people watching the electronic device  30 , and the estimated age  174   e  indicates an age of the youngest person watching the electronic device  30 . For example, if the user  20  is watching the electronic device  30  with his/her child, then the estimated age  174   e  indicates an age of the child. In some implementations, the estimated age  174   e  is determined based on facial features represented in a facial image. 
     In some implementations, each media content item  152  in the media content datastore  150  is associated with a rating value indicating a suitability of the media content item  152  for various ages. The third media content item  152 - 3  has a rating value  164 - 3  of General (G) indicating that the third media content item  152 - 3  is suitable for all ages. The eighth media content item  152 - 8  has a rating value  164 - 8  of General indicating that the eighth media content item  152 - 8  is suitable for all ages. The eleventh media content item  152 - 11  has a rating value  164 - 11  of PG-13 indicating that some information in the eleventh media content item  152 - 11  may be inappropriate for children under thirteen. The twelfth media content item  152 - 12  has a rating value  164 - 12  of Restricted (R) indicating that persons under seventeen require a parent or an adult guardian to accompany them. 
     In the example of  FIG. 1F , the content delivery platform  100  filters out media content items  152  that are not suitable for the estimated age  174   e  of 5-8 years. As such, the content delivery platform  100  filters out the eleventh media content item  152 - 11  and the twelfth media content item  152 - 12  (as indicated by the strike-through). As a result, the set  180  of media content items  152  includes the third media content item  152 - 3  and the eighth media content item  152 - 8 . 
     Referring to  FIG. 1G , in some implementations, the contextual data  174  indicates a current activity  174   f  of the user  20 . In the example of  FIG. 1G , the contextual data  174  indicates that the user  20  is currently driving. Since the user  20  is currently driving, the content delivery platform  100  determines that visual delivery is not suitable. As such, the content delivery platform  100  sets a value of the delivery medium filter  112  to audio or tactile delivery. As such, video content will be filtered out while the current activity  174   f  corresponds to driving. 
     In the example of  FIG. 1G , the content delivery platform  100  filters out the second media content item  152 - 2 , the third media content item  152 - 3 , the fifth media content item  152 - 5 , the sixth media content item  152 - 6 , the eighth media content item  152 - 8 , the tenth media content item  152 - 10 , the eleventh media content item  152 - 11 , the twelfth media content item  152 - 12 , the fourteenth media content item  152 - 14  and the fifteenth media content item  152 - 15  because of their association with visual delivery. As a result, the set  180  of media content items  152  includes the first media content item  152 - 1 , the fourth media content item  152 - 4 , the seventh media content item  152 - 7 , the ninth media content item  152 - 9  and the thirteenth media content item  152 - 13  because of their association with audio delivery or tactile delivery. 
       FIG. 2  is a block diagram of the content delivery platform  100  in accordance with some implementations. In various implementations, the content delivery platform  100  includes a data obtainer  102 , a filter value determiner  104  and a content deliverer  106 . In various implementations, the data obtainer  102  obtains the user focus indicator value  170  and the contextual data  174 . For example, in some implementations, the data obtainer  102  receives the user focus indicator value  170  and the contextual data  174  from the electronic device  30 . The data obtainer  102  provides the user focus indicator value  170  and the contextual data  174  to the filter value determiner  104 . 
     In various implementations, the filter value determiner  104  determines the filter values  130  for the media content filters  110 . In some implementations, the filter value determiner  104  determines the filter values  130  for the media content filters  110  based on the user focus indicator value  170  and the contextual data  174 . In some implementations, the filter value determiner  104  determines a filter value  130  for the subject filter  111  based on the user focus indicator value  170 . For example, the filter value determiner  104  sets a filter value  130  of the subject filter  111  to a subject indicated by the user focus indicator value  170 . 
     In some implementations, the filter value determiner  104  sets a filter value  130  for the delivery medium filter  112  based on the contextual data  174 . In some implementations, the contextual data  174  indicates a user preference for a particular delivery medium. In such implementations, the filter value determiner  104  sets the filter value  130  for the delivery medium filter  112  to the particular delivery medium indicated by the user preference. In some implementations, the contextual data  174  indicates that the user  20  is engaged in an activity such as driving. In such implementations, the filter value determiner  104  sets the filter value  130  of the delivery medium filter  112  so as to exclude visual delivery. For example, the filter value determiner  104  sets the filter value  130  of the delivery medium filter  112  to audio delivery or to tactile delivery. 
     In various implementations, the filter value determiner  104  sets the filter values  130  for the media content filters  110  based on a location of the electronic device  30 . For example, in some implementations, the filter value determiner  104  sets a filter value  130  of the rating filter  120  to General when the electronic device  30  is located in a public place (e.g., on a transit bus). In some implementations, the filter value determiner  104  sets a filter value  130  of the complexity filter  116  to low complexity when the electronic device  30  is moving (e.g., when the user  20  is walking). 
     In various implementations, the filter value determiner  104  sets the filter values  130  for the media content filters  110  based on a time of day. For example, in some implementations, the filter value determiner  104  sets a filter value  130  for the engagement level filter  114  to high in the morning (e.g., when the user is wide awake). By contrast, in some implementations, the filter value determiner  104  sets the filter value  130  of the engagement level filter  114  to low at night (e.g., the when the user is tired and sleepy). 
     In various implementations, the filter value determiner  104  sets the filter values  130  for the media content filters  110  based on a type of another device that is near the electronic device  30 . For example, in some implementations, the filter value determiner  104  sets the filter value  130  for the delivery medium filter  112  to High Definition (HD) video when there is TV near the electronic device  30 . In some implementations, the filter value determiner  104  sets the filter value  130  for the delivery medium filter  112  to a radio station when there is an audio-only device (e.g., a wireless speaker or a device configured with a virtual assistant) near the electronic device  30 . 
     In various implementations, the filter value determiner  104  sets the filter values  130  for the media content filters  110  based on a learning style of the user  20  indicated by the contextual data  174 . In some implementations, the filter value determiner  104  sets the filter value  130  for the delivery medium filter  112  to visual delivery when a learning style of the user  20  corresponds to a visual learner. In some implementations, the filter value determiner  104  sets the filter value  130  for the engagement level filter  114  to high when the contextual data  174  indicates that the user  20  is an active learner. 
     In various implementations, the filter value determiner  104  sets the filter values  130  for the media content filters  110  based on a current state of the user  20 . For example, in some implementations, the filter value determiner  104  sets the filter value  130  for the complexity filter  116  to low when the current state of the user  20  corresponds to being tired or drowsy. In some implementations, the filter value determiner  104  sets the filter value  130  for the rating filter  120  to Restricted when the user  20  is with his/her spouse and there are no children near the user. 
     In various implementations, the content deliverer  106  generates the set  180  of media content items  152  based on the filter values  130 . In some implementations, the content deliverer  106  filters the media content items  152  in the media content datastore  150  based on the filter values  130 . For example, in some implementations, the content deliverer  106  filters out media content items  152  that breach the filter values  130  (e.g., do not satisfy the filter values  130 ). In some implementations, the content deliverer  106  includes media content items  152  in the set  180  that satisfy the filter values  130 . The content deliverer  106  provides the set  180  of media content items  152  to the electronic device  30 . 
       FIG. 3  is a flowchart representation of a method  300  of delivering media content items. In various implementations, the method  300  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the electronic device  30  and/or the content delivery platform  100  shown in  FIG. 1A ). In some implementations, the method  300  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  300  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  310 , in some implementations, the method  300  includes obtaining a user focus indicator value that is associated with a subject. For example, as shown in  FIG. 1A , the content delivery platform  100  receives the user focus indicator value  170  from the electronic device  30 . In some implementations, the user focus indicator value includes a user interest value that indicates that the user is interested in the subject. 
     As represented by block  310   a , in some implementations, the method  300  includes detecting that the electronic device has presented media content items that relate to the subject within a threshold amount of time. For example, detecting that the electronic device  30  has presented media content items  152  related to the Civil War within the last 2 weeks. As such, in some implementations, the method  300  includes determining the user focus indicator value based on the user&#39;s interaction with other content media items. 
     As represented by block  310   b , in some implementations, the method  300  includes receiving a user input that specifies the subject. In some implementations, the user input specifies that the user is interested in learning about the subject. For example, in some implementations, the user  20  provides a user input, captured by the electronic device  30 , that specifies the subject. 
     As represented by block  320 , in some implementations, the method  300  includes determining a plurality of filter values for a respective set of media content filters based on the subject and contextual data associated with the device or a user of the device. For example, as shown in  FIG. 1A , the content delivery platform  100  determines the filter values  130  for the media content filters  110  based on the contextual data  174 . 
     As represented by block  320   a , in some implementations, the contextual data indicates a learning style of a user of the device. For example, as shown in  FIG. 1B , the contextual data  174  indicates the learning style  174   a  of the user  20 . In some implementations, the learning style indicates that the user is a visual learner, an auditory learner, a reading/writing learner, a kinesthetic learner (e.g., an active learner) or a passive learner. In some implementations, the method  300  includes determining the learning style of the user based on the delivery medium of media content items that the user has accessed in the past. For example, in some implementations, the method  300  includes determining that the user is an auditory learner in response to a number of auditory media content items that the user has listened to being greater than a number of visual media content items that the user has watched. In some implementations, the method  300  includes determining the learning style of the user based on a level of user engagement (e.g., a level of user participation) required by media content items that the user has accessed in the past. For example, in some implementations, the method  300  includes determining that the user is an active learner in response to an average engagement level of media content items that the user has accessed being greater than a threshold engagement level. In some implementations, the method  300  includes determining that the user is a passive learner in response to the average engagement level of the media content items that the user has accessed being less than the threshold engagement level. 
     In some implementations, the method  300  includes setting a value of a content delivery medium filter to visual delivery when the learning style of the user corresponds to a visual learner. For example, as shown in  FIG. 1B , the content delivery platform  100  sets a filter value  130  of the delivery medium filter  112  to video and/or graphic when the learning style  174   a  indicates that the user  20  is a visual learner. Setting the value of the delivery medium filter to visual delivery allows the content delivery platform to deliver media content items that are more suitable to the user&#39;s visual learning style thereby enhancing a user experience of the device. 
     In some implementations, the method  300  includes setting a value of a content delivery medium filter to audio delivery when the learning style of the user corresponds to an auditory learner. Setting the value of the delivery medium filter to audio delivery allows the content delivery platform to deliver media content items that are more suitable to the user&#39;s auditory learning style thereby enhancing a user experience of the device. 
     In some implementations, the method  300  includes setting a value of a content engagement filter to a value that is less than a threshold engagement level when the learning style of the user corresponds to a passive learner. In some implementations, the method  300  includes filtering out media content items that require user participation (e.g., in the form of answering questions). In some implementations, the method  300  includes including delivering media content items that provide information in a passive manner (e.g., displaying a documentary). 
     In some implementations, the method  300  includes setting a value of a content engagement level filter to a value that is greater than a threshold engagement level when the learning style of the user corresponds to an active learner (e.g., a kinesthetic learner, for example, a person who enjoys hands-on learning). In some implementations, the method  300  includes including media content items that provide a hands-on learning experience to the user. In some implementations, the method  300  includes including media content items that require user participation (e.g., in the form of answering questions or performing a real-world activity along with the media content item). 
     As represented by block  320   b , in some implementations, the contextual data indicates other devices that are proximate to the device. In some implementations, the contextual data indicates types of the other devices that are within a threshold distance of the device. 
     In some implementations, the method  300  includes setting a value of a content delivery medium to visual delivery when the contextual data indicates that there is a television within a threshold viewing distance of the user. In some implementations, the method  300  includes setting the value of the content delivery medium to High Definition (HD) video when the user is within a viewing distance of an HD TV. 
     In some implementations, the method  300  includes setting a value of a content delivery medium filter to tactile delivery when the contextual delivery indicates that there is a tactile device within a threshold tactile distance of the user. In some implementations, the method  300  includes setting the value to the content delivery medium filter to vibrational delivery when the user is within a vibration detection distance of a device that vibrates (e.g., when the user is sitting on a vibrating chair). 
     In some implementations, the method  300  includes setting a value of a content delivery medium filter to audio delivery when the contextual data indicates that there is a speaker within a threshold auditory distance of the user. In some implementations, the method  300  includes setting the value of the content delivery medium filter to audio delivery when the user is within a listening distance of a speaker device. 
     As represented by block  320   c , in some implementations, the method  300  includes setting a value of a content delivery medium filter based on a time of day. For example, in some implementations, the method  300  includes setting the value of the content delivery medium filter to audio delivery during a time that corresponds to a commute time (e.g., when the user is likely driving to/from work and cannot view visual content). In some implementations, the method  300  includes setting the value of the content delivery medium filter to video delivery during a time that corresponds to a leisure time (e.g., when the user is likely lounging in his/her family room). 
     In some implementations, the method  300  includes setting a value of a content delivery medium filter based on a location of the device. For example, in some implementations, the method  300  includes setting the value of the content delivery medium filter to audio delivery or tactile delivery when the device is in a state of motion (e.g., when the device is moving at a speed that is greater than a threshold speed, for example, when the user is commuting to/from work). In some implementations, the method  300  includes setting the value of the content delivery medium filter to visual delivery when the device is located at a home of the user. 
     In some implementations, the method  300  includes setting a value of a content delivery medium filter based on a user preference for a particular content delivery medium. For example, in some implementations, the method  300  includes setting the value of the content delivery medium filter to audio delivery when the user prefers audio content. In some examples, the method  300  includes setting the value of the content delivery medium filter to audiobooks when the user prefers listening to audiobooks over reading books or watching movies that are based on books. 
     As represented by block  320   d , in some implementations, the contextual data indicates a current state of the user or the device. In some implementations, the current state includes a current health state of the user, for example, an energy level of the user, a dietary state of the user, a sleep state of the user, etc. 
     In some implementations, the method  300  includes setting a value of a content complexity filter based on a current alertness of the user. For example, as shown in  FIG. 1D , the content delivery platform  100  sets the filter value  130  of the complexity filter  116  to low or medium in response to the alertness level  174   c  of the user  20  being medium. Delivering media content items based on the current alertness of the user improves the user experience of the device by providing content that the user is more likely to comprehend given the current alertness and forgoing presentation of content that is too complex for the user to comprehend given the current alertness of the user. 
     In some implementations, the method  300  includes setting a value of a content duration filter based on a current drowsiness of the user. For example, as shown in  FIG. 1E , the content delivery platform  100  sets a filter value  130  for the duration filter  118  to less than 20 minutes when the drowsiness level  174   d  is high. Controlling a duration of the delivered content tends to reduce a power consumption of the device by forgoing presentation of longer media content items that the user is unlikely to watch after having fallen asleep. 
     In some implementations, the method  300  includes setting a value of a content engagement level filter based on an estimated energy level of the user. For example, as shown in  FIG. 1C , the content delivery platform  100  sets the filter value  130  of the engagement level filter  114  to passive when the energy level  174   b  of the user  20  is low. Controlling an engagement level of the delivered content based on an estimated energy level of the user enhances a user experience of the device by allowing presentation of content that is suitable for the user&#39;s energy level. 
     As represented by block  320   e , in some implementations, the method  300  includes determining the contextual data based on sensor data captured by a set of one or more sensors. In some implementations, the sensor data includes an image of an eye of the user, a heart rate measurement, a blood glucose measurement or a brain wave exhibited by the user. 
     As represented by block  330 , in some implementations, the method  300  includes delivering based on a first combination of the plurality of filter values, a first set of one or more media content items that are associated with a first combination of content delivery mediums. In some implementations, the first set of one or more media content items is selected from a plurality of media content items that provide information about the subject. For example, as shown in  FIG. 1C , the content delivery platform  100  includes the third media content item  152 - 3 , the fifth media content item  152 - 5 , the sixth media content item  152 - 6 , the eighth media content item  152 - 8 , the eleventh media content item  152 - 11 , the twelfth media content item  152 - 12  and the fourteenth media content item  152 - 14  in the set  180  of media content items  152  in response to the delivery medium filter  112  having a value that corresponds to video or graphic content and the engagement level filter  114  having a value that corresponds to passive content. In the example of  FIG. 1C , the selected media content items are associated with visual delivery (e.g., video or graphic). 
     As represented by block  330   a , in some implementations, the first combination of content delivery mediums includes a first combination of visual delivery, audio delivery or tactile delivery, and the second combination of content delivery mediums includes a second combination of visual delivery, audio delivery or tactile delivery. For example, the selected media content items in  FIG. 1C  are associated with visual delivery, whereas the selected media content items in  FIG. 1G  are associated with audio delivery. 
     In some implementations, visual delivery includes displaying text (e.g., an ebook, a research paper or a blog article), a still image (e.g., a picture) or a video (e.g., a movie, a TV show or a documentary). In some implementations, audio delivery includes playing an audiobook, a podcast, a song or an audio portion of a video. In some implementations, tactile delivery includes generating vibrations or providing tactile feedback (e.g., presenting braille-style content). 
     As represented by block  330   b , in some implementations, delivering the first set results in a first user experience, and delivering the second set results in a second user experience that is different from the first user experience. 
     As represented by block  330   c , in some implementations, delivering the first set includes displaying the first set of one or more media content items on the display of the device (e.g., the device itself presents the first set of one or more media content items), and delivering the second set includes displaying the second set of one or more media content items on a second device that is proximate to the device (e.g., the device causes another device to present the second set of one or more media content items, for example, a nearby TV displays the second set of one or more media content items). 
     As represented by block  340 , in some implementations, the method  300  includes delivering, based on a second combination of the plurality of filter values that is different from the first combination, a second set of one or more media content items that are associated with a second combination of content delivery mediums that is different from the first combination of content delivery mediums. In some implementations, the second set of one or more media content items is selected from the plurality of media content items. In some implementations, the second set is different from the first set. For example, as shown in  FIG. 1G , the content delivery platform  100  includes the first media content item  152 - 1 , the fourth media content item  152 - 4 , the seventh media content item  152 - 7 , the ninth media content item  152 - 9  and the thirteenth media content item  152 - 13  in the set  180  of media content items  152  in response to the delivery medium filter  112  having a value that corresponds to audio delivery or tactile delivery. 
     In some implementations, the method  300  includes converting a first media content item from a first content delivery medium to a second content delivery medium based on the contextual data (e.g., converting a written article into an audio clip, a podcast or a video transcript). 
     In some implementations, the first set and the second set include a common media content item. For example, there is some overlap between the first set and the second set. Alternatively, in some implementations, the first set and the second set do not include a common media content item. For example, there is no overlap between the first set and the second set. 
       FIG. 4  is a block diagram of a device  400  that delivers media content items in accordance with some implementations. In some implementations, the device  400  implements the electronic device  30  and/or the content delivery platform  100  shown in  FIG. 1A and/or 2 . 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  400  includes one or more processing units (CPUs)  401 , a network interface  402 , a programming interface  403 , a memory  404 , one or more input/output (I/O) devices  410 , and one or more communication buses  405  for interconnecting these and various other components. 
     In some implementations, the network interface  402  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  405  include circuitry that interconnects and controls communications between system components. The memory  404  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  404  optionally includes one or more storage devices remotely located from the one or more CPUs  401 . The memory  404  comprises a non-transitory computer readable storage medium. 
     In some implementations, the memory  404  or the non-transitory computer readable storage medium of the memory  404  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  406 , the data obtainer  102 , the filter value determiner  104  and the content deliverer  106 . In various implementations, the device  400  performs the method  300  shown in  FIG. 3 . 
     In some implementations, the data obtainer  102  obtains a user focus indicator value (e.g., the user focus indicator value  170  shown in  FIGS. 1A and 2 ) and contextual data (e.g., the contextual data  174  shown in  FIGS. 1A-2 ). In some implementations, the data obtainer  102  performs the operation(s) represented by block  310  in  FIG. 3 . To that end, the data obtainer  102  includes instructions  102   a , and heuristics and metadata  102   b.    
     In some implementations, the filter value determiner  104  determines filter values for one or more media content filters (e.g., the filter values  130  for the media content filters  110  shown in  FIGS. 1A and 2 ). In some implementations, the filter value determiner  104  performs the operations(s) represented by block  320  shown in  FIG. 3 . To that end, the filter value determiner  104  includes instructions  104   a , and heuristics and metadata  104   b.    
     In some implementations, the content deliverer  106  delivers a set of one or more media content items that the content deliverer  106  selects based on the filter values determined by the filter value determiner  104  (e.g., the set  180  shown in  FIGS. 1A and 2 ). In some implementations, the content deliverer  106  performs the operation(s) represented by blocks  330  and  340  shown in  FIG. 3 . To that end, the content deliverer  106  includes instructions  106   a , and heuristics and metadata  106   b.    
     In some implementations, the one or more I/O devices  410  include one or more sensors for capturing contextual data (e.g., the contextual data  174  shown in  FIGS. 1A-1G ). For example, in some implementations, the one or more I/O devices  410  include an image sensor (e.g., a camera), an ambient light sensor (ALS), a microphone, a location sensor, a heart rate sensor and/or a blood glucose sensor. In some implementations, the one or more I/O devices  410  include a display, a speaker and/or a haptic device (e.g., a vibrational device that generates vibrations) for presenting media content items. 
     When a user wants to learn information about a subject, the user generally accesses a fixed media content item that includes information about the subject. For example, the user accesses a book, a newspaper article, a web page, a podcast and/or a video about the subject. However, the information in the fixed media content item may not be current or reliable by the time the user accesses the fixed media content item. In many instances, information in fixed media content items starts becoming out-of-date as soon as the media content item is published. 
     The present disclosure provides methods, systems, and/or devices for curating content that provides information regarding a particular subject that the user is interested in learning about. A content curation engine determine a subject that the user is interested in learning about. The content curation engine identifies media content items that include information regarding the subject that the user is interested in. The content curation engine selectively extracts portions of information from different media content items, and synthesizes a media content item based on the extracted portions of the information. 
     After identifying existing media content items that provide information regarding the subject, the content curation engine extracts portions of information that are reliable and forgoes extracting portions of information that are unreliable. As such, the media content item synthesized by the content curation engine includes reliable information and not unreliable information. 
     In some implementations, the content curation engine extracts portions of information that are current and forgoes extracting portions of information that are not current. As such, the media content item synthesized by the content curation engine includes information that is up-to-date and does not include information that is out-of-date. 
     In some implementations, the content curation engine extracts portions of information that are relevant based on contextual data indicating a context of a device or a user of the device, and forgoes extracting portions of information that are not relevant based on the contextual data. As such, the media content item synthesized by the content curation engine includes information that is relevant based on the contextual data and does not include information that is irrelevant based on contextual data. 
       FIG. 5A  is a block diagram of an example operating environment  50  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  50  includes the electronic device  30 , a content curation engine  500  and the media content datastore  150 . 
     Although the content curation engine  500  is shown as being separate from the electronic device  30 . In some implementations, the content curation engine  500  is integrated into the electronic device  30 . Although the media content datastore  150  is shown as being separate from the content curation engine  500 . In some implementations, the media content datastore  150  is integrated into the content curation engine  500 . In some implementations, the content curation engine  500  and/or the media content datastore  150  reside at the electronic device  30 . In some implementations, the content curation engine  500  is integrated into the content delivery platform  100  shown in  FIGS. 1A and 2 . As such, in some implementations, the content delivery platform  100  performs the operations described in relation to the content curation engine  500 . In some implementations, the content delivery platform  100  (shown in  FIGS. 1A and 2 ) is integrated into the content curation engine  500 . As such, in some implementations, the content curation engine  500  performs the operations described in relation to the content delivery platform  100 . 
     In various implementations, the content curation engine  500  obtains the user focus indicator value  170  that indicates a subject that the user  20  is interested in learning about. In some implementations, the content curation engine  500  receives the user focus indicator value  170  from the electronic device  30 . For example, in some implementations, the user  20  specifies the user focus indicator value  170 . In some implementations, the content curation engine  500  determines the user focus indicator value  170  based on respective subjects  154  of media content items  152  that the electronic device  30  has presented to the user  20  within a threshold amount of time (e.g., within the last 24 hours). 
     In some implementations, the content curation engine  500  obtains contextual data  174  that is associated with the electronic device  30  or with the user  20  of the electronic device  30 . In some implementations, the content curation engine  500  receives the contextual data  174  from the electronic device  30 . In some implementations, the contextual data  174  includes sensor data that is captured by sensors of the electronic device  30 . For example, in some implementations, the contextual data  174  includes a location of the electronic device  30 . In some implementations, the contextual data  174  indicates which other devices are proximate to the electronic device  30 . In some implementations, the contextual data  174  indicates user characteristics of the user  20 . For example, in some implementations, the contextual data  174  indicates a learning style of the user  20  (e.g., the contextual data  174  indicates whether the user  20  is a visual learner, an auditory learner, an active learner, a passive learner, etc.). In some implementations, the contextual data  174  indicates a current state of the user  20  (e.g., a current health state of the user  20 ). For example, in some implementations, the contextual data  174  indicates an energy level of the user (e.g., whether the user is tired or fresh). In some implementations, the contextual data  174  indicates an alertness level of the user (e.g., whether the user has had his/her morning coffee). In some implementations, the contextual data  174  indicates a drowsiness level of the user (e.g., whether the user is sleepy or wide awake). In some implementations, the contextual data  174  indicates a motion state of the electronic device  30  (e.g., the contextual data  174  indicates that the electronic device  30  is in a car that is being driven). In some implementations, the contextual data  174  indicates media content items that the electronic device  30  recently presented (e.g., within a threshold time period of a current time). In some implementations, the contextual data  174  indicates a web browsing history of the user  20 . 
     In various implementations, the content curation engine  500  identifies a set of items  510  from the various media content items  152  based on the user focus indicator value  170 . In some implementations, the set of items  510  includes a subset of the media content items  152  that provides information regarding a subject indicated by the user focus indicator value  170 . In some implementations, the set of items  510  includes a subset of the media content items  152  that provides information regarding a subject that the user is interested in learning about. In some implementations, the content curation engine  500  identifies the set of items  510  by filtering out media content items  152  that do not provide information regarding the subject indicated by the user focus indicator value  170 . For example, the content curation engine  500  filters out media content items  152  with respective subjects  154  that do not match the subject indicated by the user focus indicator value  170 . 
     In some implementations, the content curation engine  500  identifies the set of items  510  based on the contextual data  174 . In some implementations, the content curation engine  500  identifies the set of items  510  by identifying a subset of the media content items  152  that is contextually relevant to a context of the user  20  and/or the electronic device  30 . In some implementations, the content curation engine  500  identifies the set of items  510  by filtering out media content items  152  that are not contextually relevant to a context of the user  20  and/or the electronic device  30  indicated by the contextual data  174 . In some implementations, the content curation engine  500  identifies the set of items  510  by performing operations described in relation to the content delivery platform  100 . As such, in some implementations, the set of items  510  is the same as the set  180  shown in  FIGS. 1A and 2 . 
     In various implementations, the content curation engine  500  synthesizes a curated media content item  520  based on the set of items  510 . In some implementations, the content curation engine  500  selectively extracts portions of information from the set of items  510  and includes the extracted portions in the curated media content item  520 . In some implementations, the content curation engine  500  extracts, from the set of items  510 , portions of information that appear to be reliable. For example, the content curation engine  500  extracts, from the set of items  510 , portions of information that satisfy a reliability threshold. In some implementations, the content curation engine  500  forgoes extracting, from the set of items  510 , portions of information that appear to be unreliable. For example, the content curation engine  500  forgoes extracting, from the set of items  510 , portions of information that breach the reliability threshold, for example, do not satisfy the reliability threshold. As such, the curated media content item  520  includes information that appears to be reliable and does not include information that appears to be unreliable. 
     In some implementations, the content curation engine  500  extracts, from the set of items  510 , portions of information that appear to be current or up-to-date. For example, the content curation engine  500  extracts, from the set of items  510 , portions of information that satisfy a currentness threshold. In some implementations, the content curation engine  500  forgoes extracting, from the set of items  510 , portions of information that appear to be out-of-date or not current. For example, the content curation engine  500  forgoes extracting, from the set of items  510 , portions of information that breach the currentness threshold, for example, portions that do not satisfy the currentness threshold. As such, the curated media content item  520  includes information that appears to be current (e.g., up-to-date) and does not include information that does not appear to be current (e.g., out-of-date). 
     In some implementations, the content curation engine  500  extracts, from the set of items  510 , portions of information that appear to be relevant to (e.g., suitable for) a context of the user  20  and/or the electronic device  30  indicated by the contextual data  174 . For example, the content curation engine  500  extracts, from the set of items  510 , portions of information that satisfy a relevance threshold. In some implementations, the content curation engine  500  forgoes extracting, from the set of items  510 , portions of information that appear to be irrelevant to (e.g., unsuitable for) the context of the user  20  and/or the electronic device  30  indicated by the contextual data  174 . For example, the content curation engine  500  forgoes extracting, from the set of items  510 , portions of information that breach (e.g., do not satisfy) the relevance threshold. As such, the curated media content item  520  includes information that is relevant to a context of the user  20  and/or the electronic device  30 , and does not include information that is not relevant to the context of the user  20  and/or the electronic device  30 . 
     In various implementations, synthesizing the curated media content item  520  enhances a user experience of the electronic device  30  by including, from the set of items  510 , portions of information that are reliable, current, relevant or likely being sought by the user  20 , and excluding, from the set of items  510 , portions of information that are unreliable, out-of-date, not relevant or likely not being sought by the user  20 . In various implementations, synthesizing the curated media content item  520  enhances operability of the electronic device  30  by improving a battery life of the electronic device due to a reduction in a number of user inputs corresponding to the user  20  trying to determine whether information included in the curated media content item  520  is reliable and/or current. For example, synthesizing the curated media content item  520  reduces a need for user inputs that correspond to the user  20  performing searches on a search engine to determine whether or not the information included in the curated media content item  520  is reliable and/or current. 
     Referring to  FIG. 5B , the content curation engine  500  identifies the second media content item  152 - 2  and the fifteenth media content item  152 - 15  based on the user focus indicator value  170  and/or the contextual data  174 . In the example of  FIG. 5B , the set of items  510  includes the second media content item  152 - 2  and the fifteenth media content item  152 - 15 . As described in relation to  FIG. 1B , the second media content item  152 - 2  has a second delivery medium value  156 - 2  indicating that the second media content item  152 - 2  includes video content. For example, the second media content item  152 - 2  is a video clip, a documentary or a movie. The fifteenth media content item  152 - 15  has a fifteenth delivery medium value  156 - 15  indicating that the fifteenth media content item  152 - 15  is an ebook. 
     As illustrated in  FIG. 5B , in some implementations, the second media content item  152 - 2  includes a first video portion  152 - 2   a , a second video portion  152 - 2   b , a third video portion  152 - 2   c , . . . , and an nth video portion  152 - 2   n . In some implementations, the different video portions of the second media content item  152 - 2  correspond to different time periods of the video. For example, the first video portion  152 - 2   a  corresponds to the first two minutes of the video, the second video portion  152 - 2   b  corresponds to the next two minutes of the video, etc. In some implementations, the time periods corresponding to the different portions of the second media content item  152 - 2  are of equal temporal lengths (e.g., each portion of the video is 2 minutes long). Alternatively, in some implementations, the time periods corresponding to the different portions of the second media content item  152 - 2  are of different temporal lengths (e.g., the first video portion  152 - 2   a  is two minutes long, the second video portion  152 - 2   b  is thirty seconds long, etc.). 
     In some implementations, the fifteenth media content item  152 - 15  includes a first ebook portion  152 - 15   a , a second ebook portion  152 - 15   b , a third ebook portion  152 - 15   c , . . . , and an nth ebook portion  152 - 15   n . In some implementations, the different ebook portions of the fifteenth media content item  152 - 15  correspond to different chapters in the ebook. For example, the first ebook portion  152 - 15   a  corresponds to a first chapter of the ebook, the second ebook portion  152 - 15   b  corresponds to a second chapter of the ebook, the third ebook portion  152 - 15   c  corresponds to a third chapter of the ebook, . . . , and the nth ebook portion  152 - 15   n  corresponds to an nth chapter of the ebook. In some implementations, the different ebook portions of the fifteenth media content item  152 - 15  correspond to different pages, paragraphs, sentences and/or phrases in the ebook. In some implementations, the ebook portions are of equal lengths. Alternatively, in some implementations, the ebook portions are of different lengths. 
     In various implementations, the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  from the second media content item  152 - 2 , and forgoes extracting the remaining video portions of the second media content item  152 - 2 . For example, the content curation engine  500  forgoes extracting the second video portion  152 - 2   b  and the nth video portion  152 - 2   n . In some implementations, the content curation engine  500  generates a new media content item and includes the first video portion  152 - 2   a  and the third video portion  152 - 2   c  in the new media content item. Alternatively, in some implementations, the content curation engine  500  generates a copy of the second media content item  152 - 2  and removes all video portions except the first video portion  152 - 2   a  and the third video portion  152 - 2   c  from the copy of the second media content item  152 - 2 . 
     In various implementations, the content curation engine  500  extracts the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  from the fifteenth media content item  152 - 15 , and forgoes extracting the remaining ebook portions from the fifteenth media content item  152 - 15 . For example, the content curation engine  500  forgoes extracting the first ebook portion  152 - 15   a , the third ebook portion  152 - 15   c  and various other ebook portions between the third ebook portion  152 - 15   c  and the mth ebook portion  152 - 15   m . In some implementations, the content curation engine  500  includes the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  into the curated media content item  520 . 
     In various implementations, the content curation engine  500  orders the extracted portions of the second media content item  152 - 2  and the fifteenth media content item  152 - 15  based on one or more characteristics of the extracted portions. For example, in some implementations, the content curation engine  500  orders the extracted portions such that portions that convey the information in general terms are placed towards a beginning of the curated media content item  520  and portions that convey the information in more specific terms are placed towards an end of the curated media content item  520 . As an example, the first video portion  152 - 2   a  is placed before the second ebook portion  152 - 15   b  because the first video portion  152 - 2   a  may provide an overview of the subject, whereas the second ebook portion  152 - 15   b  may provide detailed information regarding the subject. 
     In some implementations, the content curation engine  500  orders the extracted portions in increasing order of their complexity. For example, the least complex portions are placed towards the beginning of the curated media content item  520  and the most complex portions are placed towards the end of the curated media content item  520 . As an example, the first video portion  152 - 2   a  is placed before the second ebook portion  152 - 15   b  because the first video portion  152 - 2   a  is less complex than the second ebook portion  152 - 15   b.    
     In some implementations, the content curation engine  500  places an extracted portion that introduces the subject (e.g., an executive summary of the subject) towards the beginning of the curated media content item  520 , an extracted portion that provides closing remarks regarding the subject (e.g., a conclusion or a closing summary) towards the end of the curated media content item  520 , and extracted portions that provide detailed information in the middle of the curated media content item  520 . As an example, the first video portion  152 - 2   a  provides an introduction to the subject that the user  20  is interested in, the mth book portion provides a closing summary or a conclusion to the subject, and the second ebook portion  152 - 15   b  and the third video portion  152 - 2   c  provide details regarding the subject. 
     Referring to  FIG. 5C , in various implementations, the content curation engine  500  extracts a portion of the information from a media content item  152  in response to determining that the portion satisfies a reliability threshold  530 . In some implementations, the content curation engine  500  determines respective reliability scores for the portions and extracts the portions with reliability scores that satisfy (e.g., exceed) the reliability threshold  530 . For example, the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because their reliability scores  532 - 2   a  and  532 - 2   c , respectively, exceed the reliability threshold  530 . Similarly, the content curation engine  500  extracts the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  because their reliability scores  532 - 15   b  and  532 - 15   m , respectively, exceed the reliability threshold  530 . 
     In various implementations, the content curation engine  500  forgoes extracting portions with reliability scores that breach (e.g., are less than) the reliability threshold  530 . For example, the content curation engine  500  forgoes extracting the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  because their respective reliability scores  532 - 2   b ,  532 - 2   n  and  532 - 2   x  are below the reliability threshold  530 . Similarly, the content curation engine  500  forgoes extracting the first ebook portion  152 - 15   a , the third ebook portion  152 - 15   c  and the ebook portions between the third ebook portion  152 - 15   c  and the mth ebook portion  152 - 15   m  because their respective reliability scores  532 - 15   a ,  532 - 15   c  and  532 - 15   x  are below the reliability threshold  530 . 
     In some implementations, the reliability threshold  530  indicates a threshold number of forward citations. In such implementations, a reliability score for a portion of information indicates a number of forward citations that the portion of information has. In some implementations, the number of forward citations indicates a number of other media content items that cite the portion of information. If the number of other media content items that cite the portion of information is greater than the threshold number of forward citations, then the portion of information is more likely to be reliable. As such, in some implementations, the content curation engine  500  extracts the portion of information if the number of other media content items that cite the portion of information is greater than the threshold number of forward citations. If the number of other media content items that cite the portion of information is less than the threshold number of forward citations, then the portion of information is less likely to be reliable. As such, in some implementations, the content curation engine  500  forgoes extracting the portion of information if the number of other media content items that cite the portion of information is less than the threshold number of forward citations. 
     In some implementations, the reliability threshold  530  indicates a threshold number of backward citations. In such implementations, a reliability score for a portion of information indicates a number of backward citations that the portion of information has. In some implementations, the number of backward citations indicates a number of other media content items that the portion of information cites. If the number of other media content items that the portion of information cites is greater than the threshold number of backward citations, then the portion of information is more likely to be reliable. As such, in some implementations, the content curation engine  500  extracts the portion of information if the number of other media content items that the portion of information cites is greater than the threshold number of forward citations. If the number of other media content items that the portion of information cites is less than the threshold number of backward citations, then the portion of information is less likely to be reliable. As such, in some implementations, the content curation engine  500  forgoes extracting the portion of information if the number of other media content items that the portion of information cites is less than the threshold number of backward citations. 
     In some implementations, the reliability threshold  530  indicates a set of trusted information sources (e.g., journal papers, news articles from well-established news outlets, textbooks, etc.). In such implementations, a reliability score for a portion of information indicates types of information sources that the portion of information cites. In some implementations, the content curation engine  500  extracts a portion of information if the portion of information cites information sources that are among the set of trusted information sources. For example, the content curation engine  500  extracts a portion of information in response to determining that the portion of the information cites peer-reviewed journal articles. In some implementations, the content curation engine  500  forgoes extracting a portion of information if the portion of information cites information sources that are not among the set of trusted information sources. For example, the content curation engine  500  forgoes extracting a portion of information in response to determining that the portion of information cites a crowd-sourced news article with unverified facts. 
     Referring to  FIG. 5D , in various implementations, the content curation engine  500  extracts a portion of the information from a media content item  152  in response to determining that the portion satisfies a currentness threshold  540 . In some implementations, the content curation engine  500  determines respective currentness scores for the portions and extracts the portions with currentness scores that satisfy (e.g., exceed) the currentness threshold  540 . For example, the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because their currentness scores  542 - 2   a  and  542 - 2   c , respectively, exceed the currentness threshold  540 . Similarly, the content curation engine  500  extracts the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  because their currentness scores  542 - 15   b  and  542 - 15   m , respectively, exceed the currentness threshold  540 . 
     In various implementations, the content curation engine  500  forgoes extracting portions with currentness scores that breach (e.g., are less than) the currentness threshold  540 . For example, the content curation engine  500  forgoes extracting the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  because their respective currentness scores  542 - 2   b ,  542 - 2   n  and  542 - 2   x  are below the currentness threshold  540 . Similarly, the content curation engine  500  forgoes extracting the first ebook portion  152 - 15   a , the third ebook portion  152 - 15   c  and the ebook portions between the third ebook portion  152 - 15   c  and the mth ebook portion  152 - 15   m  because their respective currentness scores  542 - 15   a ,  542 - 15   c  and  542 - 15   x  are below the currentness threshold  540 . 
     In some implementations, the currentness threshold  540  indicates a threshold age. In such implementations, a currentness score for a portion of information indicates an age of the portion of information. In some implementations, the more recent a portion of information is, the more current the portion of information may be. In some implementations, the content curation engine  500  extracts the portion of information, if the age of the portion of information is less than the threshold age. In some implementations, the content curation engine  500  forgoes extracting a portion of information, if the age of the portion of information is more than the threshold age. 
     In some implementations, the currentness threshold  540  indicates a threshold number. In some implementations, a currentness score for a portion of information indicates a number of later-published media content items that affirm the portion of information. In various implementations, a portion of information is more likely to be current, if later-published media content items (e.g., more recent media content items than the portion of information) affirm or verify the portion of information. As such, in some implementations, the content curation engine  500  extracts the portion of information when a number of later-published media content items that affirm the portion of information exceeds the threshold number. In some implementations, a portion of information is less likely to be current, if later-published media content items do not affirm or actively debunk the portion of information. As such, in some implementations, the content curation engine  500  forgoes extracting a portion of information when the portion of information is not affirmed by at least a number of later-published media content items that exceed the threshold number. 
     Referring to  FIG. 5E , in various implementations, the content curation engine  500  extracts a portion of the information from a media content item  152  in response to determining that the portion satisfies a relevance threshold  550 . In some implementations, the content curation engine  500  determines respective relevance scores for the portions and extracts the portions with relevance scores that satisfy (e.g., exceed) the relevance threshold  550 . For example, the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because their relevance scores  552 - 2   a  and  552 - 2   c , respectively, exceed the relevance threshold  550 . Similarly, the content curation engine  500  extracts the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  because their relevance scores  552 - 15   b  and  552 - 15   m , respectively, exceed the relevance threshold  550 . 
     In various implementations, the content curation engine  500  forgoes extracting portions with relevance scores that breach (e.g., are less than) the relevance threshold  550 . For example, the content curation engine  500  forgoes extracting the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  because their respective relevance scores  552 - 2   b ,  552 - 2   n  and  552 - 2   x  are below the relevance threshold  550 . Similarly, the content curation engine  500  forgoes extracting the first ebook portion  152 - 15   a , the third ebook portion  152 - 15   c  and the ebook portions between the third ebook portion  152 - 15   c  and the mth ebook portion  152 - 15   m  because their respective relevance scores  552 - 15   a ,  552 - 15   c  and  552 - 15   x  are below the relevance threshold  550 . 
     In some implementations, a value of the relevance threshold  550  is based on (e.g., a function of) the contextual data  174  shown in  FIG. 5A . For example, in some implementations, the value of the relevance threshold  550  corresponds to the learning style  174   a  (shown in  FIG. 1B ) of the user  20 . In such implementations, a relevance score for a portion of information indicates whether or not the portion of information conforms to the learning style  174   a  of the user  20 . The content curation engine  500  extracts the portion of information if the portion of information conforms to the learning style  174   a  and forgoes extracting the portion of information if the portion of information does not conform to the learning style  174   a.    
     In some implementations, a value of the relevance threshold  550  corresponds to the energy level  174   b  (shown in  FIG. 1C ) of the user  20 . In such implementations, a relevance score for a portion of information indicates whether or not the portion of information matches the energy level  174   b  of the user  20 . The content curation engine  500  extracts the portion of information if the portion of information matches the energy level  174   b  (e.g., if a level of engagement associated with the portion matches the energy level  174   b  of the user  20 ) and forgoes extracting the portion of information if the portion of information does not match the energy level  174   b  (e.g., if the level of engagement associated with the portion does not match the energy level  174   b  of the user  20 ). 
     In some implementations, a value of the relevance threshold  550  corresponds to the alertness level  174   c  (shown in  FIG. 1D ) of the user  20 . In such implementations, a relevance score for a portion of information indicates whether or not the portion of information matches the alertness level  174   c  of the user  20 . The content curation engine  500  extracts the portion of information if the portion of information matches the alertness level  174   c  (e.g., if a complexity of the portion matches the alertness level  174   c  of the user  20 ) and forgoes extracting the portion of information if the portion of information does not match the alertness level  174   c  (e.g., if the complexity of the portion does not match the alertness level  174   c  of the user  20 ). 
     In some implementations, a value of the relevance threshold  550  corresponds to the drowsiness level  174   d  (shown in  FIG. 1E ) of the user  20 . In such implementations, a relevance score for a portion of information indicates whether or not the portion of information matches the drowsiness level  174   d  of the user  20 . The content curation engine  500  extracts the portion of information if the portion of information matches the drowsiness level  174   d  and forgoes extracting the portion of information if the portion of information does not match the drowsiness level  174   d.    
     In some implementations, a value of the relevance threshold  550  corresponds to the estimated age  174   e  (shown in  FIG. 1F ) of nearby people. In such implementations, a relevance score for a portion of information indicates whether or not the portion of information matches (e.g., is suitable for) the estimated age  174   e  of nearby people. The content curation engine  500  extracts the portion of information if the portion of information matches the estimated age  174   e  of nearby people (e.g., if a content rating of the portion matches the estimated age  174   e  of nearby people) and forgoes extracting the portion of information if the portion of information does not match the estimated age  174   e  of nearby people (e.g., if the content rating of the portion does not match the estimated age  174   e  of nearby people). 
     Referring to  FIG. 5F , in some implementations, the content curation engine  500  extracts a portion of information from a media content item in response to determining that the portion of information satisfies the reliability threshold  530  (shown in  FIG. 5C ), the currentness threshold  540  (shown in  FIG. 5D ) and the relevance threshold  550  (shown in  FIG. 5E ). In the example of  FIG. 5F , the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because the first video portion  152 - 2   a  and the third video portion  152 - 2   c  satisfy the reliability threshold  530 , the currentness threshold  540  and the relevance threshold  550 , as indicated by the letter ‘Y’ for the reliability, currentness and relevance of the first video portion  152 - 2   a  and the third video portion  152 - 2   c . In the example of  FIG. 5F , the content curation engine  500  forgoes extracting the second video portion  152 - 2   b  and the nth video portion  152 - 2   n  because the second video portion  152 - 2   b  and the nth video portion  152 - 2   n  do not satisfy the currentness threshold  540 , as indicated by the letter ‘N’ for the currentness of the second video portion  152 - 2   b  and the nth video portion  152 - 2   n . In the example of  FIG. 5F , the content curation engine  500  forgoes extracting the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  because the video portions do not satisfy the relevance threshold  550 , as indicated by the letter ‘N’ for the relevance of the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n.    
     In the example of  FIG. 5F , the content curation engine  500  extracts the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  because the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m  satisfy the reliability threshold  530 , the currentness threshold  540  and the relevance threshold  550 , as indicated by the letter ‘Y’ for the reliability, currentness and relevance of the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m . In the example of  FIG. 5F , the content curation engine  500  forgoes extracting the first ebook portion  152 - 15   a  because the first ebook portion  152 - 15   a  does not satisfy the reliability threshold  530 , as indicated by the letter ‘N’ for the reliability of the first ebook portion  152 - 15   a . In the example of  FIG. 5F , the content curation engine  500  forgoes extracting the third ebook portion  152 - 15   c  because the third ebook portion  152 - 15   c  does not satisfy the currentness threshold  540 , as indicated by the letter ‘N’ for the currentness of the third ebook portion  152 - 15   c . In the example of  FIG. 5F , the content curation engine  500  forgoes extracting the ebook portions between the third ebook portion  152 - 15   c  and the mth ebook portion  152 - 15   m  because the ebook portions do not satisfy the relevance threshold  550 , as indicated by the letter “N” for the relevance of the ebook portions between the third ebook portion  152 - 15   c  and the mth ebook portion  152 - 15   m.    
       FIG. 6  is a block diagram of the content curation engine  500  in accordance with some implementations. In various implementations, the content curation engine  500  includes a data obtainer  560 , a content filterer  570  and a content curator  580 . In various implementations, the data obtainer  560  obtains the user focus indicator value  170  and the contextual data  174 . For example, in some implementations, the data obtainer  560  receives the user focus indicator value  170  and the contextual data  174  from the electronic device  30 . The data obtainer  560  provides the user focus indicator value  170  and the contextual data  174  to the content filterer  570 . 
     In various implementations, the content filterer  570  identifies a set of items  510  that provides information regarding the subject identified by the user focus indicator value  170 . In some implementations, the content filterer  570  generates the set of items  510  by identifying media content items  152  with content subjects  154  that match the subject identified by the user focus indicator value  170 . In some implementations, the content filterer  570  generates the set of items  510  by filtering out media content items  152  that do not provide information regarding the subject identified by the user focus indicator value  170 . 
     In some implementations, the content filterer  570  identifies the set of items  510  based on the contextual data  174 . In some implementations, the set of items  510  includes media content items  152  that are contextually relevant to a context indicated by the contextual data  174 . In some implementations, the content filterer  570  generates the set of items  510  by filtering out media content items  152  that are not contextually relevant to the context indicated by the contextual data  174 . 
     In some implementations, the content filterer  570  utilizes a set of media content filters (e.g., the media content filters  110  shown in  FIG. 2 ) to identify the set of items  510 . In some implementations, the content filterer  570  determines filter values (e.g., the filter values  130  shown in  FIG. 2 ) for the set of media content filters based on the contextual data  174 . To that end, in various implementations, the content filterer  570  performs the operations described with respect to the filter value determiner  104  (shown in  FIG. 2 ) to identify the set of items  510 . 
     In some implementations, the set of items  510  includes two or more media content items  152 . In some implementations, the media content items  152  in the set of items  510  utilize different delivery mediums to provide the information regarding the subject indicated by the user focus indicator value  170 . For example, in some implementations, one media content item  152  in the set of items  510  utilizes textual delivery (e.g., the fifteenth media content item  152 - 15  shown in  FIG. 5B ), whereas another media content item  152  in the set of items  510  utilizes video delivery (e.g., the second media content item  152 - 2  shown in  FIG. 5B ). 
     In various implementations, the content curator  580  curates (e.g., synthesizes, for example, generates) the curated media content item  520  based on the set of items  510 . In various implementations, the content curator  580  extracts portions of information from the set of items  510 , and synthesizes the curated media content item  520  by placing the extracted portions of information in a data container. In some implementations, the content curator  580  extracts portions of information from the set of items  510  such that the curated media content item  520  includes portions of information that are associated with different delivery mediums. For example, in some implementations, the content curator  580  extracts portions of information from the set of items  510  such that curated media content item  520  includes a portion of the text from the fifteenth media content item  152 - 15  and a portion of the video from the second media content item  152 - 2 . 
     In various implementations, the content curator  580  performs reliability analysis on the set of items  510  to identify portions of information that satisfy the reliability threshold  530 . In some implementations, portions of information that satisfy the reliability threshold  530  are more likely to be reliable. As such, in some implementations, the content curator  580  extracts the portions of information that satisfy the reliability threshold  530  and includes the portions of information that satisfy the reliability threshold  530  in the curated media content item  520 . In some implementations, portions of information that breach the reliability threshold  530  are less likely to be reliable. As such, in some implementations, the content curator  580  forgoes extracting the portions of information that breach the reliability threshold  530  and does not include the portions of information that breach the reliability threshold  530  in the curated media content item  520 . 
     In some implementations, the content curator  580  generates respective reliability scores for the portions of information. In such implementations, the content curator  580  extracts the portions of information with reliability scores that are greater than the reliability threshold  530  for inclusion in the curated media content item  520 . The content curator  580  forgoes extracting the portions of information with reliability scores that are less than the reliability threshold  530 . As described in relation to  FIG. 5C , in some implementations, the content curator  580  generates a reliability score for a particular portion of information by identifying a number of citations associated with the particular portion (e.g., a number of forward citations and/or a number of backward citations) and comparing the number of citations to a threshold number of citations. 
     In various implementations, the content curator  580  performs currentness analysis on the set of items  510  to identify portions of information that satisfy the currentness threshold  540 . In some implementations, portions of information that satisfy the currentness threshold  540  are more likely to be current (e.g., up-to-date). As such, in some implementations, the content curator  580  extracts the portions of information that satisfy the currentness threshold  540  and includes the portions of information that satisfy the currentness threshold  540  in the curated media content item  520 . In some implementations, portions of information that breach the currentness threshold  540  are less likely to be current. As such, in some implementations, the content curator  580  forgoes extracting the portions of information that breach the currentness threshold  540  and does not include the portions of information that breach the currentness threshold  540  in the curated media content item  520 . 
     In some implementations, the content curator  580  generates respective currentness scores for the portions of information. In such implementations, the content curator  580  extracts the portions of information with currentness scores that are greater than the currentness threshold  540  for inclusion in the curated media content item  520 . The content curator  580  forgoes extracting the portions of information with currentness scores that are less than the currentness threshold  540 . As described in relation to  FIG. 5D , in some implementations, the content curator  580  generates a currentness score for a particular portion of information by comparing an age of the particular portion with a threshold age. As further described in relation to  FIG. 5D , in some implementations, the content curator  580  generates a currentness score for a particular portion of information by determining whether later-published media content items have affirmed the particular portion of information. 
     In various implementations, the content curator  580  performs relevance analysis on the set of items  510  to identify portions of information that satisfy the relevance threshold  550 . In some implementations, portions of information that satisfy the relevance threshold  550  are more likely to be contextually relevant. As such, in some implementations, the content curator  580  extracts the portions of information that satisfy the relevance threshold  550  and includes the portions of information that satisfy the relevance threshold  550  in the curated media content item  520 . In some implementations, portions of information that breach the relevance threshold  550  are less likely to be contextually relevant. As such, in some implementations, the content curator  580  forgoes extracting the portions of information that breach the relevance threshold  550  and does not include the portions of information that breach the relevance threshold  550  in the curated media content item  520 . 
     In some implementations, the content curator  580  generates respective relevance scores for the portions of information. In such implementations, the content curator  580  extracts the portions of information with relevance scores that are greater than the relevance threshold  550  for inclusion in the curated media content item  520 . The content curator  580  forgoes extracting the portions of information with relevance scores that are less than the relevance threshold  550 . As described in relation to  FIG. 5E , in some implementations, the content curator  580  generates a relevance score for a particular portion of information by determining a relevance of the particular portion to the learning style  174   a  (shown in  FIG. 1B ) of the user  20 , the energy level  174   b  (shown in  FIG. 1C ) of the user  20 , the alertness level  174   c  (shown in  FIG. 1D ) of the user  20 , the drowsiness level  174   d  (shown in  FIG. 1E ) of the user  20  and/or the estimated age  174   e  (shown in  FIG. 1F ) of nearby people. 
       FIG. 7  is a flowchart representation of a method  700  of curating content. 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 electronic device  30  and/or the content curation engine  500  shown in  FIG. 5A ). 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). 
     As represented by block  710 , in some implementations, the method  700  includes obtaining a user focus indicator value that is associated with a subject. For example, as shown in  FIG. 5A , the content curation engine  500  receives the user focus indicator value  170  from the electronic device  30 . In some implementations, the user focus indicator value includes a user interest value that indicates that the user is interested in the subject. 
     As represented by block  710   a , in some implementations, the method  700  includes receiving a user input that specifies the subject. In some implementations, the user input specifies that the user is interested in learning about the subject. For example, in some implementations, the user  20  provides a user input, via the electronic device  30 , that specifies the subject. 
     As represented by block  710   b , in some implementations, the method  700  includes detecting that the electronic device has presented media content items that relate to the subject within a threshold amount of time. For example, the method  700  includes detecting that the electronic device  30  has presented media content items  152  related to the Civil War within the last 2 weeks. 
     As represented by block  720 , in some implementations, the method  700  includes identifying, based on the user focus indicator value, a first media content item that includes information about a first plurality of subjects including a first primary subject that matches the particular subject associated with the user focus indicator value. In some implementations, the first media content item is associated with a first content delivery medium. For example, as shown in  FIG. 5B , the content curation engine  500  identifies the second media content item  152 - 2  that has a second delivery medium value  156 - 2  indicating that the second media content item  152 - 2  includes is a video. 
     As represented by block  730 , in some implementations, the method  700  includes identifying a second media content item that includes a second plurality of subjects including a second primary subject. In some implementations, the second plurality of subjects includes the particular subject associated with the user focus indicator value and the second media content item is associated with a second content delivery medium that is different from the first content delivery medium. For example, as shown in  FIG. 5B , the content curation engine  500  identifies the fifteenth media content item  152 - 15  that has a fifteenth delivery medium value  156 - 15  indicating that the fifteenth media content item  152 - 15  is an ebook. 
     As represented by block  730   a , in some implementations, the first media content item was generated at a first time and the second media content item was generated at a second time that is after the first time. For example, in some implementations, the video represented by the second media content item  152 - 2  in  FIG. 5B  was created in the year 2010, and the ebook represented by the fifteenth media content item  152 - 15  was published in the year 2016. 
     As represented by block  730   b , in some implementations, the first content delivery medium is visual and the second content delivery medium is aural. For example, in some implementations, the first media content item is a video, a web article or an ebook, and the second media content item is a podcast, a radio station, an audio file or a song. In some implementations, the first content delivery medium is textual and the second content delivery medium is video. For example, in some implementations, the first media content item is a web article or an ebook, and the second media content item is a video. In some implementations, the first content delivery medium is textual and the second content delivery medium is aural. For example, the first media content item is a web article or an ebook, and the second media content item is a podcast, a talk show, a radio station, an audio file or a song. 
     As represented by block  730   c , in some implementations, the first content delivery medium corresponds to a first learning style and the second content delivery medium corresponds to a second learning style. In some implementations, media content items delivered via the first content delivery medium are more suitable for the first learning style, and media content items delivered via the second content delivery medium are more suitable for the second learning style. For example, the first delivery medium is more suitable for a user with an active learning style (e.g., for a user who likes to learn by doing), whereas the second content delivery medium is more suitable for a user with a passive learning style (e.g., for a user who likes to learn by watching). Alternatively, in some implementations, the first content delivery medium and the second content delivery medium correspond to (e.g., are suitable for) the same learning style (e.g., the first learning style). 
     As represented by block  740 , in some implementations, the method  700  includes synthesizing a third media content item based on a portion of the information included in the first media content item and a portion of the information included in the second media content item. For example, as shown in  FIG. 5B , the content curation engine  500  synthesizes the curated media content item  520  by selectively extracting portions of the second media content item  152 - 2  and the fifteenth media content item  152 - 15 . In some implementations, the third media content item is associated with a combination of the first content delivery medium and the second content delivery medium. For example, as shown in  FIG. 5B , the curated media content item  520  utilizes video delivery (e.g., for displaying the first video portion  152 - 2   a  and the third video portion  152 - 2   c ) and textual delivery (e.g., for displaying the second ebook portion  152 - 15   b  and the mth ebook portion  152 - 15   m ). 
     As represented by block  740   a , in some implementations, the method  700  includes extracting the portion of the information included in the first media content item in response to the portion of the information included in the first media item satisfying a reliability threshold. For example, as shown in  FIG. 5C , the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because their reliability scores  532 - 2   a  and  532 - 2   c , respectively, exceed the reliability threshold  530 . 
     In some implementations, the method  700  includes determining a reliability of the portion of the information included in the first media content item based on a number of citations associated with the portion of the information included in the first media content item satisfying a citation criterion. In some implementations, the citation criterion indicates a threshold number of citations. In some implementations, the reliability of the portion of the information indicates a likelihood of the portion of the information being accurate. In some implementations, the citation criterion indicates a threshold number of citations. In such implementations, the method  700  includes extracting the portion of the information in response to the number of citations associated with the portion of the information being greater than the threshold number of citations. 
     In some implementations, the method  700  includes determining that a number of other media content items that cite the portion of the information included in the first media content item is greater than a threshold number. For example, in some implementations, the method  700  includes determining that a number of forward citations associated with the portion of the information exceeds a threshold number of forward citations. 
     In some implementations, the method  700  includes determining that a number of other media content items cited by the portion of the information included in the first media content item is greater than a threshold number. For example, in some implementations, the method  700  includes determining that a number of backward citations associated with the portion of the information exceeds a threshold number of backward citations. 
     In some implementations, the method  700  includes determining that a number of other media content items cited by the portion of the information included in the first media content item are from a content publisher that satisfies a content publisher criterion. In some implementations, the content publisher criterion specifies a set of whitelisted content publishers. In some implementations, the method  700  includes extracting the portion of the information in response to the portion of the information citing other media content items that are from the set of whitelisted content publishers specified by the content publisher criterion. In some implementations, the content publisher criterion specifies a type of content publisher (e.g., a news organization, a book publishing company, etc.). In some implementations, the method  700  includes extracting the portion of the information in response to the portion of the information citing other media content items that are from the type of content publisher specified by the content publisher criterion. 
     As represented by block  740   b , in some implementations, the method  700  includes forgoing extraction of another portion of the information included in the first media content item that is unreliable. For example, as shown in  FIG. 5B , in some implementations, the content curation engine  500  forgoes extracting the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  in response to the content curation engine  500  determining that the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  are unreliable. 
     In some implementations, the method  700  includes determining that the other portion is unreliable in response to the other portion breaching a citation criterion. In some implementations, the method  700  includes determining that the other portion is unreliable based on a number of forward citations associated with the other portion. In some implementations, the method  700  includes determining that a number of other media content items that cite the other portion is less than a threshold number. In some implementations, the method  700  includes determining that the other portion is unreliable based on a number of backward citations associated with the other portion. In some implementations, the method  700  includes determining that a number of other media content items cited by the other portion is less than a threshold number. 
     In some implementations, determining that the other portion is unreliable includes determining that a number of other media content items cited by the other portion are from a content publisher that breaches a content publisher criterion. In some implementations, the content publisher criterion specifies a set of content publishers and the other media content items cited by the other portion are not from the set of content publishers specified by the content publisher criterion. In some implementations, the content publisher criterion specifies a type of content publisher (e.g., a news organization, a book publishing company, etc.) and the other media content items cited by the other portion are not from the type of content publisher specified by the content publisher criterion. 
     As represented by block  740   c , in some implementations, the method  700  includes extracting the portion of the information included in the first media content item in response to determining that the portion of the information included in the first media item is current. For example, as shown in  FIG. 5D , the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because their currentness scores  542 - 2   a  and  542 - 2   c , respectively, exceed the currentness threshold  540 . 
     In some implementations, the method  700  includes determining that the portion of the information included in the first media content item is current based on a recency of the first media content item. In some implementations, the method  700  includes determining that the portion of the information included in the first media content item is current based on an age of the first media content item. In some implementations, the method  700  includes determining that the portion of the information is current in response to a time at which the first media content item was published being within a threshold amount of time from a current time. 
     In some implementations, the method  700  includes determining that the portion of the information included in the first media content item is current based on a later-published media content item affirming the portion of the information. In some implementations, the method  700  includes determining that the portion of the information included in the first media content item is current in response to other media content items, published after the first media content item and within a threshold amount of time of a current time, affirming the portion of the information included in the first media content item. 
     In some implementations, the method  700  includes forgoing extraction of another portion of the information included in the first media content item in response to determining that the other portion is not current. For example, as shown in  FIG. 5D , the content curation engine  500  forgoes extracting the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  because their respective currentness scores  542 - 2   b ,  542 - 2   n  and  542 - 2   x  are below the currentness threshold  540 . 
     As represented by block  740   d , in some implementations, the method  700  includes extracting the portion of the information included in the first media content item in response to the portion of the information included in the first media content item satisfying a relevancy threshold in relation to the user focus indicator value. For example, as shown in  FIG. 5E , the content curation engine  500  extracts the first video portion  152 - 2   a  and the third video portion  152 - 2   c  because their relevance scores  552 - 2   a  and  552 - 2   c , respectively, exceed the relevance threshold  550 . 
     In some implementations, the content curation engine  500  forgoes extracting another portion of the information included in the first media content item in response to the other portion of the information included in the first media content item breaching the relevancy threshold in relation to the user focus indicator value. For example, as shown in  FIG. 5E , the content curation engine  500  forgoes extracting the second video portion  152 - 2   b , the nth video portion  152 - 2   n  and the video portions between the third video portion  152 - 2   c  and the nth video portion  152 - 2   n  because their respective relevance scores  552 - 2   b ,  552 - 2   n  and  552 - 2   x  are below the relevance threshold  550 . 
       FIG. 8  is a block diagram of a device  800  that curates content in accordance with some implementations. In some implementations, the device  800  implements the electronic device  30  and/or the content curation engine  500  shown in  FIG. 5A . 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  800  includes one or more processing units (CPUs)  801 , a network interface  802 , a programming interface  803 , a memory  804 , one or more input/output (I/O) devices  810 , and one or more communication buses  805  for interconnecting these and various other components. 
     In some implementations, the network interface  802  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  805  include circuitry that interconnects and controls communications between system components. The memory  804  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  804  optionally includes one or more storage devices remotely located from the one or more CPUs  801 . The memory  804  comprises a non-transitory computer readable storage medium. 
     In some implementations, the memory  804  or the non-transitory computer readable storage medium of the memory  804  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  806 , the data obtainer  560 , the content filterer  570  and the content curator  580 . In various implementations, the device  800  performs the method  700  shown in  FIG. 7 . 
     In some implementations, the data obtainer  560  obtains a user focus indicator value (e.g., the user focus indicator value  170  shown in  FIGS. 5A and 6 ) and contextual data (e.g., the contextual data  174  shown in  FIGS. 5A and 6 ). In some implementations, the data obtainer  560  performs the operation(s) represented by block  710  in  FIG. 7 . To that end, the data obtainer  560  includes instructions  560   a , and heuristics and metadata  560   b.    
     In some implementations, the content filterer  570  identifies a set of two or more media content items based on the user focus indicator value (e.g., the set of items  510  shown in  FIGS. 5A and 6 , for example, the second media content item  152 - 2  and the fifteenth media content item  152 - 15  shown in  FIG. 5B ). In some implementations, the content filterer  570  performs the operations(s) represented by blocks  720  and  730  shown in  FIG. 7 . To that end, the content filterer  570  includes instructions  570   a , and heuristics and metadata  570   b.    
     In some implementations, the content curator  580  synthesizes a media content item (e.g., the curated media content item  520  shown in  FIGS. 5A, 5B and 6 ) based on the set of media content items that the content filterer  570  identifies. In some implementations, the content curator  580  performs the operation(s) represented by block  740  shown in  FIG. 7 . To that end, the content curator  580  includes instructions  580   a , and heuristics and metadata  580   b.    
     In some implementations, the one or more I/O devices  810  include one or more sensors for capturing contextual data (e.g., the contextual data  174  shown in  FIGS. 5A and 6 ). For example, in some implementations, the one or more I/O devices  810  include an image sensor (e.g., a camera), an ambient light sensor (ALS), a microphone, a location sensor, a heart rate sensor and/or a blood glucose sensor. In some implementations, the one or more I/O devices  810  include a display, a speaker and/or a haptic device (e.g., a vibrational device that generates vibrations) for presenting the media content item synthesized by the content curator  580  (e.g., for presenting the curated media content item  520  shown in  FIGS. 5A, 5B and 6 ). 
     Information is curated in various fixed forms such as books, newspapers, articles, web pages, podcasts, video, etc. However, the information cannot be automatically updated. As such, portions of the information becomes inaccurate or irrelevant. Some portions of the information start becoming inaccurate and/or irrelevant as early as its curation date. 
     The present disclosure provides methods, systems, and/or devices for updating a media content item that provides information regarding a subject based on information provided by another media content item related to that subject. A media content item can be updated based on information provided by a more recent media content item. For example, a device identifies information in an old media content item that is outdated, and replaces the outdated information with up-to-date information from a newer media content item. A media content item can be updated to rectify inaccuracies. For example, a device identifies information in a media content item that is inaccurate, and replaces the inaccurate information with accurate information from another media content item. 
     A media content item can be updated to include information that is more relevant to what the user is interested in learning. For example, a device identifies information in a media content item that is not relevant to the user, and replaces the irrelevant information with relevant information from another media content item. A media content item can be updated to include information that is contextually more relevant to a context of a device or a user of the device. For example, a device identifies information in a media content item that is not relevant to a context of the user, and replaces the irrelevant information with contextually relevant information from another media content item. 
     Updating a media content item based on information provided by another media content item enhances a user experience of the device by presenting information that is more accurate, current and/or relevant. Updating the media content item based on information provided by another media content item tends to improve operability of the device by reducing battery consumption resulting from unnecessary user inputs that correspond to the user trying to verify the accuracy and/or currentness of information presented by the device. 
       FIG. 9A  is a block diagram of an example operating environment  90  in accordance with some implementations. While pertinent features are shown, those of ordinary skill in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the example implementations disclosed herein. To that end, as a non-limiting example, the operating environment  90  includes the electronic device  30 , a content updating engine  900  and the media content datastore  150 . 
     Although the content updating engine  900  is shown as being separate from the electronic device  30 . In some implementations, the content updating engine  900  is integrated into the electronic device  30 . Although the media content datastore  150  is shown as being separate from the content updating engine  900 . In some implementations, the media content datastore  150  is integrated into the content updating engine  900 . In some implementations, the content updating engine  900  and/or the media content datastore  150  reside at the electronic device  30 . 
     In some implementations, the content updating engine  900  is integrated into the content delivery platform  100  shown in  FIGS. 1A and 2 . As such, in some implementations, the content delivery platform  100  performs the operations described in relation to the content updating engine  900 . In some implementations, the content delivery platform  100  (shown in  FIGS. 1A and 2 ) is integrated into the content updating engine  900 . As such, in some implementations, the content updating engine  900  performs the operations described in relation to the content delivery platform  100 . 
     In some implementations, the content updating engine  900  is integrated into the content curation engine  500  shown in  FIGS. 5A, 5B and 6 . As such, in some implementations, the content curation engine  500  performs the operations described in relation to the content updating engine  900 . In some implementations, the content curation engine  500  (shown in  FIGS. 5A, 5B and 6 ) is integrated into the content updating engine  900 . As such, in some implementations, the content updating engine  900  performs the operations described in relation to the content curation engine  500 . 
     In some implementations, each media content item  152  is associated with a media content provider  166  that provided the media content item  152 . In some implementations, the media content provider  166  includes an authoring entity (e.g., an author of the media content item  152 ). In some implementations, the media content provider  166  includes a publishing entity (e.g., a publisher of the media content item  152 , for example, a publishing company or a publishing house). In some implementations, the media content provider  166  includes a producing entity (e.g., a producer of the media content item  152 ). In some implementations, the media content provider  166  includes a directing entity (e.g., a director of the media content item  152 ). In some implementations, the media content provider  166  is a person. In some implementations, the media content provider  166  is an autonomous entity (e.g., a bot). In some implementations, the media content datastore  150  stores media content items  152  that are associated with various different media content providers  166 . For example, some of the media content items  152  are provided by a first media content provider, some of the media content items  152  are provided by a second media content provider, some of the media content items  152  are provided by a third media content provider, etc. 
     In various implementations, the content updating engine  900  obtains a selection request  904  that specifies a particular media content item  152  that the user  20  wants to access. In some implementations, the content updating engine  900  receives the selection request  904  from the electronic device  30 . For example, in some implementations, the electronic device  30  displays representations of the media content items  152  and the user  20  selects one of the representations of the media content items  152 . In such implementations, the selection request  904  indicates the media content item  152  that the user  20  selected. In some implementations, the user  20  provides a voice command that specifies a particular media content item  152 , and the selection request  904  indicates the particular media content item  152  specified in the voice command. 
     In various implementations, the content updating engine  900  identifies a set of items  910  from the various media content items  152  based on the selection request  904 . In some implementations, the set of items  910  includes a subset of the media content items  152  that provide information regarding the same subject  154  as the media content item  152  indicated by the selection request  904 . In some implementations, the content updating engine  900  identifies the set of items  910  by filtering out media content items  152  that do not provide information regarding the same subject  154  as the media content item  152  indicated by the selection request  904 . 
     In various implementations, the content updating engine  900  updates the particular media content item  152  indicated by the selection request  904  based on the set of items  910  in order to generate an updated media content item  920 . In some implementations, the updated media content item  920  includes information that the particular media content item  152  requested by the user  20  does not include. In some implementations, the updated media content item  920  does not include an inaccurate portion of the particular media content item  152  that the user  20  requested. In some implementations, the updated media content item  920  includes an indicator to indicate that a portion of the particular media content item  152  requested by the user  20  is not current. 
     In various implementations, generating the updated media content item  920  enhances a user experience of the electronic device  30  by presenting information that is current and forgoing presentation of information that is not current. In various implementations, generating the updated media content item  920  improves operability of the electronic device  30  by reducing the need for user inputs that correspond to the user  20  trying to verify accuracy of the information included in the updated media content item  920 . 
     Referring to  FIG. 9B , in various implementations, the media content item  152  requested by the user  20  includes a first portion  152   a , a second portion  152   b , a third portion, . . . , and an nth portion  152   n . In some implementations, the content updating engine  900  displays an indicator  922  in association with a portion of the media content item  152  that breaches a currentness threshold  940 . In some implementations, the currentness threshold  940  is the same as the currentness threshold  540  shown in  FIG. 5D . 
     In some implementations, the content updating engine  900  determines respective currentness scores for the portions of the media content item  152 . For example, the content updating engine  900  determines a first currentness score  942   a  for the first portion  152   a , a second currentness score  942   b  for the second portion  152   b , a third currentness score  942   c  for the third portion  152   c , . . . , and an nth currentness score  942   n  for the nth portion  152   n . As illustrated in  FIG. 9B , the first currentness score  942   a , the third currentness score  942   c , . . . , and the nth currentness score  942   n  satisfy the currentness threshold  940  (e.g., the first currentness score  942   a , the third currentness score  942   c , . . . , and the nth currentness score  942   n  are greater than the currentness threshold  940 ). However, the second currentness score  942   b  breaches the currentness threshold  940  (e.g., the second currentness score  942   b  is less than the currentness threshold  940 ). 
     In various implementations, the content updating engine  900  includes an indicator  922  in the updated media content item  920  in order to indicate that a portion of the updated media content item  920  breaches the currentness threshold  940 . In the example of  FIG. 9B , the indicator  922  indicates that the second portion  152   b  of the media content item  152  breaches the currentness threshold  940 . In some implementations, the indicator  922  includes text  924 . In some implementations, the text  924  specifies that the second portion  152   b  of the media content item  152  may not be current. For example, in some implementations, the text  924  specifies that the second portion  152   b  of the media content item  152  may be inaccurate. In some implementations, the text  924  specifies that the second portion  152   b  of the media content item  152  does not cite information from a set of approved information sources. In some implementations, the text  924  specifies that the second portion  152   b  of the media content item  152  is stale (e.g., the information provided by the second portion  152   b  is older than a threshold amount of time). In some implementations, the indicator  922  is displayed while the second portion  152   b  is being presented and not while other portions of the media content items  152  are being presented. 
     In some implementations, the currentness threshold  940  indicates a threshold age. In such implementations, a currentness score for a portion of the media content item  152  indicates an age of the portion. In some implementations, the more recent a portion is, the more current the portion may be. In some implementations, the currentness score for a portion breaches the currentness threshold  940 , if the age of the portion is more than the threshold age. In some implementations, the content updating engine  900  displays the indicator  922  in association with the portion, if the age of the portion is more than the threshold age. 
     In some implementations, the currentness threshold  940  indicates a threshold number. In some implementations, a currentness score for a portion indicates a number of later-published media content items that disavow or debunk the information provided by the portion. In various implementations, a portion of information is less likely to be current, if later-published media content items (e.g., more recent media content items than the portion of information) disavow the information provided by the portion. As such, in some implementations, the content updating engine  900  displays the indicator  922  in associated with the portion when a number of later-published media content items that disavow the information provided by the portion exceeds the threshold number. 
     Referring to  FIG. 9C , in some implementations, the content updating engine  900  generates an updated media content item  920   a  by modifying a visual property  926  of a portion of the media content item  152  in response to a currentness score of the portion breaching the currentness threshold  940 . In the example of  FIG. 9C , the content updating engine  900  modifies the visual property  926  of the second portion  152   b  of the media content item  152  in order to indicate that the second portion  152   b  breaches the currentness threshold  940 . In some implementations, modifying the visual property  926  of the second portion  152   b  indicates that the second portion  152   b  is not current. In some implementations, modifying the visual property  926  of the second portion  152   b  indicates that the second portion  152   b  includes inaccurate information or stale information. 
     In some implementations, the visual property  926  includes a brightness value. For example, in some implementations, the content updating engine  900  lowers the brightness value of the second portion  152   b  in order to indicate that the second portion  152   b  breaches the currentness threshold  940 . In some implementations, modifying the visual property  926  of the second portion  152   b  includes graying-out the second portion  152   b  in order to indicate that the second portion  152   b  breaches the currentness threshold  940 . 
     In some implementations, modifying the visual property  926  of the second portion  152   b  includes adjusting a playback speed of the second portion  152   b . For example, in some implementations, modifying the visual property  926  of the second portion  152   b  includes playing the second portion  152   b  at a faster speed so that playback of the second portion  152   b  occupies less time. 
     Referring to  FIG. 9D , in some implementations, the content updating engine  900  generates an updated media content item  920   b  by removing (e.g., deleting) a portion of the media content item  152  that breaches the currentness threshold  940 . In the example of  FIG. 9D , the updated media content item  920   b  does not include the second portion  152   b  of the media content item  152 , for example, because the second currentness score  942   b  is less than the currentness threshold  940 . 
     Referring to  FIG. 9E , in some implementations, the content updating engine  900  generates an updated media content item  920   c  by replacing a portion of the media content item  152  that breaches the currentness threshold  940  with a portion from another media content item  152  that satisfies the currentness threshold  940 . In the example of  FIG. 9E , the content updating engine  900  replaces the second portion  152   b  of the media content item  152  with a replacement portion  928  from another media content item. 
     In some implementations, the replacement portion  928  is from another media content provider. For example, in some implementations, the media content item  152  is from a first media content provider  166   a  (e.g., a first author, a first producer, a first director, a first artist, a first person, a first bot, etc.) and the replacement portion  928  is from a second media content provider  166   b  that is different from the first media content provider  166   a  (e.g., a second author, a second producer, a second director, a second artist, a second person, a second bot, etc.). 
     In some implementations, the replacement portion  928  is associated with a different content delivery medium than the media content item  152 . For example, in some implementations, the media content item  152  is associated with a first content delivery medium  156   a , and the replacement portion  928  is associated with a second content delivery medium  156   b  that is different from the first content delivery medium  156   a . In some implementations, the first content delivery medium  156   a  includes visual delivery, and the second content delivery medium  156   b  includes aural delivery (e.g., audio delivery). In some implementations, the first content delivery medium  156   a  includes a first type of visual delivery (e.g., video delivery, for example, a documentary movie), and the second content delivery medium  156   b  includes a second type of visual delivery (e.g., textual delivery, for example, a portion of an ebook). 
     Referring to  FIG. 9F , in some implementations, the content updating engine  900  generates an updated media content item  920   d  by including an additional portion  930  from another media content item. In some implementations, the content updating engine  900  inserts the additional portion  930  after (e.g., immediately after) the portion that breaches the currentness threshold  940 . For example, as shown in  FIG. 9F , the content updating engine  900  inserts the additional portion  930  immediately after the second portion  152   b  that breaches the currentness threshold  940 . In some implementations, the content updating engine  900  inserts the additional portion  930  before (e.g., immediately before) the portion that breaches the currentness threshold  940 . 
     In some implementations, the additional portion  930  provides information that supplements the second portion  152   b . For example, in some implementations, the content updating engine  900  determines that the second portion  152   b  provides insufficient information regarding a subtopic, and the additional portion  930  provides additional information regarding the subtopic in order to account for the insufficiency of the second portion  152   b . In some implementations, the additional portion  930  provides more details regarding the information provided by the second portion  152   b . In some implementations, the additional portion  930  corrects incorrect information found in the second portion  152   b . In some implementations, the additional portion  930  clarifies unclear information in the second portion  152   b.    
     In some implementations, the additional portion  930  is from another media content provider. For example, in some implementations, the media content item  152  is from the first media content provider  166   a  (e.g., a first author, a first producer, a first director, a first artist, a first person, a first bot, etc.) and the additional portion  930  is from the second media content provider  166   b  that is different from the first media content provider  166   a  (e.g., a second author, a second producer, a second director, a second artist, a second person, a second bot, etc.). 
     In some implementations, the additional portion  930  is associated with a different content delivery medium than the media content item  152 . For example, in some implementations, the media content item  152  is associated with the first content delivery medium  156   a , and the additional portion  930  is associated with the second content delivery medium  156   b  that is different from the first content delivery medium  156   a . In some implementations, the first content delivery medium  156   a  includes visual delivery, and the second content delivery medium  156   b  includes aural delivery. In some implementations, the first content delivery medium  156   a  includes a first type of visual delivery (e.g., video delivery, for example, a documentary movie), and the second content delivery medium  156   b  includes a second type of visual delivery (e.g., textual delivery, for example, a portion of an ebook). 
       FIG. 10  is a block diagram of the content updating engine  900  in accordance with some implementations. In various implementations, the content updating engine  900  includes a data obtainer  960 , a related content identifier  970  and a content updater  980 . In various implementations, the data obtainer  960  obtains the selection request  904 . For example, in some implementations, the data obtainer  960  receives the selection request  904  from the electronic device  30 . The data obtainer  960  provides the selection request  904  or an indication of the selected media content item  152  to the related content identifier  970 . 
     In various implementations, the related content identifier  970  identifies the set of items  910  that provide information regarding the same subject as the media content item  152  specified in the selection request  904 . In some implementations, the related content identifier  970  generates the set of items  910  by identifying media content items  152  with content subjects  154  that match the subject of the media content item  152  specified by the selection request  904 . In some implementations, the related content identifier  970  generates the set of items  910  by filtering out media content items  152  that do not provide information regarding the same subject as the media content item  152  specified by the selection request  904 . 
     In some implementations, the related content identifier  970  identifies the set of items  910  based on contextual data (e.g., the contextual data  174  shown in  FIGS. 1A-2, 5A and 6 ). In some implementations, the set of items  910  includes media content items  152  that are contextually relevant to a context indicated by the contextual data  174 . In some implementations, the related content identifier  970  generates the set of items  910  by filtering out media content items  152  that are not contextually relevant to the context indicated by the contextual data  174 . 
     In some implementations, the related content identifier  970  utilizes a set of media content filters (e.g., the media content filters  110  shown in  FIG. 2 ) to identify the set of items  910 . In some implementations, the related content identifier  970  determines filter values (e.g., the filter values  130  shown in  FIG. 2 ) for the set of media content filters based on the contextual data  174 . To that end, in various implementations, the related content identifier  970  performs the operations described with respect to the filter value determiner  104  (shown in  FIG. 2 ) to identify the set of items  910 . 
     In some implementations, the set of items  910  includes two or more media content items  152 . In some implementations, the media content items  152  in the set of items  910  utilize different content delivery mediums to provide the information regarding the same subject as the media content item  152  indicated by the selection request  904 . For example, in some implementations, one media content item  152  in the set of items  910  utilizes textual delivery (e.g., the fifteenth media content item  152 - 15  shown in  FIG. 5B ), whereas another media content item  152  in the set of items  510  utilizes video delivery (e.g., the second media content item  152 - 2  shown in  FIG. 5B ). 
     In various implementations, the content updater  980  updates (e.g., modifies) the media content item  152  identified by the selection request  904  based on the set of items  910 . In some implementations, the content updater  980  updates the media content item  152  by including an indicator (e.g., the indicator  922  shown in  FIG. 9B ) in the media content item  152  in order to indicate that a portion of the media content item  152  is not current (e.g., inaccurate or out-of-date). In some implementations, the content updater  980  updates the media content item  152  by modifying a visual property (e.g., the visual property  926  shown in  FIG. 9C ) of a portion of the media content item  152  in order to indicate a deficiency with the portion of the media content item  152 . In some implementations, the content updater  980  updates the media content item  152  by removing a portion of the media content item  152  (e.g., as shown in  FIG. 9D ). In some implementations, the content updater  980  updates the media content item  152  by replacing a portion of the media content item  152  (e.g., as shown in  FIG. 9E ). In some implementations, the content updater  980  updates the media content item  152  by including an additional portion in the media content item  152  (e.g., as shown in  FIG. 9F ). 
     In various implementations, the content updater  980  performs currentness analysis on portions of the media content item  152  indicated by the selection request  904  in order to identify portions that breach the currentness threshold  940 . In some implementations, portions of the media content item  152  that breach the currentness threshold  940  are less likely to be current. In some implementations, portions of the media content item  152  that breach the currentness threshold  940  are more likely to provide outdated information. In some implementations, portions of the media content item  152  that breach the currentness threshold  940  are more likely to provide inaccurate information. 
     In some implementations, the content updater  980  determines a respective currentness score for each portion of the media content item  152  specified by the selection request  904 . In some implementations, a portion of the media content item  152  breaches the currentness threshold  940  when the currentness score for the portion is less than the currentness threshold  940 . For example, as shown in  FIG. 9B , the second portion  152   b  of the media content item  152  breaches the currentness threshold  940  because the second currentness score  942   b  is less than the currentness threshold  940 . In some implementations, a portion of the media content item  152  satisfies the currentness threshold  940  when the currentness score for the portion is greater than the currentness threshold  940 . For example, as shown in  FIG. 9B , the first portion  152   a , the third portion  152   c  and the nth portion  152   n  of the media content item  152  satisfy the currentness threshold  940  because their respective currentness scores  942   a ,  942   c  and  942   n , respectively, are greater than the currentness threshold  940 . 
     In some implementations, the content updater  980  includes an indicator (e.g., the indicator  922  shown in  FIG. 9B ) in the updated media content item  920  in order to indicate that a portion of the media content item  152  specified by the selection request  904  breaches the currentness threshold  940 . In some implementations, the content updater  980  modifies a visual property (e.g., the visual property  926  shown in  FIG. 9C ) of the portion of the media content item  152  that breaches the currentness threshold  940  in order to indicate that the portion breaches the currentness threshold  940 . In some implementations, the content updater  980  removes (e.g., deletes) the portion of the media content item  152  that breaches the currentness threshold  940  in order to avoid presenting information that is not current (e.g., as illustrated in  FIG. 9D ). In some implementations, the content updater  980  replaces the portion of the media content item  152  that breaches the currentness threshold  940  with a portion of another media content item  152  (e.g., the replacement portion  928  shown in  FIG. 9E ) that satisfies the currentness threshold  940 . In some implementations, the content updater  980  includes an additional portion (e.g., the additional portion  930  shown in  FIG. 9F ) in the media content item  152  in order to generate the updated media content item  920 . 
     In various implementations, the content updater  980  performs sufficiency analysis on portions of the media content item  152  indicated by the selection request  904  in order to identify portions that breach a sufficiency threshold  950 . In some implementations, portions of the media content item  152  that breach the sufficiency threshold  950  are less likely to provide sufficient information regarding a subject to which the media content item  152  is directed. In some implementations, portions of the media content item  152  that satisfy the sufficiency threshold  950  are more likely to provide sufficient information regarding the subject to which the media content item  152  is directed. 
     In some implementations, the content updater  980  determines a respective sufficiency score for each portion of the media content item  152  specified by the selection request  904 . In some implementations, a portion of the media content item  152  breaches the sufficiency threshold  950  when the sufficiency score for the portion is less than the sufficiency threshold  950 . In some implementations, a portion of the media content item  152  satisfies the sufficiency threshold  950  when the sufficiency score for the portion is greater than the sufficiency threshold  950 . 
     In some implementations, the content updater  980  removes (e.g., deletes) the portion of the media content item  152  that breaches the sufficiency threshold  950  in order to avoid presenting information that is insufficient (e.g., deficient). In some implementations, the content updater  980  replaces the portion of the media content item  152  that breaches the sufficiency threshold  950  with a portion of another media content item  152  (e.g., the replacement portion  928  shown in  FIG. 9E ) that satisfies the sufficiency threshold  950 . In some implementations, the content updater  980  includes an additional portion (e.g., the additional portion  930  shown in  FIG. 9F ) in the media content item  152  in order to compensate for the insufficient portion in the media content item  152 . 
     In various implementations, the content updater  980  performs clarity analysis on portions of the media content item  152  indicated by the selection request  904  in order to identify portions that breach a clarity threshold  990 . In some implementations, portions of the media content item  152  that breach the clarity threshold  990  are less likely to be clear. In some implementations, portions of the media content item  152  that satisfy the clarity threshold  990  are more likely to be clear. 
     In some implementations, the content updater  980  obtains user input from various users regarding clarity of different portions of the media content item  152 . In some implementations, the content updater  980  determines that a particular portion of the media content item  152  is unclear, if a number of users reporting that the particular portion is unclear exceeds the clarity threshold  990 . In some implementations, the content updater  980  determines that a particular portion of the media content item  152  is clear, if a number of users reporting that the particular portion is unclear is below the clarity threshold  990 . 
     In some implementations, the content updater  980  determines a respective clarity score for each portion of the media content item  152  specified by the selection request  904 . In some implementations, a portion of the media content item  152  breaches the clarity threshold  990  when the clarity score for the portion is less than the clarity threshold  990 . In some implementations, a portion of the media content item  152  satisfies the clarity threshold  990  when the clarity score for the portion is greater than the clarity threshold  990 . 
     In some implementations, the content updater  980  removes (e.g., deletes) the portion of the media content item  152  that breaches the clarity threshold  990  in order to avoid presenting information that is unclear. In some implementations, the content updater  980  replaces the portion of the media content item  152  that breaches the clarity threshold  990  with a portion of another media content item (e.g., the replacement portion  928  shown in  FIG. 9E ) that satisfies the clarity threshold  990 . In some implementations, the content updater  980  includes an additional portion (e.g., the additional portion  930  shown in  FIG. 9F ) in the media content item  152  in order to compensate for the unclear portion in the media content item  152 . 
       FIG. 11  is a flowchart representation of a method  1100  of updating a media content item. In various implementations, the method  1100  is performed by a device with a non-transitory memory and one or more processors coupled with the non-transitory memory (e.g., the electronic device  30  and/or the content updating engine  900  shown in  FIG. 9A ). In some implementations, the method  1100  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  1100  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     As represented by block  1110 , in some implementations, the method  1100  includes receiving a selection request for a first media content item that includes information about a subject. In some implementations, the first media content item is generated by a first media content provider. For example, as shown in  FIG. 9A , the content updating engine  900  receives the selection request  904  from the electronic device  30 . As described herein, in some implementations, the selection request  904  specifies a particular media content item  152  that the user  20  wants to access. 
     As represented by block  1120 , in some implementations, the method  1100  includes identifying a set of media content items that provides information about the subject. In some implementations, at least a portion of the set of media content items is generated by a second media content provider that is different from the first media content provider. For example, as shown in  FIG. 10 , the related content identifier  970  identifies the set of items  910  that provides information regarding the same subject as the media content item  152  specified in the selection request  904 . As described herein, the set of items  910  includes a media content item  152  that is provided by a different media content provider  166  than the media content item  152  specified in the selection request  904 . 
     As represented by block  1120   a , in some implementations, the first media content item was generated at a first time, and the set of media content items includes a second media content item that was generated at a second time that is after the first time. For example, the first media content item includes a book that was written in 1950, and the second media content item is a book that was written in 2010. 
     As represented by block  1120   b , in some implementations, the first media content item is associated with a first content delivery medium, and the set of media content items includes a second media content item that is associated with a second content delivery medium that is different from the first content delivery medium. For example, in some implementations, the first media content item utilizes visual delivery (e.g., the first media content item includes a book or a video), and the second media content item utilizes aural delivery (e.g., the second media content item includes an audio recording such as a podcast). 
     As represented by block  1130 , in some implementations, the method  1100  includes updating the first media content item based on the information provided by the set of media content items in order to generate an updated first media content item. For example, as shown in  FIGS. 9A and 10 , the content updating engine  900  (e.g., the content updater  980 ) updates the media content item  152  specified by the selection request  904  based on the set of items  910  in order to generate the updated media content item  920 . 
     As represented by block  1130   a , in some implementations, the method  1100  includes determining that the first media content item breaches a sufficiency threshold. In some implementations, updating the first media content item includes incorporating a portion of information from the set of media content items into the first media content item in order to satisfy the sufficiency threshold. In some implementations, the method  1100  includes determining that the first media content item lacks information regarding a subtopic, and incorporating information regarding the subtopic from the set of media content items into the first media content item. For example, if a book on the Civil War lacks sufficient information regarding muskets and the user is interested in learning about weapons that were used during the Civil War, then the content updating engine  900  incorporates a relevant portion from a video about muskets. 
     As represented by block  1130   b , in some implementations, the method  1100  includes determining, based on the set of media content items, that a portion of information in the first media content item breaches a currentness threshold. For example, as shown in  FIG. 9B , the content updating engine  900  determines that the second currentness score  942   b  for the second portion  152   b  of the media content item  152  is below the currentness threshold  940 . 
     In some implementations, the method  1100  includes displaying an indicator in association with the portion of information in order to indicate that the portion of information is not current. For example, as shown in  FIG. 9B , the updated media content item  920  includes the indicator  922  in order to indicate that the second portion  152   b  of the media content item  152  is not current. 
     In some implementations, the method  1100  includes modifying a visual property of the portion of the media content item. For example, as shown in  FIG. 9C , the content updating engine  900  modifies the visual property  926  of the second portion  152   b  of the media content item  152  in order to indicate that the second portion  152   b  is not current. For example, in some implementations, the content updating engine  900  displays an “X” through the portion of the media content item  152  in order to indicate that the portion is not current. In some implementations, the method  1100  includes graying-out the portion of the media content item in order to indicate that the portion is not current. 
     In some implementations, the method  1100  includes forgoing displaying the portion of information that breaches the currentness threshold. For example, as shown in  FIG. 9D , the content updating engine  900  removes the second portion  152   b  from the media content item  152  so that the updated media content item  920  does not include the second portion  152   b.    
     In some implementations, the method  1100  includes replacing the portion of information in the first media content item that breaches the currentness threshold with a portion of information from the set of media content items that satisfies the currentness threshold. For example, in some implementations, the method  1100  includes determining that a portion of the first media content item includes information that is outdated, and replacing the outdated information with up-to-date information from another media content item. For example, as illustrated in  FIG. 9E , in some implementations, the content updating engine  900  replaces the second portion  152   b  with the replacement portion  928 . 
     In some implementations, the currentness threshold specifies a threshold age, and determining that the portion of information breaches the currentness threshold includes determining that an age of the first media content item is greater than the threshold age. For example, if the threshold age is 100 years and the portion of information is 120 years old then the content updating engine  900  determines that the portion of information is outdated. In some implementations, the threshold age is a function of the subject. For example, for media content items related to the Civil War, the threshold age may be greater than the threshold age for media content items related to the moon landing. 
     In some implementations, determining that the portion of information breaches the currentness threshold includes determining that the portion of information is inaccurate. 
     In some implementations, determining that the portion of information breaches the currentness threshold includes determining that the portion of information has been disavowed (e.g., debunked or rejected) by at least a portion of the set of media content items. 
     As represented by block  1130   c , in some implementations, the method  1100  includes identifying, based on user feedback, a portion of the first media content item that breaches a clarity threshold (e.g., the clarity threshold  990  shown in  FIG. 10 ). For example, in some implementations, the method  1100  includes identifying, based on feedback from other users, a portion of the first media content item that is unclear. In some implementations, the method  1100  includes modifying the portion of the first media content item in order to satisfy the clarity threshold. For example, in some implementations, the method  1100  includes replacing the portion of the first media content item with a portion from another media content item that satisfies the clarity threshold. In some implementations, the method  1100  includes modifying the portion of the media content item to make the portion clearer. In some implementations, the method  1100  includes collecting the user feedback from a plurality of devices that previously presented the first media content item to their respective users. In some implementations, the method  1100  includes receiving a user input specifying the user feedback. 
     In some implementations, the first media content provider includes a first authoring entity (e.g., a first human writer or a first bot writer) and the second media content provider includes a second authoring entity (e.g., a second human writer or a second bot writer). 
     In some implementations, the first media content provider includes a first publishing entity (e.g., a first publisher, for example, a first publishing company) and the second media content provider includes a second publishing entity (e.g., a second publisher, for example, a second publishing company). 
       FIG. 12  is a block diagram of a device  1200  that updates a media content item in accordance with some implementations. In some implementations, the device  1200  implements the electronic device  30  and/or the content updating engine  900  shown in  FIG. 9A . 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  1200  includes one or more processing units (CPUs)  1201 , a network interface  1202 , a programming interface  1203 , a memory  1204 , one or more input/output (I/O) devices  1210 , and one or more communication buses  1205  for interconnecting these and various other components. 
     In some implementations, the network interface  1202  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  1205  include circuitry that interconnects and controls communications between system components. The memory  1204  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  1204  optionally includes one or more storage devices remotely located from the one or more CPUs  1201 . The memory  1204  comprises a non-transitory computer readable storage medium. 
     In some implementations, the memory  1204  or the non-transitory computer readable storage medium of the memory  1204  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  1206 , the data obtainer  960 , the related content identifier  970  and the content updater  980 . In various implementations, the device  1200  performs the method  1100  shown in  FIG. 11 . 
     In some implementations, the data obtainer  960  obtains a selection request specifying a media content item that a user wants to access (e.g., the selection request  904  shown in  FIGS. 9A and 10 ). In some implementations, the data obtainer  960  performs the operation(s) represented by block  1110  in  FIG. 11 . To that end, the data obtainer  960  includes instructions  960   a , and heuristics and metadata  960   b.    
     In some implementations, the related content identifier  970  identifies a set of one or more media content items that provide information regarding the same subject as the media content item specified by the selection request (e.g., the set of items  910  shown in  FIGS. 9A and 10 ). In some implementations, the related content identifier  970  performs the operations(s) represented by block  1120  shown in  FIG. 11 . To that end, the related content identifier  970  includes instructions  970   a , and heuristics and metadata  970   b.    
     In some implementations, the content updater  980  updates the media content item requested by the user based on the set of items identified by the related content identifier  970  in order to generate an updated media content item (e.g., the updated media content item  920  shown in  FIGS. 9A and 10 ). In some implementations, the content updater  980  performs the operation(s) represented by block  1130  shown in  FIG. 11 . To that end, the content updater  980  includes instructions  980   a , and heuristics and metadata  980   b.    
     In some implementations, the one or more I/O devices  1210  include one or more input devices for receiving a selection request (e.g., the selection request  904  shown in  FIGS. 9A and 10 ). For example, in some implementations, the one or more I/O devices  1210  include a touchscreen display, a keyboard, a mouse or a microphone. In some implementations, the one or more I/O devices  1210  include a display, a speaker and/or a haptic device (e.g., a vibrational device that generates vibrations) for presenting the updated media content item (e.g., for presenting the updated media content item  920  shown in  FIGS. 9A and 10 ). 
     Various processes defined herein consider the option of obtaining and utilizing a user&#39;s personal information. For example, such personal information may be utilized in order to provide an improved privacy screen on an electronic device. However, to the extent such personal information is collected, such information should be obtained with the user&#39;s informed consent. As described herein, the user should have knowledge of and control over the use of their personal information. 
     Personal information will be utilized by appropriate parties only for legitimate and reasonable purposes. Those parties utilizing such information will adhere to privacy policies and practices that are at least in accordance with appropriate laws and regulations. In addition, such policies are to be well-established, user-accessible, and recognized as in compliance with or above governmental/industry standards. Moreover, these parties will not distribute, sell, or otherwise share such information outside of any reasonable and legitimate purposes. 
     Users may, however, limit the degree to which such parties may access or otherwise obtain personal information. For instance, settings or other preferences may be adjusted such that users can decide whether their personal information can be accessed by various entities. Furthermore, while some features defined herein are described in the context of using personal information, various aspects of these features can be implemented without the need to use such information. As an example, if user preferences, account names, and/or location history are gathered, this information can be obscured or otherwise generalized such that the information does not identify the respective user. 
     While various aspects of implementations within the scope of the appended claims are described above, it should be apparent that the various features of implementations described above may be embodied in a wide variety of forms and that any specific structure and/or function described above is merely illustrative. Based on the present disclosure one skilled in the art should appreciate that an aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method may be practiced using any number of the aspects set forth herein. In addition, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to or other than one or more of the aspects set forth herein. 
     It will also be understood that, although the terms “first”, “second”, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first node could be termed a second node, and, similarly, a second node could be termed a first node, which changing the meaning of the description, so long as all occurrences of the “first node” are renamed consistently and all occurrences of the “second node” are renamed consistently. The first node and the second node are both nodes, but they are not the same node. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising”, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting”, that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Metadata:
Filing Date: 20210423
Publication Date: 20220426
Grant Date: 20220426
Priority Date: 20200529
Inventors: Richter, Ian M.
Assignee: APPLE INC
CPC Classifications: [{"code": "H04N21/4667", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4661", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4532", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4126", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/44222", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/016", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N21/4667", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/016", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4532", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4126", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/44222", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/435", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4661", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/44222", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/435", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/016", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4532", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4667", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N21/4661", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N21/4126", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 81260023