HK1228053B - Methods and apparatus to detect engagement with media presented on wearable media devices - Google Patents

Description

Method and apparatus for detecting engagement with media presented on a wearable media device

Related applications

This patent claims the benefit of U.S. Provisional Patent Application No. 61/923,859, filed January 6, 2014, which is hereby incorporated by reference in its entirety.

Technical Field

The present disclosure relates generally to audience measurement and, more particularly, to methods and devices for detecting engagement with media presented on a wearable media device.

Background Art

Media monitoring companies desire information about user interactions with, for example, media devices. To this end, media monitoring companies obtain monitoring information related to media presented at the media devices, allowing media monitoring entities to gain insights into, for example, exposure to advertisements, exposure to content (e.g., shows, web pages, etc.), user purchasing activity related to exposure to media, demographic information of viewers exposed to the media, and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 illustrates an example environment including an example wearable media device having a meter constructed according to the teachings of the present disclosure.

FIG. 2 is a block diagram of an example implementation of the example wearable media device of FIG. 1 .

3 is a screenshot representing a first image as viewed via the example wearable media device of FIG. 1 and/or FIG. 2 with media presented at a first opacity.

4 is a screenshot showing a second image as viewed via the example wearable media device of FIG. 1 and/or FIG. 2 with media presented at a second opacity.

5 is a block diagram of an example implementation of the example meter of FIG. 1 and/or FIG. 2 .

6 is a flow diagram representative of example machine-readable instructions that may be executed to implement the example wearable media device of FIGS. 1 and 2 .

7 is a flow diagram representative of example machine readable instructions that may be executed to implement the example meter of FIG. 1 , FIG. 2 , and/or FIG. 5 .

8 is a flow diagram representative of example machine readable instructions that may be executed to implement the example media measurement entity of FIG. 1 .

Figure 9 is a block diagram of an example processor platform capable of executing the example machine-readable instructions of Figure 6 to implement the example wearable media device of Figures 1 and/or 2, capable of executing the example machine-readable instructions of Figure 7 to implement the example meter of Figures 1, 2 and/or 5, and/or capable of executing the example machine-readable instructions of Figure 8 to implement the example media measurement entity of Figure 1.

DETAILED DESCRIPTION

Media monitoring companies desire information about user interactions with media devices. For example, a media monitoring company wishes to obtain monitoring information related to media presented at a media device so that the media monitoring entity obtains, for example, exposure to advertisements, exposure to content (e.g., shows, web pages, etc.), user purchasing activity in response to exposure to media, demographic information of viewers exposed to media, and the like. As used herein, media refers to any form of content and/or advertisement delivered by any promotional vehicle (e.g., television, radio, tablet, smartphone, wearable media device, etc.). Monitoring information includes, for example, media identification information (e.g., media identification metadata, codes, signatures, watermarks, and/or other information that can be used to identify the presented media), application usage information (e.g., an identifier of the application, the time of use of the application and/or the duration of use, a rating of the application, etc.), and/or user identification information (e.g., demographic information, panelist identifiers, user names, etc.). Media identification information can be aggregated to determine and/or estimate, for example, the exposure of one or more populations and/or demographics to particular media and/or particular types of media, ownership and/or usage statistics of media devices, relative ranking of usage and/or ownership of media devices, types of usage of media devices (e.g., whether the devices are used to browse the Internet, stream media from the Internet, etc.), and/or other types of media device information. Conventionally, such systems treat each detected media identification instance similarly for purposes of, for example, calculating exposure data (e.g., a rating), even though a first person associated with a first detection of media may have paid little or no attention to the presentation of the detected media and a second person associated with a second detection of media may have focused, for example, a high degree of attention on and/or interacted with the presentation of the media.

The examples disclosed herein acknowledge that, while media may be detected on a media device, the presentation of media does not necessarily indicate that a person is paying attention to (e.g., engaging with) the media presentation. The examples disclosed herein generate engagement information (e.g., likelihood of engagement) that indicates whether a user is paying attention to media presented on a wearable media device. Some wearable media devices have a head-mounted display that presents media (e.g., audiovisual media such as television programs, movies, streaming videos, websites, advertisements, text messages, emails, maps, augmented reality data, etc.) to the user, for example, on a portion (e.g., the upper right corner) of a reflective surface (e.g., a lens of glasses). As can be seen, the user of the wearable media device is exposed to the displayed media while still interacting with the surrounding environment. Because the user of the wearable media device can more easily perform tasks other than paying attention to (e.g., focusing on) the displayed media with the wearable media device, it is possible that the user is not paying attention to the displayed media.

To determine whether a user is engaged (e.g., paying attention) to displayed media and/or how likely the user is to be engaging with the displayed media, examples disclosed herein use display characteristics generated by a wearable media device. In the examples disclosed herein, the opacity or transparency characteristics of the display are captured and used to generate, for example, an engagement score for the displayed media (e.g., a likelihood of engagement expressed as a percentage). While the display of media on a head-mounted wearable media device (such as glasses) is often completely opaque, in some scenarios, the media is displayed with transparency. Because transparency and opacity are referred to as opposites, transparency can also be referred to as opacity or opacity percentage. Wearable media devices provide a transparent or semi-opaque display to enable a user to perceive their environment beyond the display of the media. In other words, head-mounted displays sometimes generate a display that is at least partially see-through, allowing the user to observe their surroundings while still being exposed to the media. As described in detail below, in some example wearable devices, the transparency or opacity of the display on such a head-mounted display is set based on, for example, manual input provided by the user and/or automatic detection of the user's gaze direction relative to the media display. The examples disclosed herein use data representing the opacity of a display to determine the likelihood that a user is engaging with (e.g., paying attention to) the corresponding media. The example measures of how much a user is paying attention provided by the examples disclosed herein are referred to herein as engagement levels or engagement scores. In some examples disclosed herein, a greater opacity of a display (i.e., 90% opaque) is converted to a higher engagement score for the corresponding media because (1) the manual input provided by the user corresponds to a desire to clearly view the media and/or (2) the user is likely looking at the display according to a gaze direction detector. In some examples disclosed herein, a smaller opacity (i.e., 40% opaque) is converted to a lower engagement score for the corresponding media because (1) the manual input provided by the user corresponds to the user increasing transparency in view of seeing through (e.g., ignoring or partially ignoring) the media and/or (2) the user is likely looking away from the display according to a gaze direction detector.

In some examples disclosed herein, the opacity of a display generated by a wearable media device is obtained by reference to one or more settings used by the wearable media device to generate the display. Additionally or alternatively, examples disclosed herein capture opacity characteristics via an interface with a manual input (e.g., a button) that a user has access to during use of the wearable media device. Additionally or alternatively, examples disclosed herein capture opacity characteristics based on a gaze direction detector of the wearable media device that determines or sets the opacity of a media presentation based on whether the user is looking at or approximately looking at the media presentation. While eye position information itself can indicate whether a user is paying attention to displayed media, in some scenarios, a user looks in the direction of a media presentation while simultaneously providing a manual input to make the display transparent. Therefore, some examples disclosed herein prioritize opacity characteristic data solely based on and/or relative to eye position data in determining opacity characteristic data. In some examples, eye position data is not used and/or obtained.

In some examples disclosed herein, an engagement level or score is calculated by converting opacity characteristic data into an engagement score based on a data structure (e.g., a conversion table) having multiple mappings between opacity and engagement scores. In some examples disclosed herein, the engagement score is calculated based on one or more algorithms defined by, for example, a media measurement entity. In some examples disclosed herein, the one or more algorithms combine the opacity characteristic data with additional or alternative data generated by the wearable media device, such as sensor information (e.g., motion data, location data, facial expression data, eye tracking data, etc.), to generate an engagement score. That is, some examples disclosed herein consider additional factors along with the opacity characteristic data to generate an engagement score.

The engagement information provided by the examples disclosed herein is used, for example, to generate an engagement rating for a particular media presented on a wearable media device. Traditional ratings generated using presentation information are an indication of exposure to the media, but do not indicate whether the audience member actually paid attention to the media presentation (e.g., the individual may be distracted from the media). In contrast, the engagement information provided by the examples disclosed herein can be used to generate an engagement rating that indicates how much the user of the wearable media device paid attention to a particular media. The engagement rating provided by the examples disclosed herein can be independent and/or can be used to supplement traditional (e.g., exposure-based) ratings. Compared to traditional ratings generated using only presentation and/or media identification information, the engagement ratings provided by the examples disclosed herein are more refined from multiple perspectives. For example, the engagement levels disclosed herein provide information about the degree of attention paid by the wearable media device user to a particular portion or event of the media (such as a particular scene, the appearance of a particular actor or actress, a particular song played, a particular product shown, etc.). Thus, the engagement levels or scores provided by the examples disclosed herein indicate, for example, how interested audience members become and/or remain when a particular person, brand, or object appears in the media and/or when a particular event or type of event occurs in the media. Thus, the examples disclosed herein provide more refined data about specific portions of the media (relative to data previously provided by systems based solely on exposure).

FIG1 illustrates an example environment of a user 100 wearing a wearable media device 102. In the example of FIG1 , the wearable media device 102 includes a meter 104 constructed according to the teachings of the present disclosure. As described in detail below, the example meter 104 uses opaque data related to the display of the wearable media device 102 to generate engagement information indicating a level of attention paid to the media presented on the wearable media device 102. The example wearable media device 102 of FIG1 is capable of acquiring (e.g., downloading) any suitable type of media from any suitable media source. For example, the wearable media device 102 of FIG1 communicates with a media provider 106 via a network 108 and/or directly to obtain the media displayed on the wearable media device 102. The example network 108 of FIG1 is a wide area network (WAN) such as the Internet. However, in some examples, a local area network may be used in addition or alternatively. For example, multiple networks (e.g., cellular networks, Ethernet networks, etc.) may be used to implement the example network 108 of FIG1 . The example media provider 106 of Figure 1 can be implemented via any media provider, such as a media broadcaster, multiplexer or unicaster (e.g., a cable television service, a fiber optic television service, an IPTV provider, etc.), an on-demand digital media provider (e.g., a provider of Internet streaming video and/or audio services such as, etc.), a web page, and/or any other provider of any type of electronic media.

In the illustrated example of FIG1 , wearable media device 102 is a head-mounted display device (such as, for example, Google). As can be seen, the example wearable media device 102 of FIG1 communicates with the example network 108 via a first wireless coupling 110, for example, established with a Wi-Fi access point 112. Additionally or alternatively, the example wearable media device 102 of FIG1 communicates with the network 108 via a second wireless coupling 114 (e.g., Bluetooth pairing, Wi-Fi session) established with a portable device 116 having, for example, cellular capabilities. The example portable device 116 of FIG1 is, for example, a smartphone, tablet, phablet, portable people meter, and/or any other portable device having wireless communication capabilities to communicate with the network 108. In this case, the example wearable media device 102 of FIG1 communicates data to the portable device 116 via the second wireless coupling 114, and the portable device 116 forwards the data to the network 108 via a third wireless coupling 118 (e.g., a cellular connection). In some examples, the wearable media device 102 of FIG1 utilizes a first wireless coupling 110 when the wearable media device 102 is within range of a Wi-Fi access point 112. When the example wearable media device 102 of FIG1 is not within range of the Wi-Fi access point 112 (or any other Wi-Fi access point and/or other type of short-range communication device), the wearable media device 102 of FIG1 utilizes a second wireless coupling 114 with a portable device 116 for communicating with the network 108.

In the illustrated example of FIG1 , a meter 104 collects information related to a media presentation generated by an example wearable media device 102. In the example of FIG1 , the example meter 104 (1) detects and/or measures a user's engagement with a media presentation, (2) detects and/or identifies media presented on the wearable media device 102, and/or (3) detects and/or identifies a user of the wearable media device 102. In the illustrated example of FIG1 , the example meter of FIG1 is downloadable via, for example, Internet software. In the illustrated example of FIG1 , the meter 104 is provided by a media measurement entity 120 (e.g., a monitoring entity such as the Nielsen Company) and/or an example media provider 106. For example, the media measurement entity 120 of FIG1 includes an SDK (software development kit) provider 122 that provides instructions to application developers associated with, for example, the media provider 106. In some examples, SDK provider 122 provides an SDK to application developers so that developers can integrate monitoring instructions (e.g., including instructions for implementing the example meter 104) into existing applications. In this case, media provider 106 uses the SDK to integrate meter 104 into an application associated with media provider 106 (e.g., by instrumenting the application with instructions corresponding to the SDK for meter 104), and deploys the instrumented application with meter 104 integrated therein to, for example, an application store (e.g., Apple iTunes, Google Play, etc.). In some examples, the instrumented application has basic functionality other than media monitoring, such as, for example, presenting media from a specific media provider (e.g., a television broadcaster such as ESPN, ABC, NBC, etc.) (e.g., when the instrumented application is dedicated to a specific media provider, such as, for example, a television broadcaster such as ESPN, ABC, NBC, etc.).

Members of the public, some of whom are panelists of the media measurement entity 120, can download the meter 104 (e.g., from an application memory) to a corresponding media device (such as the example wearable media device 102 of FIG. 1 ). People become panelists via, for example, a user interface (e.g., a website) presented on the wearable media device 102. People become panelists in additional or alternative ways (such as, for example, via a telephone interview, by completing an online survey, etc.). Additionally or alternatively, people can be contacted and/or recruited using any desired methodology (e.g., random selection, static selection, telephone solicitation, internet advertising, surveys, advertising in a mall, product packaging, etc.). During the recruitment of panelists, the media measurement entity 120 of FIG. 1 receives demographic information from the recruiting personnel so that subsequent correlation can be made between media exposure associated with those panelists and different demographic markets.

Although in the illustrated example of FIG1 , the meter 104 is provided via an SDK, the meter 104 and/or corresponding instructions provided via the SDK may be provided in any other suitable manner. For example, the instructions associated with the example meter 104 of FIG1 may be provided as an application programming interface (API), a plug-in, an add-on, etc. Alternatively, the instructions associated with the example meter 104 may be maintained externally, and the SDK may facilitate the installation of the monitoring instructions into one or more applications. This latter approach is advantageous because it facilitates the implementation of the monitoring equipment after the corresponding application is deployed.

As described in detail below with respect to FIG. 5 and FIG. 6 , the example meter 104 of FIG. 1 collects monitoring data (e.g., media identification information, user identification information, device identification information, etc.), generates engagement information indicating interest in the display of the wearable media device 102, and sends a record including the monitoring data and engagement information to the example media measurement entity 120 (e.g., via a communication interface between the wearable media device 102 and the network 108). To exchange information with the media device 102 via the network 104, the example media measurement entity 120 employs a server 124 (and/or any other suitable computing platform) that implements an interface 126 for receiving reported monitoring information from, for example, the wearable media device 102 via the network 108. The example interface 126 of FIG. 1 is a Hypertext Transfer Protocol (HTTP) interface. However, the example server 124 of FIG. 1 may utilize any suitable type of interface and/or protocol. In the illustrated example, the HTTP interface 126 receives an HTTP request including, for example, media monitoring information. In some examples, the HTTP request is sent with the media monitoring information in the payload portion of the request. The media monitoring information received via the HTTP request includes, for example, media identification information (e.g., media identification metadata, codes, signatures, watermarks, and/or other information that can be used to identify the presented media), user identification information (e.g., an alphanumeric identifier assigned to the current user), device identification information (e.g., model, manufacturer identification, version information, etc.), application usage information (e.g., application identifier, time and/or duration of application usage, application rating, etc.), engagement information generated by the example meter 104, and/or any other appropriate monitoring information. The request is not actually intended to retrieve media, but rather serves as a vehicle for transmitting media monitoring information. Thus, the HTTP request can be referred to as a "virtual request." The example server 124 of FIG. 1 is configured with software (e.g., a daemon process) that extracts media monitoring information from the payload of the virtual request. Additionally or alternatively, any other method of transmitting media monitoring information can be used (e.g., HTTP Secure (HTTPS) protocol), File Transfer Protocol (FTP), Secure File Transfer Protocol (SFTP), HTTP and/or HTTPS GET requests, HTTP and/or HTTPS POST requests, etc.).

The example media measurement entity 120 of FIG. 1 employs a data store 128 implemented via one or more storage devices (such as, for example, flash memory, magnetic media, optical media, etc.). The data stored in the example data store 128 of FIG. 1 can be in any data format (such as, for example, binary data, comma-delimited data, tab-delimited data, Structured Query Language (SQL) structures, etc.). Although the data store 128 is illustrated as a single database in the illustrated example of FIG. 1 , the data store 128 can be implemented via multiple databases and/or can be stored in multiple storage locations. The example data store 128 of FIG. 1 stores, for example, engagement information and monitoring information received from the example meter 104 of FIG. 1 . In some examples, the data store 128 can store personally identifiable information (e.g., demographic information, bibliographic information, etc.) about one or more panelists and/or other persons, indicating, for example, one or more characteristics of the corresponding person.

While the above discussion focuses on a single wearable media device 102, a single meter 104, a single media provider 106, and a single media measurement entity 120 for simplicity, any number of any of these elements may be present. For example, in a typical embodiment, it is expected that the media measurement entity 120 will provide multiple different meters 104 to the general public. Thus, it is expected that there will be many media devices accessing the metering application, and it is expected that a large portion of the users accessing such an application will agree to become panelists. Thus, it is expected that there will be many instances of the above-described processing being performed across many devices at overlapping times and/or at different times. Thus, for example, there may be many instantiations of the machine-readable instructions disclosed in the following flow diagrams operating at the same time or at different times. Many of these instances may be implemented as parallel threads operating on the same device.

FIG2 is a block diagram of an example embodiment of the example wearable media device 102 of FIG1 . The example wearable media device 102 of FIG2 includes a sensor 200 that monitors the environment in which the wearable media device 102 is located and/or monitors the activity of the wearable media device 102. The sensor 200 of FIG2 includes, for example, a motion sensor, an accelerometer, a location tracker (e.g., a global positioning system module), an audio sensor, a touch sensor, an image capturer, and/or any other suitable sensor that collects information related to the wearable media device 102. In some examples, the sensor 200 includes an image capture sensor that obtains image information indicating the direction of the user's gaze. For example, the user's gaze direction is calculated by determining the direction in which the center of the eye is pointing. As described below, the user's gaze direction can be used to control the opacity characteristics of the displayed media.

The example wearable media device 102 of FIG2 includes a communication interface 202 that facilitates communications, such as those described above with respect to FIG1 . For example, the communication interface 202 of FIG2 includes a Wi-Fi interface that communicates with available (e.g., within range) Wi-Fi access points. Thus, the example communication interface 202 of FIG2 facilitates the first example wireless coupling 110 described above with respect to FIG1 . In some cases, the Wi-Fi communication interface 202 is additionally or alternatively used to facilitate the second example wireless coupling 114 of FIG1 with the example portable device 116. Additionally or alternatively, the example communication interface 202 of FIG2 includes a Bluetooth interface that facilitates, for example, the first example wireless coupling 110 and/or the second example wireless coupling 114 of FIG1 . In some examples, the communication interface 202 of FIG2 includes one or more wired interfaces for exchanging information and/or receiving charge from a power source via a cable.

The example wearable media device 102 of FIG. 2 includes one or more applications 204 to be executed on the example wearable media device 102. As described above, the example wearable media device 102 of FIG. 2 can download any number and/or type of applications (e.g., an email application, a text messaging application, a map application, a browser, an augmented reality application, etc.) from, for example, an application store. The example applications 204 of FIG. 2 include a media fetcher 206 and an eye tracker 208. The example media fetcher 206 of FIG. 2 obtains media from any appropriate source (such as, for example, the media provider 106 of FIG. 1). The example media fetcher 206 of FIG. 2 implements, for example, a web browser (e.g., Google), a streaming service (e.g., ), and/or on-demand programming (e.g., ). The example media fetcher 206 of FIG. 2 receives a request for specific media (e.g., input from a user) and submits one or more queries to the appropriate media source to cause the media to be delivered to the wearable media device 102.

The example eye tracker 208 of FIG. 2 uses the detected gaze direction of the user to control the opacity of media displayed on the display surface 210 of the wearable media device 102. The example eye tracker 208 of FIG. 2 uses eye position and/or movement data provided by one or more of the sensors 200 to determine or estimate the user's gaze direction and determine whether the estimated annotation direction corresponds to a portion of the display surface 210 designated for media display (e.g., a segment of a lens). In other words, the example eye tracker 208 of FIG. 2 indicates how close the user's gaze is to the media presented on the display surface 210. In the illustrated example of FIG. 2 , the eye tracker 208 calculates the angular difference (e.g., an angle of a particular number of degrees) between the detected gaze direction and the direct line of sight between the user's eye and the designated display portion of the display surface 210. In the illustrated example of FIG. 2 , the eye tracker 208 uses the magnitude of the angular difference to provide opacity instructions for the media display to the display generator 212 of the wearable media device 102. For example, the eye tracker 208 of FIG. 2 queries a reference table 214 that includes a mapping between an angular difference (between a detected gaze direction and a direction corresponding to a designated media display portion of the display surface) and an opacity of the currently displayed media. Using the mapping table 214, the example eye tracker 208 of FIG. 2 selects a displayed opacity that corresponds to the detected gaze direction (e.g., looking away from the displayed media, looking near the displayed media, looking directly at the displayed media, etc.). In the illustrated example of FIG. 2 , the mapping table 214 includes a high opacity (e.g., 80% to 100% opaque), for example, when the user is looking directly at the designated media display portion of the display surface 210. Alternatively, the example mapping table 214 of FIG. 2 includes a medium opacity (e.g., 50% to 80% opaque), for example, when the user is looking near the designated media display portion of the display surface 210. Alternatively, the example mapping table 214 of FIG. 2 includes a low opacity (e.g., 25% to 50% opaque), for example, when the user is looking away from the designated media display portion of the display surface 210.

FIG3 illustrates an example of the eye tracker 208 of FIG2 determining that a user's eye 300 is looking directly at a media display 302 of the display surface 210 of the wearable media device 102. At a time corresponding to FIG3 , the example eye tracker 208 of FIG2 determines that the user's gaze direction 304 is directed toward the media display 302. Therefore, the example media of the display 302 of FIG3 has a high opacity (e.g., 100% opaque).

4 illustrates an example of the eye tracker 208 of FIG. 2 determining that the user's eye 300 has moved away from the media display 302 of the display surface 210 of the wearable media device 102. At a time corresponding to FIG. 4 , the example eye tracker 208 of FIG. 2 determines that the user's gaze direction 400 is directed away from the media display 302. Therefore, the example media of the display 302 of FIG. 4 has a low opacity (e.g., 25% opaque).

The example eye tracker 208 of FIG. 2 delivers the opacity setting (eg, opacity percentage) obtained from the mapping table 214 to the display generator 212 , which facilitates display of the media on the display surface 210 according to the received opacity setting.

2 delivers the obtained opacity setting to the display settings 216, which is used by the example display generator 212 to generate the display of media on the display surface 210. That is, the example display settings 216 of FIG2 includes an entry (e.g., a variable, a file, etc.) dedicated to tracking the current opacity of media displayed on the display surface 210.

The example wearable media device 102 of FIG. 2 includes a manual opacity input 218 that the user of the wearable media device 102 has access to. For example, the manual opacity input 218 of FIG. 2 is implemented via a button on the frame of the wearable media device 102 and/or via an on-screen menu presented on the display surface 210 that the user selects. The example manual opacity input 218 of FIG. 2 enables the user to instruct the display generator 212 to display media at a specific opacity. The instructions provided via the example manual opacity input 218 are stored in the example display settings 216. In some examples, the manual opacity input 218 of FIG. 2 switches across a range of opacities (which may include predetermined values). Additionally or alternatively, the example manual opacity input 218 provides a field or prompt for the user to enter a specific number (e.g., a percentage) for the opacity of the displayed media. In the illustrated example of FIG. 2 , the example display generator 212 uses the provided input to generate the display of the media on the display surface 210. 2, the display generator 212 prioritizes instructions provided by the manual opacity input 218 over settings provided by the example eye tracker 208. However, any suitable combination and/or priority settings are possible.

As described above, the example wearable media device 102 of FIG2 displays media on the display surface 210 according to the display settings 216 (including an indication of the opacity of the displayed media). As described below, the example meter 104 obtains the opacity information and uses the opacity information to generate engagement information for the media displayed on the display surface 210.

Although FIG2 illustrates an example manner of implementing the wearable media device 102 of FIG1 , one or more of the elements, processes, and/or devices illustrated in FIG2 may be combined, split, rearranged, omitted, eliminated, and/or implemented in any other manner. Further, the example communication interface 202 , the example application 204 , the example media acquirer 206 , the example eye tracker 208 , the example display generator 212 , and/or the example meter 104 may be implemented via hardware, software, firmware, and/or any combination of hardware, software, and/or firmware. Thus, for example, any of the example communication interface 202 , the example application 204 , the example media acquirer 206 , the example eye tracker 208 , the example display generator 212 , and/or the example meter 104 of FIG2 may be implemented via one or more analog or digital circuits, logic circuits, programmable processors, application specific integrated circuits (ASICs), programmable logic devices (PLDs), and/or field programmable logic devices (FPLDs). When reading any of the device or system claims of this patent covering pure software and/or firmware implementations, at least one of the example communication interface 202, example application 204, example media acquirer 206, example eye tracker 208, example display generator 212, and/or example meter 104 of FIG. 2 is expressly defined herein as comprising a tangible computer-readable storage device or storage disk (such as a memory, a digital versatile disc (DVD), a compact disc (CD), a Blu-ray disc, etc.) storing software and/or firmware. Still further, the example wearable media device 102 of FIG. 2 may include one or more elements, processes, and/or devices in addition to or in place of those of FIG. 2 , and/or may include more than one of any or all of the illustrated elements, processes, and devices.

FIG5 is a block diagram of the example meter 104 of FIG1 and/or FIG2. The example meter 104 of FIG5 includes an engagement level detector 500 that detects and/or measures the engagement level of media presented on the example wearable media device 102 of FIG1 and/or FIG2. The engagement level detector 500 of FIG5 includes an opacity obtainer 502 that obtains opacity information associated with a display generated by the wearable media device 102; a score calculator 540 that receives data from the opacity obtainer 504; and one or more transformations 506 used by the score calculator 504. The example opacity obtainer 502 of FIG5 captures display characteristic data from the wearable media device 102. In some examples, the opacity obtainer 502 communicates with (e.g., queries and receives responses to) the example display settings 216 stored in memory of the wearable media device 102. As described above, the example display settings 216 include instructions and/or settings referenced by the display generator 212, which uses the display settings 216 to generate a media display having a specific opacity. In this case, the example opacity obtainer 502 of FIG. 5 identifies and/or interprets the display settings 216 as corresponding to the specific opacity and provides corresponding data to the example score calculator 504 of FIG. 4 . Additionally or alternatively, the example opacity obtainer 502 of FIG. 5 obtains opacity information by interfacing with the example manual opacity input 218 of the wearable media device 102. That is, the example opacity obtainer of FIG. 5 receives one or more signals from the manual opacity input 218 when, for example, a user presses a button corresponding to the manual opacity input 218 and/or selects from an on-screen menu corresponding to the manual opacity input 218. In this case, the example opacity obtainer 502 of FIG. 5 identifies and/or interprets the received signals as corresponding to the specific opacity and provides data to the example score calculator 504 of FIG. 5 . Additionally or alternatively, the example opacity obtainer 502 of FIG5 obtains opacity information by interfacing with the example eye tracker 206 of the wearable media device 102. That is, the example opacity obtainer 502 of FIG5 receives one or more signals from the eye tracker 208, for example, to calculate the user's gaze direction. In this case, the example opacity obtainer 502 of FIG5 identifies and/or interprets the received signals as corresponding to a particular opacity and provides the data to the example score calculator 504 of FIG5.

The example score calculator 504 of FIG5 uses data representing the opacity provided by the example opacity obtainer bar to generate a user's attention metric (e.g., engagement level). In the illustrated example of FIG5, the engagement level calculated by the score calculator 504 is the likelihood that the user is paying attention to the media presented on the display surface 210 of the wearable media device 102. The metric generated by the example score calculator 504 of FIG5 is a value of any appropriate type (such as, for example, a numerical score based on a scale, a percentage, a classification, one of multiple levels defined by thresholds, etc.). In some examples, the metric generated by the example score calculator 504 of FIG5 is a total score or percentage formed by combining multiple individual engagement scores or percentages based on different data and/or detections corresponding to, for example, consecutive intervals.

In the illustrated example of FIG5 , the score calculator 504 uses the provided opacity to determine or estimate, for example, whether the user is paying attention to the displayed media. The example score calculator 504 of FIG5 calculates a score (e.g., likelihood) representing whether the user is paying attention to the displayed media based on the transformation 506. For example, the score calculator 504 of FIG5 compares the received opacity with one or more thresholds stored in the transformation 506 to select one of a plurality of engagement scores. For example, the transformation 506 of FIG5 includes a table in which specific ranges of opacity correspond to specific engagement scores. Table 1 is an example illustration of the example transformation 506 of FIG5 .

Table 1

Opacity (%) Participation score 100 10 90-99 9 1980-1989 8 70-79 7 60-69 6 50-59 5 40-49 4 30-39 3 20-29 2 10-19 1 0-9 0

As shown in Table 1, users are assigned larger engagement scores when the opacity is high. Although the engagement scores of Table 1 are integers, additional or alternative types of scores (such as percentages) are possible.

Additionally or alternatively, the example score calculator 504 of FIG. 5 uses any suitable algorithm or equation to convert the exact opacity into a specific engagement score. In other words, the example score calculator 504 of FIG. 5 can directly convert opacity into an engagement score in addition to or instead of using a range of likelihoods (e.g., according to the transformed Table 1) to assign a score to a corresponding user. In this case, the example transformation 506 includes one or more algorithms or functions that receive opacity as input and output a numerical representation, such as a likelihood of engagement. For example, the transformation 506 receives a first opacity percentage and generates a second percentage indicating the likelihood of a user engaging with the displayed media. In this case, a higher percentage proportionally indicates a higher level of attention or engagement.

In some examples, the example score calculator 504 of FIG5 considers data collected by, for example, the sensors 200 of the wearable media device 102, along with the opacity characteristic data provided by the opacity obtainer 502. For example, the transformation 506 of FIG5 includes one or more algorithms that combine the opacity characteristic data with additional or alternative data, such as sensor information (e.g., motion data, location data, facial expression data, etc.) generated by the sensors 200 of the wearable media device 102 to generate an engagement score. The example score calculator 504 can consider additional factors along with the opacity characteristic data to generate the engagement score.

In some examples, the score calculator 504 of FIG. 5 combines calculations taken over multiple intervals. For example, the likelihood of engagement calculated by the example score calculator 504 of FIG. 5 can be combined (e.g., averaged) over a period of multiple frames of continuous media to generate an overall likelihood of engagement for the user over that period. Detecting that a user is likely to be paying attention to the media over multiple consecutive frames can indicate a higher likelihood of engagement with the displayed media, as opposed to an indication that the user frequently switches to, for example, a lower opacity. For example, the score calculator 504 can calculate a percentage of likelihood of engagement for each of twenty consecutive frames representing the media. In some examples, the score calculator 504 calculates an average of the twenty percentages and compares the average to one or more thresholds, each indicating an engagement score, as described above with respect to Table 1. Based on the comparison of the average to the one or more thresholds, the example score calculator 504 determines a likelihood of engagement or classification for the user over the period corresponding to the twenty frames.

The example score calculator 504 of FIG5 outputs the calculated score to the example time stamper 508. The example time stamper 508 of FIG5 includes a clock and a calendar. The example time stamper 508 of FIG5 associates a time period (e.g., 1:00 AM Central Standard Time (CST) to 1:01 AM CST) and a date (e.g., January 2, 2014) with each calculated engagement score, for example, by appending the time period and date information to the end of the data. The data packet (e.g., engagement score, opacity, and time stamp) is stored in the memory 510. The example memory 510 of FIG5 includes, for example, volatile memory (e.g., synchronous dynamic random access memory (SDRAM), dynamic random access memory (DRAM), memory bus dynamic random access memory (RDRAM), etc.) and/or non-volatile memory (e.g., flash memory). The memory 510 may include one or more double data rate (DDR) memories (e.g., DDR, DDR2, DDR3, mobile DDR (mDDR), etc.)

The example timestamp 508 of FIG. 5 also receives data from the example media detector 512 and the example user identifier 514. The example media detector 512 of FIG. 5 detects the presentation of media on the wearable media device 102 and/or collects identification information associated with the detected presentation. In some examples, the media detector 512 includes an application that is equipped to extract, for example, codes and/or watermarks embedded in the media presented by the wearable media device 102. Audio watermarking is a technique for identifying media, such as television programs, radio broadcasts, advertisements, downloaded media, streaming media, pre-packaged media, etc. Existing audio watermarking techniques identify media by embedding one or more audio codes (e.g., one or more watermarks) (such as media identification information and/or identifiers that can be mapped to the media identification information) into audio and/or video components. In some examples, the audio or video components are selected to have signal characteristics sufficient to conceal the watermark. As used herein, the terms "code" or "watermark" are used interchangeably and are defined to refer to any identifying information (e.g., an identifier) that may be inserted or embedded in the audio or video of media (e.g., a program or advertisement) for the purpose of identifying the media or for another purpose such as tuning (e.g., a packet identification header). To identify the watermarked media, the watermark is extracted and used to access a reference watermark table that is mapped to the media identification information.

Additionally or alternatively, the example media detector 512 of FIG5 conveniently illustrates the generation of a watermark and/or signature for media presented on the wearable media device 102. Unlike media monitoring techniques based on codes and/or watermarks included with and/or embedded in the monitored media, fingerprint- or signature-based media monitoring techniques typically use one or more inherent characteristics of the monitored media to generate a substantially unique proxy for the media during a monitoring interval. This proxy is referred to as a signature or fingerprint and can take any form (e.g., a sequence of values, a waveform, etc.) that represents any aspect of a media signal (e.g., an audio and/or video signal forming a monitored media presentation). A good signature is one that is repeatable when processing the same media presentation but unique relative to other (e.g., different) presentations of other (e.g., different) media. Therefore, the terms "fingerprint" and "signature" are used interchangeably herein and are defined herein to refer to a proxy for identifying the media that is generated based on one or more inherent characteristics of the media.

Signature-based media monitoring typically involves determining (e.g., generating and/or collecting) a media signal (e.g., an audio signal and/or a video signal) representing the output of a monitored media device and comparing the monitored signature with one or more reference signatures corresponding to a known (e.g., a reference) media source. Various comparison criteria (such as cross-correlation values, Hamming distances, etc.) can be evaluated to determine whether the monitored signature matches a specific reference signature. When it is found that the monitored signature matches a reference signature, the monitored media can be identified as corresponding to the specific reference media represented by the reference signature that matches the monitored signature. Because attributes (such as identifiers of media, presentation time, channels, etc.) are collected for the reference signature, these attributes can then be associated with the monitored media whose monitored signature matches the reference signature. An example system for identifying media based on codes and/or signatures has long been known and first disclosed in Thomas U.S. Patent No. 5,481,294, the entire text of which is incorporated herein by reference.

In some examples, the code/watermark is sent along with the media and/or in association with the media as media identification metadata. The media identification metadata can be formatted as text or binary format (such as, for example, an ID3 tag). In some examples, the media identification metadata includes data from the code/watermark, etc. However, in some other embodiments, the media identification metadata is derived from and/or represents the code/watermark and/or signature, etc. Example methods and apparatus for transcoding watermarks into ID3 tags are disclosed in U.S. patent application Ser. No. 13/341,646, U.S. patent application Ser. No. 13/341,661, U.S. patent application Ser. No. 13/443,596, U.S. patent application Ser. No. 13/455,961, U.S. patent application Ser. No. 13/341,646, and U.S. patent application Ser. No. 13/472,170, the entirety of which is incorporated herein by reference.

In the illustrated example of FIG5 , the detection functionality of the media detector 512 stores data associated with and/or representative of the collected information in, for example, a memory 513 and/or transmits the collected monitoring information to the example media measurement entity 120 of FIG1 . In some examples, the wearable media device 102 includes additional or alternative monitoring functionality (e.g., local monitoring functionality and/or monitoring software other than the software of the media detector 512). In some examples, the monitoring functionality of the media detector 512 and/or other monitoring functionality operating on the wearable media device 102 is referred to as an “on-device meter.” The example detector 512 of FIG5 provides media identification information to the example timestamp 508.

To determine the identity of the user of the wearable media device 102, the example meter 104 of FIG. 5 includes a user identifier 514. The example user identifier 514 of FIG. 5 determines the user's identity based on user identification information stored in the memory 510 of the example wearable media device 102, for example, in conjunction with registration of the wearable media device 102 and/or installation of the example meter 104 on the wearable media device 102. For example, when a user registers to participate in a monitoring dashboard associated with the media measurement entity 120 of FIG. 1 , the user is assigned an identifier (e.g., an alphanumeric string) stored on the wearable media device 102. In this case, the example user identifier 514 of FIG. 5 references the stored identifier to obtain the user identification information. Additionally or alternatively, the example user identifier 514 of FIG. 5 utilizes a log of information provided when the user initiates (e.g., unlocks) a session with the wearable media device 102. The example user identifier 514 of FIG. 5 utilizes any other suitable technology (e.g., facial recognition data provided by an application of the wearable media device 102) to identify the current user. The example user identifier 514 of FIG. 5 provides user identification information to the example time stamper 508 .

In the illustrated example of FIG5 , output device 516 periodically and/or aperiodically outputs data (e.g., media identification information, user identification information, engagement scores, etc.) from memory 510 (e.g., via network 108 ) to, for example, media measurement entity 120 of FIG1 . In the illustrated example, output device 516 uses the communication capabilities of wearable media device 102 (e.g., communication interface 202 ) to deliver the information. In the illustrated example of FIG5 , media measurement entity 120 (e.g., Nielsen Company (USA), LLC) uses the data generated by meter 104 to generate, for example, exposure information (such as engagement ratings, traditional exposure/audience composite ratings (e.g., Nielsen ratings), etc.). Information from many meters can be compiled and analyzed to generate ratings representing media exposure across one or more populations of interest.

Although an example manner of implementing the meter 104 is illustrated in FIG5 , one or more of the elements, processes, and/or devices illustrated in FIG5 may be combined, split, rearranged, omitted, eliminated, and/or implemented in any other manner. Further, the example engagement level detector 500, the example opacity obtainer 512, the example score calculator 504, the example time stamper 508, the example media detector 512, the example user identifier, the example output device 516, and/or more generally the example meter 104 of FIG5 may be implemented via hardware, software, firmware, and/or any combination of hardware, software, and/or firmware. Thus, for example, any of the example engagement level detector 500, the example opacity obtainer 512, the example score calculator 504, the example time stamper 508, the example media detector 512, the example user identifier, the example output device 516, and/or more generally the example meter 104 can be implemented via one or more analog or digital circuits, logic circuits, programmable processors, application specific integrated circuits (ASICs), programmable logic devices (PLDs), and/or field programmable logic devices (FPLDs). When reading any of the apparatus or system claims of this patent covering pure software and/or firmware implementations, at least one of the example engagement level detector 500, the example opacity obtainer 512, the example score calculator 504, the example time stamper 508, the example media detector 512, the example user identifier, the example output device 516, and/or more generally the example meter 104 of FIG. 5 is expressly defined herein as comprising a tangible computer-readable storage device or storage disk (such as a memory, a digital versatile disc (DVD), a compact disc (CD), a Blu-ray disc, etc.) storing software and/or firmware. Still further, the example meter 104 of FIG. 5 may include one or more elements, processes, and/or devices in addition to or in place of those illustrated in FIG. 5 , and/or may include more than one of any or all of the illustrated elements, processes, and devices.

FIG6 shows a flowchart representing example machine-readable instructions for implementing the example wearable media device 102 shown in FIG1 and/or FIG2. FIG7 shows a flowchart representing example machine-readable instructions for implementing the example meter 104 shown in FIG1, FIG2, and/or FIG5. FIG8 shows a flowchart representing example machine-readable instructions for implementing the example media measurement entity 120 of FIG1. In these examples, 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 with respect to FIG9. The program may be implemented as software stored on a tangible computer-readable storage medium (such as a CR-ROM, floppy disk, hard drive, digital versatile disc (DVD), Blu-ray disc), or memory associated with the processor 912, although the entire program and/or portions of the program may alternatively be executed by a device other than the processor 912 and/or implemented in firmware or dedicated hardware. 6-8 , many other methods of implementing the example wearable media device 102 of FIG. 1 and/or FIG. 2 , the example meter 104 of FIG. 1 , FIG. 2 , and/or FIG. 5 , and/or the example SDK provider of FIG. 1 may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks may be changed, eliminated, or combined.

As described above, the example processes of Figures 6-8 can be implemented using encoded instructions (e.g., computer and/or machine-readable instructions) stored on a tangible computer-readable storage medium (such as a hard drive, flash memory, read-only memory (ROM), compact disc (CD), digital versatile disc (DVD), cache, random access memory (RAM), and/or any other storage device or storage disk that internally stores information for any duration (e.g., for an extended period of time, permanently, briefly, temporarily buffered, and/or cached). 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 is defined to exclude propagating signals and transmission media. As used herein, "tangible computer-readable storage medium" and "tangible machine-readable storage substrate" are used interchangeably. Additionally or alternatively, the example processes of Figures 6-8 may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable storage medium, such as a hard drive, flash memory, read-only memory, compact disc, digital versatile disc, cache, random access memory, and/or any other storage device or storage disk that internally stores information for any duration (e.g., for an extended period of time, permanently, briefly, temporarily buffered, and/or cached). As used herein, the term non-transitory computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk, and is defined to exclude propagating signals and transmission media. As used herein, when the phrase "at least" is used as a transitional term in the preamble of a claim, the phrase is open ended in the same manner as the term "comprising" is open ended.

FIG6 begins when the wearable media device 102 of FIG1 is activated (e.g., placed on a user's head and turned on) (block 600). The inputs and outputs of the wearable media device 102 (such as the sensors 200, communication interface 202, and manual opacity input 218 of FIG2) are activated via, for example, a basic input/output system (BIOS) (block 602). When media presentation is initiated or triggered on the wearable media device 102 (block 604), the example display generator 212 of FIG2 references the display settings 216 to determine the current opacity characteristics for displaying media on the display surface 210 (block 608). As described above, the display settings 216 receives data from, for example, the manual opacity input 218 and/or the eye tracker 208. In the illustrated example, the manual opacity input 218 is prioritized (e.g., has overriding authority) over instructions provided by the eye tracker 208. Using the opacity information from the display settings 216, the example display generator 212 displays the media at the corresponding opacity on the display surface 210 (block 608). If the media presentation is finished (block 610), control returns to block 604. Otherwise, the display generator 212 continues to refer to the display settings 216 for opacity information and, accordingly, displays the media on the display surface (blocks 606 and 608).

FIG7 begins when the example meter 104 of FIG1 , FIG2 , and/or FIG5 is triggered to generate an engagement score (block 700). In some examples, the meter 104 is scheduled to generate an engagement score (e.g., every 2 seconds, every 10 seconds, every minute, etc.). Additionally or alternatively, the example meter 104 is triggered in response to, for example, a media presentation initiated on the wearable media device 102. In the example of FIG7 , the user identifier 514 determines the identity of the user (block 702), for example, by requesting identification information from the user and/or referencing one or more sources of user identification information on the wearable media device 102. Additional or alternative techniques for obtaining user identification information may be employed (such as, for example, deriving the identity based on a social security number associated with the wearable media device 102, deriving the identity based on a phone number associated with the wearable media device 102, deriving the identity based on a hardware address of the wearable media device 102 (e.g., a media access control (MAC) address of the wearable media device 102), etc.). The example media detector 512 obtains media identification information representative of media being displayed on the wearable media device 104 (block 704 ).

The example opacity obtainer 502 of the example engagement level detector 500 of FIG5 obtains a current opacity characteristic (e.g., a degree or percentage) of the currently displayed media (block 706). The opacity obtainer 502 obtains the opacity characteristic from, for example, the display settings 216 of the wearable media device, the manual opacity input 218, and/or an eye tracker. The score calculator 504 receives the opacity characteristic data from the opacity obtainer 502 and uses the opacity characteristic data to generate an engagement score for the currently displayed media (block 708). For example, the score calculator 504 uses the example transformation 506 to convert the opacity into an engagement score (e.g., the likelihood that the user is focusing on the media displayed on the wearable media device 102).

The example meter 104 generates an information packet (e.g., via the output device 516) that includes at least user identification information, media identification information, and an engagement score (block 710). The meter 104 uses the communication interface 202 of the wearable media device 102 to transmit the packet to the media measurement entity (block 712). Thus, the media measurement entity is made aware of the user's identity, the identity of the media displayed on the wearable media device 102, and a score representing the identified user's engagement (or non-engagement) with the identified media. The example of FIG. 7 then ends (block 714).

FIG8 is a flow diagram representing example machine-readable instructions that may be executed to implement the media measurement entity 120 of FIG1 . The example of FIG8 begins when an SDK provider 122 of the media measurement entity 120 provides an SDK to an application developer (such as, for example, a media provider 106 and/or a developer associated with an application store (e.g., Apple iTunes, Google Play, etc.) (block 800). The SDK provided by the example SDK provider 122 enables the receiving application developer to create, for example, a meter 104 and/or integrate the meter 104 into one or more applications. In the illustrated example, the meter 104 of FIG1 , FIG2 , and/or FIG5 is provided via the provided SDK. However, the meter 104 of FIG1 , FIG2 , and/or FIG5 may be provided via, for example, an API, a programming library, a dynamic link library (DLL), a plug-in, an add-on, etc. In some examples, the meter 104 is provided directly to the wearable media device 102 via, for example, a website, a mailed CD, etc. In some examples, meter 104 is provided to a wearable media device manufacturer and/or an intermediary. In examples where meter 104 is provided to a wearable media device manufacturer, the wearable media device manufacturer can design (e.g., develop, produce, manufacture, etc.) wearable media device 102 with meter 104 as an integrated component. In examples where meter 104 is provided to an intermediary, the intermediary can install (e.g., modify, change, adapt, etc.) wearable media device 102 to include meter 104 before or before selling wearable media device 102 to a retailer and/or to an end user (e.g., a customer).

The example media measurement entity 120 receives demographic information from a user of the wearable media device 102, for example, regarding installation of the meter 104 on the wearable media device and/or registration with a faceplate associated with the media measurement entity 120 (block 802). In the illustrated example, the media measurement entity 120 assigns an identifier to the user (block 804). In some examples, the identifier is generated based on the demographic information. The identifier is then stored in a memory (e.g., a faceplate) on the wearable media device 102 and/or in the data store 128 of the media measurement entity 120. In the illustrated example, the media measurement entity 120 begins collecting monitoring data (such as, for example, media identification information (e.g., media identification metadata, codes, signatures, watermarks, and/or other information that can be used to identify the presented media), user identification information, usage time and/or duration, engagement scores, and/or demographic information) (block 806). The example of FIG. 8 then ends (block 808).

9 is a block diagram of an example processor platform 900 capable of executing instructions of FIG 6 to implement the example wearable media device 102 of FIG 1 and/or FIG 2 , capable of executing instructions of FIG 7 to implement the example meter 104 of FIG 1 , FIG 2 , and/or FIG 5 , and/or capable of executing instructions of FIG 8 to implement the example media measurement entity 120 of FIG 1 . The processor platform 900 may be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smartphone, a tablet (such as an iPad ^™ )), a wearable media device (e.g., Google), an Internet appliance, or any other type of computing device.

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 via 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 communicates with a main memory including a volatile memory 914 and a non-volatile memory 916 via a bus 918. The volatile memory 914 can be implemented via synchronous dynamic random access memory (SDRAM), dynamic random access memory (DRAM), memory bus dynamic random access memory (RDRAM), and/or any other type of random access memory device. The non-volatile memory 916 can be implemented via flash memory and/or any other desired type of memory device. Access to the main memories 914 and 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 via 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 circuitry 920. The input devices 922 allow a user to enter data and commands into the processor 912. The input devices may be implemented via, for example, an audio sensor, a microphone, a still camera or video recorder, a keyboard, buttons, a mouse, a touch screen, a touchpad, a trackball, an isopoint mouse, and/or a voice recognition system.

One or more output devices 924 are also connected to the illustrated example interface circuit 920. The output device 924 can be implemented, for example, via a display device (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube (CRT) display, a touch screen, a tactile output device, a light emitting diode (LED), a printer, and/or a speaker). Thus, the illustrated example interface circuit 920 typically includes a graphics driver card, a graphics driver chip, or a graphics driver processor.

The interface circuitry 920 of the illustrated example also includes communication devices (such as transmitters, receivers, transceivers, modems, and/or network interface cards) that facilitate exchanging data with an external machine (e.g., any type of computing device) via a network 926 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, a 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 drives, optical disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.

The encoded instructions 932 of Figures 6, 7, and/or 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.

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.

Claims (15)

1. A method for detecting engagement with media presented on a wearable media device, the method comprising the steps of: The first opacity of the media presentation displayed on the wearable media device is determined by instructions executed by the processor; The processor executes instructions to determine changes in manually opaque input during media presentation, wherein the manually opaque input indicates a second opacity that is different from the first opacity. In response to detecting the change in the manual opacity input, the processor executes instructions to calculate an engagement score for the media presented via the media presentation based on the second opacity. When the second opacity is less than the first opacity, the engagement score is determined to be a first value; when the second opacity is greater than the first opacity, the engagement score is determined to be a second value greater than the first value. The engagement score indicates the likelihood that the user of the wearable media device is paying attention to the media presentation. The participation score is associated with the media by instructions executed by the processor. 2. The method of claim 1, wherein the step of determining the first opacity includes determining settings associated with the display device of the wearable media device. 3. The method of claim 2, wherein the setting is based on automatic tracking of the eye position of the user of the wearable media device relative to the media presentation. 4. The method according to any one of claims 1, 2 and 3, further comprising the step of: collecting media identification information associated with the media. 5. The method according to claim 4, further comprising the step of: generating a record including the media identification information and the participation score. 6. The method of claim 5, further comprising the step of: sending the record to a media measurement entity. 7. The method according to any one of claims 1, 2 and 3, further comprising the step of: maintaining the transformation information to be used to calculate the participation score. 8. A device for detecting participation in media presented on a wearable media device, the device comprising: Glasses worn by a user, the glasses including a display for displaying media in front of the user's eyes; Manual opacity input, which receives user input to adjust the opacity setting, which indicates the opacity of the media presentation; A display generator generates a media presentation on the display based on a first opacity, and the display generator detects changes in the manual opacity input. In response to detecting a change in the manual opacity input during media rendering, the media rendering on the display is generated based on a second opacity different from the first opacity; and A meter generates an engagement score based on the second opacity of the media presentation. When the second opacity is less than the first opacity, the engagement score is generated as a first value. When the second opacity is greater than the first opacity, the engagement score is generated as a second value greater than the first value. The engagement score indicates the likelihood that a user of the wearable media device is paying attention to the media presentation, and the meter associates the engagement score with the media. 9. The device of claim 8, wherein the meter associates the participation score with media identification information representing media associated with the media presentation. 10. The device according to any one of claims 8 and 9, further comprising an eye tracker, wherein the opacity setting is set by the eye tracker when the user does not use the manual opacity input during media presentation, and the opacity setting is based on the manual opacity input when the user uses the manual opacity input during media presentation, the meter obtaining the opacity setting via an interface with the eye tracker or via an interface with the manual opacity input. 11. The device according to any one of claims 8 and 9, the device further comprising a sensor for collecting motion data associated with the device, wherein the meter generates the participation score based on a combination of the second opacity and the motion data. 12. An apparatus for detecting participation in media presented on a wearable media device, the apparatus comprising: A unit used to determine the first opacity corresponding to the media displayed on the wearable media device; A unit for detecting changes in manually opaque input during media presentation, wherein the manually opaque input indicates a second opacity different from the first opacity; A unit for calculating an engagement score for the media based on the second opacity in response to detecting a change in the manually opaque input, wherein the engagement score is determined to be a first value when the second opacity is less than the first opacity, and a second value greater than the first value when the second opacity is greater than the first opacity, the engagement score indicating the likelihood that a user of the wearable media device is paying attention to the media presentation; and A unit used to associate the participation score with the media. 13. The apparatus of claim 12, wherein the unit for determining a first opacity corresponding to media displayed on the wearable media device refers to a setting associated with a display device of the wearable media device. 14. The apparatus of claim 12 or 13, wherein the apparatus further comprises a unit for collecting media identification information associated with the media. 15. The apparatus of claim 14, wherein the apparatus further comprises a unit for generating a record including the media identification information and the participation score.