Patent Publication Number: US-2015066897-A1

Title: Systems and methods for conveying passive interest classified media content

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates to a systems and methods for automatically selecting and conveying a subset of content items from a larger collection of content items. More specifically, the present invention relates to systems and methods that observe consumer interaction with content items, then selects and conveys a subset of the larger collection of content items to the consumer based on the consumer&#39;s interaction with the content. 
     Computer systems have long been used as a medium to store and convey content, and are quickly becoming the preferred medium as storage capacities and outlet channels have increased dramatically in recent years. For example, a typical personal computer or tablet is relatively inexpensive and can easily hold hundreds of movies and thousands of pictures or songs, and business or professional computer servers can hold and convey vastly more content. When the amount of stored content exceeds a few dozen items, it becomes increasingly difficult to efficiently display relevant content items to the consumer. Consumers typically either do not want to view large quantities of content items or simply do not have time to consume large quantities of content items, much less all the content items in any particular group. Rather, consumers would rather consume the most relevant content items at any given point in time. For example, if a storage system contains 50,000 pictures, it may be too time consuming and probably impractical for the consumer to view even a fraction of the pictures (e.g., 10%-20%) in the database. Accordingly, computer systems typically display only a small subset of the total collection of content items available. In this example, the computer system may display a 100 picture subset from the total collection of 50,000 pictures. One challenge with this approach is determining which content items to include as part of the subset. Several methods for creating this content subset are known in the art; however, each method has drawbacks. 
     The most common method for creating a content subset is through a query or a “search”. Here, a consumer may enter one or more keywords or phrases into a query to conduct a search for metadata related to those keywords or phrases. The system preferably displays content items having relevant metadata matching or closely related to the keywords or phrases entered by the consumer as part of the search. For example, entering the keyword “basketball” into a streaming video service search box may search for videos having metadata matching or related to the search term “basketball”. This subset of videos (i.e., those having metadata matching or related to “basketball”) are displayed for consumer selection from the larger collection of videos. 
     This display method, however, has several drawbacks. For example, a consumer merely browsing a collection of content items may not be knowledgeable about what specific items are available and, therefore, fail to enter relevant keywords or phrases related to that content because the user lacks the proper knowledge related to the relevant search terms. This issue may be exacerbated should the consumer forget about previously created content, or if the user is unaware of other content created by a third party. For example, if a consumer wants to watch a movie but does not have a specific movie in mind, the consumer has no way of efficiently browsing the movie listings using keywords because the desired content is unknown. The consumer simply cannot devise search terms for something unknown and is relegated to two basic options: (a) use broad search terms by genre (e.g., comedy, drama, action, etc.) that may net hundreds or thousands of results that may or may not appeal to the consumer; or (b) enter random keywords, hoping to find someone of interest. Obviously, both of these processes are highly inefficient and undesirable. 
     Another method for creating a subset of content items involves consumer annotations. In this respect, a consumer may annotate (e.g., vote or rate) various content items in a collection of content items. The system can later present to the consumer a subset of items from the content collection based on the consumer annotations (e.g., movies the consumer voted for or rated with five stars). For example, a streaming video system may allow consumers to vote on movies and may then provide the consumer with a subset of the total movies available based on the voting. If the consumer tends to vote basketball movies more favorably than other types of sports movies, the subset would include more basketball movies and fewer movies relating to other sports. Additionally, this type of voting system could be used to determine what content items are initially shown to new consumers who have never voted on an item. For example, a positive vote may increase the rating of certain content, while a negative vote may decrease the content rating. New consumers (e.g., new members to the video streaming service) are shown only highly-rated content, while low-rated content may be displayed lower on the list, shown on subsequent pages, or not at all. Unfortunately, this method requires that the consumer regularly annotate content items, and preferably a large quantity of content items, as more annotations tend to produce better results. This is a tedious process that many consumers tend to neglect over time due to lack of time or patience. Moreover, some consumers may lose interest in the annotation process before fully experiencing the system&#39;s functionality as it can take several weeks or months to annotate enough content items to create relevant subsets. 
     A third approach is to have the system sort and display content items based on some objective measure (e.g., date created). For example, the system may sort the database of content items by date, and display only a subset of content items created in the last hour. This approach may be excellent for a blog or other similar website where the content consumer is likely interested in all or a substantial amount of the content items available, and has interest in following new content items as they are posted. But, if the consumer is only interested in a small fraction of the total available content items (e.g., as is typically the case with a streaming video or audio service), it will be difficult to locate relevant or interesting content items based only on objective criteria like the creation date. Alternatively, the objective criteria may be crowd-sourced information such as, inter alia, voting results, viewer ratings, number of downloads, or number of views. The system would similarly display all content items meeting a certain threshold value. For example, the system may display only videos with 100,000 or more views. Both approaches, however, are impersonal as no deference is given to the individual consumer&#39;s preferences, but rather the crowd as a whole. 
     Another approach involves letting consumers follow certain content producers identified as producing interesting or relevant content. This system allows consumers to add producers to a preferred list, and then automatically display only those content items created by the selected producers on the preferred list. One issue with this system is that consumers must search out and find the producers they want to follow. Again, a system that displays only content items from known producers may eliminate lesser or relatively unknown produces, even if those lesser or unknown producers may be producing content relevant or interesting to certain consumers. Thus, such a system does not allow consumers to discover new producers or their content. 
     Additionally, some conventional systems combine two or more of the aforementioned systems or methods to attempt to provide better results. For example, a system may provide a consumer with only highly rated content matching a specific keyword or phrase created after a specific date and created by a specific content producer listed on a preferred list. Such systems are capable of delivering highly personalized results to the consumer, but the consumer must provide continuous input to ensure optimal results. That is, the consumer must, inter alia, continuously enter search terms, manage the content producer preferred list, and vote on or rate content items. Over time this process becomes unsustainable and leads to a reduction in the quality of the content items displayed to the consumer. Moreover, the perceived quality of the overall system depreciates over time as more consumers are unable to maintain the level of input required to produce satisfactory results. Thus, regardless of the combinations, there is simply no way to balance the need for frequent consumer input and impersonal results. These systems either require extensive and unsustainable user input (e.g., annotation, creating preferred content producer lists, keyword discovery, etc.) or are reliant on crowd-sourced input that produces generalized and impersonal results. 
     Moreover, existing systems are also vulnerable to a variety of malicious activity. For example, voting systems are susceptible to tampering by fake accounts (e.g., created by a content producer) created solely for the purpose of voting or rating “up” or “down” certain content. In this respect, such fake accounts can have an adverse impact on the relevance of hundreds or even thousands of content items. Alternatively, computer viruses or malware (known as a “botnet”) may infect or hack user accounts, thereby allowing malicious consumers or producers to use controlled devices (“bots”) to vote on content items. Manipulating the voting system in this respect is representative of only a few, rather than representing the true popularity of the content. 
     Thus, there is a significant need in the art for systems and methods for creating and displaying a filtered subset of information to a consumer based on a larger collection of content items to maintain high-quality personal results without the extensive or exhaustive continuous input from the consumer. The present invention fulfills these needs and provides further related advantages. 
     SUMMARY OF THE INVENTION 
     The systems and methods disclosed herein for conveying automatic passive interest classified media content include storing a plurality of media content items on a storage device and associating metadata with each of the media content items. A media content subset is then created from the plurality of media content items to be conveyed over a communication network to an interactive presentation environment for consumption by a user. The user can consume the media content in the subset by selecting, viewing or otherwise listening to the media content. The system analyzes the consumption patterns of the media content in the subset by the user, and preferably specifically with respect to the user&#39;s interaction with the presentation environment. In response, the media content subset may be modified by adding one or more media content items to the subset or removing one or more media content items from the subset, based on the consumption habits of the user. 
     For example, in a preferred embodiment, media content consumed or selected by the consumer is retained in the subset and other similar content may be added to the subset, while unrelated or unselected content may be removed from the subset. In this respect, the analyzing step preferably includes identifying consumption habits through user interaction with the interactive presentation environment and associating those habits as metadata with respect to the consumed media content items. This, accordingly, allows the system to modify the media content subset by removing unconsumed or less relevant media content items or adding relevant media content items based on comparisons and similarities among the metadata stored in association with each media content item. 
     The storage device preferably includes a local storage medium, a network-connected storage medium, or a cloud service. The plurality of media content items preferably include some form of consumable audio content (e.g., by listing to the content), visual content (e.g., by watching the content), or a combination of audio and visual content (e.g., a movie or sit-com). In one embodiment, the metadata may be uploaded to the system by the producer of the content. Here, the metadata is pre-loaded and may include producer metadata, consumer metadata or category metadata. Furthermore, the system may form a producer relevancy table categorically listing one or more content producers that create or upload content relevant to the interests of the user. The system is able to use the aforementioned metadata to associate the media content item with other media content items on the storage device to form a media content subset relevant to the user. Moreover, the creating step may include selecting one or more of the plurality of media content items for inclusion in the media content subset based on an input received from the user through the interactive presentation environment. In one embodiment, the input may be a keyword and the media content subset may include one or more media content items having metadata matching or substantially similar to the keyword. 
     In another embodiment as disclosed herein, the systems and methods include conveying automatic passive interest classified media content to a cohort. In this embodiment, the system stores a plurality of media content items and associated metadata on a storage device and creates a media content subset from the plurality of media content items based on similarities in the metadata. The cohort is formed from a plurality of users having similar consumption habits, i.e., similar interactions with the media content items presented as part of an interactive presentation environment. To this end, the media content items in the subset are conveyed to users over a communication network for consumption by the users of the cohort via the interactive presentation environment. The system analyzes the consumption habits of the media content subset by the users of the cohort over the interactive presentation environment and modifies the media content subset from the plurality of media content items in the storage device in response to the analyzed consumption habits of the media content subset by the users of the cohort. 
     Preferably, the system generates cohort metadata for each of the plurality of the media content items based on the consumption habits of the users in the cohort. This way, the system can better gauge which users are more relevant to the cohort and which users are less relevant. To this end, consumers with similar consumption habits may be added or retained within the cohort, while other consumers with dissimilar consumption habits may be excluded or removed from the cohort. Of course, a user may be assigned to multiple cohorts and cohort assignment may change over time as determined by individual user consumption and relative to consumption by other users. In a similar respect, the consumption habits of a user in a cohort may change in response to the consumption habits of the user with the media content subset via the interactive presentation environment. The system may also include or permit the formation of multiple cohorts and associate a producer relevancy table with each cohort. 
     In another alternative embodiment of the systems and methods disclosed herein, a method for conveying automatic passive interest classified media content to a user may include storing a plurality of media content items and associated metadata on a storage device, creating multiple media content subsets from the plurality of media content items, wherein the media content items in each of the media content subsets have related metadata. The one or more media content subsets are then conveyed over a communication network to at least one media inbox associated with the user through an interactive presentation environment. The system analyzes media inbox selection and consumption of the respective media content subsets therein by the user over the interactive presentation environment and modifies the consumed media inbox and media content subset based on user selection and consumption habits with the consumed inbox and the consumed content subset over the interactive presentation environment. The media inbox may include a consumer inbox and a cohort inbox, and the system may assign multiple media inboxes to a single user. Additionally, the conveying step may include conveying to the user a pre-selected inbox, a search inquiry inbox, an objective criteria inbox, or a manually selected inbox. 
     In another embodiment, the systems and methods for conveying automatic passive interest classified media content to a user in a feedback responsive presentation environment includes storing a plurality of media content items and associated metadata on a storage device, creating a media content subset from the plurality of media content items based on a consumption habit profile unique to the user, conveying the media content subset over a communication network to the feedback responsive presentation environment, analyzing interaction of the conveyed media content items from the media content subset by the user within the feedback responsive presentation environment, and modifying the consumption habit profile of the user in response to consumption or non-consumption of the media content items in the media content subset. The modifying step may further include modifying the media content items in the media content subset in response to modification of the consumption habit profile of the user. Additionally, the feedback responsive presentation environment preferably includes a continuously moving stream of the media content items, such as a horizontal stream, a vertical stream, or a manually scrollable stream. The media content items relatively more pertinent to the user are preferably more prominent in the feedback responsive interactive presentation environment than other media content items relatively less pertinent to the user. 
     The systems and methods disclosed herein may also include conveying automatic passive interest classified media content by way of storing a plurality of media content items and associated metadata on a storage device, extracting at least one feature from one or more of the media content items, and creating a media content subset from the plurality of media content items based at least in part on similar features extracted from the media content items. The media content subset is then presented over a communication network to an interactive presentation environment for consumption by a user. The consumption of the media content subset by the user over the interactive presentation environment is analyzed and the media content subset may be modified with the plurality of media content items from the storage device in response to the analyzed user consumption. The metadata associated with the media content item may be augmented with the feature, which may include a producer feature or a media content feature. Additionally, this method may further include the steps of comparing the extracted at least one feature with the metadata and supplementing the metadata with the extracted feature, when non-duplicative. Furthermore, the extracting step may include the step of extracting a first feature from the media content item with a first extractor and extracting a second feature from the media content item with a second extractor, wherein the system is able to weigh the relevancy of the first and second features based on the relevancy of the first and second extractors relative to the first and second features. Of course, a higher relevancy corresponds with a higher weight. 
     Other features and advantages of the present invention will become apparent from the following more detailed description, when taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings illustrate the invention. In such drawings: 
         FIG. 1  is a schematic view illustrating a preferred embodiment wherein the systems and methods disclosed herein select a media content subset from a plurality of media content items for display to a consumer; 
         FIG. 2  is a schematic view similar to  FIG. 1 , further illustrating the logic behind selecting and conveying the media content subset; 
         FIG. 3  is a flowchart illustrating a method for conveying the media content subset in accordance with one embodiment disclosed herein; 
         FIG. 4  is a schematic view illustrating a producer uploading media content to a storage device; 
         FIG. 5  is a schematic view illustrating an extractor extracting one or more media content features and a set of producer features from the plurality of media content items stored on the storage device; 
         FIG. 6A  is a schematic view illustrating the use of multiple extractors simultaneously extracting the media content features and the producer features to create a set of media metadata and a set of producer metadata; 
         FIG. 6B  is a schematic view illustrating an extractor determining which of a plurality of extractors should be used to extract the media content metadata and the producer metadata from the media content items; 
         FIG. 7  is a schematic view illustrating a content selector creating a subset of the plurality of media content items; 
         FIG. 8  is a schematic view of a cohort generator grouping the consumers into one or more cohorts; 
         FIG. 9  is a schematic view illustrating an inbox generator generating one or more inboxes and an inbox selector selecting and fetching the one or more inboxes for presentation to the consumer; 
         FIG. 10  is a diagrammatic view illustrating an inbox containing a mixture of high relevance, medium relevance, low relevance, and undermined relevance selected media content items; 
         FIG. 11  is a schematic view of a presentation environment presenting an inbox containing the media content subset to the consumer; 
         FIG. 12  is a diagrammatic view illustrating the presentation environment displaying selected media content items to the consumer in different sizes based on relevance; 
         FIG. 13  is a schematic view illustrating an analyzer analyzing a set of consumed selected media content items and saving the information obtained therefrom as a set of consumer metadata; and 
         FIG. 14  is a schematic view illustrating the system creating a producer relevancy table. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     As shown in the drawings for the purposes of illustration, the present invention for the systems and methods for conveying content based on automatic passive interest classification is shown generally by reference numeral  10  in  FIGS. 1-14 . As illustrated in  FIG. 1 , the content conveyance system  10  includes a group of media content items  12  that may include individual media content items  12   a,    12   b,    12   c  . . .  12   n  stored on a storage device  14 , such as a local hard drive or solid state drive, a network-connected storage device, a cloud service, or any other local or remote storage device known in the art. The media content items  12  may have virtually any structure or format known in the art including, inter a/ia, documents, text strings, pictures, videos, audio files, or any combination thereof. Of course, a single media content item could include one or more of the aforementioned structures or formats, and the system  10  could store and use media content items  12  having different structures and/or formats or different combinations of the aforementioned structures and/or formats. 
     The system  10  further includes one or more producers  16  that create some or all of the individual media content items  12   a,    12   b,    12   c  . . .  12   n  stored on the storage device  14 . The one or more producers  16  may be, for example, a human, a machine (e.g., an automated service, a content aggregator, a sensor, or any other non-human agent capable of generating the media content items  12 , as described herein), or any combination thereof. Once created, the producer  16  uploads a new media content item (e.g., yet to be uploaded media content item  12   e ) to the system  10  for storage on the storage device  14 . Preferably, the system  10  further stores and associates a set of media metadata  18   e  with the new media content item  12   e.  In this respect, each of the media content items  12   a,    12   b,    12   c  . . .  12   n  in the storage device  14  include respective media metadata  18   a,    18   b,    18   c  . . .  18   n  for use in accordance with the embodiments disclosed herein. For example, the media content item  12   a  would include the media metadata  18   a,  the media content item  12   b  would include the media metadata  18   b,  and so on. In general, the media metadata  18  ( FIG. 2 ) may include both objective and subjective information about the respective media content item  12 , such as the name of the producer  16 , the creation date, the subject matter, etc. Moreover, the media metadata  18  may include subject matter descriptions that vary from a broad category to more specific or descriptive categories, to more accurately identify the respective media content items  12 . For example, the respective media content item  12  may include the relatively broad category “sports”, followed by more specific descriptive categories that include “basketball” and/or “slam dunk”. The media metadata  18  may be embedded and associated with the respective media content item  12  or stored elsewhere on the storage device  14  (e.g., in a media content information registry) and simply associated with the respective media content item  12 . Furthermore, the producer  16  may provide the media metadata  18 , e.g., by embedding the respective media content item  12  with the media metadata  18  before uploading the respective media content item  12  to the system  10 , as illustrated in  FIG. 1 . Alternatively, the system  10  may extract the media metadata  18  from the respective media content item  12  after upload, as illustrated in  FIG. 2 . 
     The content conveyance system  10  also preferably includes one or more consumers  20  who consume some portion of the media content items  12   a ,  12   b ,  12   c  . . .  12   n . Like the producers  16 , the consumers  20  may be a human, a machine (e.g., an automated process), or any combination thereof. As discussed in greater detail below, the system  10  includes a content selector  22  that selects and retrieves one or more of the media content items  12  for inclusion as a set of selected media content items  23  in a media content subset  24 . In the embodiment shown in  FIG. 2 , the content selector  22  creates the media content subset  24  by specifically selecting the media content items  23   a ,  23   b ,  23   c ,  23   d  from the media content items  12   a ,  12   b ,  12   c  . . .  12   n  stored on the storage device  14 . In this example, the selected media content item  23   a  may correspond to the media content item  12   a , the selected media content item  23   b  may correspond to the media content item  12   b , the selected media content item  23   c  may correspond to the media content item  12   e , and the selected media content item  23   d  may correspond to a media content item  12   i.  This subset  24  of the media content items  12  is later conveyed to and consumed by the consumer  20  in accordance with the embodiments disclosed herein. 
     The content selector  22  uses information generated by an analyzer  26  in determining which of the media content items  12  to include in the media content subset  24 . In this respect, the analyzer  26  is designed to extract a set of consumption features  28  for storage as a set of consumer metadata  30  in connection with the consumer  20 . The consumer metadata  30  preferably includes information about the consumer  20 , such as age, gender, consumption habits, interests (e.g., “basketball”, “baseball”, “traveling”, etc.), and the like. In one embodiment, the analyzer  26  obtains the consumer metadata  30  by way of information provided by the consumer  20  (e.g., a questionnaire, survey, completing a consumer profile page, voting, rating, annotating one or more of the media content items  12 , etc.). Alternatively, the analyzer  26  may extract the consumer metadata  30  from the consumer  20  by way of monitoring and analyzing consumption habits (e.g., keyword searches, watching videos, viewing pictures, etc.), as described in more detail below. 
     The content selector  22  creates the subset  24  by comparing the consumer metadata  30  with the media metadata  18  for each of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  on the storage device  14  based on one or more selection criteria. If any of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  meet the selection criteria, the content selector  22  may select and place matching media content items  12  (e.g., the selected media content items  23   a ,  23   b ,  23   c ,  23   d  shown in  FIG. 2 ) into the media content subset  24  for later selected consumption by the consumer  20 . Conversely, the content selector  22  preferably does not select any of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  that fail to meet the selection criteria of the subset  24 . The selection criteria may be any method known in the art for determining similarity (or lack thereof) between two different sets of metadata (e.g., the media metadata  18  and the consumer metadata  30 ). For example, the content selector  22  may select and place certain media content items  12  into the media content subset  24  only if there is an exact match between keywords or phrases in the two sets of metadata  18 ,  30  (e.g., the keyword “basketball” may need to be in both the media metadata  18  and the consumer metadata  30 ). Alternatively, the content selector  22  may use more inclusive selection criteria. For example, if the consumer metadata  30  includes a keyword for a specific sport (e.g., “basketball”), the content selector  20  may include in the subset  24  all media content items  12  having the metadata  18  containing keywords for any type of sport (e.g., “baseball”, “football”, “hockey”, etc.). 
     In an alternative embodiment, the content selector  22  may further allow the consumer  20  to manually search the media content items  12  via a query or search feature. Such a feature may be used independently or in conjunction with automatically identifying and selecting the media content items  12  without consumer input, as described above. Here, the content selector  22  may compare the consumer-input keywords or strings with the media metadata  18  for each of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  in the storage device  14 . If any of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  meet the selection criteria, the content selector  22  places the selected media content items (e.g., the items  23   a ,  23   b ,  23   c ,  23   d ) into the media content subset  24  for selected consumption by the consumer  20 . Moreover, the content selector  22  may allow the consumer  20  to search text within text-based content items (e.g., electronic books, magazines, news articles, etc.). The content selector  22  is preferably able to search the substance (e.g., all text) of text-based media content on the storage device  14  to better identify which of the media content items  12  contain the entered keyword or strings. Of course, the content selector  22  could implement a smart search algorithm that does not necessarily require a one-for-one keyword match, but rather searches for and identifies relevant content based on keyword associations, similar to a Google® search. 
     More specifically, the analyzer  26  preferably analyzes how the consumer  20  consumes the media content subset  24  and stores the analysis results as the consumer metadata  30  associated with each consumer  20 . The analyzer  26  determines which of the selected media content items  23  in the media content subset  24  the consumer  20  actually consumes. The analyzer  26  then extracts the consumption features  28  from each of the consumed selected media content items  23   a ,  23   b ,  23   c  and/or  23   d  and stores these consumption features  28  as keywords or phrases as the consumer metadata  30  for use by the content selector  22  in generating future media content subsets  24 . The consumption features  28  extracted from the analyzed media content items in the subset  24  may include, inter alia, the subject matter of the selected media content item (e.g., keywords such as “basketball”, “football”, or more generally “sports”); the people, places, and things portrayed in the consumed selected media content items  23   a ,  23   b ,  23   c  and/or  23   d  (e.g., location of a video or a specific product mentioned therein); the reputation of the consumed selected media content items  23   a ,  23   b ,  23   c  and/or  23   d  created by the producer  16 ; the geographical location of the producer  16 ; and other media content items  12  viewed by consumers  20  who also view the analyzed and consumed selected media content items  23   a ,  23   b ,  23   c  and/or  23   d . Alternately, the analyzer  26  may not extract the consumption features  28  from the consumed media content items  23   a ,  23   b ,  23   c  and/or  23   d . Instead, the analyzer  26  may only determine which of the selected media content items  23  the consumer  20  actually consumes, and then uses the media metadata  18  from the consumed selected media content items  23   a ,  23   b ,  23   c  and/or  23   d  to create the consumer metadata  30 . 
     The content selector  22  preferably automatically creates the media content subsets  24  from the larger collection of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  in the storage device  14 . In this embodiment, the content selector  22  may eliminate the need for input from the consumer  20 . That is, the consumer  20  no longer needs to vote on, rate, or otherwise annotate any of the media content items  12  to receive relevant media content subsets  24 . For example, relevant interests may include hobbies, age-related interests or groups, geographic locations, or virtually any other categorical area of interest. Furthermore, the media content subsets  24  are highly personalized because selections are based on metadata collected directly from each consumer  20 . Thus, the system  10  is not reliant on crowd-sourcing or objective data in selecting relevant media content subsets  24  to present to each consumer  20 . 
       FIG. 2  illustrates additional aspects of the system  10  relative to those shown and described above with respect to  FIG. 1 . Here, the system  10  may include an extractor  32  for extracting a set of media content features  34  through analysis of the media content items  12   a ,  12   b ,  12   c  . . .  12   n  on the storage device  14 . The media content features  34  extracted by the extractor  32  may include the subject matter of the media content items  12 , common words or proper names associated with the media content items  12 , the identity of a face in the media content items  12 , or any other feature known in the art. In one embodiment, the extractor  32  may extract multiple features  34  from the respective media content items  12  or multiple extractors  32  may extract different features  34  from the respective media content items  12 , as described in more detail below with respect to  FIG. 5 . For example, one extractor may extract the subject matter of the media content item, while another extractor may extract location information from the same media content item. 
     The extractor  32  may also extract a set of producer features  36  and store this information as a set of producer metadata  38 . The producer features  36  may include the name, age, occupation, geographic location, interests, past content item production history, popularity, or any other characteristic relating to the producer  16 . The producer metadata  38  may be stored in a producer profile (not shown in  FIG. 2 ) or anywhere else accessible to the system  10 . 
     The system  10  may further optionally include a producer relevancy table  40  that includes a directory or list of the producers  16  who produce the media content items  12  relevant or interesting to a specific consumer  20 . The system  10  builds the producer relevancy table  40  by comparing the consumer metadata  30  with the producer metadata  38  based on certain producer relevancy criteria such as, inter alia, objective criteria (e.g., number of downloads or views), popularity (e.g., ratings by other consumers) or similar characteristics (e.g., interests such as basketball, age, geographic location, occupation, etc.). For example, if the consumer metadata  30  of a specific consumer  20  includes the interest “basketball”, the producer relevancy table  40  may include producers who produce media content items  12  related to “basketball”. New producers or producers with little or no information will typically have little to no relevancy until the producer  16  builds a reputation among the consumers  20  based on the content produced. The system  10  may also permit the consumers  20  to manually add producers  16  to the producer relevancy table  40  as needed or desired. 
     The system  10  may also optionally include a cohort generator  42  that creates one or more cohorts  44 . The cohort  44  is essentially a group of the consumers  20  the cohort generator  42  deems similar based on one or more cohort selection criteria. The cohort selection criteria may include, for example, hobbies (e.g., sports or travel), age, gender, political affiliation, or virtually any other method of categorizing or sorting the interests or character information of individual or groups of the consumers  20 . Although, preferably, the cohort selection criteria is based, at least in part, on consumption of similar media content items  12 . In one example, the cohort generator  42  may create a cohort of democratic consumers and create another cohort of republican consumers. The system  10  may treat each cohort  44  similar to each individual consumer  20  for purposes of creating metadata and extracting relevant content items or features. 
     More specifically, each cohort  44  may have a set of cohort metadata  46  collected by the analyzer  26  that collectively represents the individual consumption habits of the consumers  20  that make up each cohort  44 . Moreover, each cohort  44  may include its own producer relevancy table  40 ′, again representative of the collective consumption habits of the consumers  20  in the cohort  44 . Alternately, each cohort  44  may simply be a group of similar consumers  20  and have no unique group characteristics (e.g., the cohort metadata  46  or the producer relevancy table  40 ′). The consumers  20  may also belong to multiple cohorts  44 . For example, one consumer  20  could be a member of a “basketball” cohort and a member of a “republican” cohort. Alternatively, the system  10  may restrict consumer enrollment to a specific number of the cohorts  44 , or to a single cohort  44 . Additionally, the system  10  preferably automatically organizes the placement of the consumers  20  in the cohorts  44  based on certain objective data, such as relevancy, popularity, interest, etc., although the consumers  20  may be given the option to manually joint or leave a cohort  44 . One benefit is that the cohorts  44  may reduce the number of the media content subsets  24  in the system  10 . For example, the system  10  may only need to create a single media content subset  24  representative of a class of consumers, instead of creating a media content subset  24  for each of the consumers  20 . Streamlining the creation and conveyance of the media content subsets  24  may reduce the overall processing and bandwidth demands placed on the system  10 . In a specific example, instead of generating multiple media content subsets  24  related to the “basketball” media content item  12  for each consumer  20  interested in basketball, the system  10  can group all these consumers  20  into one cohort  44  related to a single “basketball” media content subset  24 . 
     The system  10  may also include an inbox generator  48  for creating one or more inboxes  50  that store the media content subsets  24  of the selected media content  23  for the consumer  20  or the cohort  44 . Here, the one or more inboxes  50  may receive and store the media content subsets  24  from the content selector  22  for consumption thereof by the consumers  20  or by consumers subscribing to the cohort  44 , as described herein. In one embodiment, the media content subsets  24  related to the interests of the consumer  20  alone or related to the interests of one or more of the cohorts  44  to which the consumer  20  belongs, may be grouped together and delivered to a single inbox  50 . In another embodiment, each consumer  20  may have a separate inbox  50  for each interest and/or for each cohort  44 . Here, for example, the consumer  20  may have three inboxes  50 , one inbox for an individual interest in “basketball”, a second inbox for a first cohort related to “boating” and a third inbox for a second cohort related to “action movies”. Likewise, each cohort  44  may have one inbox  50  shared by all of its member consumers  20 , individual inboxes  50  for each of its member consumers  20 , or any number of the inboxes  50  as may be desired or needed. 
     The system  10  may further include an inbox selector  52  for fetching the one or more inboxes  50  for the consumer  20  in response to a request. As mentioned above, each consumer  20  may have several of the inboxes  50  that relate to various interests or cohort memberships. As such, the consumer  20  may have the option of viewing all inboxes at once, a select number of all the inboxes, or the consumer  20  may only be interested in viewing one inbox. Preferably, the inbox selector  52  automatically pre-selects one or more of the inboxes  50  based on a search request by the consumer  20 . Here, the consumer  20  need only provide search criteria for the inbox selector  52  to use to identify and present the relevant inboxes  50  to the consumer  20 . In another embodiment, the inbox selector  52  may automatically pre-select one or more of the inboxes  50  without input from the consumer  20 . Here, the inbox selector  52  may retrieve a newest inbox, a random inbox, an inbox the consumer  20  has never viewed, or any other inbox as the system  10  may be designed to retrieve or fetch. In another alternative embodiment, the consumer  20  may be able to review and manually select one or more of the inboxes, as desired, and the inbox selector  52  retrieves those one or more inboxes accordingly. 
     The system  10  further includes a presentation environment  54  that conveys all or a portion of the selected media content items  23  in the media content subset  24  and/or the retrieved inboxes  50  for consumption by the consumer  20 . The presentation environment  54  may be a computer monitor, television, projector, mobile device (e.g., smartphone or tablet), audio playback device, large-format display in a public venue, or any other type of device or venue for conveying the information described herein. Of course, the nature of the presentation environment  54  may depend on the type of selected media content  23  (e.g., video, audio, text, or picture). Likewise, the specific method of consumption (e.g., watching, listening, or reading) will depend on the type of selected media content  23 . The presentation environment  54  may also permit the consumer  20  to ignore certain selected media content  23 , vote or rate the selected media content  23 , comment on the selected media content  23 , or otherwise add metadata. 
       FIG. 3  illustrates one method ( 100 ) for conveying the media content subset  24  in accordance with the embodiments disclosed herein. The steps and related apparatuses of method ( 100 ) are more specifically shown and described below with respect to  FIGS. 4-14 . In this respect, the first step ( 102 ) is for one or more of the producers  16  to create one or more of the media content items  12 . The producer(s)  16  may create the media content item(s)  12 , for example, by recording video or audio, taking pictures, writing text, or by other methods known in the art for creating media content for use with the system  10 . 
     The next step ( 104 ) is for one or more of the producers  16  to upload the media content items  12  to the storage device  14 , as illustrated in  FIG. 4 . The producer(s)  16  may upload the media content item(s)  12  to the system  10  via the Internet, a local area network (LAN), a virtual private network (VPN) or some other suitable method known in the art for transferring data. The producer(s)  16  may optionally include the media metadata  18  embedded with the respective media content items  12  or otherwise associated therewith, for use by the content selector  22  in creating the media content subsets  24 . 
     In the next step ( 106 ), the extractor  32  may optionally extract one or more of the media content features  34  from the media content items  12 , as illustrated in  FIG. 5 . Here, the extractor  32  analyzes the media content items  12  and may extract one or more of the media content features  34  and/or one or more of the producer features  36 . In one embodiment, this step ( 106 ) may include a single extractor  32  that extracts all the relevant media content features  34  and/or the relevant producer features  36  from the media content items  12 . Alternately, step ( 106 ) may involve using multiple extractors  32  that extract the same or different media content features  34  from the media content items  12 . For example, one extractor may extract subject matter information and another extractor may extract location information. Although, it may not be necessary to perform step ( 106 ) if the producer(s)  16  include the media metadata  18  embedded or associated with the respective media content items  12  when the content items  12  are uploaded as part of step ( 104 ). Alternatively, the system  10  may still perform step ( 106 ) to augment, supplement or check the accuracy of the media metadata  18  provided by the producer(s)  16 . 
     The next step is for the extractor  32  to save the media content features  34  and/or the producer features  36  as metadata ( 108 ). More specifically, as illustrated in  FIG. 5 , the extractor  32  may save the media content features  34  as the media metadata  18  and the producer features  36  as the producer metadata  38 . The media metadata  18  may be embedded with the media content items  12  or saved elsewhere on the storage device  14  (e.g., in a media metadata registry) while remaining associated with the media content items  12 . Moreover, the media metadata  18  embedded with media content items  12  ( FIG. 1 ) by the producer  16  may also be copied into or placed in the media metadata registry (not shown). In an embodiment wherein the system  10  uses one extractor  32 , the media content features  34  and/or the producer features  36  extracted as part of step ( 106 ) may simply be respectively saved to the media metadata  18  and/or the producer metadata  38  as shown in  FIG. 5 . 
     In another embodiment wherein the system  10  uses multiple feature extractors  32 , a weighing system  56  may optionally aggregate the results of the multiple feature extractors  32  to provide enhanced categorization. In this respect, the weighing system  56  may accord more or less weight to specific features obtained by the multiple extractors  32  based on the relevancy of the subject matter to the respective extractor. For example, in one embodiment, the system  10  may use three feature extractors, such as a sports feature extractor  32   a , a travel feature extractor  32   b , and a general feature extractor  32   c , as illustrated in  FIGS. 6A and 6B . In  FIG. 6A , the sports feature extractor  32   a  is optimized for sports-related media content, and thus is relatively accurate when extracting sports-related features, but may be relatively inaccurate with non-sports-related features. Similarly, the travel feature extractor  32   b  may be optimized for travel-related features, but not for non-travel-related features. Conversely, the general feature extractor  32   c  is a generalized feature extractor, and is not optimized for any specific type of media content item  12 . When extracting the media content features  34  and/or the producer features  36  from a “basketball” video, the weighing system  56  may accord the sports feature extractor  32   a  the most weight, followed by the general feature extractor  32   c , and the travel feature extractor  32   b  may be given the least weight. Conversely, when extracting features from a “travel” video, the weighing system  56  may give the travel feature extractor  32   b  the most weight, followed by the general feature extractor  32   c , and the sports feature extractor  32   a  may be given the least weight. As such, the weighing system  56  improves the accuracy of step ( 108 ) by giving priority to the specific feature extractor  32  optimized for reading information from the media content items  12 . In this respect, the content selector  22  may generate the media content subset  24  based on the weight of the media metadata  18  and/or the producer metadata  38  stored on the storage device  14  in association with respective media content items  12 . 
     Alternatively, the general feature extractor  32   c  may determine the general category of a feature (e.g., “basketball” or “travel”), and the more specialized feature extractors  32   a ,  32   b  may extract the media content features  34  and/or the producer features  36  to be saved as the metadata  18 ,  38 , as illustrated in  FIG. 6B . For example, the general feature extractor  32   c  would decide the overall category of the media content item  12 , then the sports feature extractor  32   a  would extract the media content features  34  and/or the producer features  36  if the media content item  12  is sports-related, or the travel feature extractor  32   b  would extract the media content  34  and/or the producer features  36  if the media content item  12  is travel-related. In this embodiment, the weighing system  56  may be able to better assign a specific weight to the media metadata  18  and/or the producer metadata  38  based on tailored extraction from the media content items  12 . 
     The next step ( 110 ) is for the content selector  22  to create the content media subset  24  of the selected content media content items  23  relevant to the consumer  20 , as illustrated in  FIG. 7 . As discussed in greater detail above, the content selector  22  compares the media metadata  18  with the consumer metadata  30  from the media content items  12  based on one or more selection criteria, such as a consumer consumption profile. The media content item(s)  12  meeting the one or more selection criteria become the selected content media  23  and are placed into the content media subset  24 . The system  10  excludes other media content items  12  from the subset  24  that otherwise fail to meet the selection criteria. In one embodiment, the consumer  20  may manually search for media content items  12  to place in the content media subset  24  via a query or search. The content selector  22  may only compare the media metadata  18  to the search terms or may compare the media metadata  18  to both the search terms and the consumer metadata  30 . Moreover, the content selector  22  may search the text of text-based content items to determine if any of the content items  12  meet the selection criteria. The media content subset  24  may contain only selected media content  23  based on the personal preferences of the consumer  20 , the preferences of the cohort  44 , or a combination thereof. 
     The next step ( 112 ) is to optionally use the cohort generator  42  to group the consumers  20  having similar consumer metadata  30  (i.e., consumers  20  who share similar interests or characteristics) into the cohorts  44 . For example, as illustrated in  FIG. 8 , the cohort generator  42  groups the consumers  20   a ,  20   b  in the cohort  44   a  and the consumers  20   c ,  20   d  into the cohort  44   b.  The system  10  may continuously add and/or remove the consumers  20  from the cohorts  44  and create and/or delete the cohorts  44  based on changes in the consumption patterns of the selected media content  23 . In one aspect, the system  10  may change the structure of the cohort  44  if consumers of a particular cohort  44  begin exhibiting differing content consumption habits (e.g., one group of cohort members consume a particular media content item, while another group of members in the same cohort ignore that particular media content item). Alternatively, the system  10  may change the cohort  44  in response to receiving differing feedback on the consumed selected media content  23  (e.g., one group of cohort members provide positive feedback while another group of members in the same cohort provide negative feedback). If the differing content consumption or feedback patterns are limited to a small number of consumers in the cohort  44 , the system  10  may remove these consumers from the cohort  44 . If a larger portion of the cohort  44  exhibits different content consumption patterns or provides differing feedback, the system  10  may split the existing cohort  44  into different cohorts so the two new cohorts are representative of the content consumption or feedback tendencies. The cohorts  44  can also be combined when the system  10  determines that two or more of the cohorts  44  are sufficiently similar to one another (e.g., exhibit similar consumption habits and/or provide similar feedback). 
     In the next step ( 114 ), the inbox generator  48  may create one or more inboxes  50  for storing the one or more of the media content subsets  24 . For example,  FIG. 9  illustrates the inbox generator  48  creating the inboxes  50   a ,  50   b . In a preferred embodiment, the inbox generator  48  continuously creates new inboxes  50  containing different media content subsets  24  without requiring input by the consumer  20 . Thus, as soon as the consumer  20  logs on to the system  10 , one or more of the inboxes  50  containing the media content subsets  24  are ready for consumption. In this respect, as shown in  FIG. 9 , an inbox selector  52  may select one inbox (e.g., the inbox  50   a ) for presentation to the consumer  20  through the presentation environment  54 . Alternatively, the inboxes  50  may be generated on-demand when requested by the consumer  20  or may be static. 
     As illustrated in  FIG. 10 , the media content subsets  24  stored in the inbox  50   a  include a mixture of “high” relevancy (e.g., the selected media content  23   a ,  23   e ), “medium” relevancy (e.g., the selected media content  23   d ), and “low” relevancy (e.g., the selected media content  23   b ) selected media content items, including some items  23  identified as having an “undetermined” relevancy (e.g., the selected media content  23   c ). This arrangement is advantageous over a system that only presents highly relevant media content because displaying only highly relevant media content tends to produce increasingly narrow and self-reinforcing media content subsets  24  over time, thereby preventing the serendipitous discovery of new media content. Thus, the system  10  may update the consumer metadata  30  when the consumer  20  consumes selected media content  23  the system  10  previously determined was irrelevant, to further increase the future accuracy of the system  10 . Alternately, in general, the inbox  50  may contain more homogeneity in the selection of the selected media content  23 . For example, the inbox  50  may include only highly-rated media content or only media content matching a keyword search or phrase. 
     The next step ( 116 ) is for the presentation environment  54  to convey the media content subsets  24  stored in the one or more inboxes  50  to the consumer  20  for consumption thereof, as illustrated in  FIG. 11 . The media content subset  24  may be conveyed visually, audibly, or in any combination thereof. The particular method for conveying the selected media content  23  may vary depending on the type of the selected media content  23 . For example, a song or other sound recording may be conveyed only audibly (e.g., via an MP3 player without a screen). A picture may be conveyed only visually (e.g., via a display screen without speakers). More preferably, however, the selected media content  23  in the media content subset  24  is presented (or presentable) both visually and audibly (if necessary), such as by a computer, tablet or smartphone. Should the consumer  20  have more than one inbox  50  containing different media content subsets  24 , the inbox selector  52  may determine which one or more of the inboxes  50  to convey through the presentation environment  54 . For example, in  FIG. 9 , the inbox selector  52  selected and conveyed the inbox  50   a  to display to the consumer  20 . Alternately, the consumer  20  may manually select or determine the inboxes  50  conveyed by way of the presentation environment  54 . 
     In a preferred embodiment, the presentation environment  54  presents the media content subset(s)  24  in a continuously moving stream. Here, the selected media content  23  may start to appear at the bottom of the presentation environment  54  (e.g., a display screen), travel up the presentation environment  54 , and exit the top side thereof, or vice versa. The media content subset(s)  24  may also move horizontally across the presentation environment  54  (e.g., from right-to-left or left-to-right). In an alternate embodiment, the presentation environment  54  may present the media content subset(s)  24  in a scrollable list the consumer  20  can navigate manually via a mouse, touch screen, track pad, stylus, or other similar device. As illustrated in  FIG. 12 , relevancy may determine the size of the selected media content  23  presented to the consumer  20 . Here, the selected media content items  23   a  and  23   d  may be considered more relevant than the selected media content items  23   b  and  23   c , as represented by the relative size of the boxes in  FIG. 12 . This methodology of presenting the selected media content  23  to the consumer  20  may assure that more relevant content receives more attention than less relevant content, while simultaneously still presenting a diverse content base of information to the consumer  20 . That is, the more relevant media content (e.g., the selected media content items  23   a ,  23   d ) are larger and consume more of the presentation environment  54  display space than the less relevant media content (e.g., the selected media content items  23   b ,  23   c ). 
     Additionally, the presentation environment  54  may include a summary feature (e.g., a new window or pop-up box) that presents a selectable short description of the selected media content items  23   a - 23   d  presented to the consumer  20 . Here, the consumer  20  may interact with the selected media content items  23   a - 23   d  in some respect, such as hovering over one of the selected media content items  23   a ,  23   b ,  23   c , or  23   d  with a mouse or comparable pointing device. This may allow the consumer  20  to quickly view a brief description of the selected media content item  23   a ,  23   b ,  23   c , or  23   d  before deciding whether to select or ignore the hovered-over item. Moreover, the system  10  may track the interaction level of the consumer  20  with each of the selected media content items  23   a - 23   d.  In this respect, the system  10  may determine instances where the consumer  20  simply ignores the selected media content items  23   a - 23   d , previewed the selected media content items  23   a - 23   d , or consumed one or more of the selected media content items  23   a - 23   d  (e.g., by clicking or touching the selected media content item  23   b ). 
     The preview feature of the system  10  is preferably implemented in a way that does not clutter the presentation environment  54 . For example, the consumer  20  may position a pointing device (e.g., a computer mouse, stylus, finger or the like) over the selected media content  23  or click on the desired selected media content  23  to display a short summary. The consumer  20  may then click on the selected media content to view it fully. Thus, the consumer  20  can quickly obtain a preview or short description of the selected media content  23  without taking the time to fully consume the selected media content  23 . Alternatively, the summary may be displayed in the presentation environment  54  without any interaction with the selected media content  23  by the consumer  20 . 
     The next step ( 118 ) is for the consumer  20  to consume one or more of the selected media content items  23   a - 23   d  from the media content subset  24  conveyed to the consumer  20  by way of the presentation environment  54 . The specific method of consumption of each of the selected media content items  23   a - 23   d  may vary depending on the type and structure thereof. Such consumption may include, inter a/ia, reading text, viewing photos or videos, and/or listening to audio. 
     The next step ( 120 ) is for the analyzer  26  to analyze consumption of the selected media content  23   a ,  23   b ,  23   c , or  23   d  in the media content subset  24  by the consumer  20 . As illustrated in  FIG. 13 , the analyzer  26  first determines which of the selected media content  23   a ,  23   b ,  23   c , or  23   d  in the media content subset  24  the consumer  20  consumed by way of the presentation environment  54 . Next, the analyzer  26  extracts various consumption features  28  from the consumed media content  23   a ,  23   b ,  23   c , or  23   d  and stores the consumption features  28  as the consumer metadata  30  for use by the content selector  22  in generating future media content subsets  24 . In an alternate embodiment, the analyzer  26  may only record which selected media content  23   a - 23   d  the consumer  20  consumed. The system  10  then uses the media content features  34  in the media metadata  18  as the consumption features  28 . Here, the system  10  can use the media metadata  18  to obtain the consumption features  28  if the analyzer  26  does not extract the consumption features  28  from the consumed media content  23 . 
     The next step ( 122 ) is to save the consumption features  28  extracted by the analyzer  26  as the consumer metadata  30  for later use by the content selector  22 . In one embodiment, all the consumption features  28  are saved as the consumer metadata  30 . More preferably, however, the system  10  saves the consumption features  28  as the consumer metadata  30  only if that information has been extracted some threshold number of times. For example, the consumption features  28  (e.g., “basketball”) must be extracted from the media content item  12  five times before the analyzer  26  will save that consumption feature  28  as the consumer metadata  30 . Thus, random or coincidental consumption features extracted from the consumed media content  23  that do not interest the content consumer  20  will not be used by the content selector  22  when generating future subsets  24 . For example, if the consumer  20  views a video of a basketball game in New York, the analyzer  26  may extract the phrase “basketball” as a feature and the phrase “New York” as a feature. Moreover, this particular consumer  20  may be an avid basketball fan, but may have no interest in New York. Thus, assuming this consumer  20  has watched numerous other basketball videos (e.g., five) and few, if any, New York videos (e.g., one), only the feature “basketball”, and not “New York”, will be saved in the consumer metadata  30  for use by the content selector  22 . In this example, the phrase “basketball” meets the minimum threshold selection requirement seeing that the consumer  20  has watched five basketball videos, while the phrase “New York” fails to meet the minimum threshold selection requirement and is deemed merely a coincidental feature that does not interest the consumer  20 . Therefore, only consumption features  28  common to the types of selected media content  23  repeatedly consumed enough to warrant a reasonable likelihood of actual interest (e.g., basketball videos) will be used to create future media content subsets  22 . The same logic and features can be applied to consumption habits by the cohorts  44 . 
     Moreover, the analyzer  26  may give more weight to the consumption features  28  extracted from the selected media content items  23  that the consumer  20  spent a longer time consuming. For example, if the consumer  20  spent ten minutes watching a first video and five minutes watching a second video, the analyzer  26  may give more weight to the consumption features extracted from the first video than the consumption features extracted from the second video when determining if the threshold for saving in the consumer metadata  30  has been met. Alternatively, the time requirement may be based on a percentage of the media content consumer since some content may last longer than others. The analyzer  26  may update the consumer metadata  30  in real-time (i.e., in response to every selected media content consumption action that the consumer  20  takes) or at certain intervals (e.g., certain times of the day or week). The consumer metadata  30  may be saved anywhere accessible to system  10  (e.g., in a content consumer metadata registry or simply on the storage device  14 ). 
     Importantly, the analyzer  26  does not require any input from the consumer  20 . Preferably, the analyzer  26  operates automatically behind the scenes to extract the consumption features  28  from the consumed selected media content  23 . Thus, the consumer  20  need not vote on, rate, annotate, or otherwise provide any input to the system  10  as the analyzer  26  extracts and stores the relevant data as the consumer metadata  30 —such extraction preferably occurs in real-time as consumers consume media content through the system  10 . As such, the consumer  20  will receive relevant media content subsets  24  simply by consuming the selected media content  23  and defined user feedback is not necessarily needed for the system  10  to operate. Advantageously, the media content subsets  24  presented by the system  10  are highly personalized unlike traditional systems that group media content items  12  based on objective or crowd-sourced data. This is because the analyzer  26  analyzes consumer content consumption habits and stores the relevant data (i.e., the consumption features  28 ) as the consumer metadata  30  for that specific consumer  20  (or for a cohort  44 ). The specific consumer metadata  30  is then used to create the media content subset  24  for that specific consumer  20  or specific cohort  44 . No other consumer preferences, viewing habits, or opinions need be taken into account when creating the media content subset  24 . In this respect, the media content subset  24  is highly representative of consumer or cohort preferences. 
     Even though the above-described features of the system  10  eliminate the need for consumers to input data to create the media content subsets  24 , the consumption features  28  may still optionally include various consumer  20  annotations (e.g., voting, rating, or commenting on consumed selected media content items  23 ). These consumer  20  annotations may still be an integral part in forming the consumer metadata  30  or the cohort metadata  46 . 
     In one embodiment, crowd-sourced measures of popularity (e.g., content consumer rating) may be a factor in determining media content item relevancy by the content selector  22 . The content selector  22  may be more likely to place highly-rated media content into the media content subset  24  than lower-rated media content, all else being equal. For example, if the producer  16  creates a new media content item  12   e  ( FIG. 1 ), that media content item  12   e  may be consumed and annotated (e.g., rated) by a number of the consumers  20 . If the consumers  20  favorably rate the new media content item  12   e , it will be more likely to appear in the media content subset  24  than other media content items less favorably rated by the consumers  20 . Thus, highly-rated media content items are more likely to be consumed than lower-rated media content items. In this respect, popular media content items preferably quickly replicate through the media content subsets  24  and the inboxes  50  of the consumers  20  and the cohorts  44  in the system  10 , while unpopular media content items (e.g., spam or other undesirable content) quickly die off as a result of being excluded from the media content subsets  24 , and the inboxes  50 . 
     Importantly, consumers employing false account schemes or botnets solely for the purpose of rating media content items  12  up or down will be placed in their own cohort  44 , and thus not disrupt the experience of legitimate consumers. These botnet-type “consumers” create their own consumption patterns or are placed in their own cohort and thereby segregated from legitimate consumers. Thus, the botnet-type “consumer” or cohort will receive different selected media content  23  in its media content subset  24  than the legitimate consumers  20  and the legitimate cohorts  44 . That is, the media content items  12  the botnet-type “consumers” rate up will appear in a media content subset and inbox associated only with that consumer or a cohort made up of the botnet-type “consumers”, while those same media content items will not be conveyed to other consumers or cohorts because the consumption habits would be different. Moreover, the more these botnet-type “consumers” try to favorably rate particular media content items, the more the “consumers” and their cohort become segregated from legitimate consumers. In this respect, the system  10  ameliorates the effects of botnets, griefing, spamming and other unauthorized and undesirable uses thereof. 
     In step ( 124 ), the system  10  may optionally create the consumer relevancy table  40  based on the consumption features  28 , as illustrated in  FIG. 14 . To do so, the system  10  compares the consumer metadata  30  with the producer metadata  38  based on one or more producer relevancy criteria. The content selector  22  may use the producer relevancy table  40  as a factor in creating the media content subsets  24 . Thus, the media content items  12  created by the producers  16  listed on the producer relevancy table  40  are more likely to appear in the media content subset  24  for the consumer  20  or the cohort  44  than the media content items  12  produced by the producers  16  not listed on the relevancy table  40 . The consumer  20  may also manually add producers to the producer relevancy table  40 . 
     Although several embodiments have been described in detail for purposes of illustration, various modifications may be made without departing from the scope and spirit of the invention. Accordingly, the invention is not to be limited, except as by the appended claims.