Patent Publication Number: US-2020304870-A1

Title: Behavior modeling based on content genre

Description:
CROSS REFERENCE TO RELATED PATENT APPLICATION 
     This application is a continuation of U.S. application No. Ser. No. 15/668,365 filed Aug. 3, 2017, which is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Modeling of user behavior has been applied to content consumption as it relates to the behavior of users consuming the content. Current behavioral modeling focuses on predicting user preferences for what type of content the user prefers. Thus, behavioral modeling can determine that a user is a sports fan and can therefore be used to recommend a sporting event to the user. However, users consuming content exhibit different consumption behaviors based on genre of content consumed. For example, users consuming content in the sports genre exhibit different behaviors with regard to how content is consumed from users consuming content in the movies genre. Current behavioral modeling fails to predict how users consume content, but rather focus on what content the users might want to consume. These and other shortcomings are addressed by the approaches set forth herein. 
     SUMMARY 
     It is to be understood that both the following general description and the following detailed description are exemplary and explanatory only and are not restrictive. Provided are methods and systems for behavior modeling based on content genre. 
     A user&#39;s consumption of a content item (e.g., sporting event, movie, etc) can be made up of many viewing events. A viewing event can represent some interaction between the user and the content item and/or other content items. For example, starting to watch the content item, changing the channel to watch a different content item, returning to watch the original content item, etc These viewing events can be used to determine an affinity with respect to the given content item. For example, the affinity can be calculated as a function of a ratio of the duration of viewed content relative to the duration of the content item. These affinities for given content item (also referred to as “program affinities”) can be modified by various affinity modifiers. For example, briefly switching away from viewing the given content item to view or “sample” another content item having the same genre as the given content item can contribute to a “sampling bonus” added to the program affinity. As another example, a weight or “view coefficient” can be used to weight or scale program affinities based on a time, importance, or other factor of the content item viewed. Additionally, program affinities can be aggregated to determine affinities across a genre, series, sports league, or grouping of users, or can be otherwise aggregated. These affinities can be used in various ways. For example, affinities can be used to identify users that are fans of a particular genre, such as sports. Affinities can be used to identify a level of dedication a fan has to the genre. Recommendations for product packages, applications, services, and the like that are tailored to these fans can then be offered to the identified users. 
     Additional advantages will be set forth in part in the description which follows or may be learned by practice. The advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and together with the description, serve to explain the principles of the methods and systems: 
         FIG. 1  is a diagram of an example content delivery network; 
         FIGS. 2A and 2B  are example timelines of viewing habits with respect to multiple users within a time period. 
         FIGS. 3A, 3B, and 3C  are example representations of the relationship between content viewing events and program affinities; 
         FIGS. 4A, 4B, and 4C  are representations of program affinity based on view coefficients; 
         FIG. 5  is a representation of determining aggregated program affinity; 
         FIG. 6  is a behavioral model based on user groupings according to program affinities; 
         FIG. 7  is a flowchart depicting an example method; 
         FIG. 8  is a flowchart depicting an example method; 
         FIG. 9  is a flowchart depicting an example method; 
         FIG. 10  is a block diagram of an example computing device. 
     
    
    
     DETAILED DESCRIPTION 
     Before the present methods and systems are disclosed and described, it is to be understood that the methods and systems are not limited to specific methods, specific components, or to particular implementations. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. 
     As used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. 
     “Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not. 
     Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other components, integers or steps. “Exemplary” means “an example of” and is not intended to convey an indication of a preferred or ideal embodiment. “Such as” is not used in a restrictive sense, but for explanatory purposes. 
     Disclosed are components that can be used to perform the disclosed methods and systems. These and other components are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these components are disclosed that while specific reference of each various individual and collective combinations and permutation of these may not be explicitly disclosed, each is specifically contemplated and described herein, for all methods and systems. This applies to all aspects of this application including, but not limited to, steps in disclosed methods. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods. 
     The present methods and systems may be understood more readily by reference to the following detailed description of preferred embodiments and the examples included therein and to the Figures and their previous and following description. 
     As will be appreciated by one skilled in the art, the methods and systems may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the methods and systems may take the form of a computer program product on a computer-readable storage medium having computer-readable program instructions (e.g., computer software) embodied in the storage medium. More particularly, the present methods and systems may take the form of web-implemented computer software. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices. 
     Embodiments of the methods and systems are described below with reference to block diagrams and flowchart illustrations of methods, systems, apparatuses and computer program products. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create a means for implementing the functions specified in the flowchart block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including computer-readable instructions for implementing the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. 
     Accordingly, blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware-based computer systems that perform the specified functions or steps, or combinations of special purpose hardware and computer instructions. 
     In various instances, this detailed description may refer to content items (which may also be referred to as “content,” “content data,” “content information,” “content asset,” “multimedia asset data file,” or simply “data” or “information”). In some instances, content items can comprise any information or data that may be licensed to one or more individuals (or other entities, such as business or group). In various embodiments, content may include electronic representations of video, audio, text and/or graphics, which may include but is not limited to electronic representations of videos, movies, or other multimedia, which may include but is not limited to data files adhering to MPEG2, MPEG, MPEG4 UHD, HDR, 4k, Adobe® Flash® Video (.FLV) format or some other video file format whether such format is presently known or developed in the future. In various embodiments, the content items described herein may include electronic representations of music, spoken words, or other audio, which may include but is not limited to data files adhering to the MPEG-1 Audio Layer 3 (.MP3) format, Adobe®, CableLabs 1.0,1.1, 3.0, AVC, HEVC, H.264, Nielsen watermarks, V-chip data and Secondary Audio Programs (SAP). Sound Document (.ASND) format or some other format configured to store electronic audio whether such format is presently known or developed in the future. In some cases, content may include data files adhering to the following formats: Portable Document Format (.PDF), Electronic Publication (.EPUB) format created by the International Digital Publishing Forum (IDPF), JPEG (.JPG) format, Portable Network Graphics (.PNG) format, dynamic ad insertion data (.csv), Adobe® Photoshop® (.PSD) format or some other format for electronically storing text, graphics and/or other information whether such format is presently known or developed in the future. In some embodiments, content items may include any combination of the above-described examples. 
     In various instances, this detailed disclosure may refer to consuming content or to the consumption of content, which may also be referred to as “accessing” content, “providing” content, “viewing” content, “listening” to content, “rendering” content, or “playing” content, among other things. In some cases, the particular term utilized may be dependent on the context in which it is used. For example, consuming video may also be referred to as viewing or playing the video. In another example, consuming audio may also be referred to as listening to or playing the audio. 
     Note that in various instances this detailed disclosure may refer to a given entity performing some action. It should be understood that this language may in some cases mean that a system (e.g., a computer) owned and/or controlled by the given entity is actually performing the action. 
     The present disclosure relates to behavioral modeling based on content genre. Particularly, the present disclosure relates to modeling user behavior of sports fans (also referred to as sports consumers) based on activities more prevalent to the consumption of sports content items. The methods and systems disclosed can quantify how much content consumers like content items in one or more genres based on the content consumers&#39; behaviors (e.g., actions), also called an affinity towards the genre. The behaviors utilized in the present disclosure relate not just to behaviors that indicate whether a user has an affinity for a genre (e.g., a user watches a number of football games exceeding a threshold amount), but how the user consumes content items within the genre (e.g., while a user is watching a football game, the user changes the channel to one or more other football games). For example, a sports consumer viewing a sports content item (e.g., a game) can periodically switch to other sports content items (e.g., other games) in order to learn the score or state of a particular game. In contrast, a consumer of a movie or television show is more likely to watch a content item (e.g., a movie) without switching to another content item (e.g., another movie). As another example, a sports consumer is more likely to begin or resume watching a game in the middle or near the end of a game. When determining a user&#39;s affinity associated with the sports genre (e.g., with respect to a particular game, series, season, team, league, etc.) these behaviors can be taken into consideration. Although the following discussion is presented in the context of the sports genre and sports fans, it is understood that the following discussion is applicable to other genres or categorizations of content items. 
     Determining an affinity of a user for a particular content item (e.g., a game) can include aggregating viewing events associated with the user. A viewing event can be an event indicating delivery of content to a user device. Examples of viewing events include a selection of a stream of content for delivery to the user device or a tuning of a user device to a channel or frequency for reception of the content item. The viewing events can be aggregated by receiving data indicative of the viewing events generated by a user device associated with the user, such as a mobile device, set top box, or other device for consuming content items. The viewing events can indicate, for example, a particular content item viewed, a start time of viewing, an end time of viewing, a viewing duration, and potentially other data. 
     To determine the affinity of a user for a particular content item, referred to hereinafter as a “program affinity,” viewing events for a particular content item can be selected. Determining the program affinity for the particular content item can include selecting those viewing events having an identifier indicating their association with the particular content item. One or more viewing events for the particular content item can be merged. For example, a first viewing event can be merged with a contiguous second viewing event. A first viewing event can be considered contiguous to a second viewing event when the end time of the first viewing event matches the start time of the second viewing event. As another example, a first viewing event can be merged with a second viewing event when at least a portion of the durations of the first viewing event and second viewing event overlap. As a further example, a first viewing event can be merged with a second viewing event when the end time of the first viewing event and the start time of the second viewing event are separated by a duration below a threshold. In such an example, the program affinity for the particular content item would not be negatively impacted by short deviations in viewing to other content items, or “sampling,” as is described in further detail below. 
     The program affinity for the particular content item can be calculated based on a duration of the particular content item viewed relative to the total duration of the content item. For example, calculating the program affinity for the particular content item can include calculating a summation of the viewing duration of each viewing event corresponding to the particular content item and the user. The summation can then be divided by the total duration of the content item (e.g., the time duration between a start time and an end time of the content item) to calculate the program affinity. As another example, calculating the program affinity for the particular content item can include calculating an average viewing duration of each viewing event corresponding to the content item and the user. The average viewing duration can then be divided by the total duration of the content item to determine the program affinity for the particular content item. 
     Program can be calculated by applying one or more affinity modifiers. For example, the affinity modifiers can include a sampling bonus. A sampling bonus for a given content item is a bonus based on one or more viewing events for other content items occurring during a delivery of the given content item. The viewing events used to calculate a sampling bonus can include those viewing events having a duration falling below a threshold, indicating a brief period of viewing for the “sampled” other content item. Affinity modifiers can also include weights or “view coefficients” applied to the program affinities. The view coefficients for a given content item can be based on a scheduling of the given content item in a series or season. View coefficients for the given content item can also be determined based on a significance of an event corresponding to the content item, such as a rivalry game, a key game in a series or season, or an otherwise significant event. 
     One or more program affinities, or aggregate affinities based on multiple program affinities, can be used to generate a recommendation for a user. For example, a program affinity or aggregate affinity can indicate an affinity or “fandom” for a particular genre, team, sport, league, or other category of content items. Users can be grouped based on their respective affinities, and recommendations can be generated based on to which grouping a user belongs. The recommendation can include a promotion or advertisement for a content package, product, discount, or other incentive corresponding to the grouping or classification of the user. The recommendation can include a recommendation for one or more content items relevant to the grouping or classification of the user. 
       FIG. 1  illustrates various aspects of an exemplary system in which the present methods and systems can operate. Those skilled in the art will appreciate that present methods may be used in systems that employ both digital and analog equipment. One skilled in the art will appreciate that provided herein is a functional description and that the respective functions can be performed by software, hardware, or a combination of software and hardware. 
     A system  100  can comprise a central location  101  (e.g., a headend), which can receive content (e.g., data, input programming, and the like) from multiple sources. The central location  101  can combine the content from the various sources and can distribute the content to user (e.g., subscriber) locations (e.g., location  119 ) via a distribution system  116 . 
     The central location  101  can receive content from a variety of sources  102   a,    102   b ,  102   c.  The content can be transmitted from the source to the central location  101  via a variety of transmission paths, including wireless (e.g. satellite paths  103   a,    103   b ) and a terrestrial path  104 . The central location  101  can also receive content from a direct feed source  106  via a direct line  105 . Other input sources can comprise capture devices such as a video camera  109  or a server  110 . The signals provided by the content sources can include a single content item or a multiplex that includes several content items. 
     The central location  101  can comprise one or a plurality of receivers  111   a,    111   b ,  111   c,    111   d  that are each associated with an input source. For example, MPEG encoders such as an encoder  112 , are included for encoding local content or a video camera  109  feed. A switch  113  can provide access to the server  110 , which can be a Pay-Per-View server, a data server, an interne router, a network system, a phone system, and the like. Some signals may require additional processing, such as signal multiplexing, prior to being modulated. Such multiplexing can be performed by a multiplexer (mux)  114 . 
     The central location  101  can comprise one or a plurality of modulators  115  for interfacing to a network  116 . The modulators  115  can convert the received content into a modulated output signal suitable for transmission over a network  116 . The output signals from the modulators  115  can be combined, using equipment such as a combiner  117 , for input into the network  116 . The network  116  can comprise a content delivery network, a content access network, and/or the like. For example, the network  116  can be configured to provide content from a variety of sources using a variety of network paths, protocols, devices, and/or the like. The content delivery network and/or content access network can be managed (e.g., deployed, serviced) by a content provider, a service provider, and/or the like. 
     A control system  118  can permit a system operator to control and monitor the functions and performance of the system  100 . The control system  118  can interface, monitor, and/or control a variety of functions, including, but not limited to, the channel lineup for the television system, billing for each user, conditional access for content distributed to users, and the like. The control system  118  can provide input to the modulators for setting operating parameters, such as system specific MPEG table packet organization or conditional access information. The control system  118  can be located at the central location  101  or at a remote location. 
     The network  116  can distribute signals from the central location  101  to user locations, such as a user location  119 . The network  116  can comprise an optical fiber network, a coaxial cable network, a hybrid fiber-coaxial network, a wireless network, a satellite system, a direct broadcast system, an Ethernet network, a high-definition multimedia interface network, universal serial bus network, or any combination thereof 
     A multitude of users can be connected to the network  116  at one or more of the user locations. At the user location  119 , a media device  120  can demodulate and/or decode, if needed, the signals for display on a display device  121 , such as on a television set (TV) or a computer monitor. For example, the media device  120  can comprise a demodulator, decoder, frequency tuner, and/or the like. The media device  120  can be directly connected to the network (e.g., for communications via in-band and/or out-of-band signals of a content delivery network) and/or connected to the network  116  via a communication terminal  122  (e.g., for communications via a packet switched network). The media device  120  can comprise a set-top box, a digital streaming device, a gaming device, a media storage device, a digital recording device, a combination thereof, and/or the like. The media device  120  can comprise one or more applications, such as content viewers, social media applications, news applications, gaming applications, content stores, electronic program guides, and/or the like. Those skilled in the art will appreciate that the signal can be demodulated and/or decoded in a variety of equipment, including the communication terminal  122 , a computer, a TV, a monitor, or a satellite dish. 
     The communication terminal  122  can be located at the user location  119 . The communication terminal  122  can be configured to communicate with the network  116 . The communications terminal  122  can comprise a modem (e.g., cable modem), a router, a gateway, a switch, a network terminal (e.g., optical network unit), and/or the like. The communications terminal  122  can be configured for communication with the network  116  via a variety of protocols, such as internet protocol, transmission control protocol, file transfer protocol, session initiation protocol, voice over internet protocol, and/or the like. For example, for a cable network, the communication terminal  122  can be configured to provide network access via a variety of communication protocols and standards, such as Data Over Cable Service Interface Specification. 
     The user location  119  can comprise a first access point  123 , such as a wireless access point. The first access point  123  can be configured to provide one or more wireless networks in at least a portion of the user location  119 . The first access point  123  can be configured to provide access to the network  116  to devices configured with a compatible wireless radio, such as a mobile device  124 , the media device  120 , the display device  121 , or other computing devices (e.g., laptops, sensor devices, security devices). For example, the first access point  123  can provide a user managed network (e.g., local area network), a service provider managed network (e.g., public network for users of the service provider), and/or the like. It should be noted that in some configurations, some or all of the first access point  123 , the communication terminal  122 , the media device  120 , and the display device  121  can be implemented as a single device. 
     The user location  119  may not be fixed. By way of example, a user can receive content from the network  116  on the mobile device  124 . The mobile device  124  can comprise a laptop computer, a tablet device, a computer station, a personal data assistant (PDA), a smart device (e.g., smart phone, smart apparel, smart watch, smart glasses), GPS, a vehicle entertainment system, a portable media player, a combination thereof, and/or the like. The mobile device  124  can communicate with a variety of access points (e.g., at different times and locations or simultaneously if within range of multiple access points). For example, the mobile device  124  can communicate with a second access point  125 . The second access point  125  can be a cell tower, a wireless hotspot, another mobile device, and/or other remote access point. The second access point  125  can be within range of the user location  119  or remote from the user location  119 . For example, the second access point  125  can be located along a travel route, within a business or residence, or other useful locations (e.g., travel stop, city center, park). 
     The system  100  can comprise an application device  126 . The application device  126  can be a computing device, such as a server. The application device  126  can provide services related to applications. For example, the application device  126  can comprise an application store. The application store can be configured to allow users to purchase, download, install, upgrade, and/or otherwise manage applications. For example, the application device  126  can be configured to allow users to download applications to a device, such as the mobile device  124 , communications terminal  122 , the media device  120 , the display device  121 , and/or the like. The application device  126  can run one or more application services to provide data, handle requests, and/or otherwise facilitate operation of applications for the user. 
     The system  100  can comprise one or more content source(s)  127 . The content source(s)  127  can be configured to provide content (e.g., video, audio, games, applications, data) to the user. The content source(s)  127  can be configured to provide streaming media, such as on-demand content (e.g., video on-demand), content recordings, and/or the like. For example, the content source(s)  127  can be managed by third party content providers, service providers, online content providers, over-the-top content providers, and/or the like. The content can be provided via a subscription, by individual item purchase or rental, and/or the like. The content source(s)  127  can be configured to provide the content via a packet switched network path, such as via an interne protocol (IP) based connection. The content can be accessed by users via applications, such as mobile applications, television applications, set-top box applications, gaming device applications, and/or the like. An example application can be a custom application (e.g., by content provider, for a specific device), a general content browser (e.g., web browser), an electronic program guide, and/or the like. 
     The system  100  can comprise an edge device  128 . The edge device  128  can be configured to provide content, services, and/or the like to the user location  119 . For example, the edge device  128  can be one of a plurality of edge devices distributed across the network  116 . The edge device  128  can be located in a region proximate to the user location  119 . A request for content from the user can be directed to the edge device  128  (e.g., due to the location of the edge device and/or network conditions). The edge device  128  can be configured to package content for delivery to the user (e.g., in a specific format requested by a user device), provide the user a manifest file (e.g., or other index file describing segments of the content), provide streaming content (e.g., unicast, multicast), provide a file transfer, and/or the like. The edge device  128  can cache or otherwise store content (e.g., frequently requested content) to enable faster delivery of content to users. 
     The network  116  can comprise a network component  129 . The network component  129  can comprise any device, module, and/or the like communicatively coupled to the network  116 . For example, the network component  129  can comprise a router, a switch, a splitter, a packager, a gateway, a encoder, a storage device, a multiplexer, a network access location (e.g., tap), physical link, and/or the like. 
     The content source  127  and/or edge device  128  can serve to deliver content items to user devices, such as the mobile device  124 , communications terminal  122 , the media device  120 , and/or the display device  121 . Accordingly, a user device such as the mobile device  124 , communications terminal  122 , the media device  120 , and/or the display device  121  configured to receive a given content item can generate a viewing event detected by the content source  127  and/or edge device  128 . The content source  127 , edge device  128 , or another computing device in communication with the content source  127  or edge device can calculate affinities as set forth below based on the detected viewing events. 
       FIG. 2A  is an example timeline  200  of viewing habits for a sampling of users. In this example timeline, the users are drawn from a pool of “typical” users, e.g. users below the ninetieth percentile of affinities. Each entry  202  in the timeline for a respective user indicates a viewing of a program for a duration indicated by the x-axis of the timeline  200 . The example timeline  200  serves to illustrate that “typical” users tend to either tune in to a given program for an extended duration, or abandon viewing without resuming viewing. For example, entry  204  shows that user  19  viewed a given program continuously for ninety minutes, while entry  206  shows that user  24  viewed a given program for two and a half hours. Conversely, entries  208   a,b,c,d,e  show that user  7  viewed a given content program for an hour and a half, but with four deviations from viewing the content program interspersed throughout the viewing period. 
       FIG. 2B  is an example timeline  210  of viewing habits for a sampling of users. In this example timeline, the users are drawn from a pool of users having a higher affinity for the sports genre. Each entry  212  in the timeline for a respective user indicates a viewing of a program for a duration indicated by the x-axis of the timeline  210 . The example timeline  200  serves to illustrate that “fans,” e.g. users associated with high affinity values, tend to sample programs repeatedly throughout the duration of the timeline  210 . For example, entries  214   a,b,c,d  serve to show that user  10  viewed a given program (corresponding to entries  214   a,c ) with two instances of sampling another program (corresponding to entries  214   b,d ). Conversely, entry group  216  indicates repeated samplings of programs throughout the indicated period. 
       FIG. 3A  is an example depiction  300  of the relationship between viewing events and program affinities. Shown is a timeline with timeline entries  302   a,b,c  each corresponding to a respective content item. Durations of content items can be represented by a width of a respective timeline entry  302   a,b,c . Each vertically aligned darkened portion  304  can correspond to a respective viewing event, with a duration of the respective viewing event corresponding to the width of the darkened portion. A program affinity for each content item can be calculated as a function of a ratio of darkened areas to the total area of the respective timeline entry  302   a,b,c.    
       FIG. 3B  is an example depiction  310  of the relationship between viewing events and program affinities. Shown is a timeline with timeline entries  312   a,b,c  each corresponding to a respective content item. Durations of content items can be represented by a width of a respective timeline entry  312   a,b,c.  Each vertically aligned darkened portion  314  can correspond to a respective viewing event, with a duration of the respective viewing event corresponding to the width of the darkened portion. Also included are horizontally aligned darkened portions  314  representative of a sampling bonus. In this example, the width of the darkened portions  314  correspond to the width of the timeline entry  312   a,b,c . In other words, the sampling bonus is based on a duration of the respective content item. Thus, a program affinity for each content item can be calculated as a function of a ratio of darkened areas (including vertically aligned darkened portions  314  and horizontally aligned darkened portions  316 ) to the total area of the respective timeline entry  312   a,b,c.    
       FIG. 3C  is an example depiction  320  of the relationship between viewing events and program affinities. Shown is a timeline with timeline entries  322   a,b,c  each corresponding to a respective content item. Durations of content items can be represented by a width of a respective timeline entry  322   a,b,c.  Each vertically aligned darkened portion  324  can correspond to a respective viewing event, with a duration of the respective viewing event corresponding to the width of the darkened portion. Also included are horizontally aligned darkened portions  326  representative of a sampling bonus. In this example, the width of the horizontally aligned darkened portions  326  correspond to the duration between the start of a first viewing event and the end of a last viewing event for the respective timeline entry  322   a,b,c.  Thus, a program affinity for each content item can be calculated as a function of ratio of darkened areas (including vertically aligned darkened portions  324  and horizontally aligned darkened portions  326 ) to the total area of the respective timeline entry  322   a,b,c.    
       FIG. 4A  is an example representation  400  of the relationship between viewing events and program affinities. Included are timeline entries  402   a,b,c  representing respective content items. Each timeline entry  402   a,b,c  includes vertically aligned darkened portions  404  representing viewing events having durations corresponding to the width of the respective vertically aligned darkened portion  404 . A respective view coefficient  406   a,b,c  has been applied to the program affinities for each content item. Thus, viewing events occurring earlier in the content items are weighted less than viewing events occurring later in the content items for the purpose of calculating program affinities. 
       FIG. 4B  is an example representation  410  of the effect of view coefficients in relation to program affinities and viewing events. Included are timeline entries  412   a,b,c  representing respective content items. Each timeline entry  412   a,b,c  includes vertically aligned darkened portions  414  representing viewing events having durations corresponding to the width of the respective vertically aligned darkened portion  414 . A view coefficient  416  has been applied to scale the program affinities for each content item based on a time or date of the respective content item. Thus, program affinities for earlier content items are weighted less than program affinities for later content items. For example, users may abandon or cease to view content items in a given season or series as the season or series goes on. Thus, users still viewing the content items later in the series or season would receive a higher view coefficient representing their higher affinity for the content. 
       FIG. 4C  is an example representation  420  of the effect of view coefficients in relation to program affinities and viewing events. Included are timeline entries  422   a,b,c  representing respective content items. Each timeline entry  422   a,b,c  includes vertically aligned darkened portions  424  representing viewing events having durations corresponding to the width of the respective vertically aligned darkened portion  424 . A respective view coefficient  426   a,b,c  has been applied to the program affinities for each content item based on a significance or importance of the respective content item. The significance or importance of the respective content item can be defined by user input. The significance or importance of the respective content item can be determined based on ratings, numbers of viewing events, or other statistics associated with similar content items. Thus, the program affinity for the content item of timeline entry  422   b  is weighted higher than the program affinity for the content item of timeline entry  422   a,  whose program affinity is weighted higher than the program affinity for the content item of timeline entry  422   c.    
       FIG. 5  is an example representation  500  of determining an aggregated program affinity for a user. In this example, program affinities for multiple content items on multiple channels are aggregated to determine an aggregated affinity for a user with respect to a sports league. Included are timelines  502   a,    502   b,    502   n  each corresponding to a respective channel A,B or N. Each timeline  502   a,b,n  includes timeline entries  504   a,b,c,    506   a,b,c,  and  508   a,b,c.  Each of the timeline entries  504   a,b,c,    506   a,b,c,  and  508   a,b,c  corresponds to a delivery of a respective content item. In this example, content items for timeline entries  504   a,    506   a,  and  508   a  are delivered concurrently. Similarly, content items for timeline entries  504   b,    506   b,  and  508   b  are delivered concurrently, and content items for timeline entries  504   c,    506   c , and  508   c  are delivered concurrently. 
     Vertically aligned darkened portions  510  correspond to viewing events, and horizontally aligned portions  512  correspond to sampling bonuses. The vertically aligned darkened portions  510  and horizontally aligned darkened portions  512  are summed across each of the timelines  502   a,    502   b,    502   n,  as represented by an aggregate timeline  514 . Aggregate timeline  514  includes timeline entries  516   a,b,c  indicating, for a user, respective aggregated affinities across multiple content items. These aggregated affinities can then be further summed to determine an aggregated affinity for a user with respect to grouping of events, e.g. games, a specific league, combinations thereof, and the like. Although  FIG. 5  depicts a summation to determine an aggregate affinity, it is understood that another aggregate function, e.g. an average, can be used when aggregating program affinities or aggregate affinities. 
       FIG. 6  is a behavioral model  600  based on user groupings according to program affinities. In this example, users are grouped into one of three categories based on their affinities. The affinities used to group the users can be program affinities for individual content items, or aggregated affinities across multiple content items in a series, season, league, or other grouping. Each of the groups can correspond to a range or threshold of affinity scores of the member users. Included in the behavioral model  600  are three timelines  602   a,b,c  each corresponding to a “rabid fan” group (indicating a high interest by the users), an “avid fan” (indicating an interest that is less than “rabid” but more than “casual”) group, and a “casual fan” (indicating an interest that is less than “avid”) group, respectively. 
     Each timeline  602   a,b,c  includes timeline entries  604   a,b,c,    606   a,b,c,  and  608   a,b,c . Each of the timeline entries  604   a,b,c,    606   a,b,c,  and  608   a,b,c  corresponds to a delivery of a respective content item. In this example, content items for timeline entries  604   a,    606   a,  and  608   a  correspond to a same content item. Similarly, content items for timeline entries  604   b,    606   b,  and  608   b  correspond to a same content item, and content items for timeline entries  604   c,    606   c,  and  608   c  correspond to a same content item. Each timeline  602   a,b,c  also includes a line  610   a,b,c  representing activity of users of the respective grouping. The line  610   a,b,c  represents a number of users in the respective group viewing a content item at a point in the timeline. Thus,  FIG. 6  shows the relationships between a number of viewers of a given content item at a given time based on their respective grouping. For example, line  610  shows that “rabid fans” tend to begin viewing a channel before a given content item begins delivery, and continue to view the channel after the given content item ends. Conversely, “casual fans” may be more prone to viewing a channel through which a content item is delivered after the content item begins, and may cease viewing before the content item ends. 
       FIG. 7  is a flowchart  700  of an example method. One or more of steps of the method can be performed by a computing device, such as the content source  127  and/or the edge device  128  of  FIG. 1 . Beginning with step  702 , an occurrence of a viewing event can be determined. The viewing event can be associated with a user. Determining the occurrence of the viewing event can include determining, during the delivery of a first content item associated with a first viewing event by the user, an occurrence of a second viewing event associated with a second content item. For example, a user device associated with the user can receive a delivery of a first content item, thereby generating the first viewing event. The user device can include the mobile device  124 , communications terminal  122 , the media device  120 , and/or the display device  121  of  FIG. 1 . The user device can then instead switch to receiving the second content item, thereby generating the second viewing event. The user device can receive the second content item instead of the first content item in response to a user input, such as a channel change signal, a user interface input, or other input. 
     Next, in step  704 , it can be determined that the duration of the second viewing event has a duration less than a threshold. For example, the threshold can be one second, two seconds, five seconds, ten seconds, 1 minute, 5 minutes, or any other appropriate duration. One skilled in the art can modify the threshold in order to achieve desired results. The second viewing event having the duration less than the threshold can indicate that the user was “sampling” the second content item before returning to the first content item, or switching to another content item. Thus, the second viewing event can contribute to a sampling bonus when determining a program affinity with respect to the first content item, as described herein. Conversely, when the second viewing event has a duration greater than the threshold, it can indicate a switch of user attention away from the first content item on a longer term basis, and can instead contribute to the program affinity with respect to the second content item. 
     In step  706 , in response to determining that the duration of the viewing event is below the threshold, it can be determined that the first content item and the second content item are in the same genre. For example, if the first content item corresponds to a sports game, thereby being in the “sports” genre, it can be determined that the second content item is also in the “sports” genre. Thus, sampling a second content item in the sports genre can indicate a greater affinity for the sports genre, and increase the program affinity with respect to the first content item through a sampling bonus. Determining that the first content item and the second content item are in the same genre can include comparing metadata associated with the first content item and the second content item, respectively. The metadata can be included or associated with one or more data segments of the first and/or second content item. The metadata can be loaded from an electronic program guide (EPG), or another listing or index identifying the first and/or second content item. 
     Next, in step  708 , an affinity for the genre can be determined. This can include determining at least one first affinity based on one or more viewing events associated with the user and the genre. Determining the at least one first affinity based on one or more viewing events associated with the user and the genre can include selecting the one or more viewing events associated with the genre and the user. Selecting the one or more viewing events associated with the genre and the user can include merging one or more viewing events associated with a same content item and the user. For example, a first viewing event and a second viewing event can be merged into a third viewing event when the first viewing event and the second viewing event are contiguous and associated with the same content item. In such an example, the third viewing event can have a start time corresponding to the start time of the first viewing event and an end time corresponding to the end time of the second viewing event. 
     As another example, the first viewing event and the second viewing event can be merged into a third viewing event when the first viewing event and the second viewing event are separated by a duration below a threshold and associated with the same content item. In such an example, the third viewing event can have a start time corresponding to the start time of the first viewing event and an end time corresponding to the end time of the second viewing event. 
     As another example, the first viewing event and the second viewing event can be merged into a third viewing event when the first viewing event and the second viewing event at least partially overlap and are associated with the same content item. In examples in which the first viewing event and the second viewing event are merged to generate a third viewing event, the third viewing event can then be merged with a fourth viewing event according to the approaches set forth above. 
     Determining the at least one first affinity can include calculating the at least one first affinity as at least one summation of the durations of each of the one or more selected viewing events divided by the duration of their respective associated content item. Determining the at least one first affinity can include calculating the at least one first affinity as at least one average of the durations of each of the one or more selected viewing events divided by the duration of their respective associated content item. 
     Determining the affinity for the genre can include adding at least one sampling bonus to the at least one first affinity based on the second viewing event associated with the second content item. The at least one sampling bonus can be weighted or scaled based on a duration of a first content item. The at least one sampling bonus can be weighted or scaled based on a duration of the first content item viewed. The at least one sampling bonus can be weighted or scaled based on a difference between a start time of a first viewing event for the first content item and an end time of the last viewing event for the first content item. Determining the sampling bonus can include determining a scalar to be applied to one of the durations defined above. The scalar can be dynamically determined based on a duration of sampling, a number of instances of sampling, or another factor. The scalar can be a predefined value for use in determining sampling bonuses. For example, a scalar can be determined or predefined to be 0.2. The scalar can be applied to the total 1800 second (thirty minute) duration of a content item, resulting in a sampling bonus of 360. As another example, a scalar can be increased by 0.1 for each instance of sampling occurring for a given content item. Thus, for four instances of sampling occurring during a given content item, the resulting scalar would be 0.4. When applied to a viewed duration of 1000 seconds, the resulting sampling bonus would be 400. It is understood that the examples above are non-limiting, and that one skilled in the art can determine weights and/or scalars according to a genre and/or the desired results. 
     After calculating the program affinity, in step  710 , a recommendation can be generated based on the affinity. Generating the recommendation can include grouping or classifying the user based on the affinity. A recommendation can then be generated based on the grouping or classification of the user. The recommendation can include a promotion or advertisement for a content package, product, discount, or other incentive corresponding to the grouping or classification of the user. The recommendation can include a recommendation for one or more content items relevant to the grouping or classification of the user. 
       FIG. 8  is a flowchart  800  of an example method. The method can be performed by a computing device such as the content source  127  and/or edge device  128  of  FIG. 1 , or another computing device as can be appreciated. Beginning with step  802 , it can be determined that a duration of a first viewing event associated with a first content item is below a threshold, e.g. one second, five seconds, one minute, five minutes, or any other appropriate duration, thereby indicating a “sampling” of the first content item by a user. The user can be associated with a user device, such as the mobile device  124 , communications terminal  122 , the media device  120 , and/or the display device  121  of  FIG. 1 , or another user device. Thus, the first viewing event can be generated in response to a consumption of the first content item by the user device. The first viewing event can indicate a content identifier indicating the particular content item viewed, a start time of viewing, an end time of viewing, a viewing duration, a user identifier, a user device identifier, and potentially other data. 
     In step  804 , it can be determined that the first content item is within a first genre of a plurality of genres. For example, the plurality of genres can include “sports,” “movies,” and/or “television shows.” Thus, it can be determined that the first content item is within the “sports” genre, “as opposed to the “movies” or “television shows” genres. Determining that the first content item is within the first genre can comprise determining that the first content item is within the first genre based on metadata associated with the first content item. The metadata can be associated with transmitted data segments of the first content item. The metadata can be accessed via an electronic program guide (EPG) or other listing or index of the content items. 
     In step  806 , a first affinity with respect to the first genre and the user can be determined. The first viewing event can contribute to (e.g., increase) the first affinity in response to the first content item being within the first genre and having a duration below the threshold. For example, given a first genre of “sports” and a threshold of five seconds, the first viewing event can contribute to the first affinity with respect to the “sports” genre” and the user if the first content item is within the “sports” category and the duration of the first viewing event is below five seconds. Using this example, viewing events with durations below the threshold but associated with content items in other genres (e.g., “movies”) would not contribute to affinities with respect to the other genres. 
       FIG. 9  is a flowchart  900  of an example method. The method can be performed by one or more computing devices, such as the content source  127  and/or edge device  128  of  FIG. 1 , or another computing device as can be appreciated. Beginning with step  902 , a first viewing event associated with a first content item and a user can be determined. The first viewing event can be associated with a user device, such as such as the mobile device  124 , communications terminal  122 , the media device  120 , and/or the display device  121  or another user device as can be appreciated. 
     The first viewing event can be merged with one or more additional viewing events. The one or more additional viewing events can be contiguous to or overlapping with the first viewing event. The one or more additional viewing events can be separated from the first viewing event by a duration below a threshold. For example, the threshold used for merging the first viewing event and the second viewing event can correspond the sampling duration discussed below, e.g. one second, five seconds, one minute, five minutes, or any other appropriate duration. Thus, first and second viewing events separated by an instance of sampling would be merged into a single viewing event. Next, in step  904 , a second viewing event associated with a second content item and the user can be determined. The first content item and the second content item can be delivered at least partially concurrently. Thus, The second viewing event can occur with respect to the second content item during a delivery of the first content item. 
     In step  906  it can be determined that the duration of the second viewing event is less than a threshold, e.g. one second, five seconds, one minute, five minutes, or any other appropriate duration, thereby indicating a “sampling” of the second content item by the user during a viewing of the first content item. In step  908 , the genre for the first content item and second content item are determined from metadata for the respective first content item and second content item. The metadata can be associated with transmitted data segments of the first content item and second content item. The metadata can be accessed via an electronic program guide (EPG) or other listing or index of the content items. It can be determined that the genre of the first content item and second content item are the same based on the metadata. Thus, the “sampling” of the second content item indicates an affinity with respect to the first content item due to the first content item and the second content item sharing the same genre. 
     Next, in step  910 , a first affinity can be determined (e.g., calculated) based on the first viewing event. The first affinity can be based on a duration of the first viewing event relative to the duration of the first content item. The first affinity can be based on a plurality of first viewing events associated with the first content item. The first affinity can be based on a total duration of the plurality of first viewing events. 
     In step  912  an affinity bonus can be determined (e.g., calculated) based on the second viewing event. The affinity bonus can include a sampling bonus based on the second viewing event. Determining the affinity bonus, e.g. the sampling bonus, can be performed in response to determining that the genre of the first content item and the second content item are the same as set forth in step  908 . The sampling bonus can be weighted or scaled based on a duration of the first content item. The sampling bonus can be weighted or scaled based on a duration of the first content item viewed. The sampling bonus can be weighted or scaled based on a difference between a start time of a first viewing event for the first content item and an end time of the last viewing event for the first content item. Determining the sampling bonus can include determining a scalar to be applied to one of the durations defined above. The scalar can be dynamically determined based on a duration of sampling, a number of instances of sampling, or another factor. The scalar can be a predefined value for use in determining sampling bonuses. For example, a scalar can be determined or predefined to be 0.2. The scalar is to be applied to the total 1800 second (thirty minute) duration of a content item, resulting in a sampling bonus of 360. As another example, a scalar can be increased by 0.1 for each instance of sampling occurring for a given content item. Thus, for four instances of sampling occurring during a given content item, the resulting scalar would be 0.4. When applied to a viewed duration of 1000 seconds, the resulting sampling bonus would be 400. It is understood that the examples above are non-limiting, and that one skilled in the art can determine weights and/or scalars according to a genre and/or the desired results. 
     Next, in step  914 , a total affinity for the user with respect to the first content item can be determined (e.g., calculated). Determining the total affinity can include adding the affinity bonus to the first affinity. A recommendation can be generated based on the total affinity. The recommendation can be generated based on a plurality of total affinities. The recommendation can be generated based on an aggregate affinity calculated as a function of a plurality of total affinities. Generating the recommendation can include grouping or classifying the user based on the program affinity. A recommendation can then be generated based on the grouping or classification of the user. The recommendation can include a promotion or advertisement for a content package, product, discount, or other incentive corresponding to the grouping or classification of the user. The recommendation can include a recommendation for one or more content items relevant to the grouping or classification of the user. 
     The methods and systems can be implemented on a computer  1001  as illustrated in  FIG. 10  and described below. By way of example, the mobile device  124 , application server  126 , content source  127 , and/or edge device  128  of  FIG. 1  can be a computer as illustrated in  FIG. 10 . Similarly, the methods and systems disclosed can utilize one or more computers to perform one or more functions in one or more locations.  FIG. 10  is a block diagram  1000  illustrating an exemplary operating environment for performing the disclosed methods. This exemplary operating environment is only an example of an operating environment and is not intended to suggest any limitation as to the scope of use or functionality of operating environment architecture. Neither should the operating environment be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment. 
     The present methods and systems can be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that can be suitable for use with the systems and methods comprise, but are not limited to, personal computers, server computers, laptop devices, and multiprocessor systems. Additional examples comprise set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that comprise any of the above systems or devices, and the like. 
     The processing of the disclosed methods and systems can be performed by software components. The disclosed systems and methods can be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers or other devices. Generally, program modules comprise computer code, routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The disclosed methods can also be practiced in grid-based and distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices. 
     Further, one skilled in the art will appreciate that the systems and methods disclosed herein can be implemented via a general-purpose computing device in the form of a computer  1001 . The components of the computer  1001  can comprise, but are not limited to, one or more processors  1003 , a system memory  1012 , and a system bus  1013  that couples various system components including the one or more processors  1003  to the system memory  1012 . The system can utilize parallel computing. 
     The system bus  1013  represents one or more of several possible types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, or local bus using any of a variety of bus architectures. By way of example, such architectures can comprise an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, an Accelerated Graphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI), a PCI-Express bus, a Personal Computer Memory Card Industry Association (PCMCIA), Universal Serial Bus (USB) and the like. The bus  1013 , and all buses specified in this description can also be implemented over a wired or wireless network connection and each of the subsystems, including the one or more processors  1003 , a mass storage device  1004 , an operating system  1005 , content software  1006 , content data  1007 , a network adapter  1008 , the system memory  1012 , an Input/Output Interface  1010 , a display adapter  1009 , a display device  1011 , and a human machine interface  1002 , can be contained within one or more remote computing devices  1014   a,b,c  at physically separate locations, connected through buses of this form, in effect implementing a fully distributed system. 
     The computer  1001  typically comprises a variety of computer readable media. Exemplary readable media can be any available media that is accessible by the computer  1001  and comprises, for example and not meant to be limiting, both volatile and non-volatile media, removable and non-removable media. The system memory  1012  comprises computer readable media in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read only memory (ROM). The system memory  1012  typically contains data such as the content data  1007  and/or program modules such as the operating system  1005  and the content software  1006  that are immediately accessible to and/or are presently operated on by the one or more processors  1003 . 
     The computer  1001  can also comprise other removable/non-removable, volatile/non-volatile computer storage media. By way of example,  FIG. 10  illustrates the mass storage device  1004  which can provide non-volatile storage of computer code, computer readable instructions, data structures, program modules, and other data for the computer  1001 . For example and not meant to be limiting, the mass storage device  1004  can be a hard disk, a removable magnetic disk, a removable optical disk, magnetic cassettes or other magnetic storage devices, flash memory cards, CD-ROM, digital versatile disks (DVD) or other optical storage, random access memories (RAM), read only memories (ROM), electrically erasable programmable read-only memory (EEPROM), and the like. 
     Optionally, any number of program modules can be stored on the mass storage device  1004 , including by way of example, the operating system  1005  and the content software  1006 . Each of the operating system  1005  and the content software  1006  (or some combination thereof) can comprise elements of the programming and the content software  1006 . The content data  1007  can also be stored on the mass storage device  1004 . The content data  1007  can be stored in any of one or more databases known in the art. Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases can be centralized or distributed across multiple systems. 
     The user can enter commands and information into the computer  1001  via an input device (not shown). Examples of such input devices comprise, but are not limited to, a keyboard, pointing device (e.g., a “mouse”), a microphone, a joystick, a scanner, tactile input devices such as gloves, and other body coverings, and the like These and other input devices can be connected to the one or more processors  1003  via the human machine interface  1002  that is coupled to the system bus  1013 , but can be connected by other interface and bus structures, such as a parallel port, game port, an IEEE 1394 Port (also known as a Firewire port), a serial port, or a universal serial bus (USB). 
     The display device  1011  can also be connected to the system bus  1013  via an interface, such as the display adapter  1009 . It is contemplated that the computer  1001  can have more than one display adapter  1009  and the computer  1001  can have more than one display device  1011 . For example, the display device  1011  can be a monitor, an LCD (Liquid Crystal Display), or a projector. In addition to the display device  1011 , other output peripheral devices can comprise components such as speakers (not shown) and a printer (not shown) which can be connected to the computer  1001  via the Input/Output Interface  1010 . Any step and/or result of the methods can be output in any form to an output device. Such output can be any form of visual representation, including, but not limited to, textual, graphical, animation, audio, tactile, and the like. The display device  1011  and computer  1001  can be part of one device, or separate devices. 
     The computer  1001  can operate in a networked environment using logical connections to one or more remote computing devices  1014   a,b,c.  By way of example, a remote computing device can be a personal computer, portable computer, smartphone, a server, a router, a network computer, a peer device or other common network node, and so on. Logical connections between the computer  1001  and a remote computing device  1014   a,b,c  can be made via a network  1015 , such as a local area network (LAN) and/or a general wide area network (WAN). Such network connections can be through the network adapter  1008 . The network adapter  1008  can be implemented in both wired and wireless environments. Such networking environments are conventional and commonplace in dwellings, offices, enterprise-wide computer networks, intranets, and the Internet. 
     For purposes of illustration, application programs and other executable program components such as the operating system  1005  are illustrated herein as discrete blocks, although it is recognized that such programs and components reside at various times in different storage components of the computing device  1001 , and are executed by the one or more processors  1003  of the computer. An implementation of the content software  1006  can be stored on or transmitted across some form of computer readable media. Any of the disclosed methods can be performed by computer readable instructions embodied on computer readable media. Computer readable media can be any available media that can be accessed by a computer. By way of example and not meant to be limiting, computer readable media can comprise “computer storage media” and “communications media.” “Computer storage media” comprise volatile and non-volatile, removable and non-removable media implemented in any methods or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Exemplary computer storage media comprises, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. 
     The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how the compounds, compositions, articles, devices and/or methods claimed herein are made and evaluated, and are intended to be purely exemplary and are not intended to limit the scope of the methods and systems. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in ° C. or is at ambient temperature, and pressure is at or near atmospheric. 
     The methods and systems can employ Artificial Intelligence techniques such as machine learning and iterative learning. Examples of such techniques include, but are not limited to, expert systems, case based reasoning, Bayesian networks, behavior based AI, neural networks, fuzzy systems, evolutionary computation (e.g. genetic algorithms), swarm intelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g. Expert inference rules generated through a neural network or production rules from statistical learning). 
     While the methods and systems have been described in connection with preferred embodiments and specific examples, it is not intended that the scope be limited to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive. 
     Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is in no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification. 
     It will be apparent to those skilled in the art that various modifications and variations can be made without departing from the scope or spirit. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims.