Patent Publication Number: US-2023164053-A1

Title: Methods and apparatus to predict end of streaming media using a prediction model

Description:
RELATED APPLICATIONS 
     This patent arises from a continuation of U.S. patent application Ser. No. 17/726,348, filed on Apr. 21, 2022, now U.S. Pat. No. 11,563,664, which is a continuation of U.S. patent application Ser. No. 17/189,145, filed on Mar. 1, 2021, now U.S. Pat. No. 11,316,769, which is a continuation of U.S. patent application Ser. No. 16/773,785, filed on Jan. 27, 2020, now U.S. Pat. No. 10,938,704, which is a continuation of U.S. patent application Ser. No. 16/236,318, filed on Dec. 28, 2018, now U.S. Pat. No. 10,547,534, which is a continuation of U.S. patent application Ser. No. 15/954,552, filed on Apr. 16, 2018, now U.S. Pat. No. 10,193,785, which is a continuation of U.S. patent application Ser. No. 14/473,602, filed on Aug. 29, 2014, now U.S. Pat. No. 9,948,539. U.S. patent application Ser. No. 17/726,348, U.S. patent application Ser. No. 17/189,145, U.S. patent application Ser. No. 16/773,785, U.S. patent application Ser. No. 16/236,318, U.S. patent application Ser. No. 15/954,552, and U.S. patent application Ser. No. 14/473,602 are hereby incorporated herein by reference in their entireties. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to monitoring streaming media, and, more particularly, to methods and apparatus to predict the end of streaming media using a prediction model. 
     BACKGROUND 
     Streaming media, as used herein, refers to media that is presented to a user by a presentation device at least partially in parallel with the media being transmitted (e.g., via a network) to the presentation device (or a device associated with the presentation device) from a media provider. Often times, streaming media is used to present live events. However, streaming media may also be used for non-live events (e.g., a time-shifted media presentation and/or video on demand presentation). Typically, time-adjacent portions of a streaming media file are delivered to and stored in a buffer, or temporary memory cache, of a streaming media device while the streaming media is presented to the user. The buffer releases the stored streaming media for presentation while continuing to fill with un-played portions of the streaming media. This process continues until the user terminates presentation of the streaming media and/or the complete streaming media file has been delivered (e.g., downloaded). In situations where the complete streaming media file has been delivered, the streaming media device typically continues releasing the buffered streaming media for presentation until the buffer is emptied. 
     A buffer is utilized to compensate for issues such as bandwidth usage fluctuations, which create “lag,” or discontinuous delivery of the media. The buffer mitigates the occurrences of “lag” by holding a portion of the streaming media that can be presented while awaiting the transfer of additional streaming media. In some instances, as the buffer fills, the download speed (e.g., bandwidth usage rate) of the streaming media may speed up or slow down according to the remaining space of the buffer. 
     In recent years, streaming media has become a popular medium for the delivery of media to users. Services like Netflix™ and Amazon Instant Video™, as well as on-demand services provided by internet protocol (IP) based television services (e.g., AT&amp;T Uverse™) are examples of providers of such streaming media. The instant nature of streaming media and the increase in bandwidth capabilities of internet service providers have contributed to the popularity of streaming media because of the high resolutions capable of being streamed (which require increased bandwidth for delivery). For example, when a user of a streaming media device selects a movie from a streaming media distributor, such as Netflix™, the movie the presented almost instantly without having to wait for the entire move file to be downloaded to the user&#39;s device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an example system for streaming media to user devices. 
         FIG.  2    is an illustration of an example streaming media application. 
         FIG.  3    is a block diagram of an example implementation of the predictor of  FIG.  1    to predict the end of streaming media. 
         FIG.  4    is an example graph illustrating observed bandwidth rates of a streaming media application in the process of streaming media. 
         FIG.  5 A  is an example graph of (a) the observed bandwidth rates of  FIG.  4    and (b) an example prediction model. 
         FIG.  5 B  is an example graph of (a) observed bandwidth rates of a streaming media application in the process of streaming media and (b) prediction models associated with the observed bandwidth rates. 
         FIG.  6    is a flow diagram representative of example machine readable instructions that may be executed to implement the example predictor of  FIG.  3    to predict the end of streaming media. 
         FIG.  7    is a flow diagram representative of example machine readable instructions that may be executed to implement the example predictor of  FIG.  3    to generate parameters for prediction model generation. 
         FIG.  8    is a flow diagram representative of example machine readable instructions that may be executed to implement the example predictor of  FIG.  3    to generate a prediction model and forecast the predicted end time of streaming media. 
         FIG.  9    is a block diagram of an example processor system that may execute any of the machine readable instructions represented by  FIGS.  6 ,  7   , and/or  8  to implement the example predictor of  FIGS.  1  and/or  3   . 
     
    
    
     DETAILED DESCRIPTION 
     While a media device is streaming media from a streaming media distributor, data may be obtained from communications between the streaming media device and the streaming media distributor. Such data may be obtained by analyzing traffic patterns, analyzing communication packets, network tapping, etc. Example data (or metadata) about the streaming media and/or the streaming environment includes a media file format, an available buffer space of the streaming media device, bandwidth usage of the streaming media device, etc. This descriptive data may be used to compliment traditional data obtained by AMEs (e.g., audience composition and/or media identification associated with traditional media (e.g., radio and/or television) broadcasts) to create more robust data sets, and allows for finer grained statistical methods to be applied. 
     Predicting the end of the streaming media is important in instances where access to the streaming media distributor and/or the streaming media application is not available for directly obtaining information about the end. For example, predicting an end of streaming media time may allow for more precise streaming advertisement extraction for media crediting. In some instances, predicting the end of streaming media may allow for targeted survey delivery. That is, it allows a survey to be delivered near the end (e.g., slightly before the end) of the streaming media before a user diverts attention away from a media presentation device when the media presentation has ended. 
     In some instances, in media monitoring, time durations for streaming media presentation are elongated or shortened from a time duration of the streaming media. For example, by rewinding, skipping, or fast-forwarding (e.g., track mode operations) through streaming media (e.g., via progress bar manipulation), the duration of the presentation may be substantially longer or shorter than the duration of the streaming media (e.g., were it applied without any track mode operations). By extrapolating or inferring an end of streaming media (and updating the analysis during presentations), finer detailed and/or more accurate time durations may be obtained. Additionally or alternatively, targeted media may be provided to a user streaming media. Instead of waiting for a signal that streaming media is ended at a user device, it may be beneficial to predict when streaming media will end. By predicting the end time, a more seamless transition to targeted media may occur. 
     Examples disclosed herein predict the end of a streaming media presentation using a prediction model based on characteristics of the bandwidth during periods of buffer fill associated with the streaming of media. Examples disclosed herein use the characteristics of the bandwidth to extract parameters for use in a prediction model. The example prediction model is used to forecast a time at which the end of the streaming media file will be reached. 
     Examples disclosed herein are applicable to any streaming media protocol (e.g. Dynamic Adaptive Streaming over HTTP (DASH), Adaptive Bit-Rate Streaming, HTTP Live Streaming (HLS), Real-Time Streaming Protocol (RTSP), Real-Time Protocol (RTP), Real-Time Control Protocol (RTCP), and/or any suitable combination or future protocol). 
       FIG.  1    is a block diagram of an example environment in which example methods apparatus and/or articles of manufacture disclosed herein may be used for predicting end times of streaming media. In the example environment of  FIG.  1   , media streamed from a streaming media distributor  120  to example user devices  101   a - 101   c.  The example environment includes the example user devices  101   a - 101   c,  an example proxy server  105 , an example network  115 , and the example streaming media distributor  120 . In the example of  FIG.  1   , an audience measurement entity  125 , such as The Nielsen Company (US), LLC, includes an example predictor  130  for predicting the end of streaming media. 
     In the illustrated example, one of the example user devices  101   a - 101   c,  initiates a streaming media application to stream media (e.g., example user device  101   a ). A request to stream media is transmitted to the example streaming media distributor  120  by the example user device  101   a . The request is routed through the example proxy server  105  and the example network  115 . The example streaming media distributor  120  acknowledges the request, and begins streaming media to the example user device  101   a  through the example proxy server  105  using an encrypted stream. The encrypted stream prevents a proxy server  105  from accessing information regarding track mode operations and/or timestamp information associated with the streaming media. While the media is streaming to the example user device  101   a,  the example proxy server  105  extracts available data and/or metadata associated with the streaming media, (e.g., bandwidth rates, source and destination ports, internet protocol addresses, etc.) and transmits the extracted data and/or metadata to a predictor  130  at the example audience measurement entity  125 . The example predictor  130  uses the transmitted data (e.g., bandwidth rates) to predict when the media will stop (or already did stop) presenting on the user device  101   a.  For example, the predictor  130  predicts the end time when such time is not directly accessible to the example proxy server  105 , the audience measurement entity  125 , nor the example predictor  130  during the streaming of the media. 
     The example user devices  101   a - 101   c  of the illustrated example may be implemented by any device that supports streaming applications and/or streaming media (e.g. smart televisions, tablets, game consoles, mobile phones, smart phones, streaming media devices, computers, laptops, tablets, Digital Versatile Disk (DVD) players, Roku™ devices, Internet television apparati (e.g., Google™ Chromecast™, Google™ TV, Apple™ TV, etc.) and/or other electronic devices). The example user devices  101   a - 101   c  communicate with the example streaming media distributor  120  via the proxy server  105  using the network  115 . 
     The example network  115  may be any type of communications network, (e.g., the Internet, a local area network, a wide area network, a cellular data network, etc.) facilitated by a wired and/or wireless connection (e.g., a cable/DSL/satellite modem, a cell tower, etc.). The example network may be a local area network, a wide area network, or any combination of networks. 
     The example proxy server  105  of the illustrated example is a network device located in a monitored household that acts as an intermediary for communications (e.g., streaming media requests and responses including streaming media) involving one or more of the example user devices  101   a -101 c  and/or one or more other components connected to the example network  115 . Alternatively, the example proxy server  105  may be located in a separate location from the monitored household. For example, the example proxy server  105  may be a router, a gateway, a server, and/or any device capable of acting as a network traffic intermediary. For example, a broadband modem and/or router may implement the proxy server  105 . According to the illustrated example, the proxy server  105  is an intermediary for communications between the example user devices  101   a - 101   c  and the example streaming media distributor  120 . 
     For example, when the example user device  101   a  sends a request for media to the streaming media distributor  120 , the request is first routed to the example proxy server  105 . The example proxy server  105  then transmits the request to the example streaming media distributor  120  (e.g., the request may be transmitted after being modified to indicate that a response to the request should be routed to the proxy server  105 ). When the example streaming media distributor  120  responds to the request, the response is routed to the example proxy server  105 , which re-transmits the request to the example user device  101   a.    
     As the example proxy server  105  is involved in communications associated with the example user devices  101   a - 101   c,  the example proxy server  105  is capable of gathering information about those communications. While the example proxy server  105  is referred to as a “proxy” device, the proxy server  105  may not perform functions typically associated with a proxy (e.g., performing packet translation). Rather, the functions of the proxy server  105  described in examples herein, may be performed by any type of device to collect information about communications between the example user devices  101   a - 101   c  and the example streaming media provider  120  (e.g., the example proxy server  105  may not participate in the communication chain and, instead, may monitor the communications from the sidelines using, for example, packet mirroring, packet snooping, or any other technique). 
     In the illustrated example, the proxy server  105  transmits collected information to the audience measurement entity  125 . The example proxy server  105  collects, calculates, and/or correlates bandwidth information for a streaming media application. In some examples, the example proxy server  105  identifies and collects data originating from the streaming media distributor  120  and delivered to the user devices  101   a - 101   c.  For example, the example proxy server  105  may collect and correlate traffic based on one or more characteristics such as simple network management protocol (SNMP), internet protocol (IP) addresses, sub-protocols of the Internet Protocol suite (e.g., real-time streaming protocol (RTSP)), port information, service designation, user agent, etc. One or more of the above characteristics may be indicative of a specific streaming media distributor  120  (e.g., a source IP address). The example proxy server  105  also determines the rate (e.g., bandwidth rate) at which the data passes through the proxy server  105  and/or the rate at which data is streamed from the streaming media distributor  120  to the user devices  101   a - 101   c.  Combining the correlated traffic and the rate (e.g., data rate, bandwidth rate, etc.) at which the traffic passes through the device allows for application specific bandwidth rate monitoring. The proxy server  105  collects and transmits the bandwidth rate of the streaming media application and the application identification to the example predictor  130 . In this way, data (e.g., bandwidth rate) is not required to be sent from a media device presenting the streaming media nor from a streaming media distributor transmitting the streaming media to the media device. Additionally or alternatively, the example proxy server  105  mirrors the traffic to the example predictor  130  for collection, calculation, and/or correlation. 
     Other network topologies than those illustrated in  FIG.  1    may be utilized with example methods and apparatus disclosed herein. For example, the proxy server  105  may not be included in the system  100  when other devices or components can provide information about communications (e.g., bandwidth rates may be reported by the user devices  101   a - 101   c ). Additionally or alternatively, communications may be routed through the example audience measurement entity  125  and/or mirrored to the example audience measurement entity  125 . In some such examples, the audience measurement entity  125  monitors and gathers information about the communications with or without information from other devices such as the proxy server  105 . 
     The audience measurement entity  125  of the illustrated example includes an example predictor  130 . In this example, the example predictor  130  obtains the bandwidth rate from the proxy server  105  while the example user devices  101   a - 101   c  stream media from the example streaming media distributor  120 . However, as explained above, the data rate (e.g., bandwidth rate) can alternatively be provided by other device(s). In some examples control information, text overlay, etc. are embedded within the stream. Thus, it is desirable to create a threshold bandwidth rate to distinguish the transmission of streaming media from transmission of other data carried in the stream. An example selection of such a threshold is described in conjunction with  FIG.  3   . In the illustrated example of  FIG.  1   , the example predictor  130  analyzes the bandwidth rate forwarded by the example proxy server  105  and determines end of stream times for the streaming media when the bandwidth exceeds the threshold. 
     In the illustrated example, one or more of the user devices  101   a - 101   c  are associated with a panelist who has agreed to be monitored by the audience measurement entity  125 . The panelists are users registered on panels maintained by a ratings entity (e.g., an audience measurement entity  125 ) that owns and/or operates the ratings entity subsystem. Traditionally, audience measurement entities (also referred to herein as “ratings entities”) determine demographic reach for advertising and media programming based on registered panel members. That is, an audience measurement entity  125  enrolls people that consent to being monitored into a panel. During enrollment, the audience measurement entity receives demographic information from the enrolling people so that subsequent correlations may be made between advertisement/media exposure to those panelists and different demographic markets. 
     People become panelists via, for example, a user interface presented on the user devices  101   a - 101   c  (e.g., via a website). People become panelists in additional or alternative manners such as, for example, via a telephone interview, by completing an online survey, etc. Additionally or alternatively, people may be contacted and/or enlisted using any desired methodology (e.g., random selection, statistical selection, phone solicitations, Internet advertisements, surveys, advertisements in shopping malls, product packaging, etc.). 
     In the panelist system of the illustrated example, consent is obtained from the user to monitor and analyze network data when the user joins and/or registers for the panel. For example, the panelist may agree to having their network traffic monitored by the proxy server  105 . Although the example system of  FIG.  1    is a panelist-based system, non-panelist and/or hybrid panelist systems may alternatively be employed. 
       FIG.  2    illustrates an example streaming media application  201 , executing on one of the example user devices  101   a - 101   c.  The example streaming media application  201  of this example presents media obtained from the streaming media distributor  120  on the corresponding example device  101   a - 101   c.  The graphical user interface of the streaming media application  201  presents data relevant to the presentation of the streaming media. In the example streaming media application  201  of  FIG.  2   , an elapsed time indicator  202  displays a length of the media presentation session and a total length of the media. A file ID indicator  203  shows the file name of the streaming media being presented. 
     In some examples, the file ID indicator  203  is analyzed by the example predictor  130  to determine a file format when available (e.g., if the streaming media is transmitted in an unencrypted stream). The example predictor  130  may access the contents of unencrypted streaming media packets (or encrypted packets for which a decryption process is available). The example data packets may include headers, or leading data, which indicates what video and/or audio is being streamed to the streaming media application  201 . An example bandwidth indication field  204  displays the current bandwidth usage rate of the streaming media application  201 . An example time remaining indicator  206  displays the predicted end of media time as indicated by the user device (e.g.,  101   a ). An example progress bar  208 , displays the graphical representation of the time remaining based on the values of the example elapsed time indicator  202  and example time remaining indicator  206 . 
     In some examples, the data displayed by at the streaming media application  201 (e.g., codec type, file name, and/or time elapsed) may be inaccessible to the example predictor  130 . However, the example predictor  130  may measure the value of the bandwidth rate by monitoring the traffic between the user device  101   a - 101   c  and the streaming media distributor  120 . 
       FIG.  3    is a block diagram of an example implementation of the example predictor  130  of  FIG.  1   . The example predictor  130  of  FIG.  3    is provided with an example bandwidth recorder  304 , an example identifier  306 , an example threshold generator  308 , an example parameter generator  310 , an example modeler  312 , an example forecaster  314 , and an example end of stream handler  316 . 
     The example bandwidth recorder  304  of  FIG.  3    observes and records the bandwidth rates forwarded by the example proxy server  105  of  FIG.  1   . In the example  FIG.  3   , the bandwidth recorder  304  is in communication with an example identifier  306  and an example threshold generator  308 . The bandwidth rates forwarded by the example proxy server  105  are the bandwidth rates associated with the streaming media application  201  while the streaming media application  201  is streaming media. 
     The example identifier  306  determines the format of media being streamed in the streaming media application  201 . In the illustrated example of  FIG.  3   , when the streaming media application  201  begins to stream media, the example identifier  306  analyzes data delivered to the example bandwidth recorder  304  by the proxy server  105  to determine the audio and/or video codec of the streaming media. In some examples, the example identifier  306  knows the file format of the media used by the streaming media application  201  prior to streaming because such file formats may be proprietary (e.g., the example identifier  306  is informed of the file format by the example proxy server  105 ). The media format determined by the example identifier  306  is sent to the example bandwidth recorder  304 . In some examples where a media format is not determined by the example identifier  306 , the example bandwidth recorder  304  is notified that the media format is undetermined. 
     The example threshold generator  308  in the illustrated example obtains the media format of streaming media from the example identifier  306 . When the media format is obtained, the example threshold generator  308  references a look-up-table to determine a threshold for the bandwidth rate which signifies (1) that bandwidth rates should begin (e.g., when bandwidth exceeds the threshold) or cease (e.g., when bandwidth is below the threshold) to be stored in a metering dataset and (2) that a prediction should be made regarding end of stream time. For example, when bandwidth rates are above the set threshold, the bandwidth rates are recorded to a metering dataset and the predictor  130  is primed to generate a prediction. When the bandwidth rates are below the threshold, the bandwidth rates are not recorded to a metering dataset and the predictor  130  should be generating a prediction with respect to a completed metering dataset. 
     In examples where a media format is not determined by the example identifier  306 , the threshold may be set to a default, or predetermined, value by the example threshold generator  308 . In some examples, the threshold generator  308  determines that the format of the streaming media contains only streaming media and does not contain any other information such as text overlays and/or track mode commands. In such an example, a threshold may not be set for recording the bandwidth rate (e.g., a threshold may be determined to be unnecessary or may be otherwise excluded because it is not necessary to differentiate between the streaming media and other information carried in the stream). The threshold generator  308  of the illustrated example transmits the threshold to the example bandwidth recorder  304 . 
     In the illustrated example of  FIG.  3   , the example bandwidth recorder  304  monitors and/or records the bandwidth rate forwarded by the example proxy server  105  and compares the bandwidth rate to the threshold determined by the example threshold generator  308 . When the example bandwidth recorder  304  determines that the bandwidth rate meets or exceeds the threshold, the example bandwidth recorder  304  begins to store the bandwidth rates and their associated time-stamps in a metering dataset. The metering dataset is used to store the values of bandwidth rates from a first time that the bandwidth rates meet or exceed the threshold until a second time that the bandwidth rates meet or fall below the threshold after the first time. When complete, the metering dataset values are used for generating the parameters used in generating a prediction model (e.g., prediction model parameters). The example bandwidth recorder  304  monitors the bandwidth rate to determine when the bandwidth rate falls below the threshold set by the example threshold generator  308 . When the bandwidth rate falls below the set threshold after having previously met or exceeded the threshold, the example modeler  312  is notified that the metering dataset (e.g., the bandwidth rates recorded between the time that the bandwidth rate met or exceeded the threshold and the later time that the bandwidth rate met or dropped below the threshold) is ready for processing by the example parameter generator  310 . The example bandwidth recorder  304  continues to monitor the bandwidth rates forwarded by the proxy server after the completion of the dataset. 
     The example parameter generator  310  of the illustrated example calculates and/or identifies a prediction model to generate a streaming duration prediction model. Each time a prediction model is generated, the example parameter generator  310  generates a new set of parameters in the event that a characteristic of the streaming media has changed. For example, the bit rate of the streaming media may change during streaming if the streaming media is streaming using adaptive bit-rate streaming. The example parameter generator  310  accesses the metering dataset and begins a series of calculations to determine characteristic parameters of the metering dataset. In the illustrated example, the parameter generator  310  calculates the mean and the standard deviation of the metering dataset. The example parameter generator  310  also identifies a decay factor for the prediction model. The decay factor represents the rate at which the prediction model decays or decreases from a peak value of the prediction model to a zero value (or negative infinity based on the prediction model utilized). In some examples, the decay factor may be identified based on the type of streaming media (e.g., audio and/or video). In other examples, the decay factor may be identified from the codec of the media being streamed. In some examples, the decay factor may be identified based on the bit rate of the streaming media. In yet other examples, the decay factor may be uniform for all media types and/or codecs. The example parameter generator  310  stores the prediction model parameters and the decay factor associated with the prediction model parameters. When the example parameter generator  310  generates parameters for the dataset, it notifies the example modeler  312 . 
     The example modeler  312  of  FIG.  3    generates a prediction model based on the prediction model parameters created by the example parameter generator  310 . When the example modeler  312  receives notification from the example parameter generator  310  that the prediction model is to be generated, the example modeler  312  retrieves the prediction model parameters and the decay factor associated with the streaming media. The example modeler  312  generates the prediction model using the parameters provided by the example parameter generator  310 . In some examples, before releasing the prediction model to the example forecaster  314 , the prediction model parameters may be adjusted. For example, the adjustment by the example modeler  312  may align the scale (e.g., amplitude) and the mean (e.g., temporal location) of the prediction model to the scale and the mean of the metering dataset used to generate the prediction model. When the scale and mean of the example prediction model match the scale and mean of the primary dataset, the prediction model is forwarded to the example bandwidth recorder  304  and the example forecaster  314 . 
     The example bandwidth recorder  304  uses the prediction model while continuing to monitor the bandwidth rate sent by the example proxy server  105 . When a prediction model has been generated, the example bandwidth recorder  304  continues creating metering datasets as described above and also compares the observed bandwidth rate against the prediction model. If the value of the bandwidth rate exceeds the prediction model (e.g., the amplitude of the bandwidth rate exceeds the amplitude of the prediction model), the example bandwidth recorder  304  will signal the example parameter generator  310  that new parameters should be generated for a new metering dataset to generate an updated prediction model. 
     The example forecaster  314  of  FIG.  3    obtains the prediction model and begins iterating, using temporal increments (e.g. tenths, hundredths, thousandths of a second), over the prediction model starting from the mean value (the mean value being the temporal location of the maximum value of the metering dataset). The prediction model is a function of time (as will be explained in more detail below in conjunction with  FIG.  6   ). Therefore, using time values, the example forecaster  314  calculates a value for every unit of time after the temporal location of the mean. The example forecaster  314  continues iterating over increasing time values until the prediction model produces (or is indicative of) a value at or below the threshold generated by the example threshold generator  308 . The value that is at or below the threshold signifies the time at which the example forecaster  314  predicts that the streaming media will end, or more specifically, when the buffer on the user device will be emptied complete the presentation of the streaming media after the transfer of the streaming media via the network  115 . When the value at or below the threshold is reached, the example forecaster  314  determines that the time at which the value at or below the threshold was identified is the predicted end of stream time and forwards the end of stream time to the example end of stream handler  316 . In some examples, when no threshold is utilized, the iteration performed by the example forecaster  314  may stop when a value of zero (or the first negative value) is reached. 
     The example end of stream handler  316  stores and/or transmits the forecasted end of stream time to a data collection facility at or remote from, the audience measurement entity  125 . In some examples, the end of stream handler  316  may perform other actions in response to the end of stream time. For example, at the time indicated as the end of stream time, the end of stream handler  316  may transmit a survey to the user device(s)  101   a - 101   c  streaming the media. In other examples, the predicted end of stream time may be used to send a command to extract advertisement(s) embedded in the streaming media. 
     While an example manner of implementing the predictor  130  of  FIG.  1    is illustrated in  FIG.  3   , one or more of the elements, processes and/or devices illustrated in  FIG.  3    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example bandwidth recorder  304 , the example identifier  306 , the example threshold generator  308 , the example parameter generator  310 , the example modeler  312 , the example forecaster  314 , and the example end of stream handler  316  and/or, more generally, the example predictor  130  of  FIG.  1    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example bandwidth recorder  304 , the example identifier  306 , the example threshold generator  308 , the example parameter generator  310 , the example modeler  312 , the example forecaster  314 , and the example end of stream handler  316  and/or, more generally, the example predictor  130  could be implemented by one or more circuit(s), 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)), etc. 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 bandwidth recorder  304 , the example identifier  306 , the example threshold generator  308 , the example parameter generator  310 , the example modeler  312 , the example forecaster  314 , and the example end of stream handler  316  and/or the example predictor  130  are hereby expressly defined to include a tangible computer readable storage device or storage disc such as a memory, DVD, CD, Blu-ray, etc. storing the software and/or firmware. Further still, the example predictor  130  of  FIG.  1    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  3   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
       FIG.  4    illustrates a graphical illustration  400  of example bandwidth rates forwarded from the example proxy server  105  of  FIG.  1    for streaming of example media to an example one of the user devices  101   a - 101   c . The example bandwidth rates represent the rates of the traffic between the example streaming media distributor  120  and one of the example user devices  101   a - 101   c  as seen at the proxy server  105 . In the illustrated example of  FIG.  4   , one cycle of buffer filling during streaming of media is represented by bandwidth rate curve  402 . For example, a cycle of buffer filling is a period of time where a buffer fills with downloaded media at increasing, and then decreasing rates, to be presented uninterrupted. In some examples regarding adaptive bit-rate streaming, a streaming media file is partitioned into smaller packets for downloading. The downloading of the smaller packets into the buffer creates spikes in bandwidth much the same as when a buffer fill and empty cycling may create such spikes. 
     The example bandwidth rate curve  402  is observed to increase as the buffer of the user device  101   a - 101   c  is filled (or a portion of the media is downloaded) and decreases when the buffer reaches capacity (or the portion of the media has been transferred). At a certain capacity of the buffer or after a certain percentage of a packet is downloaded, 50% for example, the user device  101   a - 101   c  tapers the bandwidth rate, or speed at which data is downloaded. The tapering occurs so that the downloaded data is not lost due to lack of buffer space. This behavior is represented in the shape of the bandwidth rate curve  402 . As the buffer begins filling, the bandwidth rate gradually increases to a peak and then begins to taper off as the certain capacity is reached. This tapering continues until the entire media file is downloaded (assuming uninterrupted streaming). 
     In the illustrated example, the bandwidth recorder  304  monitors the bandwidth forwarded by the example proxy server  105 . The example identifier  306  determines that the streaming media is of, for example, a flash video format and informs the example threshold generator  308  of the media format. The example threshold generator  308  sets the threshold  404  and informs the example bandwidth recorder  304  of the threshold. When the bandwidth rate is observed to be at the threshold  404  at time  405 , the example bandwidth recorder  304  begins storing the bandwidth rates in a metered dataset. When the bandwidth rate is observed to be at the threshold  404  at time  408 , after previously exceeding the threshold at time  405 , then the bandwidth recorder  304  stops appending values to the metering dataset. Thus, the values of the bandwidth curve between time  405  and time  408  comprise the metering dataset  402  (also referred to herein as the bandwidth rate curve  402 ). Though, the example predictor  130 , in some examples, does not know the time that the media ceases streaming at the user device  101   a - 101   c  (e.g., however, the end time can be predicted by the predictor  130 ), the media is illustrated to end presentation at time  410 . Though streaming has ceased, reading out of the buffer at the user device  101   a - 101   c  continues for some time thereafter. 
       FIG.  5 A  illustrates an example graph  402  of  FIG.  4    on a timeline with an example prediction model curve  502  generated by an example predictor  130  based upon the prediction model parameters generated from the metering dataset  402 . At the time  408  that the example metering dataset  402  falls below the example threshold  404 , the example bandwidth recorder  304  notifies the example parameter generator  310  that the metering dataset  402  is complete. In the illustrated example of  FIG.  5   , the example parameter generator  310  calculates a mean  508 , an amplitude  514 , and a standard deviation  516  of the metering dataset  402 . Additionally, the parameter generator  310  determines the decay factor associated with the identified media type (e.g., flash video). The example parameter generator  310  makes the prediction model parameters (the mean, the amplitude, the standard deviation, and the decay factor) available to the example modeler  312 . The example modeler  312  then generates the prediction model curve  502  using the model based on the prediction model parameters generated by the example parameter generator  310 . In the illustrated example, the prediction model  502  is generated using an exponentially modified Gaussian (EMG) distribution function. Alternatively, other suitable prediction models may be used as described in further detail below. The example modeler  312  sends the prediction model to the example bandwidth recorder  304  and the example forecaster  314 . 
     The example forecaster  314  then iterates time values in the example prediction model  502  until a time dependent solution (or value)  512  of the example prediction model  502  is at or under the threshold  404 . The threshold  404  is used in the illustrated example to determine the end of the stream time due to the characteristics of the prediction model utilized (the exponentially modified Gaussian (EMG)). The EMG function does not go to zero until it reaches infinity, thus a threshold may be utilized to indicate a bandwidth rate below which it is determined that streaming has substantially stopped. In some examples, a second threshold may be utilized that lies substantially closer to zero than the threshold  404  used to create the metering dataset. Regardless, the temporal location of the value  512  which is determined to be at or below the threshold is reported as the predicted end of streaming time. This approach has been empirically found to predict end of stream times that are substantially close to the actual end of stream time  410  observed at the one of the user devices  101   a - 101   c.  The end of stream time represents the time at which the entire media stream has been played out of the buffer. 
       FIG.  5 B  is an example graph illustrating observed bandwidth of streaming media and associated prediction model curves. The illustrated example of  FIG.  5 B  includes the example metering dataset  402  and the example prediction model curve  502  of  FIG.  5 A . However, in the example of  FIG.  5 B , after the bandwidth rate drops below the threshold  404 , the media continues streaming, and the bandwidth recorder  304  determines that the bandwidth rate is at or exceeding the threshold  404  at a second time  520 . In response to the bandwidth rate exceeding the threshold  404  at time  520 , the example bandwidth recorder  304  creates a second metering dataset  524 . While recording the second metering dataset  524 , the example bandwidth recorder  304  compares the recorded bandwidth rate to the previously generated prediction model curve  502 . The example bandwidth recorder  304  determines that, at time  526 , the second metering dataset  524  has met or exceeded the previous prediction model curve  502 . If the bandwidth rate (e.g., the bandwidth rate of the second metering dataset  524 ) is greater than that of the previous prediction model (e.g., the prediction model curve  502 ) at the time of comparison, a new prediction model is generated. The example bandwidth recorder  304  notifies the example forecaster  314  to disregard the example prediction model curve  502 . The example parameter generator  310  generates new prediction model parameters when the second metering dataset is observed to be at or below the threshold  404  at a second time (e.g., time  527 ). The example modeler  312  generates the second prediction model  528 , and the example forecaster  314  determines a new predicted end of stream time  530 , which is substantially close to the example end of stream time  560  observed at the corresponding user device  101   a - 101   c.  The example bandwidth recorder  304  continues to monitor the bandwidth rates as they rise a third time  570 . However, no action is taken in this instance because the third spike of bandwidth rates  570  does not meet or exceed the threshold  404 . By continuing to monitor bandwidth rates and compare them with generated prediction models to trigger regeneration of the prediction model, a more accurate end of stream time can be predicted. 
     A flowchart representative of example machine readable instructions for implementing the predictor  130  of  FIG.  3    is shown in  FIG.  6   . In this example, the machine readable instructions comprise a program for execution by a processor such as the processor  912  shown in the example processor platform  900  discussed below in connection with  FIG.  6   . The program may be embodied in software stored on a tangible 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  912 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  912  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIG.  6   , many other methods of implementing the example predictor  130  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. 
     As mentioned above, the example processes of  FIGS.  6 ,  7 , and  8    may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) 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 tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. Additionally or alternatively, the example processes of  FIGS.  6 ,  7 , and  8    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 device or disc and to exclude propagating signals. 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” is open ended. 
       FIG.  6    is a flowchart representative of example machine readable instructions that may be executed to implement the example predictor  130 . The example program  600  may be initiated, for example, when the example user device  101   a  begins to stream media from the example streaming media distributor  120  in a streaming media application. 
     Initially, at block  601 , the example identifier  306  identifies the format of streaming media associated with information received from the example proxy server  105  by the example bandwidth recorder  304  and forwards the identified format to the example threshold generator  308 . For example, when the information about streaming media arrives at the example bandwidth recorder  304  of the audience measurement entity  125 , a media format is determined from the information (e.g., audio only formats, video only formats, or container formats containing both audio and video). Additionally or alternatively, the media format may be identified in the information (e.g., the proxy server  105  may determine and report the format). The example threshold generator  308  cross-references the determined media format is cross-referenced to thresholds for known media formats to establish a bandwidth rate threshold (e.g., threshold  404  of  FIGS.  4 ,  5 A, and  5 B ) for distinguishing media from ancillary information embedded in the streaming media (block  602 ). The example threshold generator  308  sets the threshold for media distinction based on the determined media format. This threshold represents a base value for the bandwidth rate, and serves to provide a more accurate prediction time than instances where no threshold is used (e.g., bandwidth below this threshold is assumed to be so insignificant that monitoring should not occur until the threshold is met). For example, without a threshold value, prediction models may be created for bandwidth rates of text overlays embedded in a stream which, in some instances, may lead to inconsistent end of stream predictions. 
     At block  603 , the example bandwidth recorder  304  monitors the bandwidth rate forwarded from the example proxy server  105 . In some examples, the bandwidth rate may fluctuate erratically while streaming media. To observe smoother bandwidth values, the example bandwidth recorder  304  utilizes a monitoring period to obtain a time-averaged bandwidth rate. For example, at the example bandwidth recorder  304 , the bandwidth value is monitored every tenth of a second for a two second period. The value recorded as the bandwidth value by the example bandwidth recorder  304  is an average of the twenty observed bandwidth usage rate values over the two second period. In other examples, the value recorded by the example bandwidth recorder  304  may be an instant bandwidth usage rate value, or may be averaged over any interval. 
     At block  604 , the example bandwidth recorder  304  determines if the media session is currently active between the user device  101   a - 101   c  and the streaming media distributor  120 . For example, the bandwidth recorder  304  of the illustrated example determines if the proxy server  105  reports that a streaming media session is still open. In the event that the example bandwidth recorder  304  determines that the streaming media session is no longer active, the example program  600  terminates. However, in the event that media session is still open, control proceeds to block  605 . 
     At block  605 , the example bandwidth recorder  304  compares the bandwidth rate to the threshold to determine if the prediction model should be generated. If the bandwidth rate is below the threshold, control returns to block  603  to await the bandwidth rate exceeding the threshold. If the example bandwidth recorder  304  determines that the measured bandwidth rate value exceeds the threshold the example bandwidth recorder  304  utilizes the bandwidth rate exceeding the threshold and the time at which the bandwidth rate exceeded the threshold as the initial values recorded in a metering dataset (block  606 ). The example bandwidth recorder  304  records the subsequent bandwidth rates and associated timestamps in the metering dataset and moves to block  618 . 
     At block  618 , the bandwidth rate is below the threshold. When the bandwidth value  400  is determined to be above the threshold, control remains at block  618  while the example bandwidth recorder  304  monitors for a bandwidth value that is at or below the threshold. 
     When the bandwidth value is determined to be below the threshold (block  618 ), the metering dataset is determined to be complete. The example parameter generator  310  determines prediction model parameters from the metering dataset (block  620 ), the prediction model parameters are to be used in generating a prediction model. For example, parameters such as a mean value, a scale (i.e. amplitude), a standard deviation, and a variance may be calculated by the example parameter generator  310 . An example flowchart illustrating example machine readable instructions that may be executed to implement block  620  (e.g., the instructions for implementing the parameter generator  310 ) are depicted in  FIG.  7   . When the prediction model parameters are calculated, the example parameter generator  310  notifies the example modeler  312  that parameters are available for generation of a prediction model  502 . Control proceeds to block  625 . 
     At block  625 , the example modeler  312  generates the prediction model using the prediction model parameters calculated from the metering dataset. The prediction model(s) may be generated by the example modeler  312  using distribution functions. For example, the predictions may be modeled using an exponentially modified Gaussian distribution. 
     
       
         
           
             
               
                 
                   
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     In Equation 1, mu (μ) represents the mean of the metering dataset, sigma squared (σ 2 ) represents the variance of the metering dataset, and lambda (λ) represents a rate of the exponential (e.g., a decay factor). Mu (μ) and sigma squared (σ 2 ) are based on the metering dataset and lambda may be customized or adjusted based on the type of media being streamed. 
     Other suitable distributions may include a chi-squared distribution, an exponential distribution, a gamma distribution, a Laplace distribution, a Pareto distribution, a Weibull distribution, a log-normal distribution, or any other suitable probabilistic distribution capable of being modeled with a right-handed decay. In some other examples, a piecewise function comprised of a plurality of functions, defining behavior over an interval may be used to generate a suitable prediction model. In other words, an example prediction model will have one maximum on the interval, (−∞&lt;t&lt;∞), and will conform to one of the following properties: 
     
       
         
           
             
               
                 
                   
                     
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     If the prediction model conforms to one of the limits set forth above, the prediction model will decay after the maximum and approach zero (or negative infinity) as time approaches infinity. 
     An example flowchart representative of machine readable instructions that may be utilized to implement block  625  is illustrated in  FIG.  8   . 
     When the prediction model is generated, the example forecaster  314  iterates over the prediction model to determine a time at which a value of the prediction model falls below the threshold and returns the time as the predicted end of stream time for the streaming media (block  627 ). Once identified, the example forecaster  314  forwards the time to the example end of stream handler  316  for reporting (block  629 ). The time returned by the example end of stream handler  316  may be a timestamp of the predicted end time and/or an amount of time remaining for the streaming media presentation. Upon the reporting of the predicted end of stream time, control returns to block  602  where the example bandwidth recorder  304  continues monitoring bandwidth rates. 
       FIG.  7    is a flowchart  700  representing example machine readable instructions that may be executed to implement block  620  of  FIG.  6    to calculate prediction model parameters. At block  708 , the example parameter generator  310  determines parameters using the values of the primary dataset to generate a distribution model. For example, the example parameter generator  310  may calculate the mean, the standard deviation, the amplitude, and the variance of the metering dataset. 
     Next, the example parameter generator  310  identifies a decay factor associated with the characteristics of the streaming media (e.g., type, bit-rate, codec, carrier stream, etc.) for use in generating the example prediction model (block  710 ). For the example exponentially modified Gaussian function, this decay factor will be lambda of Equation  1 , which determines the rate of decay. In some examples, the value used for lambda is identified based on the type of media being streamed (e.g., video and/or audio). In other examples, the value of lambda is dependent of the codec of media being streamed. For example, the example parameter generator  310  may cross reference the codec to a table having associated decay values (e.g., a flash video or “.flv” file). In other examples, the value of lambda is pre-configured before use of the example predictor  130 . 
     At block  712 , the generated parameters from block  708  and the identified decay factor from block  710  are stored for use by the example modeler  312  in the generation of the prediction model. 
       FIG.  8    is a flowchart  800  representing example machine readable instructions that may be executed to implement blocks  625  and  627  of  FIG.  6    to predict an end of stream time. Beginning at block  802 , the example modeler  312  obtains the parameters and decay factor from the example parameter generator  310 . At block  804 , the example modeler  312  uses the parameter and decay factor values to generate a prediction model. For example, the mean, the standard deviation, the amplitude, and decay factor calculated from the metering dataset are utilized in conjunction with Equation 1. Additionally, the time associated with each value in the metering dataset is inserted as the values for x in the example Equation 1, for example. Thus, utilizing the function of example Equation 1, the prediction model is generated based on the example exponentially modified Gaussian distribution. 
     At block  808 , the example forecaster  314  of the illustrated example of  FIG.  3    iterates over the prediction model generated in block  804  to determine a time at which a value of the prediction model falls below the threshold. The iterative process may iterate over the prediction model in pre-determined increments, or, in some examples, adjustable increments. For example, the iterations may be for a number of milliseconds. The example forecaster  314  begins the iteration using the identified mean value of the prediction model obtained from the generated parameters from block  708  of  FIG.  7   . As the prediction model begins to decay after the presence of a local peak, beginning the forecasting process at the mean value (e.g., the temporal location of the peak) allows for faster processing by not calculating the prediction values occurring before the peak. 
     Block  810  and block  812  of the illustrated example illustrate an iterative checking performed by the example forecaster  314 . For example, if the value observed at block  810  is not below the threshold, the example forecaster  314  observes the next increment value (block  812 ) and returns to block  810 . When the value observed at block  810  is at or below the threshold, the example forecaster  314  moves to block  814 . 
     At block  814 , the example forecaster  314  obtains the time value associated with the observed value that is at or below the threshold. In some examples, the example forecaster  314  stores this time value as the predicted end of stream time to predict an end of stream time for the streaming media based on the metering dataset. In some example, the forecaster  314  may additionally or alternatively calculate a predicted duration for the streaming media by subtracting the end of stream time from a media start time identified in information received by the example bandwidth recorder  304 . 
       FIG.  9    is a block diagram of an example processor platform  900  capable of executing the instructions of  FIGS.  6 ,  7 , and  8    to implement the predictor  130  of  FIG.  3   . The processor platform  900  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™), an Internet appliance, a digital video recorder, a smart TV, a smart Blu-ray player, a gaming console, a personal video recorder, a set top box, or any other type of computing device capable of streaming media. 
     The processor platform  900  of the illustrated example includes a processor  912 . The processor  912  of the illustrated example is hardware. For example, the processor  912  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. 
     The processor  912  of the illustrated example includes a local memory  913  (e.g., a cache). The processor  912  of the illustrated example is in communication with a main memory including a volatile memory  914  and a non-volatile memory  916  via a bus  918 . The volatile memory  914  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  916  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  914 ,  916  is controlled by a memory controller. 
     The processor platform  900  of the illustrated example also includes an interface circuit  920 . The interface circuit  920  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  922  are connected to the interface circuit  920 . The input device(s)  922  permit a user to enter data and commands into the processor  912 . 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  924  are also connected to the interface circuit  920  of the illustrated example. The output devices  924  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 light emitting diode (LED), and/or speakers). The interface circuit  920  of the illustrated example, thus, typically includes a graphics driver card. 
     The interface circuit  920  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  926  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  900  of the illustrated example also includes one or more mass storage devices  928  for storing software and/or data. Examples of such mass storage devices  928  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  932  of  FIGS.  6 ,  7 , and  8    may be stored in the mass storage device  928 , in the volatile memory  914 , in the non-volatile memory  916 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     From the foregoing, it will be appreciated that the above disclosed examples facilitate predicting an end time of streaming media using a prediction model. Additionally, the disclosed examples provide for the ability to forecast an end of stream time derived from the behavior of the traffic of the streaming media without having access to the streaming media application on the user device  101   a - 101   c.  In this way, it may be beneficial to audience measurement entities and/or data collection facilities to accurately predict the end of streaming media for targeted media delivery, more precise advertisement extraction of advertisement embedded in streaming media, presentation of user surveys, etc. 
     The disclosed examples also facilitate conservation of bandwidth in a monitored household. The disclosed examples may be used to send targeted media and/or surveys at a proper time so as not to interrupt streaming media. In a household with limited bandwidth, by predicting the end of streaming media, an audience measurement entity would not consume excess bandwidth by persistent querying to determine when to send targeted media and/or surveys. 
     The disclosed examples further facilitate conservation of system bandwidth. In examples where the proxy server sends characteristic information about the streaming media in lieu of mirroring the streaming media, required bandwidth is greatly reduced in contrast to mirroring methods. Such mirroring methods require the entirety of the streaming media to be mirrored to the audience measurement entity requiring bandwidth equal to that of the streaming media. Using the disclosed examples, the required bandwidth from the proxy server to the audience measurement entity is greatly reduced. 
     Although certain example methods, apparatus and articles of manufacture have been described 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.