Patent Publication Number: US-2023156282-A1

Title: Methods and apparatus to model on/off states of media presentation devices based on return path data

Description:
RELATED APPLICATION 
     This patent arises from a continuation of U.S. patent application Ser. No. 17/211,289, which was filed on Mar. 24, 2021, as a continuation of U.S. patent application Ser. No. 16/566,354, which was filed on Sep. 10, 2019, as a continuation of U.S. patent application Ser. No. 15/639,164, which was filed on Jun. 30, 2017, and which claims the benefit of U.S. Provisional Patent Application No. 62/428,487, which was filed on Nov. 30, 2016. U.S. patent application Ser. Nos. 17/211,289; 16/566,354; 15/639,164; and U.S. Provisional Patent Application No. 62/428,487 are hereby incorporated herein by reference in their entireties. Priority to U.S. patent application Ser. Nos. 17/211,289; 16/566,354; 15/639,164; and U.S. Provisional Patent Application No. 62/428,487 is claimed. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to audience measurement and, more particularly, to methods and apparatus to model on/off states of media presentation devices based on return path data. 
     BACKGROUND 
     Many people access media through set top boxes (STBs) or other media presentation devices provided by media content providers (e.g., cable media providers, satellite media providers, etc.). Some STBs are equipped to report tuning information indicative of the media accessed by the STBs back to the content providers. Tuning information reported back to content providers via STBs or other similar devices is sometimes referred to as return path data (RPD). RPD tuning information may be used by audience measurement entities to track or monitor people&#39;s exposure to media. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is an example environment in which the teachings disclosed herein may be implemented. 
         FIG.  2    is an example implementation of the example audience measurement module of  FIG.  1   . 
         FIG.  3    is a schematic representation of panel tuning information corresponding to media played on multiple different media sets over a period of time. 
         FIGS.  4 - 6    illustrate how RPD tuning information may be modelled based on the panel tuning information of  FIG.  3   . 
         FIG.  7    illustrates example cumulative distributions of durations panel tuning segments and modelled tuning segments. 
         FIG.  8    illustrates example cumulative distributions of durations of tail-end panel tuning segments and associated modelled tuning segments. 
         FIGS.  9 - 15    are flowcharts representative of example machine readable instructions that may be executed by one or more processors to implement the example audience measurement module of  FIGS.  1  and/or  2   . 
         FIG.  16    is a schematic illustration of an example processing system that may be used and/or programmed to execute the example machine-readable instructions of  FIGS.  9 - 15    to implement the example audience measurement module of  FIGS.  1  and/or  2   . 
     
    
    
     The figures are not to scale. Wherever possible, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. 
     DETAILED DESCRIPTION 
     As used herein, an RPD device refers to any type of device (e.g., a STB or other similar device) that is capable of accessing media from a content provider and reporting tuning information regarding the media accessed back to the content provider. Such tuning information is referred to herein as RPD tuning information or simply RPD. RPD devices are often standalone devices that connect to separate media presentation devices, such as, television sets, radios, smartphones, tablets, computers, or any other device capable of playing the media accessed by the RPD device. Media presentation devices that play media accessed by associated RPD devices are referred to herein as media sets or simply sets for purposes of brevity. 
     In some instances, a media set and an RPD device may be integrated into a single device. However, when a media set and an associated RPD device are separate devices, it is possible for one to be powered on while the other is turned off. As a result, while RPD tuning information reported by an RPD device provides data indicative of media accessed by the RPD device, such data is not necessarily indicative of media being played for consumption on the associated media set where the RPD device is located. For example, after people watch a show on their television set (e.g., a media set) accessed via a connected STB (e.g., an RPD device), they may turn off their television set without turning off the associated STB. In such a situation, the STB will continue to access media provided on the station to which the STB was last tuned and continue to report such as RPD tuning information despite the fact that no media is being played in the household because the television set is turned off. Therefore, RPD tuning information may be unreliable to determine the media to which people are exposed unless the on/off state of the associated media set can be determined. 
     Examples disclosed herein calculate capped durations for RPD tuning segments reported from an RPD device that are estimated to correspond to periods of time when an associated media set is turned on. As used herein, an RPD tuning segment refers to a period of time during which an RPD device is accessing media from a particular source of media (e.g., is tuned to a particular station or channel). Thus, each time a station or channel is changed on the RPD device corresponds to the ending of one RPD tuning segment and the beginning of a second different RPD tuning segment. RPD tuning segments may also end if the RPD device is powered off or enters a standby mode. Likewise, an RPD tuning segment begins whenever the RPD device is powered on or otherwise is removed from standby mode. The capped durations estimated in the disclosed examples will typically be at the front-end of an RPD tuning segment with unretained portions of the tuning segment extending thereafter because people are known to turn off their media sets while leaving associated RPD devices powered on. However, it may be possible that a capped duration corresponds to the tail-end of an RPD tuning segment when people turn on their media sets to begin playing media to which an RPD device that was left powered on was already accessing. Accordingly, examples disclosed herein calculated both front-end and tail-end capped durations for RPD tuning segments that may be used to estimate the period(s) of time when an associated media set is powered on and actually playing the media accessed by the RPD device. 
     The period(s) of time when a media set is on, as calculated from front-end and tail-end capped durations estimated from RPD tuning information collected from an associated RPD device, solves a problem that arises from the current state of technology in which associated media sets and RPD devices may be independently turned off or powered on. This state of technology gives rise to the situation where an RPD device is accessing media and transmitting associated RPD tuning information despite the fact that the media is not being played on an associated media set. As a result, audience measurements based on RPD tuning information are unreliable because it may not represent the media to which people were actually exposed. Examples disclosed herein resolve this problem by modelling RPD tuning segments to accurately predict when media sets are turned on and, thus, playing the media accessed by the associated RPD devices. This improves audience measurement metrics because it enables the use of RPD tuning information as a reliable source of audience exposure to media thereby significantly expanding the population from which audience measurement data is available beyond the relatively limited population pools of audience measurement panelists. 
       FIG.  1    is an example environment  100  in which the teachings disclosed herein may be implemented. In the illustrated example, a content provider  102  provides media to content subscribers and collects RPD tuning information indicative of the subscribers accessing the media. The content provider  102  may provide the RPD tuning information to an audience measurement entity (AME)  104  (e.g., the Nielsen Company (US) LLC) to enable to the AME  104  to generate audience measurement metrics. In some examples, the content provider  102  and the AME  104  communicate via a network  106  such as, for example, the Internet. 
     As shown in  FIG.  1   , the example environment  100  includes a non-panelist household  108  and a panelist household  110 . Some of the subscribers to the services of the content provider  102  may be people that have agreed to participate as panelists in a research study administered by the AME  104 . Thus, the panelist household  110  of  FIG.  1    represents a household that includes one or more individuals that have subscribed to the content provider  102  and that have enrolled as a panelist with the AME  104 . In some examples, panelists correspond to a statistically selected subset of all potential audience members that is representative of a whole population of interest. In some such panel-based monitoring systems, the panelists agree to provide detailed demographic information about themselves. In this manner, detailed exposure metrics are generated based on collected media exposure data and associated user demographics, which can then be statistically extrapolated to an entire population of interest (e.g., a local market, a national market, a demographic segment, etc.). 
     Both the non-panelist household  108  and the panelist household  110  include an RPD device  112 ,  114 . The RPD devices  112 ,  114  may be provided by the content provider  102  to enable access to media generated by content provider  102 . Further, the RPD devices  112 ,  114  are capable of reporting RPD tuning information back to the content provider  102  indicative of the media being accessed by the RPD devices  112 ,  114 . In some examples, the RPD devices  112 ,  114  access media from the content provider  102  and report RPD tuning information to the content provider  102  via the network  106 . Each of the RPD devices  112 ,  114  is connected to a corresponding media set  116 ,  118  to play the media accessed by the RPD devices  112 ,  114 . In the illustrated example, the RPD devices  112 ,  114  are separate from the corresponding media sets  116 ,  118  such that the media sets  116 ,  118  may be powered off while the RPD devices  112 ,  114  remain powered on. As a result, RPD tuning information reported by the RPD devices  112 ,  114  may not be representative of media that is actual played on the corresponding media sets  116 ,  118 . 
     The panelist household  110  is provided with a metering device  120  to track and/or monitor the media played on the media set  118  and report such to the AME  104  (e.g., via the network  106 ). In some examples, the metering device  120  also tracks and reports who is being exposed to the media being played so that the media exposure can be associated with particular individuals and their associated demographics previously collected when the household members enrolled as panelists. While the duration of media tuning segments actually played on the media sets  116 ,  118  cannot be directly confirmed from reported RPD tuning information because such information does not indicate whether the media sets  116 ,  118  are on or off, the audience measurement data reported by the metering device  120  does indicate the on/off state of the media set  118 . As a result, the AME  104  is able to know the actual duration of media tuning segments played on the media set  118  of the panelist household  110 . However, there is no direct way for the AME  104  to know the actual duration of media tuning segments played on the media set  116  of the non-panelist household  108 . 
     In some examples, the AME  104  includes an audience measurement module  122  to predict the on/off state of the media set  116  of the non-panelist household  108  as described more fully below. Briefly, the audience measurement module  122  uses the actual duration of media tuning segments played in panelist households (e.g., the panelist household  110 ), as reported from metering devices (e.g., the metering device  120 ) monitoring such, to generate a model of RPD tuning segments that would be expected if media being accessed was reported by RPD devices (e.g., the RPD devices  112 ,  114 ). Based on the resulting modelled tuning segments and a knowledge of the actual durations of tuning segments played on media devices in panelist households, the example audience measurement module  122  may estimate capped durations for the RPD tuning segments reported from RPD devices  112  in non-panelist households that correspond to when associated media sets  116  in such households are likely to be powered on. Based on these capped durations of RPD tuning segments, reliable audience measurement metrics may be generated for non-panelist households. 
       FIG.  2    is an example implementation of the example audience measurement module  122  of  FIG.  1   . The example audience measurement module  122  includes an example communications interface  202 , an example panel tuning information database  204 , an example RPD tuning information database  206 , an example RPD model generator  208 , an example distribution generator  210 , an example correlation calculator  212 , an example RPD tuning information analyzer  214 , an example set-on time calculator  216 , and an example report generator  218 . 
     The example audience measurement module  122  is provided with the example communications interface  202  to communicate with the metering device  120  installed in the panelist household  110 . That is, the metering device  120  may report audience measurement data to the AME  104  that is received by the communications interface  202 . The communications interface  202  may receive audience measurement data from other panelist households not represented in the illustrated example. The collected audience measurement data includes panel tuning information, which may be stored in the panel tuning information database  204 . The panel tuning information may include an indication of the media played on the media sets  118  in the panelist household  110 . In some example, the media may be uniquely identified. In other examples, the panel tuning information may identify a particular source of media (e.g., a station ID) from which the particular media may be identified. The panel tuning information may also include timestamps and/or other forms of timing information indicative of the start time and end time of particular media tuning segments played on the media sets  118  of panelists. Inasmuch as the panel tuning information is based on media actual played on the media sets of panelist homes, the panel tuning information will be limited to periods of time when the media set  118  is powered on and actually playing media. Each distinct period of time during which a media set  118  in the panelist household  110  is playing media associated with a particular source (e.g., a particular station or channel to which the RPD device  114  is tuned) is referred to herein as a panel tuning segment. A panel tuning segment is distinct from an RPD tuning segment defined above in that panel tuning segments are tied to times when the associated media set is powered on and actually playing media. 
     Additionally, in the illustrated example, the communications interface  202  of the audience measurement module  122  receives RPD tuning information from the content provider  102 . The content provider  102  collects the RPD tuning information reported from RPD devices (e.g., the RPD devices  112 ,  114 ) accessing media content provided by the content provider  102 . In some examples, the communications interface  202  may receive the RPD tuning information directly from the RPD devices  112 ,  114  independent of communications between the AME  104  and the content provider  102 . The RPD tuning information may be stored in the RPD tuning information database  206 . Similar to the panel tuning information, the RPD tuning information includes a media identifier (e.g., a unique identifier, a station ID, etc.) to identify the media accessed by the RPD devices. Further, the RPD tuning information includes timing information indicative of a start time and end time of RPD tuning segments. Inasmuch as the RPD devices  112 ,  114  may be separately powered from the associated media sets  116 ,  118 , it is possible that some portions of the collected RPD tuning segments correspond to media that was never actually played on a media set  116 ,  118  (e.g., when the RPD tuning device  112 ,  114  is on and reporting tuning information while the corresponding media set  116 ,  118  is turned off). 
     The example audience measurement module  122  is provided with the example RPD model generator  208  to generate a model of RPD tuning information based on predicted extensions of the durations of panel tuning segments reported in the collected panel tuning information. For example,  FIG.  3    is a schematic representation of panel tuning information  302  corresponding to media played on multiple media sets  304 ,  306 ,  308 ,  310 ,  312 ,  314  over a period of time. In the illustrated example, the media played on each media set  304 ,  306 ,  308 ,  310 ,  312 ,  314  is provided to the media set via an associated RPD device. In some examples, each media set  304 ,  306 ,  308 ,  310 ,  312 ,  314  is associated with a different panelist household. In other examples, a single panelist household may include more than one of the media sets  304 ,  306 ,  308 ,  310 ,  312 ,  314 . In any event, the panel tuning information  302  represented in  FIG.  3    is based on data collected from associated metering devices  120  in the panelist household(s)  110  and independent of RPD tuning information reported by the associated RPD devices  114 . That is, the panel tuning information  302  represents the actual duration and timing of media played on each respective media set  304 ,  306 ,  308 ,  310 ,  312 ,  314  as reported by an associated metering device  120 . 
     Individual panel tuning segments of media played on each media set  304 ,  306 ,  308 ,  310 ,  312 ,  314  are represented in  FIG.  3    by individual boxes with the dashed lines indicative of when the corresponding media set was powered off or otherwise not playing media. The different shading and/or cross-hatching within the boxes represent different media sources (e.g., stations, channels, etc.) to which the associated RPD devices  114  are tuned to provide the media. Thus, during the represented period, the first media set  304  is associated with two panel tuning segments  320 ,  322  separated by a gap in time indicating the first media set  304  was turned off between the two tuning segments  320 ,  322 . Separate panel tuning segments are not necessarily spaced in time. For example, the second media set  306  includes two panel tuning segments  324 ,  326  in which the second panel tuning segment  326  immediately follows the first panel tuning segment  324  indicating the audience member changed the channel or station to which the RPD device  114  was tuned. 
     As mentioned above, the example RPD model generator  208  may use the panel tuning information  302  to model expected RPD tuning information by modelling durations for the panel tuning segments reported in the collected panel tuning information that are extended beyond the actual duration of the panel tuning segments. It may initially be assumed that RPD devices  114  are always powered on. In such situations, an RPD device would continually report RPD tuning information such that the reported RPD tuning segments would always appear to be contiguous with each tuning segment ending when a new tuning segment begins. This situation is represented in  FIG.  4    in which the shading and cross-hatching of the actual panel tuning segments have been extended out indefinitely as modelled RPD tuning segments. Thus, the first panel tuning segment  320  played on the first media set  304  is assumed to extend to the beginning of the second panel tuning segment  322  to model a first tuning segment  402  associated with the first media set  304 . A second modelled tuning segment  404  corresponds to the second panel tuning segment  322  of  FIG.  3    but has been extended to the end of the period of time represented in the figure. 
     In actual implementation, most RPD devices  114  do not remain on indefinitely as represented in  FIG.  4   . Rather, many RPD devices  114  include a standby timer that will cause the RPD devices  114  to stop reporting RPD tuning information if there has been no activity for a set amount of time. The particular length of a standby timer will likely be different for different RPD devices  114  and/or different content providers  102 . In some examples, the content provider  102  may provide the AME  104  with the standby timer length for the RPD devices  114  associated with the content provider  102 . In other examples, the standby timer length may be determined from an analysis of RPD tuning information aggregated from multiple RPD devices  114  of the same type (and/or associated with the same content provider  102 ). More particularly, the AME  104  may determine the standby timer by identifying peaks in a distribution of durations of reported RPD tuning segments. 
       FIG.  5    illustrates how the example RPD model generator  208  may reduce the indefinite-duration modelled tuning segments of  FIG.  4    based on an example standby timer length  502 . The second RPD tuning segment  404  of the first media set  304 , as represented in  FIG.  5   , is shorter than initially assumed in  FIG.  4    because, under the model, the tuning information is expected to stop after the standby timer length has elapsed following the end of the panel tuning segment  322 . By contrast, shortening the modelled tuning segments by the standby timer length  502  does not affect the modelled duration of the first modelled tuning segment  402  of the first media set  304  because the time gap between the first panel tuning segment  320  and the second panel tuning segment  322  is less than the standby timer length  502 . 
     Typically, a standby timer is enabled by default in an RPD device  114 . However, some users may manually disable the standby timer. Accordingly, in some examples, the RPD model generator  208  randomly selects a proportion of media sets  304 ,  306 ,  308 ,  310 ,  312 ,  314  as being associated with RPD devices  114  for which the standby timer is disabled. The RPD model generator  208  will not shorten the modelled tuning segments for such media sets but will leave them to extend forward in time indefinitely (or until a subsequent panel tuning segment occurs). This is represented in  FIG.  5    in connection with the fifth media set  312  in which a modelled tuning segment  504  is modelled with a duration extending far beyond the standby timer length  502  while all other modelled tuning segments  504  have been shortened or capped. Although described as being extended indefinitely, in some examples, the RPD model generator  208  may model tuning segments associated with RPD devices  114  with disabled standby timers by extending the panel tuning segments for a finite but substantial extension period (e.g., 12 hours, 24 hours, 48 hours, etc.). 
     In some examples, the number or proportion of media sets identified to be associated with RPD devices  114  that have disabled the standby timer is derived from an analysis of aggregated RPD tuning information in a similar manner to the derivation of the standby timer length by identifying outliers on a distribution of durations of RPD tuning segments. Thus, for example, if it is determined that 85% of all RPD devices have a standby timer that is enabled, the RPD model generator  208  may select the panel tuning segments associated with 15% of the media sets  304 ,  306 ,  308 ,  310 ,  312 ,  314  to be modelled as being associated with an RPD device that does not have an enabled standby timer. In other examples, the RPD model generator  208  may generate a random number between 0 and 1 for each media set  304 ,  306 ,  308 ,  310 ,  312 ,  314  and designate the media set as either associated with a standby enabled RPD device or a standby disabled device depending on whether the number is above or below 85%. 
     In some examples, the RPD model generator  208  also accounts for the possibility that some people will turn off their RPD devices at the same time that they turn off an associated media set. In such situations, an RPD tuning segment would not be extended beyond a corresponding panel tuning segment but limited to the same duration as the panel tuning segment. Accordingly, in some examples, the RPD model generator  208  randomly selects a proportion of the panel tuning segments as corresponding to a time when the associated RPD device  114  was turned off based on the probability that people turn off an RPD device  114 . For example, assuming the probability that an RPD device  114  is powered off at the same time an associated media set is powered off is 50%, the RPD model generator may generate a random number between 0 and 1 for each panel tuning segment and then either extend the duration for a modelled tuning segment as shown in  FIG.  5    or truncate the modelled duration to be coextensive with the panel tuning segment based on whether the number is above or below 50%. This is represented in  FIG.  6    in which the first modelled tuning segment  402  for the first media set  304  is reduced to the same duration as the first panel tuning segment  320  of the first media set  304 . By contrast, the second modelled tuning segment  404  of the first media set  304  remains with an extended modelled duration in  FIG.  6    because the RPD model generator  208  randomly determined the associated RPD device  114  was not turned off at the end of the second panel tuning segment  322 . Modelled tuning segments that have the same duration as the actual duration of an associated panel tuning segment (e.g., the first modelled tuning segment  402  of  FIG.  6    or a modelled tuning segment associated with the panel tuning segment  326  of  FIG.  3   ) are referred to herein as non-extended model segments. 
     In some instances, the RPD model generator  208  may combine or merge multiple model tuning segments associated with separate panel tuning segments into a single modelled tuning segment when the model tuning segments are contiguous and associated with a single source of media. This may occur when the separate panel tuning segments correspond to successive tuning segments accessed by an RPD device  114  that are associated with a single source of media but spaced by a gap in time corresponding to when the associated media set  118  was turned off. An example situation where this may occur is when panelists turn off their televisions after watching the evening news and then turn the television back on the next morning to catch the morning news without ever changing the channel. This scenario is represented in connection with the sixth media set  314  of  FIGS.  3 - 6   . In particular, first and second panel tuning segments  328 ,  330  of the sixth media set  314  are shown in  FIG.  3    as being two separate tuning segments associated with the same source of media but spaced apart in time. As the RPD model generator  208  models the extended duration for the associated modelled tuning segments as outlined above, the two distinct panel tuning segments  328 ,  330  result in one merged modelled tuning segment  602  as represented in  FIG.  6    because separate modelled tuning segments identified for each panel tuning segment  328 ,  330  would be contiguous (e.g., one segment ends at the same time that the next segment begins). While the combined modelled tuning segment  602  corresponds to two panel tuning segments  328 ,  330  in the illustrated example, the modelled tuning segment  602  could include any number of successive panel tuning segments  328 ,  330 . 
     Returning to  FIG.  2   , the example audience measurement module  122  is provided with the example distribution generator  210  to generate a cumulative distribution of durations of the panel tuning segments reported in panel tuning information (e.g., the panel tuning information  302  represented in  FIG.  3   ) collected from metering devices  120  in panelist households  110 . Additionally, the example distribution generator  210  may generate a cumulative distribution of durations of the modelled tuning segments (e.g., as represented in  FIG.  6   ) based on the collected panel tuning segments. In some examples, the distribution generator  210  may combine or merge the cumulative distributions of durations of both the panel tuning segments and the modelled tuning segments into a single graph  700  as shown in  FIG.  7   . In the illustrated example, the dashed line  702  is a panel tuning segment distribution that represents the probability distribution of the actual duration of panel tuning segments while the solid line  704  is a modelled tuning segment distribution that represents the probability distribution of the durations of the modelled tuning segments. 
     In some examples, the distribution generator  210  generates multiple different graphs  700  corresponding to different dimensions of interest. That is, in some examples, the panel tuning segments and corresponding modelled tuning segments are aggregated based on different characteristics or dimensions such as, for example, the daypart when the media was accessed, the day of week when the media was accessed (e.g., whether on a weekend or a weekday), the station from which the media was accessed, the genre of the media, and/or the duration of the tuning segments. 
     In some examples, the distribution generator  210  may also generate a cumulative distribution of durations of tail-end panel tuning segments. As used herein, a tail-end panel tuning segment refers to the last panel tuning segment in a series of at least two successive panel tuning segments associated with a single modelled tuning segment that was merged from at least two modelled tuning segments. That is, with reference to  FIG.  6   , the second panel tuning segment  330  of the sixth media set  314  corresponds to a tail-end panel tuning segment because it is the last panel tuning segment in the modelled tuning segment  602 . In some examples, the distribution generator  210  may combine or merge the cumulative distributions of durations of the tail-end panel tuning segments with 1 minus the modelled tuning segment distribution (e.g., the distribution  704  of  FIG.  7   ) into a single graph  800  as shown in  FIG.  8   . In the illustrated example, the dashed line  802  is a tail-end panel tuning segment distribution that represents the probability distribution of the actual duration of tail-end panel tuning segments while the solid line  804  represents 1 minus the probability distribution  704  of the modelled tuning segments. In some examples, the distribution generator  210  generates multiple different graphs  800  corresponding to each different combination of dimensions of interest. 
     The example audience measurement module  122  of  FIG.  2    is provided with the example correlation calculator  212  to calculate a correlation coefficient between the duration of the panel tuning segments and the duration of the corresponding modelled tuning segments. In some examples, the correlation coefficient is a Pearson correlation coefficient. In some examples, a different correlation coefficient is calculated for each combination of particular dimensions of interest for the tuning segments. 
     As described above, some of the modelled tuning segments are non-extended model segments because they have the same duration as the actual panel tuning segment. For example, as shown in  FIG.  6   , the first panel tuning segment  320  of the first media set  304  is the same duration as the corresponding non-extended model segment  402 . In some examples, non-extended model segments are excluded in the calculation of the correlation coefficient. 
     In the illustrated example of  FIG.  2   , the example audience measurement module  122  is provided with the example RPD tuning information analyzer  214  to analyze RPD tuning information obtained from non-panelist households  108  to predict the actual duration of tuning segments to which individuals in the non-panelist households  108  were exposed to media. In some examples, the RPD tuning information analyzer  214  calculates capped durations for reported RPD tuning segments based on the distributions represented in  FIGS.  7  and  8    that estimate the periods of time when the media sets  116  in non-panelist households  108  are turned on during the reported RPD tuning segments. 
     The RPD tuning information analyzer  214  may determine capped durations for reported RPD tuning segments based on either a probabilistic approach or a deterministic approach. In the probabilistic approach, the RPD tuning information analyzer  214  generates a random number between 0 and 1 and then identifies the corresponding duration from the panel tuning segment distribution  702  shown on the graph  700  of  FIG.  7   . As a specific example, assume that a particular reported RPD tuning segment was 125 minutes long and that the RPD tuning information analyzer  214  generated the random number of 0.25. As shown in  FIG.  7   , the random number of 0.25 corresponds to a particular point  706  on the panel tuning segment distribution  702 . From the point  706  on the panel tuning segment distribution  702 , a corresponding capped duration  708  of approximately 53 minutes may be determined from the graph  700 . Thus, using the probabilistic approach, the RPD tuning information analyzer  214  determines that the reported RPD tuning segment of 125 minutes is to be shortened or capped to a probabilistic-estimated duration of 53 minutes. 
     In the probabilistic approach, as outlined above, the capped duration for the RPD tuning segment is based on random probabilities independent of the reported length of the RPD tuning segment. By contrast, the deterministic approach uses the reported length of the reported RPD tuning information to estimate the capped duration for the segment. In particular, assuming an initial reported RPD tuning segment of 125 minutes, a particular point  710  on the modelled tuning segment distribution  704  may be identified. As shown in the illustrated example of  FIG.  7   , the point  710  corresponds to a probability of approximately 0.42. The RPD tuning information analyzer  214  may use this value to identify a corresponding point  712  on the panel tuning segment distribution  702 . From the point  712  on the panel tuning segment distribution  702 , a corresponding capped duration  714  of approximately 71 minutes may be determined from the graph  700 . Thus, using the deterministic approach, the RPD tuning information analyzer  214  determines that the reported RPD tuning segment of 125 minutes is to be shortened or capped to a deterministic-estimated duration of 71 minutes. 
     In some examples, the RPD tuning information analyzer  214  may estimate a final capped duration for a reported RPD tuning segment based on a weighted average of the capped durations calculated using each of the probabilistic approach and the deterministic approach described above. In some examples, the weighting of the two approaches is based on the correlation coefficient calculated by the correlation calculator  212  and the proportion or fraction of non-extended model segments, relative to all modelled tuning segments, that were excluded from the correlation analysis. More particularly, in some examples, the RPD tuning information analyzer  214  generates a random number between 0 and 1 for a particular reported RPD tuning segment. If the random number is less than or equal to the proportion or fraction of the non-extended model segments excluded from the correlation analysis as described above, the capped duration for a reported RPD tuning segment is determined to correspond to the reported duration of the reported RPD tuning segment. That is, the reported RPD tuning segment is treated as a non-extended model segment such that the duration of the tuning segment is assumed to correspond to the actual duration that media was played via a corresponding media set. For values of the random number greater than the fraction of non-extended model segments, the capped duration (D cap ) for the reported RPD tuning segment is calculated as follows: 
         D   cap   =rD   det +(1− r ) D   prob    (Eq. 1)
 
     where r is the correlation coefficient, D det  is the deterministic-estimated duration for the RPD tuning segment, D prob  is the probabilistic-estimated duration for the RPD tuning segment. For Equation 1 to work, when the correlation coefficient is determined to be less than 0, the value is set to 0. 
     In some examples, the RPD tuning information analyzer  214  selects either the probabilistic approach or the deterministic approach to estimate a capped duration for a reported RPD tuning segment based on the length of the reported RPD tuning segment. In some examples, this is accomplished by setting the correlation coefficient to either 0 or 1. In some examples, only the deterministic approach may be implemented when the reported RPD tuning segments are relatively long (e.g., over 180 minutes) while only the probabilistic approach may be implemented for relatively short segments (e.g., less than 180 minutes). 
     The example estimates for a capped duration of a reported RPD tuning information described above (including the probabilistic-estimated duration and the deterministic-estimated duration) correspond to front-end capped durations of RPD tuning segments. That is, the shortened or capped durations estimated above predict the actual duration of media played on a particular media set  116  beginning at the start time (i.e., the front-end) of the reported RPD tuning segment. However, as described above, in some examples, a single RPD tuning segment may correspond to multiple sessions of media played on the particular media set  116  that are separated by a gap in time corresponding to when an associated media set playing media was turned off. Thus, in addition to estimating a front-end capped duration for a reported RPD tuning session, the RPD tuning information analyzer  214  may also calculated a tail-end capped duration for the reported RPD tuning segment. A tail-end capped duration for a reported RPD tuning segment may be calculated in much the same way as described above for the front-end capped durations except that different probability distributions are used. More particularly, for tail-end capped durations, the graph  800  is used rather than the graph  700 . That is, a probabilistic approach may be implemented by determining the tail-end capped duration based on where a randomly generated number falls on the tail-end panel segment distribution  802  of  FIG.  8   . Additionally or alternatively, a deterministic approach may be implemented by identifying a point on the distribution  804  (representative of 1 minus the modelled tuning segment distribution) to determine the duration associated with a corresponding point on the tail-end panel segment distribution  802 . Further still, in some examples, the two approaches may be weight averaged based on a correlation coefficient calculated between the tail-end panel tuning segments and the associated modelled tuning segments. 
     In the illustrated example of  FIG.  2   , the audience measurement module  122  is provided with the example set-on time calculator  216  to calculate the particular times when a media set  116  is powered on based on the estimated front-end and tail-end capped durations for reported RPD tuning segments collected from an RPD device  112  associated with the media set  116 . The example audience measurement module  122  is provided with the example report generator  218  to generate reports indicative of audience measurement metrics based on the RPD tuning information collected from non-panelist households  108  and the calculated set-on times of the media sets  116  in the non-panelist households  108 . For example, the report generator  218  may generate a report crediting the non-panelist household  108  with exposure to the media accessed by the associated the RPD device  112  during the period(s) of time when the corresponding media set  116  is estimated to be powered on based on RPD tuning information collected from the RPD device  112 . 
     While an example manner of implementing the audience measurement module  122  of  FIG.  1    is illustrated in  FIG.  2   , one or more of the elements, processes and/or devices illustrated in  FIG.  2    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example communications interface  202 , the example panel tuning information database  204 , the example RPD tuning information database  206 , the example RPD model generator  208 , the example distribution generator  210 , the example correlation calculator  212 , the example RPD tuning information analyzer  214 , the example set-on time calculator  216 , the example report generator  218 , and/or, more generally, the example audience measurement module  122  of  FIG.  2    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example communications interface  202 , the example panel tuning information database  204 , the example RPD tuning information database  206 , the example RPD model generator  208 , the example distribution generator  210 , the example correlation calculator  212 , the example RPD tuning information analyzer  214 , the example set-on time calculator  216 , the example report generator  218 , and/or, more generally, the example audience measurement module  122  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example communications interface  202 , the example panel tuning information database  204 , the example RPD tuning information database  206 , the example RPD model generator  208 , the example distribution generator  210 , the example correlation calculator  212 , the example RPD tuning information analyzer  214 , the example set-on time calculator  216 , and/or the example report generator  218  is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. including the software and/or firmware. Further still, the example audience measurement module  122  of  FIG.  1    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  2   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
     Flowcharts representative of example machine readable instructions for implementing the audience measurement module  122  of  FIGS.  1  and/or  2    is shown in  FIGS.  9 - 15   . In this example, the machine readable instructions comprise a program for execution by a processor such as the processor  1612  shown in the example processor platform  1600  discussed below in connection with  FIG.  16   . The program may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor  1612 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  1612  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowcharts illustrated in  FIGS.  9 - 15   , many other methods of implementing the example audience measurement module  122  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, a Field Programmable Gate Array (FPGA), an Application Specific Integrated circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. 
     As mentioned above, the example processes of  FIGS.  9 - 15    may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. “Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim lists anything following any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, etc.), it is to be understood that additional elements, terms, etc. may be present without falling outside the scope of the corresponding claim. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. 
     Turning in detail to the flowcharts, the example process of  FIG.  9    begins at block  902  where the example communications interface  202  obtains panel tuning information indicative of media sets (e.g., the media set  118 ) playing media accessed via associated RPD devices (e.g., the RPD device  114 ). In some examples, the panel tuning information is obtained from metering devices  120  in panelist households  110 . The panel tuning information may be stored in the panel tuning information database  204 . 
     At block  904 , the example RPD model generator  208  generates a model of RPD tuning segments based on the panel tuning information. Further detail regarding the implementation of block  904  is provided below in connection with  FIGS.  10  and  11   . At block  906 , the example distribution generator  210  generates distributions of the panel tuning segments and the modelled tuning segments. Further detail regarding the implementation of block  906  is provided below in connection with  FIG.  12   . 
     At block  908 , the example communications interface  202  obtains reported RPD tuning information reporting RPD tuning segments. In some examples, the RPD tuning information is received from a content provider  102  that collected the data from RPD devices  112 ,  114 . The RPD tuning information may be associated with panelist households  110  and/or non-panelist households  108 . In some examples, the RPD devices  112 ,  114  may report the RPD tuning information directly to the communications interface  202 . 
     At block  910 , the example RPD tuning information analyzer  214  estimates front-end capped durations for the reported RPD tuning segments. Further detail regarding the implementation of block  910  is provided below in connection with  FIG.  13   . At block  912 , the example RPD tuning information analyzer  214  estimates tail-end capped durations for the reported RPD tuning segments. Further detail regarding the implementation of block  912  is provided below in connection with  FIG.  14   . 
     At block  914 , the example set-on time calculator  216  calculates a set-on times for the media sets associated with the RPD tuning information. Further detail regarding the implementation of block  914  is provided below in connection with  FIG.  15   . At block  916 , the example communications interface  202  determines whether there is more audience RPD tuning information. If so, control returns to block  908 . Otherwise, control advances to block  918  where the example report generator  218  generates audience measurement reports. Thereafter, the example process of  FIG.  9    ends. 
       FIG.  10    illustrates an example process to implement block  904  of  FIG.  9   . The example process begins at block  1002  where the example RPD model generator  208  identifies panel tuning information associated with a particular media set  118  in a panelist household  110 . At block  1004 , the example RPD model generator  208  calculates the actual duration of a panel tuning segment played on the media set  118 . While the media being played may be accessed via an associated RPD device  114 , the actual duration of the media being played is independently determined based on feedback from the metering device  120  monitoring when the media set  118  is powered on and what media is being played. 
     At block  1006 , the example RPD model generator  208  calculates a modelled duration for a modelled tuning segment by extending the actual duration a substantial extension period. In some examples, the substantial extension period is a period of time that extends forward indefinitely until a subsequent panel tuning segment occurs. In other examples, the substantial extension period is any suitable period of time that is significantly longer than a typical duration for a person to consume media (e.g., 12 hours, 24 hours, 48 hours, etc.). 
     At block  1008 , the example RPD model generator  208  determines whether to treat a standby timer as enabled on the associated RPD device  114 . As described above, standby timers are typically enabled by default but some users may manual disable the timers. In some examples, whether the RPD model generator  208  treats the standby timer as enabled is based on whether a randomly generated number between 0 and 1 falls above or below a percentage probability that users of the particular type of RPD device  114  disable the standby timer. If the example RPD model generator  208  determines to treat the standby timer as enabled, control advances to block  1010  where the example RPD model generator  208  reduces the modelled duration according to a standby timer length. That is, rather than extend the actual duration of the panel tuning segment a substantial extension period (e.g., an indefinite period of time), the modelled duration is limited to the length of the standby timer. Thereafter, control advances to block  1012 . If the example RPD model generator  208  determines to not treat the standby timer as enabled (block  1008 ), control advances directly to block  1012 . 
     At block  1012 , the example RPD model generator  208  determines whether to assume the RPD device  114  is shut off after the panel tuning segment. In some examples, whether the RPD model generator  208  assumes the RPD device is shut off is based on whether a randomly generated number between 0 and 1 falls above or below a percentage probability that users of the particular type of RPD device  114  shut off the RPD device  114  when they turn off their associated media sets. If the example RPD model generator  208  determines to assume the RPD device  114  is shut off, control advances to block  1014  where the example RPD model generator  208  reduces the modelled duration to the actual duration. That is, the duration of the modelled tuning segment is assumed to be the same as the actual duration of the corresponding panel tuning segment. In other words, the modelled tuning segment is designated as a non-extended model segment. After reducing the RPD duration (block  1014 ), control advances to block  1016 . If the example RPD model generator  208  determines not to assume the RPD device  114  is shut off, control advances directly to block  1016 . 
     At block  1016 , the example RPD model generator  208  determines whether there is another panel tuning segment associated with the particular media set. In some examples, the number of panel tuning segments associated with a particular set depends upon the period of time being analyzed to generate the model. In some examples, the RPD model generator  208  generates new models each day so that the models correspond to current data. In some such examples, the models generated for a particular day are based on RPD tuning information spanning multiple days surrounding the particular day of interest. For example, the RPD model generator  208  may analyze panel tuning segments reported during a four-week period (28) ending on the day of interest. In some examples, the period of time over which the panel tuning segments are analyzed may include data collected one or more days following the particular day of interest. In any event, if the example RPD model generator  208  determines there is another panel tuning segment associated with the particular media set to be analyzed, control returns to block  1004 . Otherwise, control advances to block  1018  where the example RPD model generator  208  calculates tail-end durations for the modelled tuning segments. Further detail regarding the implementation of block  1018  is provided below in connection with  FIG.  11   . After calculating the tail-end durations (block  1018 ), control advances to block  1020  where the example RPD model generator  208  determines whether there is another media set. If so, control returns to block  1002 . Otherwise, the example process of  FIG.  10    ends and returns to complete the process of  FIG.  9   . 
       FIG.  11    illustrates an example process to implement block  1018  of  FIG.  10   . The example process begins at block  1102  where the example RPD model generator  208  determines whether a panel tuning segment is part of a series of consecutive panel tuning segments associated with the same media source and contiguous modelled tuning segments. If the example RPD model generator  208  determines that a panel tuning segment is not part of a series of consecutive panel tuning segments, control advances to block  1104 . At block  1104 , the example RPD model generator  208  sets the tail-end duration for the associated modelled tuning segment to zero. Thereafter, control advances to block  1112  where the example RPD model generator  208  determines whether there is another panel tuning segment to analyze. 
     Returning to block  1102 , if the example RPD model generator  208  determines that a panel tuning segment is part of a series of consecutive panel tuning segments, control advances to block  1106 . At block  1106 , the example RPD model generator  208  merges the modelled durations associated with the consecutive panel tuning segments. In other words, the adjacent modelled tuning segments are combined and treated as a single RPD tuning segment. At block  1108 , the example RPD model generator  208  merges the actual durations of the consecutive panel tuning segments except for the last panel tuning segment in the series. That is, the duration of the consecutive panel tuning segments are combined and treated as a single panel tuning segment. However, the merging of the panel tuning segments does not include the last panel tuning segment in the series. Thus, if the series includes only two consecutive segments, the merged actual duration would correspond to the actual duration of the first panel tuning segment. At block  1110 , the example RPD model generator  208  defines the tail-end duration for the associated modelled tuning segment as the actual duration of the last panel tuning segment in the series. 
     At block  1112 , the example RPD model generator  208  determines whether there is another panel tuning segment. If so, control returns to block  1102 . Otherwise, the example process of  FIG.  11    ends and returns to complete the process of  FIG.  10   . 
       FIG.  12    illustrates an example process to implement block  906  of  FIG.  9   . The example process begins at block  1202  where the example distribution generator  210  groups the panel tuning segments and the modelled tuning segments based on dimensions of interest. The dimensions of interest may include daypart, day of week, station, genre, and/or tuning segment length. 
     At block  1204 , the example distribution generator  210  selects a group of the tuning segments. At block  1206 , the example distribution generator  210  calculates the fraction of non-extended model segments relative to all modelled tuning segments. That is, as described above, some modelled tuning segments will have a modelled duration that is the same as the associated panel tuning segment from which each tuning segment is modelled. The proportion of these non-extended model segments relative to the total number of modelled tuning segments is determined as the fraction at block  1206 . At block  1208 , the example distribution generator  210  calculates a correlation coefficient between the actual durations and the modelled durations of the tuning segments except for those associated with non-extended model segments. In some examples, the distribution generator  210  may set the correlation coefficient to either 0 or 1 (e.g., based on the duration of the tuning segments being analyzed). 
     At block  1210 , the example distribution generator  210  generates a panel tuning segment distribution (e.g., the distribution  702  of  FIG.  7   ). At block  1212 , the example distribution generator  210  generates a modelled tuning segment distribution (e.g., the distribution  704  of  FIG.  7   ). At block  1214 , the example distribution generator  210  combines the panel and modelled distributions. At block  1216 , the example distribution generator  210  calculates a tail-end panel tuning segment cumulative distribution (e.g., the distribution  802  of  FIG.  8   ). At block  1218 , the example distribution generator  210  combines the tail-end distribution and 1 minus the modelled distribution (e.g., the solid line  804  of  FIG.  8   ). 
     At block  1220 , the example distribution generator  210  determines whether there is another group of tuning segments. If so, control returns to block  1204 . Otherwise, the example process of  FIG.  12    ends and returns to complete the process of  FIG.  9   . 
       FIG.  13    illustrates an example process to implement block  910  of  FIG.  9   . The example process begins at block  1302  where the example RPD tuning information analyzer  214  calculates a duration of a reported RPD tuning segment. Typically, RPD tuning information includes start times and end times for different tuning segments. Accordingly, the example RPD tuning information analyzer  214  calculates the duration by subtracting the start time from the end time of the particular RPD tuning segment being analyzed. 
     At block  1304 , the example RPD tuning information analyzer  214  calculates a probabilistic-estimated duration for the reported RPD tuning segment. This is accomplished by randomly generating a number and determining the corresponding duration on a panel tuning segment distribution associated with panel tuning segments sharing the same dimensions as the reported RPD tuning segment. At block  1306 , the example RPD tuning information analyzer  214  calculates a deterministic-estimated duration for the reported RPD tuning segment. This is accomplished by determining the probability percentage on a modelled tuning segment distribution corresponding to the duration of the reported RPD tuning segment and then determining the corresponding duration on an associated panel tuning segment distribution. 
     At block  1308 , the example RPD tuning information analyzer  214  determines whether to treat the reported RPD tuning segment as a non-extended model segment. In some examples, the RPD tuning information analyzer  214  accomplishes this by comparing a randomly generated number to the fraction of non-extended model segments relative to all modelled tuning segments calculated at block  1206  of  FIG.  12   . If the example RPD tuning information analyzer  214  determines to treat the reported RPD tuning segment as a non-extended model segment, control advances to block  1310  where the example RPD tuning information analyzer  214  sets the front-end capped duration for the RPD tuning segment as the reported RPD duration. Thereafter, control advances to block  1314  where the example RPD tuning information analyzer  214  determines whether there is another RPD tuning segment. If the example RPD tuning information analyzer  214  determines to not treat the reported RPD tuning segment as a non-extended model segment (block  1308 ), control advances to block  1312 . 
     At block  1312 , the example RPD tuning information analyzer  214  calculates the front-end capped duration for the reported RPD tuning segment based on a weighted average of the probabilistic-estimated duration and the deterministic estimated duration. This is accomplished using the correlation coefficient calculated at block  1208  of  FIG.  12   . In some examples, the correlation coefficient is set to either 0 or 1 such that the front-end capped duration is based exclusively on one of the probabilistic or deterministic approaches. In some examples, the process of  FIG.  13    may be simplified by only calculating the front-end capped duration for the reported RPD tuning segment based on one of the probabilistic or deterministic approaches while the other approach and the weight averaging blocks are omitted. 
     After calculating the front-end capped duration at block  1312 , control advances to block  1314 . If the example RPD tuning information analyzer  214  determines there is another RPD tuning segment, control returns to block  1302 . Otherwise, the example process of  FIG.  13    ends and returns to complete the process of  FIG.  9   . 
       FIG.  14    illustrates an example process to implement block  912  of  FIG.  9   . The example process begins at block  1402  where the example RPD tuning information analyzer  214  calculates a probabilistic-estimated tail-end duration for the reported RPD tuning segment. At block  1404 , the example RPD tuning information analyzer  214  calculates a deterministic-estimated tail-end duration for the reported RPD tuning segment. At block  1406 , the example RPD tuning information analyzer  214  calculates the tail-end capped duration for the reported RPD tuning segment based on a weighted average of the probabilistic-estimated and the deterministic-estimated tail-end durations. In some examples, the correlation coefficient is set to either 0 or 1 such that the tail-end capped duration is based exclusively on one of the probabilistic or deterministic approaches. In some examples, the process of  FIG.  14    may be simplified by only calculating the tail-end capped duration for the reported RPD tuning segment based on one of the probabilistic or deterministic approaches while the other approach and the weight averaging blocks are omitted. 
     After calculating the tail-end capped duration at block  1406 , control advances to block  1408  where the example RPD tuning information analyzer  214  determines whether there is another reported RPD tuning segment. If so, control returns to block  1402 . Otherwise, the example process of  FIG.  14    ends and returns to complete the process of  FIG.  9   . 
       FIG.  15    illustrates an example process to implement block  914  of  FIG.  9   . The example process begins at block  1502  where the example set-on time calculator  216  identifies RPD tuning information from an RPD device associated with a particular media set. At block  1504 , the example set-on time calculator  216  selects an RPD tuning segment associated with the RPD device. At block  1506 , the example set-on time calculator  216  determines whether the sum of the front-end capped duration and the tail-end capped duration is greater than or equal to the reported RPD duration. If so, control advances to block  1508  where the example set-on time calculator  216  defines a set-on time for the media set as corresponding to the reported RPD duration beginning at the start time of the reported RPD tuning segment. Thereafter, control advances to block  1516  where the example set-on time calculator  216  determines whether there is another reported RPD tuning segment. If the example set-on time calculator  216  determines the sum of the front-end capped duration and the tail-end capped duration is less than the reported RPD duration, control advances to block  1510 . 
     At block  1510 , the example set-on time calculator  216  defines a set-on time for the media set as corresponding to the front-end capped duration beginning at the start time of the reported RPD tuning segment. At block  1512 , the example set-on time calculator  216  determines whether the tail-end capped duration is greater than 0. If so, control advances to block  1514  where the example set-on time calculator  216  generates a new tuning segment with a set-on time for the media set corresponding to the tail-end capped duration that ends at the end time of the reported RPD tuning segment. Thereafter, control advances to block  1516 . If the example set-on time calculator  216  determines that the tail-end capped duration is not greater than 0 (block  1512 ), control advances directly to block  1516 . 
     At block  1516 , the example set-on time calculator  216  determines whether there is another reported RPD tuning segment. If so, control returns to block  1504 . Otherwise, control advances to block  1518  where the example set-on time calculator  216  determines whether there is another media set. If so, control returns to block  1502 . Otherwise, the example process of  FIG.  15    ends and control returns to complete the process of  FIG.  9   . 
       FIG.  16    is a block diagram of an example processor platform  1600  capable of executing the instructions of  FIGS.  9 - 15    to implement the audience measurement module  122  of  FIGS.  1  and/or  2   . The processor platform  1600  can be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, or any other type of computing device. 
     The processor platform  1600  of the illustrated example includes a processor  1612 . The processor  1612  of the illustrated example is hardware. For example, the processor  1612  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. The hardware processor may be a semiconductor based (e.g., silicon based) device. In this example, the processor implements the example communications interface  202 , the example RPD model generator  208 , the example distribution generator  210 , the example correlation calculator  212 , the example RPD tuning information analyzer  214 , the example set-on time calculator  216 , and the example report generator  218 . 
     The processor  1612  of the illustrated example includes a local memory  1613  (e.g., a cache). The processor  1612  of the illustrated example is in communication with a main memory including a volatile memory  1614  and a non-volatile memory  1616  via a bus  1618 . The volatile memory  1614  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  1616  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1614 ,  1616  is controlled by a memory controller. 
     The processor platform  1600  of the illustrated example also includes an interface circuit  1620 . The interface circuit  1620  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     In the illustrated example, one or more input devices  1622  are connected to the interface circuit  1620 . The input device(s)  1622  permit(s) a user to enter data and/or commands into the processor  1612 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  1624  are also connected to the interface circuit  1620  of the illustrated example. The output devices  1624  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a printer and/or speakers). The interface circuit  1620  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  1620  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  1626  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  1600  of the illustrated example also includes one or more mass storage devices  1628  for storing software and/or data. In the illustrated example, the mass storage devices  1628  implements the example panel tuning information database  204  and the example RPD tuning information database  206 . Examples of such mass storage devices  1628  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     The coded instructions  1632  of  FIGS.  9 - 15    may be stored in the mass storage device  1628 , in the volatile memory  1614 , in the non-volatile memory  1616 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     From the foregoing, it will be appreciated that example methods, apparatus and articles of manufacture have been disclosed that improve the accuracy of estimating the on-off state of media sets based on RPD tuning information reported from RPD devices associated the media sets. This is important for accurate audience measurement metrics because people should not be counted as audience members exposed to media accessed by RPD devices unless the associated media set is powered on and actually playing the media being accessed and reported on in the collected RPD tuning information. In this manner, more accurate audience measurement metrics may be generated based on collected RPD tuning information. 
     Example 1 is an apparatus that includes a distribution generator, implemented via a processor, to generate a modelled tuning segment distribution indicative of modelled durations of modelled tuning segments. The modelled tuning segments based on panel tuning segments during which panelists were exposed to first media. The apparatus includes a return path data (RPD) tuning information analyzer, implemented via the processor, to analyze RPD tuning information reported from an RPD device. The RPD tuning information is indicative of a reported RPD tuning segment during which the RPD device was accessing second media. The apparatus includes a set-on time calculator, implemented via the processor, to estimate a set-on time for a media set associated with the RPD device based on the RPD tuning information and the modelled tuning segment distribution. The set-on time is indicative of a period of time when the media set is powered on. 
     Example 2 includes the subject matter of Example 1, wherein the distribution generator is to generate a panel tuning segment distribution indicative of actual durations of the panel tuning segments. The RPD tuning information analyzer is to determine a probability percentage on the modelled tuning segment distribution corresponding to a reported duration of the reported RPD tuning segment and to determine a deterministic-estimated duration based on where the probability percentage falls on the panel tuning segment distribution. The set-on time is estimated based on the deterministic-estimated duration. 
     Example 3 includes the subject matter of Example 2, wherein the RPD tuning information analyzer is to determine a probabilistic-estimated duration based on where a randomly generated number falls on the panel tuning segment distribution. The set-on time calculator is to determine the set-on time based on a weighted average of the deterministic-estimated duration and the probabilistic-estimated duration. 
     Example 4 includes the subject matter of anyone of Examples 1-3 and further includes an RPD model generator to determine actual durations of the panel tuning segments, calculate modelled durations based on the actual durations of the panel tuning segments, and define the modelled tuning segments based on the modelled durations. 
     Example 5 includes the subject matter of Example 4, wherein the RPD model generator is to calculate the modelled durations by: extending the actual durations of the panel tuning segments by the shorter of (1) a substantial extension period and (2) a gap in time between ones of the panel tuning segments and a next subsequent panel tuning segment; reducing the modelled durations associated with a first portion of the panel tuning segments based on a standby timer length associated with the RPD device; and reducing the modelled durations associated with a second portion of the panel tuning segments to the actual durations of the corresponding panel tuning segments of the second portion. 
     Example 6 includes the subject matter of Example 5, wherein the RPD model generator is to identify the first portion of the panel tuning segments based on a comparison of a randomly generated number to a probability that RPD devices used to access the first media have enabled standby timers. 
     Example 7 includes the subject matter of anyone of Examples 5 or 6, wherein the RPD model generator is to identify the second portion of the panel tuning segments based on a comparison of a randomly generated number to a probability that RPD devices used to access the first media are powered off at a same time that associated media sets are powered off. 
     Example 8 includes the subject matter of anyone of Examples 4-7, wherein the RPD model generator is to identify different series of consecutive ones of the panel tuning segments associated with a same media source and associated with contiguous modelled tuning segments. The RPD model generator to generate a tail-end panel tuning segment distribution indicative of actual durations of the last panel tuning segments in the different series of consecutive panel tuning segments. The RPD tuning information analyzer to estimate a tail-end duration for the reported RPD tuning segment based on the tail-end panel tuning segment distribution. 
     Example 9 includes the subject matter of Example 8, wherein the RPD model generator is to merge the contiguous modelled tuning segments associated with the different series of consecutive panel tuning segments into different single modelled tuning segments. The set-on time calculator is to calculate a second set-on time for the media set corresponding to the tail-end duration for the reported RPD tuning segment when a reported duration for the reported RPD tuning segment is greater than the sum of the tail-end duration for the reported RPD tuning segment and a front-end duration for the reported RPD tuning segment. 
     Example 10 is a method that involves generating a modelled tuning segment distribution indicative of modelled durations of modelled tuning segments. The modelled tuning segments are based on panel tuning segments during which panelists were exposed to first media. The method includes obtaining return path data (RPD) tuning information reported from an RPD device. The RPD tuning information indicative of a reported RPD tuning segment during which the RPD device was accessing second media. The method includes estimating a set-on time for a media set associated with the RPD device based on the RPD tuning information and the modelled tuning segment distribution. The set-on time indicative of a period of time when the media set is powered on. 
     Example 11 includes the subject matter of Example 10 and further includes generating a panel tuning segment distribution indicative of actual durations of the panel tuning segments, determining a probability percentage on the modelled tuning segment distribution corresponding to a reported duration of the reported RPD tuning segment, and determining a deterministic-estimated duration for the set-on time based on where the probability percentage falls on the panel tuning segment distribution. 
     Example 12 includes the subject matter of Example 11 and further includes determining a probabilistic-estimated duration for the set-on time based on where a randomly generated number falls on the panel tuning segment distribution, and determining the set-on time based on a weighted average of the deterministic-estimated duration and the probabilistic-estimated duration. 
     Example 13 includes the subject matter of 10, further including:
         determining actual durations of the panel tuning segments, calculating modelled durations based on the actual durations of the panel tuning segments, and defining the modelled tuning segments based on the modelled durations.       

     Example 14 includes the subject matter of 13, wherein the modelled durations are calculated by: extending the actual durations of the panel tuning segments by the shorter of (1) a substantial extension period and (2) a gap in time between ones of the panel tuning segments and a next subsequent panel tuning segment, reducing the modelled durations associated with a first portion of the panel tuning segments based on a standby timer length associated with the RPD device, and reducing the modelled durations associated with a second portion of the panel tuning segments to the actual durations of the corresponding panel tuning segments of the second portion. 
     Example 15 includes the subject matter of 14, further including identifying the first portion of the panel tuning segments based on a comparison of a randomly generated number to a probability that RPD devices used to access the first media have enabled standby timers. 
     Example 16 includes the subject matter of 14, further including identifying the second portion of the panel tuning segments based on a comparison of a randomly generated number to a probability that RPD devices used to access the first media are powered off at a same time that associated media sets are powered off. 
     Example 17 includes the subject matter of 13, further including identifying different series of consecutive ones of the panel tuning segments associated with a same media source and associated with contiguous modelled tuning segments, generating a tail-end panel tuning segment distribution indicative of actual durations of the last panel tuning segments in the different series of consecutive panel tuning segments, and estimating a tail-end duration for the reported RPD tuning segment based on the tail-end panel tuning segment distribution. 
     Example 18 includes the subject matter of 17, further including merging the contiguous modelled tuning segments associated with the different series of consecutive panel tuning segments into different single modelled tuning segments, and calculating a second set-on time for the media set corresponding to the tail-end duration for the reported RPD tuning segment when a reported duration for the reported RPD tuning segment is greater than the sum of the tail-end duration for the reported RPD tuning segment and a front-end duration for the reported RPD tuning segment. 
     Example 19 is a non-transitory computer readable medium comprising instructions that, when executed, cause a machine to at least generate a modelled tuning segment distribution indicative of modelled durations of modelled tuning segments. The modelled tuning segments is based on panel tuning segments during which panelists were exposed to first media. The instructs cause the machine to obtain return path data (RPD) tuning information reported from an RPD device. The RPD tuning information indicative of a reported RPD tuning segment during which the RPD device was accessing second media. The instructions cause the machine to estimate a set-on time for a media set associated with the RPD device based on the RPD tuning information and the modelled tuning segment distribution. The set-on time is indicative of a period of time when the media set is powered on. 
     Example 20 includes the subject matter of 19, wherein the instructions further cause the machine to generate a panel tuning segment distribution indicative of actual durations of the panel tuning segments, determine a probability percentage on the modelled tuning segment distribution corresponding to a reported duration of the reported RPD tuning segment, and determine a deterministic-estimated duration for the set-on time based on where the probability percentage falls on the panel tuning segment distribution. 
     Example 21 includes the subject matter of 20, wherein the instructions further cause the machine to determine a probabilistic-estimated duration for the set-on time based on where a randomly generated number falls on the panel tuning segment distribution, and determine the set-on time based on a weighted average of the deterministic-estimated duration and the probabilistic-estimated duration. 
     Example 22 includes the subject matter of anyone of Examples 19-21, wherein the instructions further cause the machine to determine actual durations of the panel tuning segments, calculate modelled durations based on the actual durations of the panel tuning segments, and define the modelled tuning segments based on the modelled durations. 
     Example 23 includes the subject matter of Example 22, wherein the modelled durations are calculated by: extending the actual durations of the panel tuning segments by the shorter of (1) a substantial extension period and (2) a gap in time between ones of the panel tuning segments and a next subsequent panel tuning segment, reducing the modelled durations associated with a first portion of the panel tuning segments based on a standby timer length associated with the RPD device, and reducing the modelled durations associated with a second portion of the panel tuning segments to the actual durations of the corresponding panel tuning segments of the second portion. 
     Example 24 includes the subject matter of Example 23, wherein the instructions further cause the machine to identify the first portion of the panel tuning segments based on a comparison of a randomly generated number to a probability that RPD devices used to access the first media have enabled standby timers. 
     Example 25 includes the subject matter of anyone of Examples 23 or 24, wherein the instructions further cause the machine to identify the second portion of the panel tuning segments based on a comparison of a randomly generated number to a probability that RPD devices used to access the first media are powered off at a same time that associated media sets are powered off 
     Example 26 includes the subject matter of anyone of Examples 22-25, wherein the instructions further cause the machine to: identify different series of consecutive ones of the panel tuning segments associated with a same media source and associated with contiguous modelled tuning segments, generate a tail-end panel tuning segment distribution indicative of actual durations of the last panel tuning segments in the different series of consecutive panel tuning segments, and estimate a tail-end duration for the reported RPD tuning segment based on the tail-end panel tuning segment distribution. 
     Example 27 includes the subject matter of Example 17, wherein the instructions further cause the machine to merge the contiguous modelled tuning segments associated with the different series of consecutive panel tuning segments into different single modelled tuning segments. The instructions further cause the machine to calculate a second set-on time for the media set corresponding to the tail-end duration for the reported RPD tuning segment when a reported duration for the reported RPD tuning segment is greater than the sum of the tail-end duration for the reported RPD tuning segment and a front-end duration for the reported RPD tuning segment. 
     Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.