Patent Publication Number: US-2009226046-A1

Title: Characterizing Or Recommending A Program

Description:
BACKGROUND 
     Nearly everyone has faced the struggle of trying to select a good movie. Unfortunately, the conventional manner of classifying movies by genre is not very informative as to full complexity of the movie. For examples, movies placed within a single genre, such as action, can vary tremendously in their pace, subject matter, and whether the movie is serious or lighthearted. If one has an abundance of time, one can attempt to survey reviews of a movie. However, trusting a review of a movie is questionable because of the varying tastes among the reviewers, which may or may not match your own tastes. 
     With the advent of the Internet, IPTV, services such as NetFlix®, and the mass production and distribution of DVDs, there is an even wider selection of programs from which to choose. In addition to movies, available programs include TV shows, sports events, educational programs, and so on. However, even with this expanded volume of available programs, classification by genre still dominates the selection process. 
     Accordingly, consumers and content distributors are left with crude tools for handling an ever increasing supply of content. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is diagram illustrating a method of characterizing a program, according to one embodiment of the present disclosure. 
         FIG. 2  is a graph illustrating an emotional profile of a program, according to one embodiment of the present disclosure. 
         FIG. 3  is a chart representing a series of scenes of a program, according to one embodiment of the present disclosure. 
         FIG. 4  is a diagram illustrating a scene index, according to one embodiment of the present disclosure. 
         FIG. 5  is a block diagram illustrating a system for recommending and accessing a program, according to one embodiment of the present disclosure. 
         FIG. 6  is block diagram of a user interface of a program recommendation system, according to one embodiment of the present disclosure. 
         FIG. 7  is a block diagram of a manager of a program characterization and recommendation system, according to one embodiment of the present disclosure. 
         FIG. 8  is diagram illustrating a rule set for a series of scenes of a program, according to one embodiment of the present disclosure. 
         FIG. 9  is a diagram of a resource description of a scene of a program, according to one embodiment of the present disclosure. 
         FIG. 10  is a flow diagram of a method of characterizing a program, according to one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following Detailed Description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. In this regard, directional terminology, such as “top,” “bottom,” “front,” “back,” “leading,” “trailing,” etc., is used with reference to the orientation of the Figure(s) being described. Because components of embodiments of the present invention can be positioned in a number of different orientations, the directional terminology is used for purposes of illustration and is in no way limiting. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims. 
     Embodiments of the present disclosure relate to a method and system for characterizing and/or recommending a program, such as a movie. In one embodiment, a program is characterized according to an emotional state of one or more characters throughout the program. In one aspect, the program is apportioned into a sequence of scenes in which each scene is defined by a change in the emotional state of a character. After defining or differentiating the scenes of the program, an emotional profile of the program is built on a scene-by-scene basis. 
     In another aspect, the emotional state of the character remains the same throughout the scene but a physical transition or change in the settings is made, thereby differentiating that scene from other scenes. In another aspect, while in some instances the emotional state of a character does not change, a scene is identified as a separate scene because other aspects (e.g., soundtrack, physical settings, etc.) evoke an emotional response in the viewer. 
     In a yet another embodiment, a scene is defined (and differentiated from other scenes) as a part of a script between two consecutive Scene Headings which includes at least two elements: (1) an exterior or interior indicator; and (2) a location or setting. In one aspect, the scene is further defined by a time of the day in the story of the program. 
     A program is recommended to a user based on comparing a user preference profile and the emotional profile of the program(s) to determine a correlation between the user preference profile and the emotional profile of the program(s). In one aspect, the user preference profile comprises one or more parameters relating to the emotional preferences of the user. By using this correlation, a highly accurate recommendation of a program is made to the user. 
     Moreover, because this method characterizes programs on a scene-by-scene basis, the method makes it practical for the user to become aware of many programs the user would not otherwise consider viewing. For example, in the long tail phenomenon associated with digital media, there are many programs available for viewing but that are generally unknown to a viewer. With the method and system of the present disclosure, programs within the long tail of the universe of digital content can be characterized emotionally and then automatically compared with a user preference profile to produce a list of recommended programs that would have otherwise been unknown to the user. This strategy benefits both the user and owners of programs falling within the long tail of digital content. 
     These embodiments, as well as others, are described and illustrated in association with  FIGS. 1-10 . 
     A method  20  of characterizing and/or recommending a program  30  to a user is illustrated in  FIG. 1 , according to one embodiment of the present disclosure. In method  20 , a program  30  comprises a series  32  of scenes  34  and user profile  40  provides information about the tastes and habits of the user. Moreover, while one program  30  is shown for illustrative clarity, program  30  represents just one program  30  of a universe of programs that could be recommended to a user. In one aspect, the program  30  comprises any one of a movie, TV show, video, sports event, or other program. In another aspect, each scene  34  comprises one or more characters  50  that display an emotion  52 . 
     One scene  34  is differentiated from other scenes  34  of program  30  in that each scene  34  includes a display of a different emotion of the character or a display of a change from one emotion to another emotion for that character. Accordingly, each scene  34  includes a beginning emotion  54  and an ending emotion  56 . 
     In another aspect, even though some scenes  34  maintain the same emotion for a character, they are defined as a separate scene  34  because of a noticeable increase or decrease (represented by up and down arrows  58 ) in that emotion. In another non-limiting example, scenes  34  are differentiated from each other based on whether the emotion is a positive emotion  56  (e.g., happiness) or a negative emotion  57  (e.g., sadness). Many other aspects of defining a scene  34  and differentiating one scene  34  from another are described further in association with  FIGS. 2-10 . 
     Because each scene  34  is defined based on the emotional display for one or more characters, the scene  34  is not defined by its duration. Moreover, each scene  34  is not necessarily defined by the number of, or type of camera shots, as some scenes  34  include a character maintaining the same emotional state through series of shots. 
     In another aspect, the program  30  comprises a sports event and scenes  34  are differentiated from each other based on separate plays (e.g., first down play in football, each pitched ball in baseball, etc.) within the sports event. In one embodiment, each play is tagged with an emotional indicator that represents the type and intensity of emotion displayed by one or more players or the type and intensity of emotion evoked in the viewer or announcer based upon the respective play. 
     In another aspect, an emotional index or other indicator is provided for each scene  34  to represent the emotional nature of the respective scene  34 . When considered in sequence, this series of scenes  34  provides an emotional profile of the program  30 . Using this tool, each program  30  in the universe of programs is evaluated to build an emotional profile, scene-by-scene, for that program  30 . The emotional profile of each respective program  30 , in turn, is used to recommend a program  30  (from the universe of programs) to the user by identifying which programs best match the tastes and habits of the user as provided via user profile  40 . 
     Accordingly, in order to recommend a suitable program  30  to a user, information is obtained about the user and maintained via user profile  40 . In one embodiment, user profile  40  comprises viewing history parameter  70 , demographics parameter  72 , peer parameter  74 , and stated preferences parameter  76 . In one aspect, viewing history parameter  70  maintains a history of the programs  30  viewed by the user. This history is automatically tracked via a user interface of a viewing device owned or operated by the user, as described later in association with  FIGS. 5-7 . In addition, the user or the content provider, such as NetFlix®, is capable of logging entries into the history to identify programs  30  that were viewed prior to the start of automatic tracking or that were viewed in venues not associated with the automatic tracking mechanism. In this manner, viewing history parameter  70  enables maintaining a comprehensive history of programs  30  viewed by the user. 
     In addition to simply providing a history of viewing, this information is used as one factor in identifying other programs  30  that might be of interest to the user. In particular, one can see which types of movies (e.g., genre) that a user tends to watch with some frequency. Moreover, with method  20 , the emotional profile of the programs  30  previously viewed by the user is compared with the universe of programs to determine which other programs  30  may be of interest to the user. In one embodiment, method  20  includes identifying other programs  30  (not yet viewed by the user) that include emotional profiles similar to the emotional profile of programs  30  previously viewed by the user, and then recommending those identified programs  30 . 
     The demographics parameter  72  of user profile  40  enables tracking of demographic information about the user, such as their age, gender, ethnicity, religious affiliation (if any), etc. In one embodiment, the demographics parameter  72  is used to identify programs  30  that have an emotional profile known to be attractive to the one of the many different demographic groups within society. 
     The peer parameter  72  of user profile  40  enables tracking the viewing history, preferences, etc. of one or more peers of the user. In one aspect, the user defines or lists their peers (e.g., friends, family, etc.) and a manager ( FIGS. 5-7 ) tracks the viewing history of those peers in order to access user profile information for those peers to facilitate in recommending a program or movie for the user. 
     The stated preferences parameter  76  of user profile  40  enables the user to explicitly identify their preferences. For example, a user specifies a preference, non-preference, or dislike for which type of emotion, the intensity of emotions, or the frequency of emotion changes within a program  30 . This aspect is described in more detail in association with  FIGS. 5-7 . 
     Finally, the user profile  40  is not exclusively limited to the viewing history parameter  70 , the demographics parameter  72 , the peer parameter  74 , and the stated preferences parameter  76 . 
     Using the information about the user tracked via these parameters  70 - 76  of user profile  40 , method  20  compares the emotional profile of each program  30  of the universe of programs with the user profile  40  to identify programs  30  that correlate well with the user profile  40  and that are likely to be enjoyed by the user. By employing a scene-based emotional profile of each program  30  in recommending a program  30 , method  20  avoids the conventionally crude technique of choosing programs  30  solely according to genre, age, or other low level information. 
       FIG. 2  is a graph  100  illustrating an emotional profile  102  of a character in a program according to one emotion, such as happiness (represented along the vertical y axis), according to one embodiment of the present disclosure. Accordingly, the happiness of the character (a positive emotion) is illustrated by portions of the profile  102  extending above the zero mark of the y-axis. Conversely, the unhappiness of the character (a negative emotion, such as sadness, anger, etc.) is illustrated by portions of the profile  102  extending below the zero mark of the y-axis. The sequence of scenes of the program is represented by the horizontal x-axis of the graph so that profile  102  reveals the relative happiness of the character on a scene-by-scene basis throughout the program. 
     In one example, the character comprises a protagonist of the program. However, emotional profiles are also developed for other characters of the program, such as other protagonists, antagonists, or neutral characters. 
     After plotting the emotional profile  102  illustrated in  FIG. 2 , additional aspects of the emotional profile  102  are identified to help characterize the program. In one example, a maximum duration  110  of a negative emotion (e.g., unhappiness) is identified in the early scenes of the program. In another aspect, a maximum negative emotion  112  is identified, as some users would prefer to avoid portrayals of deep unhappiness. On the other hand, some users might prefer large swings of emotion in their programs. Accordingly, a maximum drop  114  is identified in emotional profile  102 , which represents a swing from a significantly positive emotion to a significantly negative emotion. While not explicitly labeled, the emotional profile  102  also could exhibit the converse situation of a maximum rise from a significantly negative emotion to a significantly positive emotion. Finally, one can recognize other simple patterns or more complex patterns to facilitate characterizing the emotional profile  102  of the program and then use those recognizable patterns for comparing the program relative to the user profile  40  ( FIG. 1 ) in making recommendations to the user regarding that program or other programs. 
     Accordingly, in just one example, if a user&#39;s stated preferences include a happy ending, one aspect of a method of recommending a program includes identifying programs having a scene-based emotional profile in which a significant duration of positive emotion is portrayed in the scenes at or near the end of the program. 
     In another embodiment, more than one emotional profile is tracked for a program. For example, the emotional profile of a second character based on the same emotion is developed. Moreover, in yet another embodiment, emotional profiles of other emotions of those same characters are developed and used for comparison with the user preference profile  40 . 
       FIG. 3  is a chart  150  illustrating a scene-by-scene characterization of one emotion of a character, according to one embodiment of the present disclosure. The chart represents the data supporting a graphically-represented emotional profile, such as profile  102  of  FIG. 2 . As illustrated in  FIG. 3 , chart  150  includes a scene column  152 , a settings column  154 , a beginning intensity  156 , and an ending intensity  158 . The scene column  152  identifies the different scenes of the program by a sequential alphanumeric identifier. The settings column  154  identifies an aspect of a scene, such as a location (e.g. rooftop, office, shipyard) in which the scene takes place. The beginning intensity  156  identifies an intensity of the tracked emotion at the beginning of the scene while the ending intensity  158  identifies an intensity of the tracked emotion at the end of the scene. Accordingly, chart  150  illustrates the change in emotional intensity that provides the basis to differentiate one scene from another. For example, scene one is characterized by the emotional intensity changing from zero to five, while scene three is characterized by the intensity changing from five to negative five. On the other hand, scenes two and eleven represent a scene in which the emotional intensity remains level throughout the scene but wherein the segment of the program is defined as a scene because of a change in setting that provides a physical transition for the tracked character or because of some other reason. For example, while there is no change in emotional intensity in scene eleven (zero to zero), the setting of shipyard provides a transition from scene ten (e.g., Midge&#39;s room) to scene twelve (e.g., office) and there is a change in emotional intensity from scene ten to scene eleven (e.g., four to zero) and then again from scene eleven to scene twelve (e.g., zero to two). 
     In one aspect, one can use these numerical indications of emotional intensity for sorting emotional profiles. For example, to provide a recommendation of a mild program, one could apply a filter to exclude programs having negative or positive intensities above four points. Alternatively, one can edit a program according to an emotional intensity preference by excluding all scenes having intensity levels above a desired number, such as five. In one embodiment, a substitute scene is available for replacement of the excluded scene or the scenes are originally made so that a relatively smooth transition takes place between the remaining scenes after excluding one or more scenes. 
       FIG. 4  is a diagram illustrating an index  175  of one scene of a program, according to one embodiment of the present disclosure. In one embodiment, scene index  175  includes a set of parameters that characterize and define a scene to distinguish one scene from another and to enable identifying one or more scenes that would be attractive to a user. As illustrated in  FIG. 4 , scene index  175  is defined by one or more of a script parameter  180 , an audio parameter  182 , an image parameter  184 , a content parameter  186 , a scene ID  270 , a type parameter  272 , a duration parameter  274 , and a resource descriptor  276 . These parameters  180 - 186  and  270 - 276  represent performance of a function and/or storage of information gathered by a particular function. 
     In one embodiment, script parameter  180  enables identifying elements and portions of a screenplay of the program that uniquely identify a scene. In one aspect, script parameter  180  includes text parameter  190 , settings parameter  192  (e.g., a location of the character), and one or more character parameters  210 ,  220 . The text  190  provides the narrative of the program including words that describe action (e.g. running) to be portrayed by the actor (represented by action descriptor  196 ), words (e.g., crying) that describe a facial expression (e.g., sad) of the character (represented by facial descriptor  198 ). A verbal emotive descriptor  194  of text parameter  190  includes verbal speech expressed in words or utterances spoken by a character that reveals their emotion. In one non-limiting example, verbal emotive descriptor  194  would denote anger as an emotion when the character&#39;s spoken words includes words such as “I hate you.” 
     The character parameters  210 ,  220  also include identifying a beginning emotion parameter  212  and an ending emotion parameter  214 . If the emotion remains the same throughout a scene, then parameters  212 ,  214  represent a beginning intensity and an ending intensity, respectively, of one emotion. In either case, parameters  212 ,  214  are configured to indicate a relative change of emotion within a scene. As noted in connection with  FIG. 2 , in some instances a scene includes no change in emotion when the scene is differentiated as a separate scene for other reasons, such as a physical transition. 
     Audio parameter  182  of scene index  175  enables identifying elements (represented by a set  230  of verbal, music, and special effects) of an audio soundtrack of the program that uniquely identify an emotion of a scene. For example, the audio parameter  182  identifies sounds associated with various emotions, such as crying to reveal sadness, laughter to reveal happiness, yelling to reveal anger, etc. Moreover, audio parameter  182  identifies music (e.g., scary music) for association with fear of a character or special effects (e.g., birds chirping) for association with happiness. 
     Image parameter  184  of scene index  175  enables identifying visual elements of the program that are observable in images of the media and that reveal an emotion of the character. These visual elements (represented by numeral  240 ) include a facial expression (e.g., smile), an action taken by the character (e.g., dancing), or an overall situation. Accordingly, by viewing the relevant images one can discern the emotion of the character. In another aspect, as described further in association with  FIG. 7 , techniques for automatically recognizing facial expressions are used to identify the visual elements to assist in differentiating one scene from another. 
     Content parameter  186  of scene index  175  enables tracking a format of a scene, such as whether the scene is recorded in standard definition (SD) format  252  or a high definition (HD) format  250 . Content parameter  186  also includes modification parameter  254  which identifies whether the scene is suitable for inclusion in one of several modified versions of the program (e.g., mobile, condensed, etc.), as further described in association with  FIG. 7 . In one embodiment, modification parameter  254  additionally identifies whether a particular scene is a core scene to be included in full, condensed, or mobile versions of the program. In this regard, non-core scenes are excluded from the modified version of the program. In this embodiment, the method retains core scenes (and omits non-core scenes) of the program to maintain a baseline emotional pattern of a program despite the program having a shortened length. 
     Scene ID  270  of scene index  175  identifies an alphanumeric identifier of a scene (e.g., 73 rd  scene of 120 scenes) within a sequence of scenes to uniquely identify a scene. Type parameter  272  of scene index  175  identifies a type of program (e.g., movie, event, TV show) to which the scene belongs. Duration parameter  274  identifies the duration of the scene and/or an elapsed time within the program at which the scene occurs. Resource descriptor  276  identifies a scene via a universal resource descriptor to enable access to the emotion-based scene index  175  via web searching or other networking resources. In one aspect, resource descriptor  276  includes a semantic web parameter  280  enabling the information of scene index  175  to be made available in a semantic web format. In another aspect, resource descriptor  276  includes a meta parameter enabling the information of scene index  175  to be made available in a meta data format or other web-based resource paradigm. 
     In one aspect, whether identified via script parameter  180 , audio parameter  182 , or image parameter  184 , some non-limiting examples of a physical state of a character include a presence in a location, an absence from a location, a running state, a standing state, a sitting state, a walking state, an eating state, a talking state, a silent state, a sleeping state, etc. 
       FIG. 5  is a block diagram illustrating a system for characterizing and/or recommending a movie, according to one embodiment of the present disclosure. As illustrated in  FIG. 5 , system  350  includes a user  352 , a manager  356 , a programs resource  358 , a content producer  360 , a physical distribution resource  380 , and a network communication link  385 . In one aspect, the programs resource  358  includes a single source  362  and a distributed network source  366 , which corresponds to a universe  370  of programs  372 . 
     Manager  356  is configured to characterize programs to produce an emotional profile of each program and to make recommendations to the user  352  based on the emotional profiles of the respective programs. Manager  356  also is described further in association with at least  FIGS. 6-7 . 
     Programs resource  358  comprises a plurality of programs available to a user  352  via network communication link  385 . The programs comprise any one or more of full length feature movies, videos, TV shows, sports events, other events. The programs  358  are provided by one or more single source providers  362  (e.g., an online retail movie provider) for rent or purchase. Alternatively, the programs  358  are made available through a variety of sources in a distributed network  366  across the World Wide Web or other electronic networks. Accordingly, the distributed network  366  provides a universe  370  of programs  372 . In one aspect, the distributed network  366  includes a peer-to-peer storage network in which the programs and/or portions of the program(s) are stored in different nodes of a peer-to-peer network. 
     Content producer  360  creates, produces, and distributes programs to retail providers (e.g., single source provider  362  or distributed network  366 ) available via network communication link  385 . Alternatively, content producer distributes its programs via a physical distribution resource  380 , such as bricks-and-mortar stores, mail delivery, etc. In one aspect, content producer  360  makes an electronic version or physical copy of each program available for characterization by manager  356  so that the program is available for recommendation to a user whether or not the program is accessible via network communication link  385 . Moreover, in some instances, content producer  360  cooperates with manager  356  to characterize a program as the program is being made, rather than having manager  356  characterize a program after it is produced. In addition, when a modified version of a program is produced via content producer  360 , that modified program is deliverable via physical distribution resource  380 . 
       FIG. 6  is a block diagram of a user interface  400  of a program characterization and recommendation system, according to one embodiment of the present disclosure. As illustrated in  FIG. 6 , user interface  400  includes user profile  402 , program module  404 , and search module  406 . In one embodiment, user profile  402  of user interface  400  comprises substantially the same features and attributes of user profile  401  previously described in association with  FIG. 1 , as well as additional features described in association with  FIGS. 6-7 . For example, as illustrated in  FIG. 6 , viewing history parameter  410  of user profile  402  includes a rating mechanism  412  to enable the user to provide a rating of a viewed program. These ratings made by the viewer assist the manager  356  in identifying and recommending programs with emotional profiles comparable with positively rated programs while identifying and excluding programs having emotional profiles corresponding to negatively rated programs. 
     In other respects, viewing history parameter  410 , demographic parameter  414 , peer parameter  416 , and stated preference parameter  418  have substantially the same features and attributes of the corresponding parameters  70 - 76  of user profile  40  of  FIG. 1 . 
     Program module  404  of user interface  400  enables selecting a type of programs that a user would like to view. In one embodiment, the program comprises any one or more of a movie  430 , a video  432 , a TV show  434 , a sports program  436 , and an event  438 . However, this listing is not an exhaustive listing of all the type of programs suitable for characterization or recommendation via a method according to principles of the present disclosure. 
     The search module  406  enables a user to specify preferences of a program they would like to obtain and view. In one aspect, these preferences are stored in stated preference parameter  418  of user profile  402 . 
     In one embodiment, search module  406  comprises an actor parameter  450 , a genre parameter  452 , a single scene parameter  454 , and a tone module  456 . The actor parameter  450  enables specifying the name of one or more actors and actresses that play characters in a movie. In aspect, the actor parameter  450  is used to specify the name of a character in a program, as many people are familiar with the name of a character as well as the name of actor or actress. 
     The genre parameter  452  enables a user to specify a genre (e.g., action, science fiction, etc.) to aid in searching. However, this genre parameter  452  is sometimes not employed if it is believed that it would interfere with the scene-based emotional profile matching performed according to principles of the present disclosure. 
     The single scene parameter  454  enables a user to specify the nature of a single scene, such as “nervous breakdown” or “sacrificial death”, to find programs with that type of scene. Moreover, in one embodiment, the single scene parameter  454  is employed in concert with the actor parameter to identify programs including a particular type of scene and a particular actor or actress. 
     The tone module  456  facilitates specifying a preference in the tone of a program. In one embodiment, the tone module  456  comprises a positive tone parameter  460 , a negative tone parameter  462 , a slow tone parameter  464 , and a fast tone parameter  466 . The positive tone parameter  460  enables a user to specify a preference, non-preference, or dislike for programs having a positive tone (e.g., happy, victory, loving) while negative tone parameter  462  enables a user to specify a preference, non-preference, or dislike for programs having a negative tone (e.g., anger, sadness, hate). Similarly, the slow parameter  464  enables a user to specify a preference, non-preference, or dislike for programs having a slow pace (e.g., nature documentary) while fast parameter  466  enables a user to specify a preference, non-preference, or dislike for programs having a fast pace (e.g., action thriller). 
     In addition, in some embodiments, tone module  456  comprises a heavy parameter  470 , a light parameter  472 , and a dominant emotion parameter  474 . The heavy parameter  470  enables specifying a preference, non-preference, or dislike for programs with a heavy subject matter or a heavy feel (e.g., holocaust) while the light parameter  472  enables specifying a preference, non-preference, or dislike for programs with a light subject matter or a light feel (e.g., gardening). In one embodiment, the dominant emotion parameter  476  is configured to specify a dominant emotion (e.g., sadness, happiness, anger, etc.) of a program, if such a dominant emotion is present in the program. 
     In one embodiment, the tone module  456  further includes a filter module  480  comprising an explicit parameter  482 , a minimizer parameter  484 , and a maximizer parameter  486 . The filter module  480  enables a user to select a program or request a recommendation of a program with the additional provision that the program be edited or filtered to remove certain types of scenes. In one embodiment, the explicit parameter  482  of filter module  480  acts to filter out programs including explicit subject matter (e.g., images or audio) so that they are excluded from the universe of programs to be recommended. Alternatively, the explicit parameter  482  enables specifying that any programs including explicit subject matter by automatically edited for removal of explicit scenes. 
     The minimizer parameter  484  of filter module  480  enables specifying a preference that high intensity emotions of a recommended program be minimized by excluding those high intensity emotional scenes. The maximizer parameter  486  of filter module  480  enables specifying a preference for programs including high intensity emotional scenes. 
       FIG. 7  is a block diagram of a manager  500 , according to one embodiment of the present disclosure. In one embodiment, the manager  500  comprises at least substantially the same features and attributes as manager  356  of system  350  of  FIG. 5 . In another embodiment, manager  356  ( FIG. 5 ) comprises at least substantially the same features and attributes as manager  500  of  FIG. 7 . 
     In one embodiment, as illustrated in  FIG. 7 , manager  500  includes user interface  400  ( FIG. 6 ), parsing module  510 , program profile module  512 , tagging module  514 , and builder module  516 . 
     The parsing module  510  of manager  500  is configured to analyze a program to define and differentiate scenes within the program. In particular, the parsing module  510  parses the program to identify each unique scene according to a display of an emotion by a character. In some instances, a physical transition within the program (e.g., a move to a new location for a character, or form one character to another) will define a scene. 
     In one embodiment, parsing module  510  comprises scene identifier function  530  including a script module  540 , an audio identifier  570 , an image identifier  572 , a stream identifier  574 , and an auto facial recognizer  576 . In one aspect, scene identifier  530  uniquely identifies a scene within a program via an alphanumeric identifier, in accordance with the scene ID  270  of scene index  175  described in association with  FIG. 4 . 
     The script module  540  is configured to automatically evaluate aspects within a screenplay or textual script of a program that identify an emotion associated with a character. In one embodiment, the script module  540  comprises a verbal parameter  542 , an action parameter  544 , and a facial parameter  546 . The verbal, action, and facial parameters  542 ,  544 ,  546  of script module  540  have substantially the same features and attributes as the verbal emotive, action, and facial descriptors  194 ,  196 ,  198  of text parameter  190  (respectively) of scene index  175  as previously described in association with  FIG. 4 . Accordingly, these verbal, action, and facial parameters  542 ,  544 ,  546  enable manager  500  to gather information regarding an emotion of character by analyzing the text of a screenplay. 
     The script module  540  also comprises a settings parameter  548 , and a character parameter  550 . The settings and character parameters  548 ,  550  of script module  540  have substantially the same feature and attributes as the settings and character parameters  192 ,  210 ,  220  of script parameter  180  of scene index  175  as previously described in association with  FIG. 4 . Accordingly, the settings parameter  548  enables identifying a scene by a physical location (e.g., an office, a garden,) of a character, while character parameter  550  enables identifying a scene by which, if any, characters are present within a particular scene. While a character is generally present within most scenes and displays an emotion within a scene, some scenes omit a character because the scene is used for a physical transition and/or to evoke an emotion in the viewer based on non-character thematic elements (e.g., showing an eagle fly, showing waves roll in to shore, showing city traffic, etc.). 
     In some embodiments, the scene identifier module  530  of parsing module  510  also comprises an audio identifier  570 , an image identifier  572 , a stream identifier  574 , and an auto facial recognizer  576 . The audio identifier  570  and image identifier  572  of scene identifier module  530  have substantially the same features and attributes as the audio function and the image function  182 ,  184 , respectively, as previously described in association with  FIG. 4 . 
     The stream identifier  574  is configured to analyze a digital signal of the audio and video portions of a program to assist in differentiating scenes from each other. One example of a stream identifier is provided in Zhang, U.S. Patent Publication 2006/0230414, assigned to Hewlett Packard Company. 
     The auto facial recognizer  576  is configured to identify characters via automatic facial recognition, as known by those skilled in the art such as those reported in Face Recognition Vendor Tests (FRTV) 2006 and Iris Challenge Evaluation (ICE) 2006 Large-Scale Results, National Institute of Standards and Technology NISTIR 7408. In one aspect, the auto facial recognizer  576  complements the textual recognition (via character parameter  550 ) of a particular character or actor in identifying scenes including a particular character (or actor). 
     In one embodiment, scenes are characterized and differentiated via scene identifier  530  at the time that the program is first being produced. In this embodiment, the different scenes of the program are defined according to the emotion displayed by a character in a manner substantially the same as previously described in association with  FIGS. 1-6 , except that manager  500  will not have to differentiate the scenes at a later time. Instead, each scene is tagged prior to release of the program by the producer or distributor. 
     The program profile  512  of manager  500  is configured to produce a profile of one or more emotions of a character or characters in a program. One non-limiting example of an emotional profile produced via program profile is illustrated in  FIG. 2 , in which a profile  102   f  the relative happiness of one character is plotted over the sequence of scenes of the program. As previously described in association with  FIG. 2 , recognizable patterns in the graphically-represented emotional profile  102  are used for comparison with criteria or information in a user profile. This comparison determines whether a program matches the tastes and habits of a user, and therefore whether that program is recommended for viewing by the user. 
     In one embodiment, program profile  512  comprises an emotional categories function  590 , a duration function  592 , a peak function  594 , a frequency function  596 , and a transitions function  598 . The emotional categories function  590  is configured to specify which emotion(s) of a character are to be tracked and plotted in a graphically-represented emotional profile. The duration function  592  enables specifying various parameters for which a duration of an emotional display will be tracked and/or recognized. For example, duration function  592  enables tracking the maximum duration (counted by time or number of scenes) of a negative or positive emotion of a character. The peak function  594  enables specifying various parameters for which a peak intensity of an emotion will be tracked and/or recognized. For example, peak function  594  enables recognizing and tracking the peak intensity of an emotion (positive or negative) of a character. The frequency function  596  enables tracking a frequency of changes between different emotions (e.g., happiness and confusion) or changes between positive and negative poles of a single emotion (e.g. happiness and unhappiness). 
     The transitions function  598  enables specifying and tracking the number of physical transitions within a program. For example, a fast paced action movie would have a large number of physical transitions and recognizing a pattern of a large number of physical transitions will assist manager  500  in recommending (or avoiding) programs with such a profile. 
     The tagging module  514  of manager  500  is configured to electronically mark or tag a scene and/or elements of a scene, thereby enabling automatically searching, grouping, access, or other handling of each scene of a program. In particular, electronically tagging each scene (and elements of a scene) facilitates building an emotional profile of a program as well as comparing the emotional profile (as a whole or on a scene-by-scene basis) with a user profile. 
     In one embodiment, as illustrated in  FIG. 7 , the tagging module  514  includes a scene ID  610 , a character ID  612 , an emotion indicator  614 , a link ID  618 , and a resource descriptor  620  with a meta parameter  622  and a semantic parameter  624 . The scene ID  610  substantially corresponds to the scene ID  270  of scene index  175  of  FIG. 4  that uniquely identifies a scene within a sequence of scenes of a program. The character ID  612  enables specifying the name or alphanumeric identifier of each character within a program, as well as the name or alphanumeric identifier of the actor or actress corresponding to a respective character. Accordingly, the character ID  612  substantially corresponds to the character parameters  210 ,  220  of scene index  175  of  FIG. 4 . 
     The emotion indicator  614  identifies an emotion (or change in emotion) of a character in a scene of the program, and substantially corresponds to the beginning emotion parameter  212  and ending emotion parameter  214  of a scene, as previously described in association with  FIG. 4 . 
     The link ID  618  is configured to assign a rule identifier (e.g. preview, mobile, full), in cooperation with rules module  640 , to a scene so that at a later time, scenes with that respective rule identifier are collated or aggregated into an appropriate sequence to provide a desired version of the program. In one aspect, link ID  618  cooperates with modification parameter  254  to tag scenes for inclusion into a modified version of a program. 
     In another embodiment, link ID  618  and modification parameter  254  cooperate to enable building a compilation of scenes from different programs to act as a preview or other modified version of a program. For example, one could compile a greatest hits or anthology of scenes for an actor or character into one new program. 
     The resource description  620  is configured to provide the electronic tagging information of a scene and its elements in a universal resource descriptor format. This arrangement facilitates broad access to the information of the emotional profile of a program across a wide spectrum of computing infrastructure, such as the World Wide Web, the Semantic Web, or other network resource paradigms. In one embodiment, the resource descriptor  620  (including meta parameter  622  and semantic parameter  624 ) comprises substantially the same features and attributes as resource descriptor  276  of scene index  175  of  FIG. 4  (including semantic parameter  280  and meta parameter  282 ). 
     The builder module  516  of manager  500  is configured to aggregate a plurality of scenes into a program according to one or more rules. Accordingly, the builder module  516  is used by manager  500  after a program has been apportioned into a sequence of scenes according to the principles of the present disclosure. 
     In one embodiment, the builder module  516  comprises a rules module  640 , a scene selector module  670 , and an advertisement module  680 . The rules module  640  comprises full parameter  650 , preview parameter  652 , a condensed parameter  654 , a mobile parameter  656 , and a custom parameter  658 . The full parameter  650  is configured to maintain all the scenes of the program that correspond to a full length of the program. 
     The preview parameter  652  is configured to specify that a limited number of the scenes of a program be aggregated into a preview version of the program. Accordingly, upon all the scenes within a program being identified and indexed, one can specify the preview parameter  652  to automatically build a preview version of a program. The preview parameter  652  collates all scenes that are tagged (via link ID  618  and modification parameter  254  of content function  186 ) as preview scenes and aggregates them together in a desired sequence to form a preview. 
     In another aspect, condensed parameter  654  collates all scenes indexed or tagged (via link ID  618  and modification parameter  254 ) as being a condensed-type scene and aggregates them together in the proper sequence (i.e., according to an event timeline of the plot) to form a condensed version of the program. A substantially similar arrangement is provided for mobile parameter  656  in which all scenes tagged or indexed as mobile-type scenes are collated into a mobile version of the program. The custom parameter  658  enables a producer to select whichever scenes they choose for inclusion into a rule to define a sequence of scenes as a custom program. 
     The scene selector module  670  of builder module  516  is configured to enable selecting certain scenes to achieve a modified version of a program. In one embodiment, the scene selector module  670  comprises link parameter  672 , alternate parameter  674 , and format parameter  676 . The alternate parameter  674  is configured to tag or index certain scenes that act as alternate scenes when one or more scenes are excluded from a rule (i.e., modified program) because of the subject matter of the excluded scene or for other reasons. The format parameter  676  is configured to specify the format of a particular scene, such as whether the scene is in standard definition or high definition. Accordingly, the format parameter  676  enables automatic or manual selection of the high definition parameter  250  or standard definition parameter  252  of scene index  175  (see  FIG. 4 ) for a particular scene. 
     The advertisement module  680  is configured to insert advertisements into a program via an interruptive function  682  or a parallel function  684 . The interruptive function  682  places an advertisement between otherwise consecutive scenes of the program while the parallel function  684  displays advertisements in parallel with one or more scenes. In other words, in the parallel function  684 , the advertisement is displayed simultaneously with one or more scenes in the form of a caption, picture-in-picture, subtitle or other mechanism. 
     In one embodiment, memory  502  represents the storage of manager  500  in a memory within a web site or other network accessible resource. 
       FIG. 8  is a diagram  700  illustrating conversion of a first rule  702  (represented as Rule A) set of scenes to a second rule  704  (i.e., Rule B) set of scenes upon inserting an advertisement  720 , via advertisement module  680  in  FIG. 7 , into a series of scenes. In one aspect, diagram  700  also illustrates the interruptive parameter  682  of  FIG. 7  because the advertisement  720  is inserted between two otherwise consecutive scenes  710 , thereby interrupting the sequence of the scenes. In another aspect, diagram  700  illustrates the application of format parameter  676  of scene selector module  670  by insertion of a high definition scene  712  just prior to a high definition advertisement  720 . With this arrangement, a user would better appreciate the smoother flow from a high definition scene to high definition advertisement. 
       FIG. 9  is a diagram  750  of elements of a scene represented in a resource descriptor scheme, according to one embodiment of the present disclosure. As illustrated in  FIG. 9 , the elements of the scene include a first character  752  (i.e., Charlotte), second character  754  (i.e., Bob), and an emotion  756  (i.e., happiness). In addition, the emotion  756  is represented as a type of the Property. Finally, diagram  750  demonstrates a set  760  of resource descriptor definitions, in the RDFS framework, for the character Charlotte and for the emotion Happiness. By using such universal resource descriptors to index elements of a scene, these universal resource descriptors are available to build rule sets as well as make the tagged or indexed scenes searchable throughout a distributed communication network. In another aspect, use of such universal resource descriptors enables indexing each scene to apportion the various scenes of a program as well to facilitate re-building the scenes into the original program or a modified program. 
       FIG. 10  is a flow diagram of a method  800  of characterizing a program, according to one embodiment of the present disclosure. In one embodiment, method  800  is performed using any one of the system and methods previously described in association with  FIGS. 1-9 . In other embodiments, systems and methods other than those described in association with  FIGS. 1-9  are used to perform method  800 . 
     As illustrated in  FIG. 10 , at block  802  method  800  comprises defining a scene as a portrayal of a character displaying an emotion or having an emotional state (e.g., happy, sad, etc.). At block  804 , each scene is identified within a movie (or other program) to apportion the program into a series of scenes. Next, method  800  includes building, via the series of scenes, an emotional profile of the program. As previously described, in some embodiments this characterization of the program via a scene-based emotional profile in further used to recommend one or more such programs upon comparison of the respective emotional profiles with a user preference profile. 
     Embodiments of the present disclosure enable accurate characterization and/or recommendation of a program. Accordingly, users gain greater access to the extensive and diverse universe of programs available as digital content, as well as available in more traditional formats. Likewise, owners of more obscure or less publicized digital content now have the opportunity to become more visible to users, distributors, producers, etc. Finally, in addition to the generally greater access afforded to the user, the user will enjoy more programs because of the accuracy in identifying programs suited to their preferences. 
     Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that a variety of alternate and/or equivalent implementations may be substituted for the specific embodiments shown and described without departing from the scope of the present invention. This application is intended to cover any adaptations or variations of the specific embodiments discussed herein. Therefore, it is intended that this invention be limited only by the claims and the equivalents thereof.