Abstract:
A system and method for electronically monitoring audience attentiveness and receptiveness including: collecting user data concerning a running of and interaction with, media content received via the first network interface by a user of the network terminal; converting the collected user data into user metrics; and analyzing the data to create at least one user Brand Affinity Index (BAI) score for the network terminal user.

Description:
FIELD 
       [0001]    This invention relates generally to systems and methods for electronically delivering media content, and more particularly to systems and methods for monitoring users of connected devices for their receptivity to receiving media content. 
       BACKGROUND 
       [0002]    Electronic commerce, often known as “e-commerce”, includes the buying, selling and advertising of products, services and brands over electronic systems such as the Internet. The amount of trade conducted electronically has grown immensely with the widespread adoption of Internet technology. One particularly explosive area of growth in e-commerce is in the field of advertising and, in particular, video advertising on the Internet. 
         [0003]    Advertising is a common way or seller of goods and/or services to generate sales and/or to initiate, maintain and increase brand awareness. In traditional media, such as television and print media, an advertisement may be seen by a wide demographic audience. Generally, only a small percentage of the audience will have any interest in purchasing the goods or services. Also, with traditional media, there is typically a limited supply of space for advertisements. In the art, the amount of resources (e.g., physical space, time, etc.) available for advertising is sometimes referred to as “inventory.” 
         [0004]    The inherent nature of the Internet is that it creates ever-increasing amounts of advertising inventory. This is because web technology can generate an advertising message image (called an “impression”) each time a web page (or other, for example, html based platform) is accessed. Since multiple users can access Internet content simultaneously, and since the number of Internet users and web pages is constantly increasing, the “inventory” of advertising space on the Internet is almost limitless. 
         [0005]    In order to optimize their advertising investment, online advertisers try to choose among the immense inventory available so as to place their video advertisements for optimal results. This can be a hit-or-miss process whereby brand managers assume certain demographics for their ideal audience (e.g. males ages 18-35 who like fast cars) and then choose publishers that cater to that demographic profile (e.g. a website dedicated to reviewing race cars). However, this does not entirely address the ultimate goal of most brand advertisers: i.e. not only making the right placement for their advertisement but also reaching the right audience at the right time. 
         [0006]    In today&#39;s connected world of devices, most of consumers&#39; media time is spent in front of four video screens, namely their computer, smartphone, tablet and television screens. As used herein, the term “screen” may be used synonymously with a “user device” and “terminal” The screens users want to use depend upon the context of where they are located (workplace, home, travelling, etc.), what we they want to achieve (shop, make travel plans, watch video, etc.) and how long will it take to achieve their desired results. 
         [0007]    Google in  The New Multi - screen World: Understanding Cross - platform Consumer Behavior , dated August 2012, and hereafter referred to as Google Multi-Screen, calls this phenomenon as “the new multi-screen world” As explained in Google Multi-Screen, there are at least two different modes of consumer&#39;s behavior in context of multi-screen usage, namely: 1) sequential screening as a user moves between screens; and 2) Simultaneous screening where we use multiple screens at the same time. 
         [0008]    The multiscreen phenomenon is very familiar in many family homes. For example, if a family is all in television room and the television is on, some or all of the family members are likely also using Internet connected mobile devices. For example, family members are likely using their smartphones for such activities as texting or emailing, or surfing the Internet on their tablets, or playing a game or doing work on their laptops. As a result, the attention to television and to television advertisements has declined. 
         [0009]    Due to this device fragmentation &amp; diversion of attention to mobile and CTV, advertising has become challenging for brands, and their ability to reach an attentive, receptive audience at TV scale has become far more difficult. Furthermore, there is increasing evidence that the multiscreen phenomenon increases the incidences of attention deficit disorder (ADD), whereby viewers are unable to focus their full attention on one screen for extended periods of time. 
         [0010]    This shift in digital viewing has caused a dramatic fragmentation of content. Thirty years ago, content was delivered on four or five broadcast television channels. It then moved to hundreds of channels with the advent of cable television. Now, content is being delivered by tens of thousands of Internet “channels.” This makes it difficult for advertisers to efficiently reach a large “TV-scale” audience. 
         [0011]    In addition to content fragmentation, screen fragmentation has increased; the average number of devices used by a person has doubled from two in 2000 to four in 2012. Screen fragmentation affects frequency quality, because no longer are viewers watching advertisements on the same TV and paying attention at all times. Therefore, most of the screens are mobile devices. To compound matters, the devices are typically based on different technologies, e.g. different operating systems, user interfaces and hardware. 
         [0012]    This is also the problem of data fragmentation. In search and display advertising, though the amount of data volume was large, the focus was only on one piece of data, e.g. the “click” on a button or hyperlink. With video and the increase in complexity due to sight, sound and motion, the volume, variety and velocity of data has increased. Content is viewed on apps and in browsers. The video may be viewed on a smartphone or tablet, and could be on an iOS or Android operating system. Finally, the action a person takes with video is different data. For example; when someone swipes a video or clicks on a TV remote, or waves a hand in front of a Samsung Galaxy G4®, these things all need to be analyzed and processed to help find brand receptive, attentive viewers. All of these different pieces of data mean different things to brand advertisers and must be analyzed to provide a receptive, attentive audience at scale. 
         [0013]    Consumer viewing habits are trending inexorably towards online and more specifically to mobility. As a result of these changes in viewing behavior, the TV advertising market is being disrupted. This is not the first time markets have been disrupted by changing consumer behavior, newspapers have moved to the web, and the yellow pages have been affected by search, real-estate has moved to the web. But unlike in other markets, where solutions have been created, the challenge of finding a receptive, attentive audience in a multi-device, fragmenting world has not yet been addressed by a solution. 
         [0014]    These and other limitations of the prior art will become apparent to those of skill in the art upon a reading of the following descriptions and a study of the several figures of the drawing. 
       SUMMARY 
       [0015]    Various examples are set forth herein for the purpose of illustrating various combinations of elements and acts within the scope of the disclosures of the specification and drawings. As will be apparent to those of skill in the art, other combinations of elements and acts, and variations thereof, are also supported herein. 
         [0016]    In a non-limiting example, tracking software or “SDK” is embedded in user devices that run video ads. Data concerning the running and interaction of the video ads is collected to determine the receptiveness and attentiveness of the users of the devices to the video ads. This data is converted into metrics which can be analyzed to create a Brand Affinity Index or BAI score. The BAI scores can then be used to present the right ad to the right screen at the right time with the goal of achieving a brand campaign with a reach and frequency that rivals a TV-scale audience. 
         [0017]    In an embodiment, set forth by way of example and not limitation, system for electronically monitoring audience attentiveness and receptiveness includes a network terminal and an analysis server. In this example, the network terminal has a first digital processor, a first non-transient computer readable media, and a first network interface, where the first computer readable media includes program instructions executable on the first digital processor for: collecting user data concerning a running of, and interaction with, content received via the first network interface by a user of the network terminal; and transmitting the collected user data via the first network interface to an analysis server. Also in this example, the analysis server includes a second digital processor, a second non-transient computer readable media, and a second network interface, the second computer readable media including program instructions executable on the second digital processor for: receiving the collected user data via the second network interface; converting the collected user data into user metrics; and analyzing the data to create at least one user Brand Affinity Index (BAI) score for the network terminal user. 
         [0018]    In an embodiment, set forth by way of example and not limitation, a computer-implemented method for electronically monitoring audience attentiveness and receptiveness includes: collecting user data concerning a running of, and interaction with, media content received via the first network interface by a user of the network terminal; converting the collected user data into user metrics; and analyzing the data to create at least one user Brand Affinity Index (BAI) score for the network terminal user. 
         [0019]    An advantage of various example embodiments disclosed herein is that the viewing experiences of audiences (as measured by their attentiveness and receptiveness to such viewing experiences) is enhanced by delivering the right media content to the the right audience at the right time. 
         [0020]    These and other examples of combinations of elements and acts supported herein as well as advantages thereof will become apparent to those of skill in the art upon a reading of the following descriptions and a study of the several figures of the drawing. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0021]    Several examples will now be described with reference to the drawings, wherein like elements and/or acts are provided with like reference numerals. The examples are intended to illustrate, not limit, concepts disclosed herein. The drawings include the following figures: 
           [0022]      FIG. 1  illustrates an example system supporting a receptivity and attentiveness (“Brand Affinity”) scoring process; 
           [0023]      FIG. 2  is a block diagram of an example computer, computerized device, proxy and/or server which may form a part of the system of  FIG. 1 ; 
           [0024]      FIG. 3  is a block diagram of an example receptivity and attentiveness scoring system; 
           [0025]      FIG. 4  is a state diagram of an example receptivity and attentiveness scoring process; 
           [0026]      FIG. 5  is a flow diagram of an example scoring database update process; and 
           [0027]      FIG. 6  is a block diagram of an example ad fulfillment system which can implement a network terminal or “client device” identification process 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0028]    Brand advertising is about reach and frequency. Reach is the number of people or households watching an advertisement. Frequency is the number of times people see the advertisement. Systems and methods are disclosed herein to increase reach and frequency by measuring and analyzing the metrics of receptivity and attentiveness. As used herein, “receptivity” means how receptive a person is to the message of the video advertisement and “attention” means how attentive the person is to the video advertisement in the context (e.g. time, place, application), that it is being presented. Collectively, the combination of receptivity and attention (i.e. the right audience at the right time) will be referred to as “Brand Affinity.” 
         [0029]      FIG. 1  illustrates a system  10 , set forth by way of example and not limitation, for supporting a receptiveness and attentiveness (Brand Affinity) scoring process, referred to herein as a “Brand Affinity Index” (BAD. In this example, the network system  10  includes one or more analysis servers  12 , one or more advertiser servers  14  and one or more publisher servers  16 . The system at  10  may further include other computers, servers or computerized systems such as user devices  18 . In this example, the analysis servers  12 , advertiser servers  14 , publisher servers  16 , and user devices  18  can communicate by a wide area network such as the Internet  20  (also known as a “global network” or a “wide area network” or “WAN” operating with TCP/IP packet protocols). 
         [0030]    The analysis servers  12  can be implemented as a single server or as a number of servers, such as a server farm and/or virtual servers, as will be appreciated by those of skill in the art. Alternatively, the functionality of the analysis servers  12  may be implemented elsewhere in the network system  10  such as on an advertiser server  14 , as indicated at  12 A, on the publisher server  16 , as indicated at  12 B, or as part as cloud computing as indicated at  12 C, all being non-limiting examples. As will be appreciated by those of skill in the art, the processes of analysis servers  12  may be distributed within network system  10 . 
         [0031]    In the example of  FIG. 1 , the network system  10  includes a plurality of advertiser servers  14  {ADV.  1 , ADV.  2 , . . . , ADV. N}. ADV.  1  can be, for example, a manufacturer of soft drinks, ADV.  2  can be a computer manufacturer and ADV. N can be, for example, an accounting firm. Alternatively, an advertiser can be an advertising agency acting as a middleman in the purchase of advertising for a client, can be an advertising (“ad”) network, or be an ad exchange. While each of the advertiser servers  14  may be implemented as a single computer, such as a network server, they can also represent other computer configurations, such as a computing cluster on a local area network (LAN). 
         [0032]    The publisher servers  16  can each represent one or more servers, such as a server farm. In the example of  FIG. 1 , the network system  10  includes a plurality of publisher servers  16  {PUB.  1 , PUB.  2 , . . . , PUB. M}. For example, PUB.  1  can be an Internet portal, PUB.  2  can be a search engine, and PUB. M can be a news website. As noted previously, one or more of the publisher servers  16  can implement some or all of the functionality of analysis servers  12 . 
         [0033]    It should be noted that the selection of publishers can be enhanced by categorizing the publishers by, for example, content. That is, a “publisher” can be a single legal entity, or a subset of that entity, or a part of a group of entities, by way of several non-limiting examples. For example, a publisher entity may have 1000 publications of which 100 are directed to dramatic content, 100 are directed to comedy, etc. The subset of publications of the publisher entity having a common thematic content may be considered a “publisher.” Furthermore, “publishers” may include a group of publications provided by different agencies which conform to a theme such as, by way of non-limiting examples, drama, sports or entertainment. 
         [0034]    User devices  18  can be any type of terminal, screen or device including, by way of non-limiting examples, a computer  18 A, a connected TV (a/k/a Smart TV or CTV)  18 D, a tablet  18 B and a smartphone  18 C. The distinguishing characteristics of user devices  18  include connectivity to the Internet  20  (“connected devices”) and display screens which can display, for example, advertisements delivered to the user devices over the Internet. Some connected devices are relatively immobile (e.g. CTV  18 D), while other connected devices are considered to be “mobile devices”, e.g. table  18 B and smartphone  18 C. By further examples, computer  18 A may be a “mobile device” if it is a laptop computer but a relatively immobile device if it is a desktop computer. 
         [0035]      FIG. 2  is a simplified block diagram of a computer and/or server  22  suitable for use in system  10 . By way of non-limiting example, computer  22  includes a microprocessor (a/k/a “processor” or “digital processor”)  24  coupled to a memory bus  26  and an input/output (I/O) bus  30 . A number of memory and/or other high speed devices may be coupled to memory bus  26  such as the RAM  32 , SRAM  34  and VRAM  36 . Attached to the I/O bus  30  are various I/O devices such as mass storage  38 , network interface  40 , and other I/O  42 . As will be appreciated by those of skill in the art, there are a number of non-transient computer readable media available to the microprocessor  24  such as the RAM  32 , SRAM  34 , VRAM  36  and mass storage  38 . The network interface  40  and other I/O  42  also may include computer readable media such as registers, caches, buffers, etc. Mass storage  38  can be of various types including hard disk drives, optical drives and flash drives, to name a few. 
         [0036]    It should be noted that the selection of publishers can be enhanced by categorizing the publishers by, for example, content. That is, a “publisher” can be a single legal entity, or a subset of that entity, or a part of a group of entities, by way of several non-limiting examples. For example, a publisher entity may have 1000 publications of which 100 are directed to dramatic content, 100 are directed to comedy, etc. The subset of publications of the publisher entity having a common thematic content may be considered a “publisher.” Furthermore, “publishers” may include a group of publications provided by different agencies which conform to a theme such as, by way of non-limiting examples, drama, sports or entertainment. 
         [0037]    It should further be noted that, in some instances, an ad network is, essentially, transparent to advertisers, publishers or both. That is, an ad network may be considered to be a publisher or collection of publishers to an advertiser and/or an ad network may be considered to be an advertiser or collection of advertisers to a publisher. 
         [0038]    In an embodiment, set forth by way of example and not limitation, software can be provided in each user device  18  to derive metrics, for example, concerning receptivity and attentiveness. For example, YuMe, Inc. of Redwood City, Calif. embeds software known as a “Software Developer Kit” (SDK) into user devices such as CTVs, smartphones, tablets and personal computers (PCs). These multi-screen SDKs are paired often with video ad serving technology to comprise a YuMe® OS. These “audience aware” SDKs, embedded into publisher video players, developer apps and CE manufacturer devices, collect valuable real-time, continuous, screen-level data that can be saved and aggregate into a central decision-making engine, such as on an analysis server  12 , where they can be analyzed, filtered, and processed to provide real-time, actionable metrics. These metrics can include user/household identities, contexts (e.g. what application or “app” is being used) and time. Other common metrics are location (via GPS services), interactivity with the screen, etc. 
         [0039]    By way of non-limiting example, if a user closes an application repeatedly when a diaper ad is displayed on a user device, the receptivity of that user to diaper commercials can be considered to be low. As another example, if the user interacts with the ad such as by a swipe on a tablet, the use of a remote control movement on a CTV, etc., it can be assumed that the user&#39;s receptivity is both high to diaper ads and that the user is being attentive to that ad. In other times or places, such as during work hours at work, the user may be just as receptive to the ad, but not attentive. Attentiveness can also be determined by such metrics as whether there is another multiscreen device being used by the user at the time that the video ad is playing, by using eye-tracking technology. 
         [0040]    In  FIG. 3 , a block diagram of an example receptivity and attentiveness scoring system  14  includes a scoring system controller  46 , a metrics database  48 , a parameter database  50 , a scoring engine  52 , a scoring database  54  and a report generator  56 . It should be noted that the various elements of scoring system  14  may be real and/or virtual and some or all of the elements may comprise computer implements processes. 
         [0041]    In this example, the video advertisement may be associated an application or “app” on a user&#39;s mobile device. Typically, the video advertisement includes a “play” button which, when activated by the click of a mouse, will start to play the video advertisement (this is referred to herein as a “click-through”). Also typically, the video advertisement can be played to completion or stopped before completion. The amount of the video advertisement which is played is referred to herein as “play-through”, and may be measured in, for example, as percentages (e.g. Video Completion Rate or “VCR”) or in seconds. In some cases, the video advertisement can include links to other resources to provide additional information, content, the ability to order a product, or feeds which can enhance the video advertisement experience, by way of non-limiting examples. The embedded SDK can monitor and report such activity for later analysis concerning user receptivity and attentiveness. 
         [0042]    In the example of  FIG. 3 , metrics derived from embedded SDKs can be stored in metrics database  48  for concurrent and/or subsequent analysis. The metrics database  48  may be localized and/or distributed and may be found, in part or in whole, in various locations in the example system of  FIG. 1 , by way of non-limiting examples. Scoring system controller  46  can engage in bidirectional communication with the metrics database  48  as indicated at  49 . 
         [0043]    A parameter database  50  can also be seen in the example of  FIG. 3 . Parameter database  50  can include weighting factors for metrics of the metric database  48 . The parameter database  50  may be localized and/or distributed and may be found, in part or in whole, in various locations in the example system of  FIG. 1 , by way of non-limiting examples. Scoring system controller  46  can engage in bidirectional communication with the parameter database  50  as indicated at  51 . Furthermore, the metrics database  48  and parameter database  50  may be integrated as a unified real and/or virtual database or may be linked as real and/or virtual databases. 
         [0044]    Scoring system  44 , in this example, further includes a scoring engine  52  which can be used to generate a score associate with an Internet receptivity and attentiveness. In the present example, scoring engine  52  operates on one or more metrics derived from metrics database  48  to develop a score which can characterize the receptivity and attentiveness. If the scores thus derived are directly related to the receptivity and attentiveness, the score can be considered to be a Brand Affinity Score or BAI. By developing standardized BAI scores for the purpose of making advertising decisions and/or making improvements, the “quality” of the receptivity and attentiveness can be increased for brand managers. Scoring engine  52  is, in this example, in bidirectional communication with scoring system controller  46  as indicated at  53 . 
         [0045]    Scores developed by scoring engine  52  may be stored in a scoring database  54  which, in this example, is in bidirectional communication with scoring system controller  46  as indicated at  55 . The scoring database  54  may be localized and/or distributed and may be found, in part or in whole, in various locations in the example system of  FIG. 1 . Furthermore, the scoring database  54 , metrics database  48  and parameter database  50  may be integrated as a unified real and/or virtual database or may be linked as real and/or virtual databases. By “database” it is meant herein any ordered storage of data allowing for its systematic retrieval. For example, a database may be a flat database, a table, a relational database, etc. 
         [0046]    Report generator  56  is, in this example, coupled to scoring system controller  46  for bidirectional communication as indicated at  57 . Report generator  56  may be used, for example, to create reports derived from data in the scoring database  54  or elsewhere. 
         [0047]    In  FIG. 4 , a state diagram of an example receptivity and attentiveness scoring process  58  includes a central control process  60 , a metrics process  62 , a parameter process  64 , a scoring database update process  66  and a report process  68 . Central control  60 , in this example, can implement a metrics process  62 , such as retrieving stored metrics from the metrics database  48  (see  FIG. 3 ). Likewise, central control  60 , by way of example, can implement parameter process  64 , such as storing weights and/or demographic parameters in, for example, parameter database  50 . Central control  60  can also implement a scoring database update process  66  and/or an implement report process  68  on, for example, scoring engine  52  and/or report generator  56 , respectively, of  FIG. 3 . 
         [0048]    In  FIG. 5 , an example scoring update process  66  of  FIG. 4  is illustrated in greater detail. Process  66  begins at  70  and, in a computer implemented act or “operation”  72 , it is determined if the update process is complete. If it is, process  66  is done as indicated at  74  and process control returns to central control  60  (see  FIG. 4 ). If not, the next parameters and metrics are retrieved in an operation  74 . An operation  78  then generates one or more scores, which are stored in, for example, the scoring database (see  FIG. 3 ) in an operation  80 . 
         [0049]    Generating BAI Quality Scores 
         [0050]    BAT quality scores may be generated, by way of non-limiting example, using a weight function. A weight function is a mathematical technique used when performing, for example, a sum, integral or average in order to give some elements more “weight” or influence on the result than the other elements in the same set. In this example, the elements of a set are selected from metrics associated with an audience segment and the weights are either constants or functions associated with the receptivity and attentiveness and, in certain examples, associated demographics. 
         [0051]    One type of weight function is the weighed sum, as given by Equation 1, below: 
         [0000]      Σ i=1   n   f ( i ) m ( i )  Equation 1
 
         [0052]    Where m(i) is the i th  metric of n selected metrics and f(i) is a weighting function associated with the metric m(i). The weighting function can be, as noted above, a constant stored in, for example, an array, table or other data structure in the parameter database  50 . Alternatively, f(i) can be a function of a number of constants and/or variables, including demographic variables, which also can also be, for example, stored in parameter database′ 50 . 
         [0053]    Another form of weight function is the weighted average. Weighted averages or “weighted means” are commonly used in statistics to compensate for the presence of bias. The weighted mean is similar to the arithmetic mean (the most common type of “average”) except instead of the metrics contributing equally to the final average, some metrics contribute more than other. The notion of weighted mean plays a role in descriptive statistics and also occurs in a more general form in several other areas of mathematics. As is well known to those skilled in the art, there are other forms of weighted means, including weighted geometric means and weighted harmonic means. 
         [0054]    Once a raw quality score is obtained, it may be normalized to be more easily compared by human analysts. For example, if the raw quality scores are in the range of 0 to 1, they may be normalized to range from 0 to 100 by multiplying by 100. Normalized scores tend to be easier for the human brain to retain and compare. 
         [0055]    Given a sufficiently large scoring database  66 , an artificial neural network can also be trained to provide quality scores. An artificial neural network (ANN), often referred simply to a “neural network”, is a computational model which simulates the structural and/or functional aspects, of biological neural networks. Neural networks include an interconnected group of artificial neurons and process information using a connectionist approach to computation. In most cases, neural networks are adaptive systems that change their structures based upon external or internal information that flows through the network during the learning phase. Most neural networks are non-linear statistical data modeling tools which can be used to model complex relationships between inputs and outputs or to find patterns in data. 
         [0056]    In order to be properly “trained”, many examples should be applied to the neural net during the training phase. For a particular receptivity and attentiveness, the metrics and parameters are applied to inputs of the neural net, and the quality score, as stored in the scoring database  54 , is applied to the output. The neural network then internally adjusts the “weights” of its neurons such that the output is a weighted function of the inputs. After many examples the neural net “learns” how to generate the proper quality score based upon any arbitrary set of inputs. 
         [0057]    An advantage of a trained neural network is that it is not necessary to know how the correct answer is derived. In fact, many more metrics can be input into a neural network than could be conveniently handled by human-assisted calculations. This has the advantage of increased robustness and the possibility of the neural network “discovering” transfer function relationships not considered by human designers. Once properly trained, a neural network can operate without any human interaction with respect to the selection of weights for a weight function. 
         [0058]    For a new system, e.g. a system where the scoring database has not yet been started, it is preferable to start with a simple weight function scoring engine where a human operator chooses a few metrics to follow and assigns weight constants to those metrics based upon expert knowledge and, to a degree, human intuition. The weights are all fractions, and the sum of the weights is “1.” As the scoring database is populated and additional experience is accumulated, the weight constants can be adjusted by changing the weights and/or additional metrics can be added. In addition, weight functions can be selectively assigned and different sets of weights can be associated with different demographics or “demos.” For example, one set of weights can be associated for the audience segment of male viewers and another set of weights can be associated with the audience segment of female viewers. 
         [0059]    The scoring engine  52  can therefore become increasingly sophisticated and accurate through incremental human intervention. However, at some point the interrelationships between a many potential metric and parameters may limit the sophistication of the scoring engine  52 . At that point, if a sufficiently large scoring database  54  has been developed, the scoring engine  54  may be supplemented by, or replaced with, a neural network. 
         [0060]    It should be noted that the examples set forth above for scoring engine  52  are not exhaustive of potential technologies. For example, the scoring engine can also be implemented using expert system technologies. Furthermore, scoring engine performance may be an interactive process with other inputs, processes and systems. 
         [0061]    Homogeneous Metrics Example 
         [0062]    The following example illustrates a generation of BAI by, for example, scoring engine  52  implementing a weight function. Suppose that, for a particular receptivity and attentiveness, such as on a web page, two metrics are tracked: 1) a click-through rate of 5%; and 2) a view-through rate of 75%. Also, further assume that the weight of the click-through rate (CTR) is 0.6 and the weight of the view-through rate (VCR) is 0.4, i.e. click-through is weighted more heavily in this example than view-through rate. Using Equation 1, the BAI for the receptivity and attentiveness as a weighted sum is: 
         [0000]        Q= 0.6(5)+0.4(75)=3+30=33 
         [0063]    Since the units of the metrics, in this example, are percentages (i.e. the metrics are homogeneous), no normalization is need. 
         [0064]    Continuing with the same example, assume that the weights given above were for the demographic “female” and that the weights for the demographic “male” are 0.4 for click-through rate and 0.6 for view-through rate. Then, applying Equation 1 for the receptivity and attentiveness as a weighted sum for the demographic “male”, we obtain: 
         [0000]        Q′= 0.4(5)+0.6(75)=2+45=47 
         [0065]    It can therefore be seen that the BAI for the given receptivity and attentiveness is 33 for females but 47 for males. As a result, advertisements targeting males will be more effective at this receptivity and attentiveness than advertisements for females. 
         [0066]    Iterative Updates to Scoring Database 
         [0067]    In an example embodiment, the scoring database may be updated on a periodic basis, e.g. every 15 minutes. In this example, central control  60  activates the process  66  to implement the scoring database update process every 15 minutes, drawing from the then-current metrics from metrics database  48  and parameter database  50 . 
         [0068]    To prevent the quality scores varying widely with each update, the most recent metrics and/or parameters can be averaged with historical metrics and/or parameters. For example, the metrics applied to the scoring database update process can be the average of metrics and parameters during a “window” of time moving forward in 15 minute steps. The window can be chosen to be of sufficient time-length to smooth out any short-term spikes or dips in quality scores but not so long as to understate or overstate the current quality level. For example, the window can be 1-5 days in length. 
         [0069]    It should also be noted that second, third, etc. order information can be derived from the iterative collection of metric data. For example, velocity (e.g. speed of change of a metric) and acceleration (e.g. acceleration of change of a metric) can be calculated and input into the scoring database update process. 
         [0070]      FIG. 6  illustrates, by way of example and not limitation, a user device (a/k/a “network terminal”)  18 , a Publisher  16  and an Ad Fulfillment System  14 . The user device  18  is a “connected” device in that it communicates with the Publisher  16  and the Ad Fulfillment System  14  via the Internet. In this non-limiting example, user device  18  sends a Request to an Ad Network  82  of Ad Fulfillment System  14  via an SDK, as described in greater detail below. The user device  18 , Publisher  16  and various subsystems of the Ad Fulfillment System  14 , e.g. Ad Network  82 , comprise one or more computer and/or servers  22  (see  FIG. 2 ). 
         [0071]    The Ad Network  82  of this example is associated with a database  84 . The Ad Network  82  will reply to the user device Request with a Reply (Ad). The Ad Network, in this example, is coupled to one or more Advertisers  86  and to one or more Ad Exchanges  88 . The Ad Exchanges, in turn, can be coupled to one or more Advertisers  90 , one or more Ad Networks  92 , etc. 
         [0072]    It will be appreciated that the network of the Ad Fulfillment System  14  can include other computers, databases and servers, e.g. Advertisers  94  and  96  connected to the Ad Network  92 . However, at some point latency becomes an issue in that the person using the user device will typically only wait for a short period of time for an advertisement before “clicking out” and moving on to another screen. 
         [0073]    It will be further appreciated that, in this non-limiting example, the Ad Network  82  is a gateway for the fulfillment of the ad request by the user device  18 . The request to the Ad Network  82  can be accomplished, by way of example, with an ad network SDK (Software Development Kit)  19  which allows the user device to send a request to the URL (Universal Resource Locator) of, in this example, Ad Network  82 . The SDK can, for example, be embedded in a player provided to the user device  18  by Publisher  16 . A Request will include, as a minimum, the IP address of user device  18  so that the Ad Network  82  may send its Reply. However, the SDK may provide additional information concerning, by way of non-limiting example, the user, the user device, its environment and/or how it is being used to the Ad Network  82  that can be useful in determining an appropriate advertisement to be sent to the user device  18 . 
         [0074]    When the user device  18  is a computer  18 A, or another user device that can support a web browser, part of the Request can include what is known as a “cookie.” A cookie is a relatively small file of information about a user device which may include demographics, personal information, browser history, context and other information or Attributes that can help with the ad selection process. However, cookies are being increasingly disabled and/or blocked for privacy purposes and they are not generally used on user devices (such as many mobile devices) by application programs (“apps”) that don&#39;t implement a web browser. 
         [0075]    In an embodiment, set forth by way of example and not limitation, software can be provided in each user device  18  which can provide terminal information that can form the basis of a “fingerprint” for that terminal. For example, YuMe, Inc. of Redwood City, Calif. embeds the customized software SDK  59  into user devices such as CTVs, smartphones, tablets and personal computers (PCs) which can provide a variety of information to, for example, their analysis servers  12  or advertisers  14 . SDKs can be used to collect valuable real-time, continuous, user device information (“data”) that can be saved and aggregated into a central decision-making engine. By way of non-limiting examples, information that can be derived from a terminal device  18  for the purpose of fingerprinting can include the size of the screen, fonts, the time zone, GPS, operating system versions, what plugins are available, what application the user is currently in, and other features or information that can, for example, be provided to an advertiser  14  as part of an advertisement (“ad”) request. 
         [0076]    By way of further non-limiting example, a user device  18  can be defined as a screen user device which has had installed upon it a unique SDK  59  which communicates with a server, such as an analysis server  12  or an advertiser server  14 . By using information sent by the SDK for a user device  18  a terminal “fingerprint” can be developed using, for example, configuration settings and other observable characteristics by the SDK. Terminal fingerprinting allows for the identification or re-identification of a visiting terminal for such purposes as authenticating a terminal, to identify a user, to track and correlate a user&#39;s activity within and across sessions, and to collect information from which inferences can be drawn about a user. 
         [0077]    In an embodiment, set forth by way of example but not limitation, a “terminal fingerprint” can include a homogeneous set of fields that describe a specific user device at a specific point in time. In this example, the fields can be collected via a variety of mechanism. In certain embodiments, missing fields can be considered part of the fingerprint. 
         [0078]    It will be appreciated that a fingerprint of a given user device may change over time due to changes in software versions, browser plugins, network configurations etc. To address this fact, prior versions (“historical set”) of a user device&#39;s fingerprint may be stored in a database. In a non-limiting example, a new fingerprint preferably matches the most recent fingerprint of the historical set within a certain threshold. 
         [0079]    As used herein, a “terminal ID” is preferably a unique, algorithmically generated identification (“ID”) that is assigned to the historical set of terminal fingerprints for a given terminal. A “match probability” reflects the probability that two fingerprints are from the same user device. The match probability can be normalized between the values of 0 and 1, for example, such that two fingerprints are more similar when the probability is closer to 1 and more dissimilar when the probability is closer to 0. A “match threshold” can be defined as the threshold of the match probability above which a fingerprint is considered to be from the same user device. If, for example, multiple fingerprints have a match probability above the threshold then the one with the highest score can be considered to be a match. 
         [0080]    Although various examples have been described using specific terms and devices, such description is for illustrative purposes only. The words used are words of description rather than of limitation. It is to be understood that changes and variations may be made by those of ordinary skill in the art without departing from the spirit or the scope of any examples described herein. In addition, it should be understood that aspects of various other examples may be interchanged either in whole or in part. It is therefore intended that the claims be interpreted in accordance with the true spirit and scope of the invention without limitation or estoppel.