Patent Publication Number: US-11659241-B2

Title: Methods, systems, articles of manufacture, and apparatus for adaptive metering

Description:
FIELD OF THE DISCLOSURE 
     This disclosure relates generally to data collection and, more particularly, to methods, systems, articles of manufacture, and apparatus for adaptive metering. 
     BACKGROUND 
     Media content can be delivered to and presented by a wide variety of content presentation devices such as desktop computers, laptop computers, tablet computers, personal digital assistants, smartphones, etc. Because a significant portion of media content is presented to such devices, monitoring of media content can provide valuable information to advertisers, content providers, and the like. 
     SUMMARY 
     An example apparatus disclosed herein includes a condition analyzer to determine a condition associated with a mobile device, a meter selector to select a meter for the mobile device based on the condition, and a data collector to collect data pertaining to the mobile device based on the selected meter. 
     An example apparatus disclosed herein includes a memory storing instructions and a processor to execute the instructions to determine a condition associated with a mobile device, select a meter for the mobile device based on the condition, and collect data pertaining to the mobile device based on the selected meter. 
     An example non-transitory computer readable medium disclosed herein includes instructions that, when executed, cause at least one processor to determine a condition associated with a mobile device, select a meter for the mobile device based on the condition, and collect data pertaining to the mobile device based on the selected meter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example environment in which examples disclosed herein can be implemented. 
         FIG.  2    is a block diagram of an example adaptive metering controller in accordance with teachings of this disclosure. 
         FIG.  3    is a block diagram of a first example process flow that can be implemented in examples disclosed herein. 
         FIG.  4 A  is a block diagram of a second example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    utilizing an accessibility service. 
         FIG.  4 B  is a block diagram of a third example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    utilizing intent filters. 
         FIG.  5 A  is a block diagram of a fourth example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    utilizing custom firmware. 
         FIG.  5 B  is a block diagram of a fifth example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    utilizing one or more external devices. 
         FIG.  6 A  is a block diagram of a sixth example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    utilizing network traffic. 
         FIG.  6 B  is a block diagram of a seventh example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    utilizing user-generated inputs. 
         FIG.  7    is a block diagram of an eighth example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    to process call and/or SMS data. 
         FIG.  8    is a block diagram of a ninth example process flow that can be implemented by the example adaptive metering controller of  FIG.  2    to process media event data associated with an application of a mobile device. 
         FIG.  9    is a flowchart representative of machine readable instructions which may be executed to implement examples disclosed herein. 
         FIG.  10    is a block diagram of an example processing platform structured to execute the instructions of  FIG.  7    to implement the example adaptive metering controller of  FIG.  2   . 
     
    
    
     The figures are not to scale. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. 
     Unless specifically stated otherwise, descriptors such as “first,” “second,” “third,” etc. are used herein without imputing or otherwise indicating any meaning of priority, physical order, arrangement in a list, and/or ordering in any way, but are merely used as labels and/or arbitrary names to distinguish elements for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for identifying those elements distinctly that might, for example, otherwise share a same name. 
     DETAILED DESCRIPTION 
     Adaptive metering is disclosed. In many cases, media content presented on a mobile device can be monitored using one or more meters implemented on the mobile device. In some cases, data collected using each of the meters may be different, and some or all of the data collected for one of the meters may overlap with data collected for different ones of the meters. Furthermore, ones of the meters can become unavailable, or can provide incomplete and/or inaccurate data. As such, the meters used for monitoring media content can be selected to ensure that a desired accuracy and/or type of data is collected. 
     In examples disclosed herein, an example adaptive metering controller is configured to select between one or more meters for collecting data associated with a mobile device. In some examples, each of the meters collects the data based on a different number and/or type of inputs. In examples disclosed herein, the adaptive metering controller determines a condition associated with the mobile device, where the condition includes at least one of a desired accuracy of the data and/or one or more panels to which a user of the mobile device belongs. In some examples, based on the condition, the adaptive metering controller selects a meter and/or a combination of meters, and collects input data from the mobile device using the selected meter or combination of meters. In some examples, the input data is associated with media presented via an application on the mobile device. In some examples, the adaptive metering controller can obtain output data directly from the input data. The output data may include information associated with the presented media such as a title, episode, start time, end time, audio language, etc. In other examples, the adaptive metering controller selects and/or generates a mapping to map the input data to the output data. 
     Advantageously, examples disclosed herein enable the adaptive metering controller to adaptively configure the number and/or type of meters used for collecting data. For example, the adaptive metering controller can collect data using a first meter, then switch to a second meter different from the first meter in response to the first meter becoming unavailable and/or providing inaccurate data. As such, examples disclosed herein enable continuous (e.g., without interruption) monitoring of media content on the mobile device while ensuring that the collected data satisfies a threshold accuracy. 
       FIG.  1    illustrates an example environment  100  in which examples disclosed herein can be implemented. The example environment  100  supports monitoring of media presented at one or more monitored sites, such as an example monitored site  102  illustrated in  FIG.  1   , and includes example media devices (e.g., a media presentation devices)  104 . Although the example of  FIG.  1    illustrates one of the monitored site  102  and five of the media devices  104 , examples disclosed herein can be implemented in environments  100  supporting any number of monitored sites  102  having any number of the media devices  104 . Further, examples disclosed herein can be implemented in any appropriate network configuration and/or topology. 
     The environment  100  of the illustrated example includes an example adaptive metering controller  106  to monitor media presented by the media devices  104 . In the illustrated example, the media monitored by the adaptive metering controller  106  can correspond to any type of media presentable by the media devices  104 . For example, monitored media can correspond to media content, such as television programs, radio programs, movies, Internet video, video-on-demand, etc., as well as commercials, advertisements, etc. In this example, the adaptive metering controller  106  determines metering data that may identify and/or be used to identify media presented by the media devices  104  (and, thus, infer media exposure) at the monitored site  102 . The adaptive metering controller  106  then stores and reports this metering data via an example network  108  to an example data processing facility  110 . 
     In this example, the data processing facility  110  performs any appropriate post-processing of the metering data to, for example, determine audience ratings information, identify targeted advertising to be provided to the monitored site  102 , etc. In this example, the data processing facilities includes example servers  112  and an example central database  114 . In some examples, the post-processing of the metering data is performed on one or more of the servers  112 . In some examples, the central database  114  can store the metering data from the adaptive metering controller  106  and/or processed metering data from the servers  112 . In the illustrated example, the network  108  can correspond to any type(s) and/or number of wired and/or wireless data networks, or any combination thereof. 
     In the illustrated example, each of the media devices  104  monitored by the adaptive metering controller  106  can correspond to any type of audio, video and/or multimedia presentation device capable of presenting media audibly and/or visually. For example, each of the media devices  104  can correspond to a multimedia computer system, a personal digital assistant, a cellular/mobile smartphone, a radio, a tablet computer, etc. 
     In examples disclosed herein, the adaptive metering controller  106  can be implemented by or otherwise included in each of the media devices  104 . This example implementation can be especially useful in scenarios in which a media monitoring application is executed on the media devices  104 , but the media devices  104  prevents (e.g., via digital rights management or other techniques) third-party applications, such as the media monitoring application, from accessing protected media data stored on the media device  104 . 
       FIG.  2    is a block diagram of the example adaptive metering controller  106  of  FIG.  1   . In examples disclosed herein, the adaptive metering controller  106  can be implemented in the media device  104  of  FIG.  1   , and the adaptive metering controller  106  is configured to select between one or more meters for collecting metering data associated with the media device  104 . In the illustrated example of  FIG.  2   , the adaptive metering controller  106  includes an example condition analyzer  204 , an example meter selector  206 , an example data collector  208 , an example mapping controller  210 , an example data processor  212 , and an example meter adjuster  214 . In this example, the adaptive metering controller  106  is communicatively coupled to the example network  108  and/or the media device  104  of  FIG.  1    via an example data interface  216 . Furthermore, the adaptive metering controller  106  is communicatively coupled to an example database  218 . In some examples, the database  218  is implemented on one or more of the media devices  104 . 
     In this example, the condition analyzer  204  determines a condition associated with the media device  104 . In some examples, the condition includes a desired accuracy of the metering data collected from the media device  104 . In some such examples, accuracy of the metering data is based on a number and/or types of meters available for the media device  104 . Additionally or alternatively, the condition corresponds to one or more panels to which a user of the media device  104  belongs. In such examples, the condition analyzer  204  identifies the one or more panels, and further identifies a type of output data required by each of the one or more panels. 
     The meter selector  206  of the illustrated example selects one or more meters for the media device  104  based on the condition. For example, when the condition corresponds to a desired accuracy level of the metering data, the meter selector  206  selects the type and/or number of the meters that satisfies a threshold, where the threshold is based on the desired accuracy level. In other examples, the meter selector  206  selects the one or more meters based on the type of output data required by each of the one or more panels associated with the media device  104 . In some examples, the meter selector  206  selects the one or meters based on a lack of data (e.g., incomplete data, inconsistent data, etc.). Additionally or alternatively, the meter selector  206  selects the one or more meters based on a triggering event (e.g., an opening of an application, initiating content streaming, selecting a menu option, etc.). 
     The example data collector  208  collects the metering data pertaining to the media device  104  based on the one or more selected meters. For example, the data collector  208  can collect the metering data using at least one of an accessibility service, intent filters, firmware, one or more external devices associated with the mobile device, user-generated inputs, or network traffic. In some examples, the data collector  208  collects the metering data from the media device  104  via the data interface  216 . 
     The mapping controller  210  of the illustrated example generates a mapping to map the metering data to one or more outputs (e.g., events). For example, the mapping controller  210  generates the mapping based on the metering data collected by the data collector  208  and the type of output data required. In some examples, the mapping includes at least one of a machine learning model or a neural network model. In some examples, the mapping controller  210  selects the mapping from one or more generated mappings previously generated by the mapping controller  210 . In some such examples, the generated mappings can be stored in the database  218 . In some examples, the mapping controller  210  maps the metering data to the one or more outputs, and/or the mapping controller  210  provides the selected and/or generated mapping to the data processor  212  to perform the mapping. 
     The example data processor  212  organizes and/or processes the metering data. For example, the data processor  212  can use the mapping provided by the mapping controller  210  to generate the output data and/or events based on the metering data. In other examples, based on the type of metering data being collected, the data processor  212  can directly obtain the output data from the metering data (e.g., without the mapping). In some examples, the data processor  212  can provide the output data and/or the metering data to the database  218  and/or to the central database  114  of  FIG.  1    via the data interface  216 . 
     The example meter adjuster  214  adjusts and/or modifies the one or more meters associated with the media device  104 . For example, in response to the metering data not satisfying the condition (e.g., not satisfying a threshold accuracy), the meter adjuster  214  can select a new meter for use by the data collector  208 . In some examples, the meter adjuster  214  can remove and/or inactivate one or more of the meters in response to the meters becoming unavailable and/or providing inaccurate data. In other examples, the meter adjuster  214  can add one or more new meters in response to new input data from the media device  104  becoming available. In some such examples, the meter adjuster  214  can add the one or more new meters in response to a new external device (e.g., camera, microphone, etc.) being connected to the media device  104 . In some other examples, the meter adjuster  214  can adjust and/or vary settings of selected meters (e.g., to increase an accuracy thereof). 
     In this example, the database  218  stores metering data and/or processed metering data utilized and/or generated by the adaptive metering controller  106 . The example database  218  of  FIG.  2    is implemented by any memory, storage device and/or storage disc for storing data such as, for example, flash memory, magnetic media, optical media, solid state memory, hard drive(s), thumb drive(s), etc. Furthermore, the data stored in the example database  418  may be in any data format such as, for example, binary data, comma delimited data, tab delimited data, structured query language (SQL) structures, etc. While, in the illustrated example, the example database  218  is illustrated as a single device, the example database  218  and/or any other data storage devices described herein may be implemented by any number and/or type(s) of memories. 
       FIG.  3    is a block diagram of a first example process flow  300  that can be implemented in examples disclosed herein. The first process flow  300  can be executed by the adaptive metering controller  106  of  FIGS.  1  and/or  2    to collect data from one or more example inputs  302  associated with the media device  104  of  FIG.  1   , and map the inputs  302  to example outputs (e.g., events)  304  using an example mapping  306 . In this example, the outputs  304  are associated with media presented via an application on the media device  104 . 
     The example inputs  302  can include at least one of image data, audio data, video data, data from an accessibility service, data from intent filters, data from memory and/or storage of the media device  104 , data from user-generated inputs, data from custom firmware, data from one or more external devices, or network data. In some examples, the inputs  302  are accessible to the adaptive metering controller  106  and based on the number and/or type of meters selected by the adaptive metering controller  106 . 
     In the illustrated example, the adaptive metering controller  106  selects the mapping  306  based on whether data is available from a third-party application on which the media is being presented. In this example, the adaptive metering controller  106  selects an example first mapping  306 A when data is available from the third-party application, and selects an example second mapping  306 B when data is not available from the third-party application. In this example, in response to the first mapping  306 A being selected, the adaptive metering controller  106  can use one or more of the inputs  302  that are available using the one or more selected meters. In such examples, the adaptive metering controller  106  maps the available inputs  302  to desired ones of the outputs  304  using the first mapping  306 A. Alternatively, in response to the second mapping  306 B being selected, the adaptive metering controller  106  uses a subset of the inputs  302 , where the subset corresponds to data that can be collected without the third-party application. In some examples, the subset of the inputs  302  includes data from the external devices and/or the network traffic. In such examples, the adaptive metering controller  106  maps the subset of the inputs  302  to the desired ones of the outputs  304  using the second mapping  306 B. 
     According to the illustrated example, the outputs  304  include example metadata  304 A and example state information  304 B associated with the media presented via the application. In some examples, the metadata  304 A can include information pertaining to a user of the media device  104 . In some examples, the media corresponds to a television show, such that the metadata  304 A corresponds to a show, season, episode, start time, end time, media type, audio language, codec, and/or player associated with the presented media. In this example, the state information  304 B is determined based on the user interacting with the media. For example, the user can play, pause, seek, stop, resume, and/or end the media based on user-generated inputs, where the user-generated inputs can include pressing a button, giving an audio command, physical contact with a portion of the media device  104 , etc. In some such examples, the state information  304 B can include timestamps corresponding to each of the user-generated inputs. 
       FIG.  4 A  is a block diagram of a second example process flow  400  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    utilizing an accessibility service. In this example, the meter selector  206  of  FIG.  2    selects a first meter for collect data pertaining to the media device  104  of  FIG.  1   , where the first meter utilizes the accessibility service of the media device  104 . 
     In examples disclosed herein, the accessibility service is installed on the media device  104  to assist users with disabilities in using the media device  104 . In some examples, a user of the media device  104  can configure the accessibility service to enable the adaptive metering controller  106  to access to data from an application (e.g., third party application) of the media device  104 . For example, the accessibility service can be configured to enable the adaptive metering controller  106  to access example accessibility data  402  using the first meter. 
     In this example, the data collector  208  of  FIG.  2    collects the accessibility data  402  based on the first meter. The accessibility data  402  is based on accessibility events monitored by the accessibility service, where each of the accessibility events indicates a state transition in a user interface of the application. For example, an accessibility event may correspond to the user clicking a button in the application. In this example, the accessibility data  402  can include at least one of the application, an event type, event text, an event class, a view ID, source text, content description, device time, or an event time corresponding to each of the accessibility events. 
     In the illustrated example, the mapping controller  210  of  FIG.  2    can select an example first mapping  404  to map the accessibility data  402  to the example outputs  304 . For example, the mapping controller  210  can generate the metadata  304 A and the state information  304 B based on the accessibility data  402  and the first mapping  404 . In some examples, the metadata  304 A and the state information  304 B can be stored in the database  218  of  FIG.  2   , or sent to the data processing facility  110  of  FIG.  1    via the network  108  of  FIG.  1   . 
       FIG.  4 B  is a block diagram of a third example process flow  406  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    utilizing intent filters. In this example, the meter selector  206  of  FIG.  2    selects a second meter for collecting data pertaining to the media device  104  of  FIG.  1   , where the second meter utilizes the intent filters of the media device  104 . 
     In examples disclosed herein, intents can be utilized in the media device  104  to start an activity in an application, where the activity can include presenting media in the application. In some examples, the intents include data associated with the presented media, and intent filters can be configured to specify a type of intent that can be received by the application. In this example, the second meter is configured to access example intent data  408  from the intents. In this example, the intent data  408  includes at least one of a uniform resource indicator (URI) passed with the intent, user actions within the application, or media data including a title of the media and/or captured video frames from the media. 
     In the illustrated example, the data collector  208  of  FIG.  2    collects the intent data  408  based on the second meter. In this example, the mapping controller  210  of  FIG.  2    can select an example second mapping  410  to map the intent data  408  to the example outputs  304 . For example, the mapping controller  210  can use the second mapping  410  to generate the metadata  304 A based on the URI and the media data passed with the intent. In some examples, the mapping controller  210  can determine the state information  304 B based on the user actions passed with the intent. In some examples, the metadata  304 A and the state information  304 B can be stored in the database  218  of  FIG.  2   , or sent to the data processing facility  110  of  FIG.  1    via the network  108  of  FIG.  1   . 
       FIG.  5 A  is a block diagram of a fourth example process flow  500  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    utilizing custom firmware. For example, a user of the media device  104  of  FIG.  1    can install the custom firmware onto the media device  104  to enable data collection by the adaptive metering controller  106 . In some examples, the custom firmware enables data to be collected from third-party applications. In this example, the meter selector  206  of  FIG.  2    selects a third meter for collecting data pertaining to the media device  104 , where the third meter utilizes the custom firmware of the media device  104 . 
     In this example, the data collector  208  of  FIG.  2    collects example custom firmware data  502  based on the third meter. In some examples, the custom firmware data  502  includes at least one of data from memory and/or storage of the media device  104 , audio data, video data, image data, and/or network traffic data. In some such examples, the custom firmware data  502  is associated with media presented by the third-party applications installed on the media device  104 . In this example, the mapping controller  210  of  FIG.  2    selects an example third mapping  504  to map the custom firmware data  502  to the example outputs  304 . For example, the mapping controller  210  can use the third mapping  504  to generate the metadata  304 A and the state information  304 B based on the custom firmware data  502 . In some examples, the data processor  212  can process the custom firmware data  502  prior to mapping, where the processing of the custom firmware data  502  reduces an amount of the data input to the third mapping  504  and, thus, reduces computational load on the adaptive metering controller  106 . 
       FIG.  5 B  is a block diagram of a fifth example process flow  506  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    utilizing external devices. In this example, the external devices include a camera and/or a microphone of the media device  104  of  FIG.  1   . In this example, the meter selector  206  of  FIG.  2    selects a fourth meter for collecting data pertaining to the media device  104 , where the fourth meter utilizes the external devices of the media device  104 . 
     In this example, the data collector  208  of  FIG.  2    collects example external device data  508  based on the fourth meter. In some examples, the external device data  508  includes at least one audio data captured via the microphone, video data captured via the camera, image data captured via the camera, and/or network traffic data. In some such examples, the external device data  508  is associated with media presented by an application installed on the media device  104 . In this example, the mapping controller  210  of  FIG.  2    can select an example fourth mapping  510  to map the external device data  508  to the example outputs  304 . For example, the mapping controller  210  can use the fourth mapping  510  to generate the metadata  304 A and the state information  304 B based on the external device data  508 . In some examples, the data processor  212  can process the external device data  508  prior to mapping, where the processing of the external device data  508  reduces an amount of the data being input to the fourth mapping  510  to reduce computational load on the adaptive metering controller  106 . 
       FIG.  6 A  is a block diagram of a sixth example process flow  600  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    utilizing network traffic. In this example, example network traffic data  602  is associated with Hypertext Transfer Protocol (HTTP) traffic on a virtual private network (VPN). In this example, the meter selector  206  of FIG.  2  selects a fifth meter for collecting data pertaining to the media device  104 , where the fifth meter utilizes the network traffic across the VPN. 
     In this example, the data collector  208  of  FIG.  2    collects the network traffic data  602  based on the fifth meter. In some examples, a subset of the network traffic data  602  is associated with media presented by one or more third-party applications installed on the media device  104 . In some examples, the data collector  208  uses artificial intelligence (AI) and/or machine learning (ML) techniques to develop a trained model to select the subset of the network traffic data  602  that includes information relevant to the outputs  304 . In some such examples, the mapping controller  210  of  FIG.  2    can select an example fifth mapping  604  to map the network traffic data  602  and/or the subset of the network traffic data  602  to the example outputs  304 . For example, the mapping controller  210  can use the fifth mapping  604  to generate the metadata  304 A and the state information  304 B based on the network traffic data  602 . In some examples, the mapping controller  210  generates and/or trains a neural network/ML model to be implemented to generate the fifth mapping  604 . In particular, the neural network can be trained based on data parameters (e.g., accuracy metrics, etc.) associated with the inputs  602  and/or the outputs  304 . In other examples, the fifth mapping  604  is generated manually or using string comparisons. 
       FIG.  6 B  is a block diagram of a seventh example process flow  606  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    utilizing user-generated inputs. In this example, the meter selector  206  of  FIG.  2    selects a sixth meter for collecting data pertaining to the media device  104 , where the sixth meter utilizes the user-generated inputs. 
     In this example, a dialog and/or request for input may be presented to a user of the media device  104  when media is played on the media device  104 . In some examples, the user may be prompted by the dialog to input information pertaining to the media being played. In some examples, the dialog is generated based on the accessibility service of the media device  104  or based on network traffic. In this example, example user-generated data  608  includes the information input by the user in the dialog. As such, the mapping controller  210  of  FIG.  2    can select an example sixth mapping  610  to map the user-generated data  608  to the example outputs  304 . In some examples, the information input by the user can include one or more of the outputs  304 . In such examples, the data collector  208  of  FIG.  2    can directly (e.g., without the sixth mapping  610 ) obtain the outputs  304  using the fifth meter. 
       FIG.  7    is a block diagram of an eighth example process flow  700  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    to process call and/or SMS (short message service) data. In this example, a mobile device (e.g., the media device  104  of  FIG.  1   ) can at least receive an incoming call, receive an incoming SMS message, send an outgoing call, and/or send an outgoing SMS message. In one example, in response to an example incoming or outgoing call or SMS message  702 , the mobile device provides call logs to the adaptive metering controller  106 . In some examples, the meter selector  206  of  FIG.  2    selects an example seventh meter, and the data collector  208  of  FIG.  2    can obtain the call logs using the seventh meter. The call logs may include a call or SMS event based on the incoming or outgoing call or SMS message  702 . In such examples, at the call log provision  704 , the data processor  212  of  FIG.  2    can determine the call or SMS events based on the call logs. In this example, at the sending of the call or SMS events  706 , the data processor  212  sends the call or SMS events to one or more of the servers  112  of the data processing facility  110  of  FIG.  1   . For example, the data processor  212  can send the call or SMS events to the one or more of the servers  112  via the network  108  of  FIG.  1   . 
     Alternatively, in response to the incoming or outgoing call or SMS message  702 , the mobile device provides call and SMS notifications to the adaptive metering controller  106 . In some examples, the meter selector  206  selects an example eighth meter, and the data collector  208  can obtain the call and SMS notifications using the eighth meter. In such examples, at the call or SMS event generation  708 , the adaptive metering controller  106  generates the call or SMS events based on the call and SMS notifications and/or based on data from an application class (e.g., AudioManager, MediaSession, etc.) of the mobile device. For example, the mapping controller  210  of  FIG.  2    can select an example seventh mapping to map the call and SMS notifications to the call or SMS events. In some examples, in response to the sending of the call or SMS events  706 , the data processor  212  provides, transmits and/or sends the generated call or SMS events to one or more of the servers  112 . In the illustrated example, the call or SMS events based on the call logs are the same as or similar to the call or SMS events generated based on the call and SMS notifications. As such, in some examples, at the processing of the call and SMS events  710 , the servers  112  similarly process the call or SMS events based on the call logs and notifications pertaining to the call and SMS. 
       FIG.  8    is a block diagram of a ninth example process flow  800  that can be implemented by the example adaptive metering controller  106  of  FIG.  2    to process media event data associated with an application  802  of the mobile device (e.g., the media device  104  of  FIG.  1   ). In this example, the application  802  is configured to present media to a user of the mobile device. In one example, in response to the application  802  presenting the media, metadata (e.g., MediaSession metadata) associated with the media is available to the adaptive metering controller  106 . In such an example, the meter selector  206  of  FIG.  2    selects an example ninth meter, and the data collector  208  of  FIG.  2    can obtain the metadata using the ninth meter. The metadata may include information associated with the presented media, such as a media title, show episode, content length, etc. In such an example, at the obtaining of the metadata  804 , the data processor  212  of  FIG.  2    can determine the media event data based on the metadata, where the media event data can include the information associated with the presented media (e.g., the media title, the show episode, the content length, etc.). In this example, at the sending of the media events  806 , the data processor  212  sends the media events to one or more of the servers  112  of the data processing facility  110  of  FIG.  1   . For example, the data processor  212  can send the media events to the one or more of the servers  112  via the network  108  of  FIG.  1   . 
     In another example, in response to the application  802  presenting the media, a portion of the metadata is available to the adaptive metering controller  106 . In such an example, the meter selector  206  selects a combination of meters including the first meter of  FIG.  4 A  and the ninth meter, for example. Accordingly, the data collector  208  of  FIG.  2    can obtain the portion of the metadata using the ninth meter, and the data collector  208  can further obtain the accessibility data  402  of  FIG.  4 A  using the first meter. In this example, at the obtaining of the accessibility data and metadata  808 , the mapping controller  210  of  FIG.  2    can select an example eighth mapping to map both the accessibility data  402  and the metadata to the media event data. The data processor  212  sends the media events to the one or more of the servers  112  at the sending of the media events  806 . 
     In yet another example, in response to the application  802  presenting the media, the metadata can be unavailable to the adaptive metering controller  106 . In such an example, the meter selector  206  selects the first meter, and the data collector  208  obtains the accessibility data  402  using the first meter. In this particular example, in response to obtaining the accessibility data  810 , the mapping controller  210  selects the example first mapping  404  of  FIG.  4    to map the accessibility data  402  to the media event data. In this example, the data processor  212  sends the media events to the one or more of the servers  112  at the sending of the media events  806 . In the illustrated example, at or in response to the processing of the media events  712 , the servers  112  similarly process the media events based on the metadata, the accessibility data  402 , and/or a combination of the metadata and the accessibility data  402 , etc. 
     While an example manner of implementing the adaptive metering controller  106  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 condition analyzer  204 , the example meter selector  206 , the example data collector  208 , the example mapping controller  210 , the example data processor  212 , the example meter adjuster  214 , and/or, more generally, the example adaptive metering controller  106  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 condition analyzer  204 , the example meter selector  206 , the example data collector  208 , the example mapping controller  210 , the example data processor  212 , the example meter adjuster  214  and/or, more generally, the example adaptive metering controller  106  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(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 condition analyzer  204 , the example meter selector  206 , the example data collector  208 , the example mapping controller  210 , the example data processor  212 , and/or the example meter adjuster  214  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 adaptive metering controller  106  of  FIG.  2    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  4   , and/or may include more than one of any or all of the illustrated elements, processes and devices. As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events. 
     A flowchart representative of example hardware logic, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the adaptive metering controller  106  of  FIG.  2    is shown in  FIG.  9   . The machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by a computer processor and/or processor circuitry, such as the processor  1012  shown in the example processor platform  1000  discussed below in connection with  FIG.  10   . 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 DVD, a Blu-ray disk, or a memory associated with the processor  1012 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  1012  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIG.  9   , many other methods of implementing the example adaptive metering controller  106  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, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. The processor circuitry may be distributed in different network locations and/or local to one or more devices (e.g., a multi-core processor in a single machine, multiple processors distributed across a server rack, etc). 
     The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data or a data structure (e.g., portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc. in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and stored on separate computing devices, wherein the parts when decrypted, decompressed, and combined form a set of executable instructions that implement one or more functions that may together form a program such as that described herein. 
     In another example, the machine readable instructions may be stored in a state in which they may be read by processor circuitry, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc. in order to execute the instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, machine readable media, as used herein, may include machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit. 
     The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc. 
     As mentioned above, the example process of  FIG.  9    may be implemented using executable 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 employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc. may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, and (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. 
     As used herein, singular references (e.g., “a”, “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” entity, as used herein, refers to one or more of that entity. The terms “a” (or “an”), “one or more”, and “at least one” can be used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements or method actions may be implemented by, e.g., a single unit or processor. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous. 
       FIG.  9    is a flowchart representative of machine readable instructions  900  which may be executed to implement examples disclosed herein. For example, the instructions  900  can be executed by the example adaptive metering controller  106  of  FIG.  2    to collect data pertaining to a mobile device (e.g., the media device  104  of  FIG.  1   ) using one or more meters. The instructions  900  begin as the adaptive metering controller  106  interfaces with the mobile device via the example data interface  216  of  FIG.  2   . 
     At block  902 , the example adaptive metering controller  106  determines a condition associated with the mobile device. For example, the condition analyzer  204  of  FIG.  2    determines the condition including at least one of a desired accuracy of the data to be monitored, a panel associated with the mobile device, or an availability of the one or more meters. 
     At block  904 , the example adaptive meter controller  106  selects a meter from the one or more meters. For example, the example meter selector  206  of  FIG.  2    selects the meter for the mobile device based on the condition. In some examples, the example meter selector  206  selects a combination of meters from the one or more meters based on the condition. 
     At block  906 , the example adaptive meter controller  106  determines whether to generate a mapping. For example, in response to the example mapping controller  210  of  FIG.  2    determining that the mapping is to be generated (e.g., block  906  returns a result of YES), the process proceeds to block  908 . Alternatively, in response to the mapping controller  210  determining that the mapping is not to be generated (e.g., block  906  returns a result of NO), the process proceeds to block  910 . In some examples, the mapping controller  210  determines that the mapping is to be generated in response to the meter selector  206  selecting the fifth meter, where the fifth meter corresponds to the network traffic data  602  of  FIG.  6 A . 
     At block  908 , the example adaptive meter controller  106  generates the mapping. For example, the example mapping controller  210  generates the mapping based on a neural network model. In some examples, the mapping controller  210  stores the generated mapping in the example database  218  of  FIG.  2   . 
     At block  910 , the example adaptive metering controller  106  selects the mapping from one or more generated mappings. For example, the mapping controller  210  selects the mapping based on the selected meter. In some examples, the mapping controller  210  selects the mapping from the example mappings  404 ,  410 ,  504 ,  510 ,  604 ,  610  described above in connection with  FIGS.  4 A,  4 B,  5 A,  5 B,  6 A , and/or  6 B. In some examples, the mapping controller  210  selects a combination of mappings from the mappings  404 ,  410 ,  504 ,  510 ,  604 ,  610 , where the combination of mappings corresponds to the combination of meters selected by the meter selector  206 . 
     At block  912 , the example adaptive metering controller  106  collects data pertaining to the mobile device. For example, the example data collector  208  of  FIG.  2    collects the data based on the selected meter. In some examples, the collected data is associated with media presented at the mobile device. In some examples, the data collector  208  stores the collected data in the database  218 . 
     At block  914 , the example adaptive metering controller  106  determines whether the collected data satisfies the condition. For example, the example meter adjuster  214  of  FIG.  2    determines whether the collected data is at the desired accuracy. In response to the example meter adjuster  214  determining that the collected data satisfies the condition (e.g., block  914  returns a result of YES), the process proceeds to block  916 . Alternatively, in response to the example meter adjuster  214  determining that the collected data does not satisfy the condition (e.g., block  914  returns a result of NO), the process returns to block  902 . 
     At block  916 , the example adaptive metering controller  106  organizes and/or processes the collected data. For example, the data processor  212  obtains the example outputs  304  of  FIG.  3    from the collected data, and sends the collected data and/or the example outputs  304  to the data processing facility  110  of  FIG.  1    for further processing. In other examples, the mapping controller  210  uses the selected and/or generated mapping to generate the outputs  304  based on the collected data. 
     At block  918 , the example adaptive metering controller  106  determines whether to repeat the process. For example, the condition analyzer  204  determines whether more data pertaining to the mobile device is to be collected. In response to the condition analyzer  204  determining that the process is to be repeated (e.g., block  918  returns a result of YES), the process returns to block  902 . Alternatively, in response to the condition analyzer  204  determining that the process is to be repeated (e.g., block  918  returns a result of NO), the process ends. 
       FIG.  10    is a block diagram of an example processor platform  1000  structured to execute the instructions of  FIG.  9    to implement the adaptive metering controller  106  of  FIG.  2   . The processor platform  1000  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), 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, a DVD player, a CD player, a digital video recorder, a Blu-ray player, a gaming console, a personal video recorder, a set top box, a headset or other wearable device, or any other type of computing device. 
     The processor platform  1000  of the illustrated example includes a processor  1012 . The processor  1012  of the illustrated example is hardware. For example, the processor  1012  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, 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 condition analyzer  204 , the example meter selector  206 , the example data collector  208 , the example mapping controller  210 , the example data processor  212 , and the example meter adjuster  214 . 
     The processor  1012  of the illustrated example includes a local memory  1013  (e.g., a cache). The processor  1012  of the illustrated example is in communication with a main memory including a volatile memory  1014  and a non-volatile memory  1016  via a bus  1018 . The volatile memory  1014  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  1016  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1014 ,  1016  is controlled by a memory controller. 
     The processor platform  1000  of the illustrated example also includes an interface circuit  1020 . The interface circuit  1020  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), a Bluetooth® interface, a near field communication (NFC) interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  1022  are connected to the interface circuit  1020 . The input device(s)  1022  permit(s) a user to enter data and/or commands into the processor  1012 . 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  1024  are also connected to the interface circuit  1020  of the illustrated example. The output devices  1024  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 (LCD), a cathode ray tube display (CRT), an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer and/or speaker. The interface circuit  1020  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  1020  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  1026 . The communication can be via, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  1000  of the illustrated example also includes one or more mass storage devices  1028  for storing software and/or data. Examples of such mass storage devices  1028  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, redundant array of independent disks (RAID) systems, and digital versatile disk (DVD) drives. 
     The machine executable instructions  1032  of  FIG.  9    may be stored in the mass storage device  1028 , in the volatile memory  1014 , in the non-volatile memory  1016 , and/or on a removable non-transitory 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 provide adaptive metering of data pertaining to a mobile device. Examples disclosed herein enable an adaptive metering controller to adaptively configure the number and/or type of meters used for collecting data. The disclosed methods, apparatus and articles of manufacture improve the efficiency of using a computing device by enabling continuous (e.g., without interruption) monitoring of media content on the mobile device while ensuring that the collected data satisfies a desired accuracy. The disclosed methods, apparatus and articles of manufacture are accordingly directed to one or more improvement(s) in the functioning of a computer. 
     Example 1 includes an apparatus including a condition analyzer to determine a condition associated with a mobile device, a meter selector to select a meter for the mobile device based on the condition, and a data collector to collect data pertaining to the mobile device based on the selected meter. 
     Example 2 includes the apparatus of Example 1, and further includes a mapping controller to map the data to one or more outputs, the one or more outputs associated with media presented on the mobile device. 
     Example 3 includes the apparatus of Example 2, where the mapping controller is to map the data to the one or more outputs via at least one of a machine learning model or a neural network model. 
     Example 4 includes the apparatus of Example 2, where the one or more outputs identify at least one of a user, a show, a season, an episode, a start time, an end time, a media type, an audio language, or a player associated with the media. 
     Example 5 includes the apparatus of Example 1, where the data is collected using at least one of an accessibility service, intent filters, firmware, an external device associated with the mobile device, user-generated inputs, or network traffic. 
     Example 6 includes the apparatus of Example 5, where the external device includes at least one of a video or a microphone, the data to include at least one of audio data, video data, or image data. 
     Example 7 includes the apparatus of Example 1, where the condition includes at least one of a desired accuracy of the data or a panel associated with the mobile device. 
     Example 8 includes the apparatus of Example 1, where the data is associated with at least one of an incoming call to the mobile device, an outgoing call from the mobile device, an incoming SMS message to the mobile device, or an outgoing SMS message from the mobile device. 
     Example 9 includes the apparatus of Example 1, where the data is associated with media presented by an application of the mobile device. 
     Example 10 includes an apparatus including a memory storing instructions and a processor to execute the instructions to determine a condition associated with a mobile device, select a meter for the mobile device based on the condition, and collect data pertaining to the mobile device based on the selected meter. 
     Example 11 includes the apparatus of Example 10, where the processor is to map the data to one or more outputs, the one or more outputs associated with media presented on the mobile device. 
     Example 12 includes the apparatus of Example 11, where the processor is to map the data to the one or more outputs by mapping the data via at least one of a machine learning model or a neural network model. 
     Example 13 includes the apparatus of Example 11, where the one or more outputs identify at least one of a user, a show, a season, an episode, a start time, an end time, a media type, an audio language, or a player associated with the media. 
     Example 14 includes the apparatus of Example 10, where the processor is to collect the data by collecting the data using at least one of an accessibility service, intent filters, firmware, an external device associated with the mobile device, user-generated inputs, or network traffic. 
     Example 15 includes the apparatus of Example 14, where the external device includes at least one of a video or a microphone, the data to include at least one of audio data, video data, or image data. 
     Example 16 includes the apparatus of Example 10, where the processor is to determine the condition by determining at least one of a desired accuracy of the data or a panel associated with the mobile device. 
     Example 17 includes a non-transitory computer readable medium including instructions that, when executed, cause at least one processor to determine a condition associated with a mobile device, select a meter for the mobile device based on the condition, and collect data pertaining to the mobile device based on the selected meter. 
     Example 18 includes the non-transitory computer readable medium of Example 17, where the instructions, when executed, cause the at least one processor to map the data to one or more outputs, the one or more outputs associated with media presented on the mobile device. 
     Example 19 includes the non-transitory computer readable medium of Example 18, where the instructions, when executed, cause the at least one processor to map the data to the one or more outputs via at least one of a machine learning model or a neural network model. 
     Example 20 includes the non-transitory computer readable medium of Example 18, where the instructions, when executed, cause the at least one processor to identify at least one of a user, a show, a season, an episode, a start time, an end time, a media type, an audio language, or a player associated with the media. 
     Example 21 includes the non-transitory computer readable medium of Example 17, where the instructions, when executed, cause the at least one processor to collect the data using at least one of an accessibility service, intent filters, firmware, an external device associated with the mobile device, user-generated inputs, or network traffic. 
     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. 
     The following claims are hereby incorporated into this Detailed Description by this reference, with each claim standing on its own as a separate embodiment of the present disclosure.