Method and apparatus for compensating media impressions for misidentification errors

Abstract

The present application relates to a method and apparatus for compensating impressions of media for misidentification errors. An exemplary method includes: receiving a first network communication from a first type of computing device at a first server in a first Internet domain, which indicates media access at the first type of computing device; sending a second network communication from the first server to request demographic information, which corresponds to the first network communication and a third network communication received from the first type of computing device; generating an aggregate audience distribution, which includes a first audience distribution, the first audience distribution is based on the access of the media by a first family member; normalizing the aggregate audience distribution to generate a misidentification correction matrix, which includes the following probabilities: when a database owner determines that an impression of the media corresponds to a person in a second demographic group, the impression belongs to the first demographic group; compensating for misidentification errors in the impression by reallocating the impression to the first demographic group using the misidentification correction matrix.

Description

[0001] This application is a divisional application of an invention patent application with an application date of December 4, 2014, application number "201480077152.X", and invention name "Method and device for compensating impression data for erroneous identification and / or non-coverage by database owner".

[0002] This patent claims priority to U.S. Provisional Patent Application Serial No. 61 / 952,726 filed on March 13, 2014, U.S. Provisional Patent Application Serial No. 61 / 979,391 filed on April 14, 2014, U.S. Provisional Patent Application Serial No. 61 / 986,784 filed on April 30, 2014, U.S. Provisional Patent Application Serial No. 61 / 991,286 filed on May 9, 2014, U.S. Provisional Patent Application Serial No. 62 / 014,659 filed on June 19, 2014, U.S. Provisional Patent Application Serial No. 62 / 023,675 filed on July 11, 2014, and U.S. Provisional Patent Application Serial No. 62 / 030,571 filed on July 29, 2014. The entire contents of U.S. Provisional Patent Application Serial Number 61 / 952,726, U.S. Provisional Patent Application Serial Number 61 / 979,391, U.S. Provisional Patent Application Serial Number 61 / 986,784, U.S. Provisional Patent Application Serial Number 61 / 991,286, U.S. Provisional Patent Application Serial Number 62 / 014,659, U.S. Provisional Patent Application Serial Number 62 / 023,675, and U.S. Provisional Patent Application Serial Number 62 / 030,571 are incorporated herein by reference. Technical Field

[0003] The present invention relates generally to monitoring media and, more particularly, to methods and apparatus for compensating impression data for misidentification and / or non-coverage by database owners. Background Art

[0004] Traditionally, audience measurement entities determine audience engagement with media programs based on registered panelists. That is, the audience measurement entity registers people who agree to be monitored into a panel. The audience measurement entity then monitors those panelists to determine the media (e.g., television or radio programs, movies, DVDs, advertisements, etc.) to which those panelists are exposed. In this manner, the audience measurement entity can determine exposure measurements for different media based on the collected media measurement data.

[0005] The technology for monitoring user access to Internet resources (such as web pages, advertisements, and / or other media) has evolved significantly over the years. Some existing systems perform such monitoring primarily through server logs. In particular, an entity serving media on the Internet may use such existing systems to record the number of requests received for its media at its servers. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 An exemplary system is shown for collecting impressions of media presented at a mobile device and collecting user information from a distributed database owner for association with the collected impressions.

[0007] Figure 2 is an exemplary impression data compensator, which can be implemented in Figure 1 An exemplary audience measurement server of the invention is provided to compensate impression data for inaccuracies related to misidentification and non-coverage resulting from the database owner's impression collection technology.

[0008] FIG. 3A to FIG. 3C Together, an exemplary data flow is shown, which may be Figure 2 An exemplary impression data compensator of executes to compensate impression data for inaccuracies related to misidentification and non-coverage resulting from a database owner's impression collection techniques.

[0009] Figure 4 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to compensate impression data for inaccuracies related to misidentification and non-coverage originating from a database owner's impression collection technology.

[0010] Figure 5 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to calculate a misidentification calibration matrix and / or a co-viewing matrix.

[0011] Figure 6 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided for determining the probability of misidentification for multiple pairs of demographic groups.

[0012] Figure 7 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to generate a misidentification calibration matrix and / or a co-viewing matrix.

[0013] Figure 8 is a flow chart of exemplary machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator for generating a co-viewing matrix.

[0014] Fig. 9 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to generate an alpha factor associated with a media audience not covered by a database owner.

[0015] Fig.10 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to adjust impressions based on an error identification calibration matrix.

[0016] Fig.11 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to adjust impressions based on an uncovered factor corresponding to a database owner's impression collection technology.

[0017] Fig.12 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator is provided to calculate demographics (or non-coverage factors) associated with media audiences that are not covered by the database owner.

[0018] Fig.13 is a flowchart representing example machine readable instructions that may be executed to implement Figure 2 An exemplary impression data compensator of to adjust impressions and / or unique audiences based on an uncovered factor for a database owner.

[0019] Fig.14 An exemplary system configured to determine the type of media impression data to collect in accordance with the teachings of the present invention is shown.

[0020] Fig.15 Show Fig.14 An example of a type predictor for determining a type of media impression data to collect.

[0021] Fig.16 Show Fig.15 Treatment by Fig.15 An example of a data classifier that is used by a type analyzer to predict the type of collected media impression data.

[0022] Fig.17 Shown drawn by Fig.14 and Fig.15 An example graph of example daytime parts and example daytime categories used by the type predictor.

[0023] Fig.18 Show Fig.14 An example of a type modeler that can be used to construct type models.

[0024] Fig.19 is a flowchart representing example machine readable instructions that may be executed to implement Fig.14 and Fig.18 An exemplary type modeler for constructing a type model.

[0025] Fig. 20 is a flowchart representing example machine readable instructions that may be executed to implement Fig.14 and Fig.15 An exemplary type predictor for assigning types to collected media impression data.

[0026] Fig.21 is an exemplary processor platform that can be used to execute Figures 4 to 13 Example instructions for implementing the example devices and systems disclosed herein.

[0027] Fig. 22 is another exemplary processor platform, which can be used to perform Fig.19 and / or Fig. 20 Example instructions for implementing the example devices and systems disclosed herein. DETAILED DESCRIPTION

[0028] The technology used to monitor user access to Internet resources (such as web pages, advertisements, and / or other media) has evolved significantly over the years. At one time in the past, such monitoring was primarily accomplished through server logs. In particular, entities serving media on the Internet would record the number of requests received for their media at their servers. It is problematic to base Internet usage research on server logs for several reasons. For example, server logs can be tampered with directly or with the help of a bot that repeatedly requests media from the server to increase the server log count corresponding to the requested media. Second, media is sometimes retrieved once, cached locally, and then repeatedly viewed from the local cache without involving the server in the repeated viewings. Server logs cannot track these views of cached media because reproducing the locally cached media does not require requesting the media from the server again. Therefore, server logs are susceptible to both overcounting errors and undercounting errors.

[0029] The invention disclosed in Blumenau's U.S. Patent 6,108,637 fundamentally changes the way Internet monitoring is performed and overcomes the limitations of the server-side log monitoring techniques described above. For example, Blumenau discloses a technique in which Internet media to be tracked is tagged with beacon instructions. In particular, the monitoring instructions are associated with the Hypertext Markup Language (HTML) of the media to be tracked. When a client requests the media, the media and the beacon instructions are both downloaded to the client. The beacon instructions are therefore executed whenever the media is accessed from the server or from a cache.

[0030] The beacon instructions cause monitoring of data reflecting information about access to the media to be sent from the client that downloaded the media to the monitoring entity. Typically, the monitoring entity is an audience measurement entity (AME) (e.g., any entity interested in measuring or tracking audience reach of advertising, media, and / or any other media) that does not provide the media to the client and is a trusted third party (e.g., Nielsen, Inc.) used to provide accurate usage statistics. Advantageously, because the beacon instructions are associated with the media and are executed by the client browser whenever the media is accessed, the monitoring information is provided to the AME regardless of whether the client is a panel member of the AME.

[0031] However, it would be useful to link demographics and / or other user information to monitoring information. To address this problem, AME sets up groups of users who have agreed to provide their demographic information and have their Internet browsing activity monitored. When individuals join the group, they provide AME with detailed information about their identity and demographics (e.g., gender, race, income, home address, occupation, etc.). AME sets up a web tracker on the group member's computer that enables AME to identify the group member (whenever the group member accesses tagged media) and therefore send monitoring information to AME.

[0032] Since most of the clients providing monitoring information from the tagged pages are not panel members and are therefore unknown to AME, statistical methods need to be used to attribute demographic information based on data collected for panel members to a larger population of users providing data for tagged media. However, the panel size of AME is small compared to the total population of users. Therefore, a problem is presented as to how to increase the panel size while ensuring that the panel's demographic data is accurate.

[0033] There are many database owners that operate on the Internet. These database owners provide services (e.g., social networking services, email services, media access services, etc.) to a large number of subscribers. In exchange for providing such services, subscribers register with the owner. As part of this registration, subscribers provide detailed demographic information. Examples of such database owners include social networking providers such as Facebook, Myspace, Twitter, etc. These database owners set up web trackers on the computers of their subscribers to enable the database owners to recognize such registered users when they visit their website.

[0034] The examples disclosed herein may be used to determine media impressions, advertising impressions, media exposure, and / or advertising exposure using user information that is distributed across different databases (e.g., different website owners, service providers, etc.) on the Internet. The exemplary methods, apparatus, and articles of manufacture disclosed herein not only achieve accurate correlation of Internet media exposure with user information, but also effectively expand the panel specifications and composition beyond the panel of participating audience measurement entities and / or viewership rating entities to persons registered in other Internet databases, such as wireless service operators, mobile software / service providers, social media websites (e.g., Facebook, Twitter, Google, etc.), and / or any other Internet website, such as Yahoo!, MSN, Apple iTunes, Experian, etc. This expansion effectively leverages AME's media impression tracking capabilities and the use of databases of non-AME entities (such as social media and other websites) to create a large, demographically accurate panel that forms an accurate, reliable measurement of exposure to Internet media (such as advertisements and / or programs). Examples of such media include websites, images presented on websites, and / or streaming media accessible via a computing device (Amazon Video, Netflix, Hulu, etc.).

[0035] Traditionally, AME (also referred to herein as "ratings entity") determines the demographic range for advertising and media programs based on registered panel members. That is, AME registers people who agree to be monitored into the panel. During registration, AME receives demographic information from the registered personnel, so that the subsequent correlation between advertising / media exposure rates and different demographic markets for those panel members can be formulated. Unlike traditional techniques in which AME relies solely on their own panel member data to collect audience measurement based on demographic information, the exemplary methods, devices and / or products disclosed herein enable AME to share demographic information with other entities that operate based on a user registration model. As used herein, the user registration model is a model in which users subscribe to the services of those entities by creating an account and providing information about their own demographics. The sharing of demographic information associated with the registered users of the database owner enables AME to expand or supplement its panel data with substantially reliable demographic information from external sources (e.g., the database owner), thereby expanding the coverage, accuracy and / or completeness of its audience measurement based on demographic information. This type of access also enables AME to monitor people who would not otherwise join the AME panel. Any entity that has a database that identifies the demographics of a group of individuals can work with AME. Such entities may be referred to as "database owners" and include entities such as wireless service operators, mobile software / service providers, social media sites (e.g., Facebook, Twitter, Google, etc.), and / or any other Internet site (such as Yahoo!, MSN, Apple iTunes, Experian, etc.) that collects demographic data about users in exchange for services.

[0036] The examples disclosed herein can be implemented by AME (e.g., any entity interested in measuring or tracking audience reach of advertisements, content, and / or any other media) in cooperation with any number of database owners, such as online web service providers that develop online media reach metrics. Such database owners / online web service providers can be wireless service operators, mobile software / service providers, social networking sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, Experian, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), and / or any other web service site that maintains user registration records.

[0037] The use of demographic information from heterogeneous data sources (e.g., high-quality demographic information from a panel of audience measurement entities and / or registered user data from network service providers) improves the reporting efficiency of metrics for both online and offline advertising campaigns. The exemplary techniques disclosed herein use online registration data to identify user demographics, and / or other user information, and use server impression counting and / or other techniques to track the amount of impressions caused by those users. Online network service providers (such as wireless service operators, mobile software / service providers, social networking sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, Experian, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.)) (collectively and individually referred to herein as online database owners) maintain detailed demographic information (e.g., age, gender, geographic location, race, income level, education level, religious beliefs, etc.) collected through the user registration process. An impression corresponds to a household or individual that has been exposed to the corresponding media and / or advertisement. Therefore, an impression represents a household or individual that has been exposed to an advertisement or media or a group of advertisements or media. In Internet advertising, the number of impressions or impression count is the total number of times an advertisement or advertising campaign has been visited by networked people (e.g., including visits that are reduced by, for example, pop-up blockers and / or augmented by, for example, retrieval from a local cache).

[0038] Examples disclosed herein adjust impression information obtained from one or more client devices and one or more database owners to improve the accuracy of demographics corresponding to recorded impressions. When using database owner data to provide demographic information for impressions, audience demographics and / or impression information obtained from one or more client devices and one or more database owners can be biased due to errors, including: 1) misidentification errors arising from device sharing, and / or 2) database owner non-coverage errors. In some cases, these two different sources of bias appear to form similar error factors, but are actually different biases. Examples disclosed herein generate calibration factors and apply the calibration factors to audience data to correct these errors.

[0039] Misidentification error refers to a measurement bias that occurs when a first person belonging to a first demographic group is considered to be the person associated with a media impression on a device when in fact a second person belonging to a second demographic group is the person for whom the media impression occurred. In some examples of such situations, a mobile device is shared among multiple people in a household. Initially, a first person in the household uses a mobile device to access a website associated with a database owner (e.g., via a web browser of the mobile device, via an application installed on the mobile device, etc.), and the database owner may identify the first person as being associated with the mobile device based on the access by the first person (e.g., a login event). Subsequently, the first person stops using the device but does not log out of the database owner system on the device, and / or the second person does not log into the database owner system, allowing the database owner to identify the second person as a different user than the first person. Therefore, when the second person begins to access media using the same mobile device, the database owner continues (in this case, incorrectly) to identify the use of the mobile device (e.g., media impression) as being associated with the first person. Therefore, impressions that should be attributed to the second person and the second demographic group are incorrectly attributed to the first person and the first demographic group. The effect of large-scale misidentification errors can create measurement bias errors by misrepresenting the demographic distribution of media impressions among a large audience and thereby distorting the audience demographics of impressions collected for advertisements and / or other media whose exposure is monitored by the audience measurement entity.

[0040] Exemplary Technical Advantages of the Disclosed Examples

[0041] Prior art techniques for correcting false positive identification errors include determining an adjustment factor by comparing: A) demographic information collected for a computation session using panelist metering software installed at a client computer, and B) demographic information determined for the same computation session using a cookie-based impression from a database owner. Examples of such techniques are disclosed in U.S. Patent Application Serial No. 13 / 756,493, filed on January 31, 2013. The entire contents of U.S. Patent Application Serial No. 13 / 756,493 are incorporated herein by reference. The examples disclosed in U.S. Patent Application Serial No. 13 / 756,493 rely on panelist metering software installed locally at a client computer to accurately identify panelists registered in a panel of an audience measurement entity. The examples disclosed in U.S. Patent Application Serial No. 13 / 756,493 also rely on cookies or cookie-like data to determine the adjustment factor. Such techniques are not suitable for correcting false positive identification errors on devices that do not have panelist metering software installed and / or do not provide cookies that can be used as client device identifiers, such as some mobile devices (e.g., iOS devices). In other words, the prior art relies on locally installed panelist metering software and cookies to generate the misidentification adjustment factor. Without such locally installed panelist metering software and / or without such cookies, the prior art will not be able to successfully generate the misidentification adjustment factor.

[0042] Compared to existing systems and methods, the examples disclosed herein generate misidentification factors using a misidentification correction matrix based on responses to surveys conducted on randomly selected persons and / or households, and do not rely on or use web trackers to generate misidentification correction factors and / or misidentification correction matrices. As used herein, the term "database owner registration status" as used with reference to a person refers to whether the person is registered with one or more specific database owners. Existing techniques that rely on locally installed panelist metering software to collect database owner registration status (e.g., whether individual family members are registered with a specific database owner) and collect media access data conducted by family members may not accurately correct impression information for misidentification bias errors and / or non-coverage bias errors, which are for impressions that occur on device types that are not monitored by the panelist metering software. The examples disclosed herein generate misidentification correction factors without relying on panelist metering software installed locally at a client computer to collect database owner registration status data. The examples disclosed herein also generate misidentification correction factors without relying on such locally installed panelist metering software to collect data about media access by family members. Thus, the examples disclosed herein determine misidentification correction factors for any device type, including device types for which database owner registration status data, database owner login data, and / or data regarding media access of family members are not collected (and / or are not collectable) using locally installed panelist metering software. Such devices are referred to herein as "non-local metering devices."

[0043] In some of these disclosed examples, the panelist data is collected and used differently in a manner different from the prior art. In such examples, the panelist data is used to adjust the error identification correction factor to more accurately determine the incidence of people in the demographic groups living together. As used in this article, the "incidence of people in the demographic groups living together" refers to the relative frequency of people in the first demographic group living with people in the second demographic group (for example, the percentage of people in demographic group A who live with someone in demographic group B). However, the aggregated audience distribution corresponding to the collected impressions is still generated without reference to the panelist data. In the examples disclosed in this article, the panelist data is only used to adjust the generated aggregated audience distribution. The adjusted aggregated audience distribution is used to generate and / or adjust the error identification correction factor. For example, the panelist data that can be used to adjust the incidence of people in the demographic group living together can include the corresponding number of people in the first demographic group who live with people in the second demographic group (for example, the same demographic group as the first demographic group or a different demographic group). In some examples, panelist data used to adjust the incidence of people in a demographic group living together does not indicate whether the panelist is a registered user of the database owner (eg, does not include web trackers) and does not include information indicating media access using a computing device.

[0044] The disclosed examples determine the probability of misidentification by determining the probability that a person in demographic group (j) is an actual viewer of the media when the database owner identifies a person in demographic group (i) as a viewer of the media. In some examples, the probability of misidentification is calculated by redistributing the audience and / or impressions observed in association with households using a survey calibration data source. In such examples, the survey calibration data source is used to aggregate and adjust the redistributed audience and / or impressions for multiple households for oversampling and / or undersampling. In some such examples, the formed probabilities are normalized to reflect the probability of misidentification for each impression observed by the database owner and associated with the demographic group.

[0045] Other disclosed examples determine the probability of misidentification due to device sharing as a combination of three separate probabilities: a) probability of living in the same household, b) probability of having access to a mobile device (of any type) in the household, and c) probability of sharing a mobile device for a particular content type. Examples disclosed herein apply the formed probability of misidentification to impression data as a factor to compensate for errors in the unique audience represented by the collected impression data. In some examples, providing such error compensation involves constructing a misidentification correction matrix to reflect the probability that an impression identified by a database owner as associated with a first demographic group should actually be associated with a second demographic group.

[0046] As used herein, non-coverage error is defined to refer to measurement bias that occurs due to the database owner's inability to identify a portion of the audience (e.g., their demographics) that uses mobile devices to view media. In some instances, when a request is sent from a mobile device to the database owner, as in the examples disclosed above, the database owner is unable to match the data in the request to a person. The database owner may not be able to identify the person associated with a given impression for the following reasons: 1) the person accessing the media that gave rise to the impression has not provided information about themselves to the database owner (e.g., the person is not registered with the database owner (e.g., Facebook) and therefore the database owner has no record of the person, the registration profile corresponding to the person is incomplete, the registration profile corresponding to the person has been flagged as suspected of containing inaccurate information, etc.), 2) the person is registered with the database owner but has not accessed the database owner using the specific mobile device on which the impression occurred (e.g., the database owner is only accessed from a computer and / or a mobile device other than the mobile device associated with the current request, and / or a user identifier for the person is not available on the mobile device on which the impression occurred), and / or 3) the person is registered with the database owner and accesses the database owner using the mobile device on which the impression occurred (e.g., the person has previously logged into the database owner from the mobile device), but has taken other active or passive measures that prevent the database owner from associating the mobile device with the person (e.g., blocking or deleting web trackers). In some examples, a user identifier for a person is not available on the mobile device on which the impression occurred because the mobile device and / or application / software on the mobile device is not a cookie-based device and / or application.

[0047] The examples disclosed herein generate device sharing matrices and / or uncovered factors for different combinations of media categories and / or mobile device types. Examples of media categories (for which separate device sharing matrices and / or uncovered factors can be generated) include advertising, children's programs, comedies, dramas, feature films, information and / or news programs, sports, variety shows (e.g., game shows, reality shows, talk shows), and / or other categories. Examples of device types (for which separate device sharing matrices and / or uncovered factors can be generated) include smartphones (e.g., iPhones, smartphones based on Android operating systems, Blackberry smartphones, smartphones based on Windows Mobile, etc.), tablet computers (e.g., iPads, tablet computers based on Android operating systems, etc.), portable media players (e.g., iPods, etc.), and / or other device types. Such device types can be network tracker-based devices (e.g., devices running network tracker-based applications / software) and / or non-network tracker-based devices (e.g., devices running applications / software that do not use network trackers, such as Apple iOS devices).

[0048] The disclosed exemplary methods and apparatus for compensating impression information for misidentification and / or non-coverage errors solve the technical problem of accurately determining demographic information associated with impressions of media that are transmitted and monitored via a network such as the Internet. Such computing devices are used to collect media impressions that occur at the computing device and data indicating that the media impressions occur. Calibration of such data may include collecting, processing, and / or analyzing hundreds, thousands, millions, or more impressions. Therefore, calibration and / or correction of such large amounts of data presents a huge technical challenge. The disclosed examples can be applied to a set of media impressions collected via a computing device to generate accurate demographic information for a huge number of media impressions (e.g., 100,000 impressions per week) in an efficient and timely manner. This provides significant improvements in the technical field of audience measurement.

[0049] The examples disclosed herein can be used with an online activity rating (OCR) system developed by Nielsen, Inc. (USA). The OCR system is an effective system for collecting and analyzing large amounts of data. The OCR system does not require panelist software to obtain the data to be processed. The technology disclosed herein is able to generate adjustment factors without the need to introduce panelist software. This reduces the amount of software required and eliminates the need for end-user computers. These are technical advantages that reduce the cost and use of computing resources. The technology disclosed herein is also used to effectively calibrate the OCR automatic system to correct misidentification errors that may occur in its operation. Therefore, the disclosed technology solves the technical problem of calibrating the OCR system to accurately reflect real-world conditions, and therefore, calibration is achieved by eliminating the need for panelist software distributed throughout the system.

[0050] The disclosed exemplary method includes: receiving a first request from a first type of computing device at a first Internet domain. In the exemplary method, the first request indicates access to media at the computing device. The exemplary method also includes: sending a request for demographic information corresponding to a plurality of requests received from the first type of computing device at the first Internet domain. The plurality of requests includes the first request. The exemplary method also includes: obtaining a count of media impressions occurring on the first type of computing device, a first portion of the media impressions corresponding to persons whose demographic information is identifiable by a database owner, and a second portion of the media impressions corresponding to persons whose demographic information is not available to the database owner; and determining demographic information for the second portion of the media impressions based on a first probability of accessing the media corresponding to the media impression on the first type of computing device and based on a second probability of accessing the media on the second type of computing device.

[0051] In some exemplary methods, determining the demographic information includes multiplying a ratio of the first probability to the second probability by the number of media impressions attributed to the first demographic group. In some exemplary methods, the first probability is a probability that a person in the first demographic group accesses the media on a first type of computing device, and the second probability is a probability that a person in the first demographic group accesses the media on a second type of computing device. Some exemplary methods also include adjusting the media impressions to compensate for incorrectly attributing a subset of the media impressions to a second person in a second demographic group, and determining the number of media impressions attributed to the first demographic group from the subset of media impressions adjusted to compensate for the incorrectly attributed media impressions.

[0052] In some exemplary methods, the first type of computing device includes a mobile device and the second type of computing device includes a television. In some exemplary methods, the first type of computing device includes at least one of the following: a smartphone, a tablet computer, or a portable media player. In some exemplary methods, the first probability and the second probability correspond to a media category of the media. In some exemplary methods, the media category is at least one of comedy, drama, politics, reality, or a combination of media categories.

[0053] Some exemplary methods also include: before determining the demographic information, adjusting the media impression for a situation in which the media impression is being incorrectly attributed to a person who did not cause the media impression. Some exemplary methods also include: calculating a first probability and a second probability based on survey responses to a survey of the person, calculating the first probability including determining a weight associated with at least one of a demographic group, a media category, a type of computing device, or a geographic region from the survey responses, the weight indicating a corresponding probability of accessing the media associated with the media impression on the device type of interest.

[0054] In some exemplary methods, the survey is at least one of a random panel or a panel of audience members maintained by an audience measurement entity. In some exemplary methods, determining demographic information for the portion of the media impressions includes determining proportions of media impressions attributed to different demographic groups corresponding to the person, and scaling the proportions of the media impressions to the portion of the media impressions.

[0055] Some exemplary methods also include: providing instructions to an issuer, wherein the instructions are to be provided by the issuer to a computing device. When executed by the computing device, the instructions provided by the issuer cause the computing device to send a first request. Some exemplary methods also include: saving computer processing resources by not communicating with individual online users about their online media access activities and by not requesting survey responses from online users to determine a first probability of accessing media corresponding to a media impression on a first type of computing device or to determine a second probability of accessing the media on a second type of computing device. Some exemplary methods also include: saving network communication bandwidth by not communicating with individual online users about their online media access activities and by not requesting survey responses from online users to determine a first probability of accessing media corresponding to a media impression on a first type of computing device or to determine a second probability of accessing the media on a second type of computing device.

[0056] The disclosed exemplary apparatus includes an impression collector for receiving a first request from a computing device at a first Internet domain and sending a request for demographic information, the demographic information corresponding to a plurality of requests received from a computing device of a first type at the first Internet domain, the plurality of requests including the first request. In the disclosed exemplary apparatus, the first request indicates access to media at the computing device. The disclosed exemplary apparatus also includes an impression information collector for accessing counts of media impressions occurring on computing devices of the first type, a first portion of the media impressions corresponding to persons whose demographic information is identifiable by a database owner, and a second portion of the media impressions corresponding to persons whose demographic information is not available to the database owner. The disclosed exemplary apparatus also includes a non-coverage detector for determining demographic information for the portion of the media impressions based on a first probability of accessing the media corresponding to the media impressions on a computing device of the first type and based on a second probability of accessing the media on a computing device of the second type, at least one of the impression information collector or the non-coverage detector being implemented by logic circuitry.

[0057] Some example apparatuses also include a non-coverage calculator for multiplying a ratio of the first probability to the second probability by the number of media impressions attributed to the first demographic group. In some example apparatuses, the first probability is a probability that a person in the first demographic group accesses the media on a first type of computing device, and the second probability is a probability that a person in the first demographic group accesses the media on a second type of computing device.

[0058] Some exemplary apparatuses also include an error identification corrector for adjusting media impressions to compensate for incorrectly attributing a subset of media impressions to a second person in a second demographic group, and determining a number of media impressions attributed to the first demographic group from the subset of media impressions adjusted to compensate for the incorrect attribution. In some exemplary apparatuses, the first type of computing device includes a mobile device and the second type of computing device includes a television. In some exemplary apparatuses, the first type of computing device includes at least one of the following: a smartphone, a tablet computer, or a portable media player. In some exemplary apparatuses, the non-coverage calculator is used to calculate the first probability and the second probability based on survey responses to a survey of people, and the non-coverage calculator is used to calculate the first probability by determining a weight associated with at least one of a demographic group, a media category, a type of computing device, or a geographic region from the survey responses, the weight indicating a corresponding probability of accessing the media associated with the media impression on the device type of interest.

[0059] In some exemplary arrangements, the survey is at least one of a random panel or a panel of audience members maintained by an audience measurement entity. In some exemplary arrangements, the first probability and the second probability correspond to a media category of the media. In some exemplary arrangements, the media category is at least one of comedy, drama, politics, reality, or a combined media category. In some exemplary arrangements, the uncovered corrector is used to determine demographic information for the portion of the media impressions by determining a proportion of media impressions that are attributed to different demographic groups corresponding to the person, and scaling the proportion of the media impressions to the portion of the media impressions.

[0060] An additional disclosed exemplary method includes: receiving a first request from a computing device of a first type at a first Internet domain and sending a request for demographic information corresponding to a plurality of requests received from the computing device of the first type at the first Internet domain, the plurality of requests including the first request. In the disclosed exemplary method, the first request indicates access to media at the computing device. The disclosed exemplary method also includes: generating an aggregated audience distribution, the aggregated audience distribution including a first audience distribution of a first household aggregated with a second audience distribution of a second household, based on survey responses of the first household, the first audience distribution including a distribution of first family members of the first household among demographic groups of the first family members registered with a database owner, the first audience distribution based on access to the first media by the first family members; normalizing the aggregated audience distribution to generate a misidentification correction matrix, the misidentification correction matrix including probabilities of the following events: when the database owner determines that an impression of the media corresponds to a person in the second demographic group, the impression is attributed to the first demographic group; and compensating for misidentification errors in the impression by reallocating the impression from the second demographic group to the first demographic group using the misidentification correction matrix.

[0061] Some exemplary methods also include: generating a correction indicator to correct the aggregate audience distribution for at least one of oversampling or undersampling associated with a survey calibration data source indicating access to media by a first family member and a registration status of the first family member with a database owner. In some exemplary methods, the correction indicator is based on a second probability that a first person in a first demographic group lives with a second person in a second demographic group. In some exemplary methods, generating the correction indicator includes: determining a first number of first person pairs based on first survey calibration data, which represents first person pairs in the first person pair including a first person in the first demographic group and a second person in the second demographic group living together; determining a second number of second person pairs based on second survey calibration data, which represents second person pairs in the second person pair including a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; and determining a ratio of the first number to the second number.

[0062] In some exemplary methods, for each first demographic group, the aggregate audience distribution describes the number of persons in the second demographic group who will be attributed to the first demographic group, and normalizing the aggregate audience distribution includes scaling the number of persons in the second demographic group so that the total number of persons for the second demographic group is a specified value. Some exemplary methods also include: determining a sharing pattern for the first household, the sharing pattern indicating first family members of the first family members who access the media category and other first family members of the first family members who do not access the media category; and determining a probability density function of device sharing probabilities for first family members in the first household based on the sharing pattern for the media category, the probability density function indicating corresponding probabilities of first family members accessing media in the media category, the first audience distribution being based on the device sharing probabilities.

[0063] Some exemplary methods also include: aggregating the device sharing probability for the first family members when the first family has two or more registered first family members of the first family members who are in the same second demographic group. Some exemplary methods also include: generating a first audience distribution by distributing a portion of the numbers into the demographic groups, the portion of the numbers totaling a count of those first family members of the first family members who are registered with the database owner. In some exemplary methods, generating the first audience distribution does not use a web tracker. In some exemplary methods, generating the first audience distribution and generating the aggregated audience distribution do not use metering software to collect the registration status of the family members with the database owner or to collect media access data. In some exemplary methods, reallocating impressions includes determining the product of an error identification correction matrix and an impression matrix, the impression matrix indicating the number of impressions corresponding to the respective demographic groups determined by the database owner. In some exemplary methods, reallocating impressions results in a total number of impressions being the same as the total number of impressions in the impression matrix.

[0064] Some exemplary methods also include providing instructions to an issuer, which are provided by the issuer to a computing device and which, when executed by the computing device, cause the computing device to send a first request. Some exemplary methods also include saving computer processing resources by not communicating with individual online users about their online media access activities and by not requesting survey responses from online users to generate an aggregate audience distribution. Some exemplary methods also include saving network communication bandwidth by not communicating with individual online users about their online media access activities and by not requesting survey responses from online users to generate an aggregate audience distribution.

[0065] The additionally disclosed exemplary apparatus includes an impression collector for receiving a first request from a computing device of a first type at a first Internet domain and sending a request for demographic information corresponding to a plurality of requests received from the computing device of the first type at the first Internet domain, the plurality of requests including the first request. In the disclosed exemplary apparatus, the first request indicates access to the media at the computing device. The disclosed exemplary apparatus also includes an aggregate distribution generator for generating an aggregate audience distribution, the aggregate audience distribution including a first audience distribution of a first household aggregated with a second audience distribution of a second household, based on a survey response of the first household, the first audience distribution including a distribution of a first family member of the first household in a demographic group of the first family members registered with a database owner, the first audience distribution based on access to the first media by the first family member. The exemplary apparatus also includes a matrix normalizer for normalizing the aggregate audience distribution to generate a false positive correction matrix, the false positive correction matrix including probabilities of the following events: when the database owner determines that an impression of the media corresponds to a person in the second demographic group, the impression is attributed to the first demographic group. The disclosed exemplary apparatus also includes a misidentification corrector for compensating for misidentification errors in impressions by reallocating impressions from the second demographic group to the first demographic group using a misidentification correction matrix, wherein at least one of the aggregate distribution generator, the matrix normalizer, or the misidentification corrector is implemented by logic circuitry.

[0066] Some exemplary devices also include a matrix corrector for generating a correction indicator to correct the aggregate audience distribution for at least one of oversampling or undersampling associated with a survey calibration data source indicating access to media by a first family member and a registration status of the first family member with a database owner. In some exemplary devices, the correction indicator is based on a second probability that a first person in a first demographic group lives with a second person in a second demographic group. In some exemplary devices, the matrix corrector is used to generate the correction indicator by: determining a first number of first person pairs based on first survey calibration data, i.e., the number of first person pairs that respectively include a first person in the first demographic group and a second person in the second demographic group living together; determining a second number of second person pairs based on second survey calibration data, i.e., the number of second person pairs that respectively include a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; and determining a ratio of the first number to the second number.

[0067] In some exemplary apparatuses, for each first demographic group, an aggregate audience distribution describes the number of persons in a second demographic group who would be attributed to the first demographic group, and a matrix normalizer is used to normalize the aggregate audience distribution by scaling the corresponding number of persons in the second demographic group so that the number of persons for the second demographic group is a specified value.

[0068] Some exemplary devices also include a household distribution generator for: determining a sharing pattern for a first household, the sharing pattern indicating first family members among the first family members who access a media category and first family members among the first family members who do not access the media category; and determining a probability density function of device sharing probability for first family members in the first household based on the sharing pattern for the media category, the probability density function indicating a corresponding probability of the first family members accessing media in the media category, the first audience distribution being based on the device sharing probability.

[0069] In some exemplary devices, an aggregate distribution generator is used to aggregate device sharing probabilities for first family members when the first family has two or more registered first family members of the first family members who are in the same second demographic group. Some exemplary devices also include a household distribution generator, which is used to generate a first audience distribution by distributing partial numbers into demographic groups, the partial numbers totaling a count of those first family members of the first family members registered with a database owner. In some exemplary devices, an error positive correction is used to reallocate impressions by determining the product of an error positive correction matrix and an impression matrix, the impression matrix indicating the number of impressions corresponding to respective demographic groups determined by the database owner. In some exemplary devices, the error positive correction is used to reallocate impressions so that the total number of reallocated impressions is the same total number of impressions as the total number of impressions in the impression matrix.

[0070] An additional disclosed exemplary method includes: receiving a first request from a computing device of a first type at a first Internet domain and sending a request for demographic information corresponding to a plurality of requests received from the computing device of the first type at the first Internet domain, the plurality of requests including the first request. In the disclosed exemplary method, the first request indicates access to media at the computing device. The disclosed exemplary method also includes: generating an aggregated audience distribution, the aggregated audience distribution including a first audience distribution for a first household aggregated with a second audience distribution for a second household, the first audience distribution including a distribution of first family members of the first household among a demographic group of those first family members registered with a database owner, the first audience distribution based on access to first media by the first family members, the aggregated audience distribution generated without using a web tracker; normalizing the aggregated audience distribution to generate a false positive correction matrix, the false positive correction matrix including probabilities of the following events: when the database owner determines that an impression of the media corresponds to a person in the second demographic group, the impression is attributed to the first demographic group; and compensating for false positive errors in the impression by reallocating the impression from the second demographic group to the first demographic group using the false positive correction matrix, the impression indicating media accessed on a mobile device.

[0071] Some exemplary methods also include: generating a correction indicator to correct the aggregate audience distribution for at least one of oversampling or undersampling associated with a survey calibration data source indicating access to media by a first family member and a registration status of the first family member with a database owner. In some exemplary methods, the correction indicator is based on a second probability that a first person in a first demographic group lives with a second person in a second demographic group. In some exemplary methods, generating the correction indicator includes: determining a first number of first person pairs based on first survey calibration data, which represents the number of first person pairs including a first person in a first demographic group and a second person in a second demographic group living together; determining a second number of second person pairs based on second survey calibration data, which represents the number of second person pairs including a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; and determining a ratio of the first number to the second number.

[0072] In some exemplary methods, for each first demographic group, the aggregate audience distribution describes the number of persons in the second demographic group who will be attributed to the first demographic group, and normalizing the aggregate audience distribution includes scaling the number of persons in the second demographic group so that the number of persons for the second demographic group is a specified value. Some exemplary methods also include: determining a sharing pattern for the first household, the sharing pattern indicating first family members of the first family members who access the media category and other first family members of the first family members who do not access the media category; and determining a probability density function of device sharing probabilities for first family members in the first household based on the sharing pattern for the media category, the probability density function indicating corresponding probabilities of first family members accessing media in the media category, the first audience distribution being based on the device sharing probabilities.

[0073] Some exemplary methods also include: aggregating the device sharing probability for the first family members when the first family has two or more registered first family members of the first family members who are in the same second demographic group. Some exemplary methods also include: generating a first audience distribution by distributing a portion of the numbers into the demographic groups, the portion of the numbers totaling a count of those first family members of the first family members who are registered with the database owner. In some exemplary methods, generating the first audience distribution does not use a web tracker. In some exemplary methods, generating the first audience distribution and generating the aggregated audience distribution do not use metering software to collect the registration status of the family members with the database owner or to collect media access data. In some exemplary methods, reallocating impressions includes determining the product of an error identification correction matrix and an impression matrix, the impression matrix indicating the number of impressions corresponding to the respective demographic groups determined by the database owner. In some exemplary methods, reallocating impressions results in a total number of impressions being the same as the total number of impressions in the impression matrix.

[0074] Some exemplary methods also include providing instructions to an issuer, which are provided by the issuer to a computing device and which, when executed by the computing device, cause the computing device to send a first request. Some exemplary methods also include saving computer processing resources by not communicating with individual online users about their online media access activities and by not requesting survey responses from online users to generate an aggregate audience distribution. Some exemplary methods also include saving network communication bandwidth by not communicating with individual online users about their online media access activities and by not requesting survey responses from online users to generate an aggregate audience distribution.

[0075] An additional disclosed exemplary apparatus includes an impression collector for receiving a first request from a computing device of a first type in a first Internet domain and sending a request for demographic information corresponding to a plurality of requests received from computing devices of the first type in the first Internet domain, the plurality of requests including the first request. In the disclosed exemplary apparatus, the first request indicates access to media at the computing device. The disclosed exemplary apparatus also includes an aggregate distribution generator for generating an aggregate audience distribution, the aggregate audience distribution including a first audience distribution for a first household aggregated with a second audience distribution for a second household, the first audience distribution including a distribution of first family members of the first household among a demographic group of those first family members registered with a database owner, the aggregate distribution generator generating the aggregate audience distribution without using a web tracker, and the first audience distribution based on access to the first media by the first family members. The exemplary apparatus also includes a matrix normalizer for normalizing the aggregate audience distribution to generate a misidentification correction matrix, the misidentification correction matrix including probabilities of an impression of media being attributed to a first demographic group when a database owner determines that the impression corresponds to a person in a second demographic group. The exemplary apparatus also includes a misidentification corrector for compensating for misidentification errors in impressions by reallocating the impressions to the first demographic group using the misidentification correction matrix, the impressions indicating media accessed on a mobile device. At least one of the aggregate distribution generator, the matrix normalizer, or the misidentification corrector is implemented by logic circuitry.

[0076] Some exemplary devices also include a matrix corrector for generating a correction indicator to correct the aggregate audience distribution for at least one of oversampling or undersampling associated with a survey calibration data source indicating access to media by a first family member and a registration status of the first family member with a database owner. In some exemplary devices, the correction indicator is based on a second probability that a first person in a first demographic group lives with a second person in a second demographic group. In some exemplary devices, the matrix corrector is used to generate the correction indicator by: determining a first number of first person pairs based on first survey calibration data, i.e., the number of first person pairs that respectively include a first person in the first demographic group and a second person in the second demographic group living together; determining a second number of second person pairs based on second survey calibration data, i.e., the number of second person pairs that respectively include a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; and determining a ratio of the first number to the second number.

[0077] In some exemplary apparatuses, for each first demographic group, an aggregate audience distribution describes the number of persons in a second demographic group who would be attributed to the first demographic group, and a matrix normalizer is used to normalize the aggregate audience distribution by scaling the number of persons in the second demographic group so that the number of persons for the second demographic group is a specified value.

[0078] Some exemplary devices also include a household distribution generator for: determining a sharing pattern for a first household, the sharing pattern indicating first family members among the first family members who access a media category and other first family members among the first family members who do not access the media category; and determining a probability density function of device sharing probability for first family members in the first household based on the sharing pattern for the media category, the probability density function indicating corresponding probabilities of first family members accessing media in the media category, the first audience distribution being based on the device sharing probability.

[0079] In some exemplary devices, an aggregate distribution generator is used to aggregate device sharing probabilities for first family members when the first family has two or more registered first family members of the first family members who are in the same second demographic group. Some exemplary devices also include a household distribution generator, which is used to generate a first audience distribution by distributing partial numbers into demographic groups, the partial numbers totaling a count of those first family members of the first family members registered with a database owner. In some exemplary devices, an error positive correction is used to reallocate impressions by determining the product of an error positive correction matrix and an impression matrix, the impression matrix indicating the number of impressions corresponding to respective demographic groups determined by the database owner. In some exemplary devices, the error positive correction is used to reallocate impressions so that the total number of reallocated impressions is the same total number of impressions as the total number of impressions in the impression matrix.

[0080] An additional disclosed exemplary method includes: collecting media impressions from a first type of computing device; requesting demographic information for the media impressions from a database owner, a first portion of the media impressions corresponding to persons for whom the database owner stores demographic information, and a second portion of the media impressions corresponding to persons for whom demographic information is not available to the database owner; receiving demographic information corresponding to the first portion of the media impressions from the database owner; determining, using a processor, a number of media impressions in the second portion of the media impressions; and determining, using the processor, demographic information for the second portion of the media impressions based on a first probability of accessing media corresponding to the media impressions on the first type of computing device and based on a second probability of accessing the media on a second type of device.

[0081] An additional disclosed exemplary method includes: receiving a first request from a first type of computing device at a first Internet domain and receiving a second request from the computing device at the first Internet domain, the first request indicating access to media at the computing device, the second request indicating access to duration units of the media at the computing device. The exemplary method also includes: sending a request for demographic information corresponding to a plurality of requests received from the first type of computing device at the first Internet domain, the plurality of requests including the second request. The disclosed exemplary method also includes: obtaining a count of duration units occurring on the first type of computing device, a first portion of the duration units corresponding to persons whose demographic information is identifiable by a database owner, and a second portion of the duration units corresponding to persons whose demographic information is not available to the database owner; and determining demographic information for the second portion of the duration units based on a first probability of accessing the media corresponding to the duration units on the first type of computing device and based on a second probability of accessing the media on the second type of computing device.

[0082] An additionally disclosed exemplary apparatus includes an impression collector. In the disclosed exemplary apparatus, the impression collector is used to receive a first request from a computing device of a first type at a first Internet domain, the first request indicating access to media at the computing device. In the disclosed exemplary apparatus, the impression collector is also used to receive a second request from the computing device at the first Internet domain, the second request indicating access to duration units of media at the computing device. In the disclosed exemplary apparatus, the impression collector is also used to send a request for demographic information corresponding to a plurality of requests received from the computing device of the first type at the first Internet domain, the plurality of requests including the second request. The disclosed exemplary apparatus also includes an impression information collector for accessing a count of duration units occurring on the computing device of the first type, a first portion of the duration units corresponding to persons whose demographic information is identifiable to a database owner, and a second portion of the duration units corresponding to persons whose demographic information is not available to the database owner. The disclosed exemplary apparatus also includes an uncovered corrector for determining demographic information for a second portion of the duration unit based on a first probability of accessing the media corresponding to the duration unit on a first type of computing device and based on a second probability of accessing the media on a second type of computing device, and at least one of the impression information collector or the uncovered corrector is implemented by a logic circuit.

[0083] Although the examples disclosed herein are described with reference to compensating or adjusting impression information obtained from a mobile device, the examples are also applicable to non-mobile devices, such as desktop computers, televisions, video game consoles, set-top boxes, and / or other devices.

[0084] Impression and Demographic Information Collection

[0085] Figure 1 An exemplary system 100 is shown for collecting user information (e.g., user information 102a, 102b) from distributed database owners 104a, 104b for association with impressions of media presented at a client device 106. In the illustrated example, the user information 102a, 102b or user data includes one or more of the following: demographic data, purchase data, and / or other data indicating user activities, behaviors, and / or preferences regarding information accessed via the Internet, purchases, media accessed on electronic devices, physical locations visited by users (e.g., retail or commercial establishments, restaurants, venues, etc.), etc. The examples disclosed herein are described in conjunction with mobile devices, which may be mobile phones, mobile communication devices, tablet computers, gaming devices, portable media presentation devices, etc. However, the examples disclosed herein may be implemented in conjunction with non-mobile devices, such as Internet appliances, smart televisions, Internet terminals, computers, or any other device capable of presenting media received via network communications.

[0086] exist Figure 1 In the illustrated example of , in order to track media impressions on the client device 106, the audience measurement entity (AME) 108 cooperates or collaborates with the application publisher 110 to download and install the data collector 112 on the client device 106. The application publisher 110 of the illustrated example can be a software application developer that develops applications and distributes them to mobile devices, and / or a distributor that receives applications from software application developers and distributes them to mobile devices. The data collector 112 can be included in other software loaded on the client device 106, such as an operating system 114, an application (or app) 116, a web browser 117, and / or any other software. Figure 1 The exemplary client device 106 is a non-natively metered device. That is, the client device 106 does not support and / or is not configured with metering software (eg, metering software provided by AME 108).

[0087] Any of the exemplary software 114-117 can present media 118 received from a media publisher 120. The media 118 can be an advertisement, video, audio, text, graphic, web page, news, educational media, entertainment media, or any other type of media. In the illustrated example, a media ID 122 is provided in the media 118 to enable identification of the media 118 so that when the media 118 is presented on the client device 106 or any other device monitored by the AME 108, the AME 108 can believe that the media 118 has a media impression.

[0088] The data collector 112 of the illustrated example includes instructions (e.g., Java, Java script, or any other computer language or script) that, when executed by the client device 106, causes the client device 106 to collect media IDs 122 of media 118 presented by the application 116 and / or the client device 106, and to collect one or more device / user identifiers 124 stored in the client device 106. The one or more device / user identifiers 124 of the illustrated example include identifiers that can be used by corresponding ones of the collaborating database owners 104a-b to identify one or more users of the client device 106 and to locate user information 102a-b corresponding to the one or more users. For example, one or more device / user identifiers 124 may include software identifiers (e.g., International Mobile Equipment Identity (IMEI), Mobile Equipment Identifier (MEID), Media Access Control (MAC) address, etc.), application storage identifiers (e.g., Google Android ID, Apple ID, Amazon ID, etc.), open source unique device identifier (OpenUDID), open device identification number (ODIN), login identifier (e.g., user name), email address, user agent data (e.g., application type, operating system, software vendor, software version, etc.), third-party service identifiers (e.g., advertising service identifier, device usage analysis service identifier, demographic information collection service identifier), network storage data, document object model (DOM) storage data, local shared objects (also known as "Flash Cookies"), etc. In some examples, fewer or more device / user identifiers 124 may be used. In addition, although in Figure 1 Only two cooperating database owners 104a-b are shown in FIG. 1 , but AME 108 may cooperate with any number of cooperating database owners to collect distributed user information (eg, user information 102a-b).

[0089] In some examples, the client device 106 may not allow access to identifying information stored in the client device 106. For such instances, the disclosed examples enable the AME 108 to store an AME-provided identifier (e.g., an identifier managed and tracked by the AME 108) in the client device 106 to track media impressions on the client device 106. For example, the AME 108 may provide instructions in the data collector 112 to set the AME-provided identifier in a storage space accessible to and / or assigned to the application 116, and the data collector 112 uses the identifier as the device / user identifier 124. In such examples, the AME-provided identifier set by the data collector 112 persists in the storage space even when the application 116 and the data collector 112 are not running. In this manner, the same AME-provided identifier may remain associated with the client device 106 for an extended duration. In some examples where the data collector 112 sets an identifier in the client device 106, the AME 108 can recruit the user of the client device 106 as a panelist and can store user information collected from the user during the panelist registration process and / or collected by monitoring the user's activities / behaviors via the client device 106 and / or any other device used by the user and monitored by the AME 108. In this manner, the AME 108 can associate the user's user information (from the panelist data stored by the AME 108) with media impressions attributed to the user on the client device 106.

[0090] In the illustrated example, the data collector 112 sends the media ID 122 and the one or more device / user identifiers 124 to the application publisher 110 as collected data 126. Alternatively, the data collector 112 can be configured to send the collected data 126 to another collection entity (other than the application publisher 110) that has been contracted by or is working with AME 108 to collect media IDs (e.g., media ID 122) and device / user identifiers (e.g., one or more device / user identifiers 124) from mobile devices (e.g., client devices 106). In the illustrated example, the application publisher 110 (or collection entity) sends the media ID 122 and the one or more device / user identifiers 124 as impression data 130 to an impression collector 132 at AME 108. The impression data 130 of the illustrated example may include one media ID 122 and one or more device / user identifiers 124 for a single impression of the reported media 118, or the impression data 130 may include multiple media IDs 122 and one or more device / user identifiers 124 for multiple impressions of the reported media based on multiple instances of collected data (e.g., collected data 126) received from the client device 106 and / or other mobile devices.

[0091] In the illustrated example, the impression collector 132 stores the impression data 130 in an AME media impression storage 134 (e.g., a database or other data structure). Subsequently, the AME 108 sends the one or more device / user identifiers 124 to the corresponding cooperative database owners (e.g., cooperative database owners 104a-b) to receive user information (e.g., user information 102a-b) corresponding to the one or more device / user identifiers 124 from the cooperative database owners 104a-b, so that the AME 108 can associate the user information with the corresponding media impression of the media (media 118) presented at the mobile device (e.g., the client device 106).

[0092] In some examples, the media identifier 122 and / or the one or more device / user identifiers 124 are encrypted before being sent to the AME 108 and / or the collaborating database owners 104a-b in order to protect the privacy of the user of the client device 106. In other examples, the media identifier 122 and / or the one or more device / user identifiers 124 are not encrypted.

[0093] After the AME 108 receives one or more device / user identifiers 124, the AME 108 sends the device / user identifier logs 136a-b to the corresponding collaborative database owners (e.g., collaborative database owners 104a-b). In some examples, each of the device / user identifier logs 136a-b may include a single device / user identifier, or each of the device / user identifier logs 136a-b may include multiple aggregated device / user identifiers received from one or more mobile devices over time. After receiving the device / user identifier logs 136a-b, each of the collaborative database owners 104a-b looks up its user corresponding to the device / user identifier 124 in the respective logs 136a-b. In this manner, each of the collaborative database owners 104a-b collects user information 102a-b corresponding to the users identified in the device / user identifier logs 136a-b for sending to the AME 108. For example, if the cooperative database owner 104a is a wireless service provider and the device / user identifier log 136a includes an IMEI code that can be recognized by the wireless service provider, the wireless service provider accesses its subscriber records to find a user with an IMEI code that matches the IMEI code received in the device / user identifier log 136a. When the user is identified, the wireless service provider copies the user's user information to the user information 102a for transmission to the AME 108.

[0094] In some other examples, the data collector 112 is configured to collect one or more device / user identifiers 124 from the client device 106. The example data collector 112 sends the one or more device / user identifiers 124 to the application publisher 110 in the collected data 126, and the example data collector 112 also sends the one or more device / user identifiers 124 to the media publisher 120. In such other examples, the data collector 112 does not collect the media ID 122 from the media 118 at the client device 106 because the data collector 112 is not Figure 1106. Instead, the media publisher 120 that publishes the media 118 to the client device 106 retrieves the media ID 122 from the media 118 it publishes. The media publisher 120 then associates the media ID 122 to the one or more device / user identifiers 124 received from the data collector 112 executing in the client device 106, and sends the collected data 138 to the application publisher 110, the collected data 138 including the media ID 122 and the associated one or more device / user identifiers 124 of the client device 106. For example, when the media publisher 120 sends the media 118 to the client device 106, it is achieved by identifying the client device 106 as the target device for the media 118 using one or more of the one or more device / user identifiers 124 received from the client device 106. In this manner, the media publisher 120 may associate the media ID 122 of the media 118 with one or more device / user identifiers 124 of the client device 106 , indicating that the media 118 was sent to a particular client device 106 for presentation (eg, generating an impression of the media 118 ).

[0095] In some other examples where the data collector 112 is configured to send the one or more device / user identifiers 124 to the media publisher 120, the data collector 112 does not collect the media ID 122 from the media 118 at the client device 106. Instead, the media publisher 120 that issues the media 118 to the client device 106 also retrieves the media ID 122 from the media 118 it issues. The media publisher 120 then associates the media ID 122 with the one or more device / user identifiers 124 of the client device 106. The media publisher 120 then sends the media impression data 130 (including the media ID 122 and the one or more device / user identifiers 124) to the AME 108. For example, when the media publisher 120 sends the media 118 to the client device 106, it does so by identifying the client device 106 as a target device for the media 118 using one or more of the one or more device / user identifiers 124. In this manner, the media publisher 120 may associate the media ID 122 of the media 118 with one or more device / user identifiers 124 of the client device 106, indicating that the media 118 was sent to a particular client device 106 for presentation (e.g., to generate an impression of the media 118). In the illustrated example, after the AME 108 receives the impression data 130 from the media publisher 120, the AME 108 may then send the device / user identifier logs 136a-b to the partner database owners 104a-b to request the above-described combination. Figure 1 Described user information 102a-b.

[0096] Despite Figure 1 106, but application publisher 110 may implement at least some of the operations of media publisher 120 to send media 118 to client device 106 for presentation. For example, an advertisement provider, media provider, or other information provider may send media (media 118) to application publisher 110, which may distribute the media to client device 106 via, for example, application 116 (when it is executed on client device 106). In such examples, application publisher 110 implements the operations described above as performed by media publisher 120.

[0097] Additionally or alternatively, compared to the above examples where the client device 106 sends the identifier to the audience measurement entity 108 (e.g., via the application publisher 110, the media publisher 120, and / or other entity), in other examples, the client device 106 (e.g., the data collector 112 installed on the client device 106) sends the identifier (e.g., one or more device / user identifiers 124) directly to the corresponding database owners 104a, 104b (e.g., without the aid of the AME 108). In such examples, the exemplary client device 106 sends the media identifier 122 to the audience measurement entity 108 (e.g., directly or through an intermediary, such as via the application publisher 110), but does not send the media identifier 122 to the database owners 104a-b.

[0098] As described above, the exemplary collaborative database owners 104a-b provide user information 102a-b to the exemplary AME 108 for matching with the media identifier 122 to form media impression information. As also described above, the database owners 104a-b are not provided with a copy of the media identifier 122. Instead, the client provides the database owners 104a-b with an impression identifier 140. The impression identifier uniquely identifies the impression event relative to other impression events of the client device 106, so that the occurrence of the impression at the client device 106 can be distinguished from other occurrences of the impression. However, the impression identifier 140 itself does not identify the media associated with the impression event. In such examples, the impression data 130 from the client device 106 to the AME 108 also includes the impression identifier 140 and the corresponding media identifier 122. To match the user information 102a-b with the media identifier 122, the exemplary collaborative database owner 104a-b provides the user information 102a-b to the AME 108 associated with the impression identifier 140 that is used to trigger the impression event that collects the user information 102a-b. In this manner, the AME 108 can match the impression identifier 140 received from the client device 106 to the corresponding impression identifier 140 received from the collaborative database owner 104a-b to associate the media identifier 122 received from the client device 106 with the demographic information in the user information 102a-b received from the database owner 104a-b. The impression identifier 140 can additionally be used to reduce or avoid duplication of demographic information. For example, the exemplary collaborative database owner 104a-b may provide the user information 102a-b and the impression identifier 140 to the AME 108 on a per-impression basis (e.g., each time a client device 106 sends a request including the encrypted identifier 208a-b and the impression identifier 140 to the collaborative database owner 104a-b) and / or on an aggregate basis (e.g., sending a set of user information 102a-b to the AME 108 presented at the client device 106, which set of user information 102a-b may include indications of multiple impressions (e.g., multiple impression identifiers 140) at the mobile devices 102a-b).

[0099] The impression identifier 140 provided to the AME 108 enables the AME 108 to distinguish between unique impressions and avoid overcounting the number of unique users and / or devices viewing the media. For example, the relationship between the user information 102a from the cooperative A database owner 104a and the user information 102b from the cooperative B database owner 104b for the client device 106 is not readily apparent to the AME 108. By including the impression identifier 140 (or any similar identifier), the exemplary AME 108 can associate user information corresponding to the same user between the user information 102a-b based on matching impression identifiers 140 stored in both of the user information 102a-b. The exemplary AME 108 can use such matching impression identifiers 140 on the user information 102a-b to avoid overcounting mobile devices and / or users (e.g., by only counting unique users rather than counting the same user multiple times).

[0100] For example, if an impression causes the client device 106 to send multiple device / user identifiers to multiple different database owners 104a-b without an impression identifier (e.g., impression identifier 140), the same user may be counted multiple times. For example, a first of the database owners 104a sends first user information 102a to AME 108, which signals that the impression occurred. Additionally, a second of the database owners 104b sends second user information 102b to AME 108, which signals (separately) that the impression occurred. Additionally, separately, the client device 106 sends an indication of the impression to AME 108. Without knowing that the user information 102a-b are from the same impression, AME 108 has an indication of a single impression from the client device 106 and an indication of multiple impressions from the database owners 104a-b.

[0101] To avoid over-counting impressions, the AME 108 may use the impression identifier 140. For example, after looking up the user information 102a-b, the exemplary cooperating database owners 104a-b transmit the impression identifier 140 to the AME 108 having the corresponding user information 102a-b. The AME 108 matches the impression identifier 140 obtained directly from the client device 106 to the impression identifier 140 received from the database owner 104a-b having the user information 102a-b, thereby associating the user information 102a-b with the media identifier 122 and generating the impression information. This is possible because the AME 108 receives the media identifier 122 associated with the impression identifier 140 directly from the client device 106. Therefore, the AME 108 can map the user data from two or more database owners 104a-b to the same media contact event, thereby avoiding double counting.

[0102] Each unique impression identifier 140 in the illustrated example is associated with a particular impression of media on the client device 106. The collaborating database owners 104a-b receive the respective device / user identifiers 124 and generate the user information 102a-b independently (e.g., without regard to the others of the collaborating database owners 104a-b) and without knowledge of the media identifiers 122 involved in the impressions. Without indicating that specific user demographics in user information 102a (received from collaborative database owner 104a) are associated with (e.g., are a result of) the same impression as specific user demographics in user information 102b (received from collaborative database owner 104b independently of user information 102a being received from collaborative database owner 104a) at client device 106, and without reference to impression identifier 140, AME 108 may be unable to associate user information 102a with user information 102b and / or determine that different pieces of user information 102a-b are associated with the same impression, and therefore count user information 102a with user information 102b as corresponding to two different users / devices and / or two different impressions.

[0103] The above examples illustrate methods and devices for collecting data at an audience measurement entity (or other entity). The examples discussed above can be used to collect impression information for any type of media, including static media (e.g., advertising images), streaming media (e.g., streaming video and / or audio, including content, advertising, and / or other types of media), and / or other types of media. For static media (e.g., media without a time component, such as images, text, web pages, etc.), the exemplary AME 108 records an impression for each occurrence of the media being presented, transmitted, or otherwise provided to the client device 106. For streaming media (e.g., video, audio, etc.), the exemplary AME 108 measures demographic data for media that appear over a period of time. For example, when the media is located in the client application / software 114-117, the AME 108 (e.g., with the help of the application publisher 110 and / or the media publisher 120) provides a beacon instruction to the client application or client software (e.g., OS 114, web browser 117, application 116, etc.) executed on the client device 106. In some examples, the beacon instructions cause the client application / software 114-117 to send requests (e.g., broadcast messages) to the impression collector 132 at regular intervals and / or irregular intervals (e.g., every minute, every 30 seconds, every 2 minutes, etc.). By monitoring and / or counting the requests that occur at intervals, the exemplary AME 108 monitors the duration of individual impressions of duration-based media (e.g., video, audio, etc.). The exemplary AME 108 can determine the number of impressions (e.g., initial loads) of duration-based media, the unique audience of duration-based media, and / or the total duration (in units such as seconds or minutes) of duration-based media viewed across multiple impressions. As used herein, the term "impression information" can include impressions and / or duration units. The exemplary impression collector 132 identifies requests from the web browser 117 and matches the impression information for the media with the demographics of the user of the web browser 117 in conjunction with one or more database owners.

[0104] In some examples, a user loads (e.g., via a browser 117) a web page from a website publisher, where the web page corresponds to a particular 60-minute video. As part of the exemplary web page or in addition to the exemplary web page, the website publisher causes the data collector 112 to send a broadcast message (e.g., a beacon request) to the beacon server 142, for example, by providing beacon instructions to the browser 117. For example, when the beacon instructions are executed by the exemplary browser 117, the beacon instructions cause the data collector 112 to send a broadcast message (e.g., a beacon request, an HTTP request, a sound pulse) to the impression collector 132 at a specified interval (e.g., every minute or any other suitable interval). The exemplary beacon instructions (or, for example, a redirect message from the impression collector 132 or the database owners 104a-b) also cause the data collector 112 to send the broadcast message or beacon request to one or more database owners 104a-b, which collect and / or retain demographic information about the user. The database owners 104a-b transmit demographic information about the users associated with the data collector 112 for combination or association with impressions determined by the impression collector 132. If the user closes the webpage containing the video before the video ends, the beacon instruction is stopped and the data collector 112 stops sending broadcast messages to the impression collector 132. In some examples, the broadcast messages include timestamps and / or other information indicating the locations in the video to which multiple broadcast messages correspond. By determining the number and / or content of broadcast messages received at the impression collector 132 from the client device 106, the example impression collector 132 can determine that the user watched a particular length of the video (e.g., a portion of the video for which the broadcast message was received at the impression collector 132).

[0105] The client device 106 of the illustrated example executes a client application / software 114-117 directed to a host website (e.g., www.acme.com) from which media 118 (e.g., audio, video, interactive media, streaming media, etc.) is obtained for presentation via the client device 106. In the illustrated example, the media 118 (e.g., advertisements and / or content) is tagged with identifier information (e.g., media ID 122, creation type ID, layout ID, publisher source URL, etc.) and beacon instructions. The exemplary beacon instructions cause the client application / software 114-117 to request additional beacon instructions from a beacon server 142, which will instruct the client application / software 114-117 how and where to send beacon requests to report impressions of the media 118. For example, the exemplary client application / software 114-117 sends a request including an identification of the media 118 (e.g., media identifier 122) to the beacon server 142. The beacon server 142 then generates and returns beacon instructions 144 to the exemplary client device 106. Although the beacon server 142 and the impression collector 132 are shown separately, in some examples, the beacon server 142 and the impression collector 132 are combined. In the illustrated example, the beacon instructions 144 include the URL of one or more database owners (e.g., one or more of the cooperating database owners 104a-b) or any other server to which the client device 106 should send a beacon request (e.g., an impression request). In some examples, the broadcast message or beacon request can be implemented as an HTTP request. However, although the HTTP request sent identifies a web page or other resource to be downloaded, the broadcast message or beacon request includes audience measurement information (e.g., an advertising campaign identifier, a content identifier, and / or device / user identification information) as its payload. The server to which the broadcast message or beacon request is directed is programmed to record the audience measurement data of the broadcast message or beacon request as an impression (e.g., an advertising and / or content impression depending on the nature of the media tagged with the beacon instructions). In some examples, the beacon instructions received with the tagged media 118 include the beacon instructions 144. In such examples, the client application / software 114 - 117 does not need to request the beaconing instructions 144 from the beaconing server 142 because the beaconing instructions 144 are already provided in the tagged media 118 .

[0106] When the beacon instructions 144 are executed by the client device 106, the beacon instructions 144 cause the client device 106 to send a beacon request (e.g., repeatedly at a specified interval) to a remote server (e.g., impression collector 132, media publisher 120, database owner 104a-b, or other server) specified in the beacon instructions 144. In the illustrated example, the specified server is a server of the audience measurement entity 108, i.e., at the impression collector 132. The beacon instructions 144 can be implemented using Java script or any other type of instructions or scripts (e.g., including Java, HTML, etc.) that can be executed with the help of a client application (e.g., a web browser).

[0107] In U.S. patent application serial number 14 / 127,414 filed on August 28, 2013, U.S. patent application serial number 14 / 261,085 filed on April 24, 2014, U.S. provisional patent application serial number 61 / 952,726 filed on March 13, 2014, U.S. provisional patent application serial number 61 / 979,391 filed on April 14, 2014, U.S. provisional patent application serial number 61 / 986,784 filed on April 30, 2014, U.S. provisional patent application serial number 61 / 991,286 filed on May 9, 2014, and U.S. provisional patent application serial number 62 / 014,659 filed on June 19, 2014, methods that can be used to implement Figure 1 The entire contents of U.S. Patent Application Serial No. 14 / 127,414, U.S. Patent Application Serial No. 14 / 261,085, U.S. Provisional Patent Application Serial No. 61 / 952,726, U.S. Provisional Patent Application Serial No. 61 / 979,391, U.S. Provisional Patent Application Serial No. 61 / 986,784, U.S. Provisional Patent Application Serial No. 61 / 991,286, and U.S. Provisional Patent Application Serial No. 62 / 014,659 are incorporated herein by reference.

[0108] Figures 2 to 11Examples of may be used to compensate for misidentification and / or non-coverage errors in impression information collected from a client device through which a user accesses media. Such impression information may be collected using any suitable technique, including the exemplary techniques discussed above. For example, the impression information collected from database owners 104a-b may be aggregate impression information describing a number of impressions for a media item of interest (e.g., an advertisement, a streaming media, a website, etc.), a number of duration units (e.g., minutes, seconds, etc.) during which the duration-based media was presented, and / or a count of audience members to which the impressions correspond. Aggregate impression information obtained from database owners 104a-b may be subject to misidentification errors (e.g., errors caused by a database owner erroneously associating an impression with a first person in a first demographic group when the impression should be associated with a second person in a second demographic group) and / or non-coverage errors (e.g., errors caused by a database owner being unable to associate an impression with a person). Since impressions and / or duration units that cannot be associated with demographic information by the database owner 104a-b may not be included in the aggregated demographic information, in some disclosed examples, non-coverage errors in the aggregated demographic information may be detected using, for example, counting impressions at the AME 108 and comparing the counted impressions to a number of impressions for which the database owner 104a-b identified demographic information. In some other examples, the database owner 104a-b returns a number of impressions and / or duration units for which the database owner 104a-b was unable to determine demographic information. The number of impressions and / or duration units for which the database owner 104a-b was unable to determine demographic information may be used as the number of uncovered impressions.

[0109] The examples disclosed herein use survey calibration data to estimate respective errors and generate compensated impression information that is adjusted to correct for misidentification errors and / or non-coverage errors. The examples disclosed herein can be used for impressions and / or duration units collected from client devices (e.g., mobile devices and / or non-mobile devices), can be applied to impressions and / or duration units collected only from mobile devices, can be applied to impressions and / or duration units collected from mobile devices (separate from impressions and / or duration units collected from non-mobile devices), and / or can be applied to impressions and / or duration units collected from a first type of mobile device (separate from impressions collected from a second type of mobile device). In some examples, the compensated impression information is presented or reported separately for mobile devices and non-mobile devices, and / or is reported as aggregated data corresponding to both mobile devices and non-mobile devices.

[0110] Examples disclosed herein can be applied to input data in real time or substantially real time (e.g., within seconds or minutes of receiving the data), and can be used to compensate impression information (e.g., impressions, duration units) over any desired time period (e.g., hourly, daily, weekly, monthly, etc.) and / or cumulatively (e.g., applied to impressions and / or duration units collected over multiple time periods). Thus, examples disclosed herein can provide advertisers and / or media publishers with accurate demographic information to enable faster adjustment of media campaign strategies to conform to measured demographics than known methods.

[0111] Figure 2 is a block diagram of an exemplary impression data compensator 200, which may be used to implement Figure 1 The exemplary impression collector 132 compensates the impression information for misidentification and / or non-coverage errors. Figure 2 The exemplary impression data compensator 200 compensates or adjusts the impression data received from the client device (e.g. Figure 1 client device 106) and / or impression information obtained from database owners 104a-b to reduce (e.g., avoid) errors, such as those mentioned above.

[0112] Figure 2 The exemplary impression data compensator 200 includes a calibration data collector 202, a sharing matrix generator 204, an error identification corrector 206, an impression information collector 208, a non-coverage calculator 210, a non-coverage corrector 212, and an impression information adjuster 214.

[0113] Figure 2 The exemplary calibration data collector 202 of collects or obtains survey calibration data describing the audience's mobile device usage characteristics. For example, the survey calibration data may include and / or be based on responses to a survey of randomly selected households. In some examples, the calibration survey obtains information including the number of people in the household, demographic characteristics of the household (e.g., age and gender, race, ethnicity, language characteristics, household income, geographic location, etc.), the number and / or type of mobile devices (e.g., smartphones, tablets, portable media players, etc.) present in the household, and / or the number of users in the household who have requested a specified database owner (e.g., Figure 1The calibration survey obtains, for each person in the household, usage characteristics of each mobile device and / or the types of mobile devices present in the household; categories of media typically viewed by the person; categories of media typically viewed by the person on each mobile device and / or the types of mobile devices in the household; applications used by the person on each mobile device and / or the types of mobile devices in the household; and / or characteristics of interactions with a specified database owner on each mobile device and / or the types of mobile devices in the household. The exemplary calibration data collector 202 obtains survey calibration data from at least a threshold number of households and, if appropriate, weights the results to reflect a general population or audience.

[0114] In some other examples, the survey calibration data source includes a survey of one or more established respondent panels, such as the Nielsen National People's Survey (NPM) Panel for ratings. Established panel surveys often provide higher quality survey calibration data. In some examples, data from multiple surveys are used to calculate different compensation factors and / or are combined to calculate compensation factors.

[0115] Error Correction

[0116] Figure 2 The exemplary sharing matrix generator 204 of calculates the device sharing matrix based on the survey calibration data. Figure 2 In the example of FIG. 2 , the shared matrix generator 204 creates a separate false positive correction matrix for each combination of device type and media category represented in the survey calibration data.

[0117] To generate the misidentification correction matrix for the device types and media categories of interest, the exemplary shared matrix generator 204 includes a household distribution generator 216 , an aggregate distribution generator 218 , a matrix corrector 220 , and a matrix normalizer 222 .

[0118] Figure 2 The exemplary household distribution generator 216 generates an audience distribution for a household based on the survey calibration data. For example, the household distribution generator 216 determines the corresponding likelihood that the person represented in the survey calibration data uses the device type of interest to view media of the media type of interest. To illustrate, consider the following example. The exemplary household from which the survey calibration data was collected includes four types of members: 1) males aged 45-54, 2) females aged 35-44, 3) females aged 18-24, and 4) males aged 12-17. Females aged 18-24 and males aged 12-17 have been reported to the household. Figure 1 An exemplary database owner 104a (e.g., Facebook) registers (e.g., can be Figure 1The exemplary database owner 104a is identified as a registered user) and uses a tablet computer (e.g. Figure 1 45-54 year old males and 35-44 year old females are not identifiable on the tablet computer by database owner 104a. Table 1 below shows an exemplary sharing pattern for a tablet computer by media category. In Table 1, cells marked with an "X" indicate that the person identified at the demographic group label of the cell views media having the category identified in the content type label. Conversely, blank cells in Table 1 indicate that the person identified at the demographic group label of the cell does not view media having the category identified in the content type label. Media categories can be defined based on, for example, media categories used in survey calibration data and / or media categories used in television and / or other media evaluations.

[0119]

[0120] Example sharing patterns for tablets by media category based on survey calibration data in an example first home

[0121] Table 1

[0122] As shown in Table 1, males aged 45-54 use tablets to watch media (e.g., websites, streaming, etc.) classified as political media, females aged 35-44 watch media (e.g., websites, streaming, etc.) classified as drama, comedy, and / or reality on tablets, and females aged 18-24 use tablets to watch media (e.g., websites, streaming, etc.) classified as drama and comedy. When males aged 12-17 use tablets to log in to database owner 104a, they do not watch media monitored by audience measurement entity 108 on the tablet. Based on the sharing patterns of Table 1, the exemplary household distribution generator 216 calculates device sharing probabilities for each media category of Table 1, as shown in Table 2 below. The device sharing probabilities are shown in Table 2 as probability density functions (PDFs) of content types (e.g., media categories) viewed on devices by people identified in the demographic group annotations.

[0123]

[0124] Example Device Sharing Probability by Media Category for a First Example Household

[0125] Table 2

[0126] In this example, if a 12-17 year old male logs into the database owner on a tablet (via a browser and / or an application) and does not log out of the database owner, and a 35-44 year old female subsequently uses the same tablet (while the 12-17 year old male is still logged into the database owner 104a) and does not log into the database owner 104a with her own credentials, when the 35-44 year old female views media on the tablet, the database owner 104a misattributes impressions and / or duration units that were correctly attributed to the 35-44 year old female to the 12-17 year old male. Thus, in such examples, using the database owner information to associate impressions and / or duration units with demographic information results in impressions and / or duration units not being attributed (or under-attributed) to the 45-54 year old male and the 35-44 year old female, and impressions and / or duration units being over-attributed to the 18-24 year old female and / or the 12-17 year old male.

[0127] To determine the false positive correction matrix for households for the tablet and 'comedy' categories, the exemplary household distribution generator 216 converts the 'comedy' probabilities in Table 2 above into the exemplary redistributed audience matrix shown in Table 3 below. In Table 3, the columns (Identified Demographic Group i) represent the demographic groups identified by the database owner 104a as being associated with the impression, and the rows (Actual Viewer Demographic Group j) represent the demographic groups that actually viewed (e.g., actual viewers) the media to which the impression corresponds. Thus, Table 3 includes a PDF for the event that when the database owner identifies a person as being in the identified demographic group i, the actual or real viewer is a person in the actual viewer demographic group j. The value in each cell is the probability γ of the following event: ij : When the database owner 104a associates impressions of a media with the identified demographic group i in the column, the actual viewer demographic group j of that row is viewing the media.

[0128] j\i M45-54 F35-44 M12-17 F18-24 M45-54 0 0 0 0 F35-44 0 0 0.5 0.5 M12-17 0 0 0 0 F18-24 0 0 0.5 0.5 total 0 0 1 1

[0129] Example Redistribution Audience Matrix for the "Comedy" Media Category for a First Example Household

[0130] Table 3

[0131] As an example of determining impressions from the redistributed audience matrix for a household of Table 3, for 10 impressions in the 'Comedy' media category identified by database owner 104a as viewed by 12-17 year old males in identified demographic group i, 5 impressions should be attributed to 35-44 year old females in actual viewer demographic group j (e.g., 10 impressions multiplied by the probability of 0.5 in Table 3), 5 impressions should be attributed to 18-24 year old females in actual viewer demographic group j (e.g., 10 impressions multiplied by the probability of 0.5 in Table 3), and no impressions should be attributed to 12-17 year old males or 45-54 year old males in actual viewer demographic group j (e.g., 10 impressions multiplied by the probability of 0 in Table 3).

[0132] The exemplary household distribution generator 216 may use the PDFs in Table 2 and / or Table 3, wherein the PDFs have different probabilities annotated for different demographic groups (Table 2) and / or different probabilities for different actual viewer demographic groups (Table 3). For example, when one of the family members watches a media category of significant interest more often than another member of the family, the different probabilities in the PDF may be determined from the survey calibration data. For example, if the 35-44 year old females of Table 2 report watching media in the 'comedy' category 'often', while the 18-24 year old females of Table 2 report watching media in the 'comedy' category 'rarely', then the PDF for the 'comedy' category in Table 2 may be (0, 0.75, 0, 0.25) to reflect the different viewing frequencies. Additionally or alternatively, the exemplary household distribution generator 216 may use a PDF with different probabilities in the exemplary sharing matrix of Table 2 based on the presence of multiple persons in the same demographic group. For example, a family with two females aged 12-17 and one female aged 35-44 may have a PDF where the probability of the F12-17 demographic group is twice the probability of the F35-44 demographic group.

[0133] Figure 2 The example aggregate distribution generator 218 generates an aggregate redistributed audience matrix for each combination of device type and media category based on all households in the example survey calibration data.

[0134] In some examples, the household distribution generator 216 generates individual device sharing matrices from the survey responses of individual households, and the aggregate distribution generator 218 aggregates the individual redistribution audience matrices into an aggregate redistribution audience matrix. For example, the household distribution generator 216 may redistribute the persons in the identified demographic group i that are identifiable by the database owner 104a within the households. Thus, the household distribution generator 216 also redistributes the impressions associated with those persons to the actual viewer demographic group j based on the survey calibration data.

[0135] In another example of a redistributed audience matrix, Table 4 below shows exemplary device sharing probabilities by media category for a household having two females of the same identified demographic group i (e.g., identified by database owner 104a as viewers of the media to which the impression corresponds), the two females being shown as females aged 18-24 (F18-24). In the illustrated example, the two females in the F18-24 demographic group are identifiable registered users of database owner 104a belonging to the F18-24 demographic group.

[0136] Content Type M45-54 F35-44 F18-24 F18-24 all 0.33 0.33 0 0.33 politics 1 0 0 0 drama 0 0.5 0 0.5 comedy 0 0.5 0 0.5 Reality 0 1 0 0

[0137] Example Device Sharing Probabilities by Media Category for a Second Example Household

[0138] Table 4

[0139] The cells of Table 4 above include a probability density function (PDF) indicating the probability of sharing a media device between persons belonging to a specified identified demographic group i (e.g., one person in the M45-54 demographic group, one person in the F35-44 demographic group, and two persons in the F18-24 demographic group) for a specified media category (e.g., all, politics, drama, comedy, reality). For example, for each of a person in demographic group i identified by M45-54, a person in demographic group i identified by F35-44, and one of the persons in demographic group i identified by F18-24, the PDF for viewing media in the "all" media category is 0.33. In the illustrated example, for another person in demographic group i identified by F18-24, the PDF is 0. The data in Table 4 is based on a survey-calibrated data source (e.g., a survey of randomly selected persons and / or households) that provides information about media viewed by persons in a household.

[0140] In the illustrated example, two females in demographic group i identified by F18-24 of the exemplary household represented in Table 4 above are registered users of database owner 104a (e.g., a social networking service). Based on the data in Table 4 above, household distribution generator 216 redistributes the audience (and thus associated impressions) of each registered database owner user (e.g., viewers in the F18-24 demographic group) among the actual viewer demographic groups j M45-54, F35-44, and F18-24 that watch the media of the category of interest in the household based on the device sharing patterns and viewing patterns collected from the household in the exemplary survey calibration data source. An exemplary redistributed audience for the "all" media category and the second exemplary household of Table 4 are shown in Table 5 below.

[0141] j\i M45-54 F35-44 F18-24 F18-24 M45-54 0 0 0.33 0.33 F35-44 0 0 0.33 0.33 F18-24 0 0 0 0 F18-24 0 0 0.33 0.33 total 0 0 1 1

[0142] Example Redistribution Audience Matrix for the "All" Category

[0143] Table 5

[0144] In Table 5 above, the household distribution generator 216 applies the same PDF (e.g., 0.33) to each registered database owner user in the same identified demographic group i (e.g., two family members of demographic group F18-24). In the illustrated example of Table 5, the cell values ​​indicate the corresponding probability of the following event: when a person in the household is identified by the database owner as a person in identified demographic group i (i.e., the person and / or demographic group indicated in the column), the actual or real viewer is a person in actual viewer demographic group j (i.e., the person and / or demographic group indicated in the row). For example, the probability of the following event is 0.33: when the database owner identifies the first person in demographic group i identified by F18-24, the real viewer of the media in the "all" category is a person in actual viewer demographic group j M45-54. In this example, the probability of the following events is the same (e.g., 0.33): when the database owner identifies a second person in demographic group i identified by F18-24, the actual or real viewer of the media in the "All" category is a person in actual viewer demographic group j by M45-54.

[0145] In the illustrated example, it does not matter whether the female viewers in demographic group i identified by F18-24 indicate that they watch a particular media category represented by the exemplary redistributed audience matrix of Table 5 on a particular device. As long as the database owner 104a captures impressions and / or duration units on that particular device, the household distribution generator 216 can redistribute impressions and / or duration units equally among all actual viewer demographic groups j M45-54, F35-44, and F18-24 of the household. Thus, the two columns labeled F18-24 (e.g., corresponding to the two daughters of the household) are filled with equal redistributions of 0.33 (for M45-54 actual viewer demographic group j), 0.33 (for F35-44 actual viewer demographic group j), and 0.33 (for F18-24 actual viewer demographic group j). The probability values ​​in the same row for the F18-24 actual viewer demographic group j are however summed across the F18-24 columns because these values ​​correspond to the same identified demographic group i F18-24. The cells in the "Total" row indicate the number of audience members within the corresponding identified demographic group i (M45-54, F35-44, F18-24) who are registered users of the database owner 104a.

[0146] Table 6 below shows an exemplary redistributed audience matrix of type "all", where the summed values ​​for demographic group i identified by F18-24 are shown.

[0147] j\i M45-54 F35-44 F18-24 M45-54 0 0 0.66 F35-44 0 0 0.66 M12-17 0 0 0 F18-24 0 0 0.66 total 0 0 2

[0148] Example Redistributed Audience Matrix for the "All" Media Category for a Household with Two Persons in the F18-24 Demographic Group

[0149] Table 6

[0150] In the exemplary upper Table 6, each column (identified demographic group i M45-54, F35-44, and F18-24) corresponds to the total number of registered users of the database owner 104a in the identified demographic group i in the household. The cells in the "Total" row indicate the number of audience members who are registered users of the database owner 104a within the corresponding identified demographic group i (e.g., M45-54, F35-44, F18-24). In some examples, the actual viewer demographic group j (e.g., row) of the redistributed audience matrix of Table 6 is expanded to include all identified demographic groups i used by the audience measurement entity 108 and / or the database owner 104a to achieve aggregation of the matrix. Therefore, although the household represented by the upper Table 6 does not have any family members in the actual viewer demographic group M12-17, the exemplary upper Table 6 includes the M12-17 rows to achieve aggregation of the PDF of Table 6 with the PDF of Table 7 below.

[0151] After the device sharing probabilities shown in Table 4 are used to generate the redistributed audience matrix for the second household described above in conjunction with Tables 5 and 6, the redistributed audience matrices of Tables 6 and 7 are aggregated (e.g., summed) across households to combine registered users of the database owner 104a in the identified demographic group i and sum the redistributed audiences for the actual viewer demographic group j. For example, the redistributed audiences of Tables 6 above and 7 below are aggregated to generate the aggregated redistributed audience across households shown in Table 8 below. In the illustrated example, Tables 6 and 7 correspond to two different households.

[0152] j\i M45-54 F35-44 M12-17 F18-24 M45-54 0 0 0.33 0.33 F35-44 0 0 0.33 0.33 M12-17 0 0 0 0 F18-24 0 0 0.33 0.33 total 0 0 1 1

[0153] Example Redistribution Audience Matrix for the "All" Media Category for a Household with One M12-17 Person and One F18-24 Person

[0154] Table 7

[0155]

[0156]

[0157] Example Aggregate Redistribution Audience Matrix for "All" Media Categories for Multiple Households

[0158] Table 8

[0159] The probabilities in Table 8 above reflect the redistributed audiences for registered users of database owner 104a in the two households represented in the corresponding tables in Tables 6 and 7. The sum of the columns for demographic group i identified by M12-17 and F18-24 reflects the total number of registered users of database owner 104a in the identified demographic group i. The cells in the "Total" row indicate the number of audience members who are registered users of database owner 104a within the corresponding identified demographic group i (M45-54, F35-44, M12-17, F18-24).

[0160] In some examples, the matrix corrector 220 generates a Nielsen National Population Metrics (NPM) metric to account for the probability of demographic pairs i, j (e.g., one person from identified demographic group i and one person from actual viewer demographic group j) living together in the same household. For example, P(L) ij is the probability that the first person in the identified demographic group i lives in the same household as the person in the actual viewer demographic group j. In the illustrated example, the NPM indicator is another source of calibration data. In the example disclosed herein, NPM data is collected from a household, where the AME 108 ( Figure 1 ) installed meters in the home that collect identifications of family members. Because NPM data is collected in the home using locally installed meters that facilitate family members to identify themselves (or otherwise accurately collect identifications of family members viewing / listening to media), the NPM data has a high degree of accuracy with respect to identifying which family member is actually viewing the media presented by the media device (e.g., is the actual viewer of the media).

[0161] The NPM indicator described below can be used to explain P(L) ij probability. In these examples, the matrix corrector 220 applies the NPM metrics to the corresponding i, j cells in the table of registered users of the redistribution database owner (e.g., the exemplary redistribution audience matrix for the "all" category shown in Table 5 above). Table 9 below shows estimates for demographic groups i, j pairs of people living together based on survey responses collected from households (in these examples, this also serves as the survey calibration data source from which the redistribution audience table is determined). Table 10 below shows estimates for demographic groups i, j pairs of people living together based on the collected NPM data. Table 11 below shows the NPM metrics generated by the matrix corrector 220 based on the estimates of Tables 9 and 10.

[0162] j\i M45-54 F35-44 M12-17 F18-24 M45-54 100 60 10 55 F35-44 30 100 10 80 M12-17 20 50 80 15 F18-24 50 20 20 90

[0163] Exemplary estimators for demographic groups i, j pairs of people living together based on survey responses

[0164] Table 9

[0165] j\i M45-54 F35-44 M12-17 F18-24 M45-54 105 50 15 48 F35-44 35 102 12 80 M12-17 25 40 70 20 F18-24 40 15 18 98

[0166] Exemplary estimators for demographic groups i, j pairs of people living together based on NPM data

[0167] Table 10

[0168] j\i M45-54 F35-44 M12-17 F18-24 M45-54 1.05 0.83 1.50 0.87 F35-44 1.17 1.02 1.20 1.00 M12-17 1.25 0.80 0.88 1.33 F18-24 0.80 0.75 0.90 1.09

[0169] Example NPM Metrics

[0170] Table 11

[0171] The exemplary NPM metric of Table 11 above is calculated by dividing the value of Table 10 (the estimated number of demographic group i, j pairs of people living together based on the panelist data) by the corresponding value of Table 9 (the estimated number of demographic group i, j pairs of people living together based on the survey responses). In the illustrated example, Table 11 is used to explain the oversampling / undersampling of any viewers from different demographic groups i, j living together in the same household. For example, Table 9 estimates that 100 people in demographic groups i, j of M45-54 live together based on the survey responses. Based on more accurate NPM data, Table 10 estimates that 105 people in demographic groups i, j of the same M45-54 live together in the same household. Therefore, the exemplary NPM metric of Table 11 for this demographic group M45-54 is 1.05, which is greater than 1 to compensate for the fact that Table 9 undersamples the number of people living together from demographic groups i, j of M45-54 (e.g., 100) relative to the corresponding value of 105 in Table 10. For instances where the estimates in Table 9 are oversampled relative to the corresponding amounts in Table 10, the corresponding NPM metric in Table 11 will be less than 1 (e.g., for actual viewer demographic group j of F18-24 and identified demographic group i of F35-44, NPM metric in Table 11 = 0.75).

[0172] In some examples, the estimates of some demographic group pairs i, j living together from Table 9 (e.g., determined based on survey responses) are aligned with the estimates based on NPM data from Table 10 because the NPM data is a higher quality data source than the demographic data from the database owner 104a. Tables 12 and 13 show exemplary data where the estimates of demographic group i, j pairs of people living together are aligned closer to the NPM data. In Table 12, the matrix corrector 220 adjusts the probability P(L) of Table 8 above for the first person in the identified demographic group i to live in the same household as a person in the actual viewer demographic group j ij In the illustrated example, the matrix corrector 220 determines each i, j cell value in Table 12 by multiplying the redistributed audience value in the i, j cell of Table 8 above by the corresponding NPM metric in the i, j cell value of Table 11 above. In this manner, the shared matrix corrector 220 applies the NPM metric to the redistributed impressions collected by the database owner 104a across different households to account for the probability P(L) that the first person in the identified demographic group i lives in the same household as a person in the actual viewer demographic group j ij .

[0173]

[0174]

[0175] For P(L) ij Example database owner redistribution audience for adjustment

[0176] Table 12

[0177] j\i M45-54 F35-44 M12-17 F18-24 M45-54 0 0 0.417 0.295 F35-44 0 0 0.333 0.338 M12-17 0 0 0 0 F18-24 0 0 0.250 0.368 total 0 0 1 1

[0178] Example Normalized Redistributed Audience

[0179] Table 13

[0180] In Table 13 above, the matrix normalizer 222 normalizes the adjusted impression redistribution from Table 12 above so that the columns for identified demographic group i (e.g., M12-17 and F18-24 identified demographic group i) sum to 1. In this manner, each column (e.g., M12-17 and F18-24 identified demographic group i) represents a probability density function (PDF) of what the AME 108 determines to be the actual viewer demographic group j of the real viewer of the media when the database owner 104a detects a particular identified demographic group i. In the illustrated example, the normalized redistribution audience of Table 13 is the misidentification correction factor, or probability γ for each pair of demographic groups i, j. ij , the probability γ ijis the probability that when a person in identified demographic group i is identified as a media viewer by the database owner 104a, a person in actual viewer demographic group j is an actual viewer (e.g., γ ij =0.417, 0.295, 0.333, 0.338, 0.250 and 0.368).

[0181] In some examples, for each pair of identified demographic group i detected by the database owner and actual viewer demographic group j assigned as a real or actual viewer, the probability γ ij It can be weighted and / or averaged in all individual household matrices to determine the aggregate probability. In the illustrated example, the real viewer or actual viewer indicates a person in a specific demographic group who is considered to be an actual audience member who contacts (e.g., watches, listens to, consumes, etc.) the media on the device. For example, the actual viewer can be a viewer determined by AME 108 as a person who actually watches or contacts the monitored media. The determination that the person is an actual viewer can be based on statistical probability, which indicates the possibility of the actual viewer based on the response to the randomly selected person and / or family survey discussed above. The determination of the actual viewer as perceived by AME 108 can also be based on observations or other collected data (e.g., NPM data) representing the actual viewer in the family. In any case, the actual viewer is a strong inference of AME 108 to who is the actual viewer, but as used in this article, the actual viewer is not necessarily an absolute determination of the identity of the actual viewer. However, the inference strength of the actual viewer used in conjunction with the examples disclosed herein is sufficient to accurately combine the examples disclosed herein to provide a correction with high accuracy Impression and / or duration units. In some examples, the sharing matrix generator 204 generates the device sharing matrix based on additional and / or alternative distinctions, such as different demographic markets, different stations, and / or different time periods.

[0182] As described above for using the redistributed audience to calculate γ ij As an alternative to the exemplary method of , in some examples, the aggregate distribution generator 218 calculates the aggregate redistribution audience matrix by calculating the compound probability, as shown in Equation 1:

[0183] γ ij =P(L) ij ×P(D|L) ij ×P(S x |D) ij (Equation 1)

[0184] In the above equation 1, P(L) ij is the probability that the first person in the identified demographic group i lives in the same household as the person in the actual viewer demographic group j; P(D|L)ij is the probability of the following event: assuming that the first person in the identified demographic group i and the person in the actual viewer demographic group j live in the same household, both of them access a mobile device (of any type) in that household; and P(S x |D) ij is the probability of the following event: assuming that the first person in the identified demographic group i accesses the same selected type of mobile device as a person in the actual viewer demographic group j, the two people share the mobile device for media of the selected category. As used in this article, "selected category" refers to the category of interest that is subject to analysis. Therefore, "selected" means "selected for analysis" as used in this context. The same meaning applies to "selected device type", "selected demographic group" and "pair of selected demographic groups".

[0185] In some examples, aggregate distribution generator 218 uses data from one or more calibration data sources (e.g., survey calibration data) to determine one or more of the probabilities. For example, in some examples, the shared matrix calculator determines the probability P(L) from a survey of the established panel. ij and P(D|L) ij and P(S x |D) ij , which establishes a group such as the group used to determine the NPM indicator data discussed above.

[0186] Referring to the examples described above with reference to Tables 1-13, the exemplary aggregate distribution generator 218 calculates the following probability γ for each pair of demographic groups i, j using the redistributed aggregate audience matrix ij : (1) persons in identified demographic group i are identified by database owner 104a as viewers of the media, and (2) persons in actual viewer demographic group j are actual viewers. The exemplary aggregate distribution generator 218 generates a false positive correction matrix that includes the calculated probabilities γ ij The example aggregate distribution generator 218 may use the example equation (1) above and / or may use the NPM indicator method described above to calculate the probability γ of the false positive correction matrix ij An exemplary aggregated redistributed audience matrix for the selected tablet device type and the selected comedy media type is shown below as an exemplary misidentification correction matrix in Table 14. The subsequent sections of the exemplary Table 14 extend to the right so that the table has an equal number of rows and columns.

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193]

[0194]

[0195] Exemplary False Positive Correction Matrix

[0196] Table 14

[0197] As shown in the exemplary misidentification correction matrix of Table 14 above, the values ​​of each column total 100%. Therefore, when redistributing impressions, duration units, and / or audiences based on the exemplary misidentification correction matrix of Table 14 above, the impressions, duration units, and / or audiences attributed to the identified demographic group i are accounted for. As described in more detail below, Figure 2 The misidentification corrector 206 applies the exemplary misidentification correction matrix of Table 14 above to compensate the impression information for misidentification errors.

[0198] Co-viewing Matrix

[0199] In some examples, the sharing matrix generator 204 also includes a co-viewing matrix generator 224, which generates a co-viewing matrix (e.g., for each combination of media category and device type) in addition to the exemplary false positive correction matrix of Table 14 above. The co-viewing matrix is ​​a matrix that provides the PDF of the probability of simultaneous viewing between two demographic groups. For example, the co-viewing probability κ ij is the probability that, when the database owner identifies a person in identified demographic group i as being associated with a media impression, a person in actual viewer demographic group j is also watching (e.g., co-watching) the media with the identified person. Thus, for the case where an impression can be correctly associated with multiple people, the probability κ in the co-viewing matrix ij May be used to compensate for impression information.

[0200] Like the exemplary false positive correction matrix of Table 14 above, the co-viewing matrix also generated by the co-viewing matrix generator 224 has an equal number of rows and columns. However, unlike the exemplary false positive correction matrix of Table 14 above, the columns of the co-viewing matrix do not necessarily sum to any particular number (e.g., 100%). In the illustrated example, the exemplary co-viewing matrix generator 224 uses the probability P(L) discussed aboveij and P(D|L) ij And also use the probability P(C x |D) ij To calculate the co-viewing probability κ ij , probability P(C x |D) ij describes the probability that a person in the identified demographic group i who lives in the same household and has access to a mobile device of the selected device type concurrently views media of the selected media category using the same mobile device of device type x as a person in the actual viewer demographic group j. In some examples, the co-viewing matrix generator 224 uses P(C x |D) ij Substituting P(S) in equation (1) above x |D) ij The co-viewing probability κ is calculated by ij .

[0201] In some examples, false positive corrector 206 applies the co-viewing matrix to the impressions and / or duration units adjusted for device sharing false positives. In some other examples, false positive corrector 206 applies the probability κ in the co-viewing matrix to the impressions and / or duration units adjusted for device sharing false positives, because co-viewing can be considered to represent additional impressions and / or duration units that are not accounted for in the data collection. ij Applied to the impressions and / or duration units adjusted by the database owner 104a for device sharing misidentification and adjusted for non-coverage, thereby determining co-viewing based on compensated (eg, corrected) impressions and / or duration units.

[0202] Misidentification Correction Example 1

[0203] After generating the exemplary false positive correction matrix of Table 14 above, the exemplary false positive corrector 206 applies the false positive correction matrix to Figure 2A set of impressions obtained by the impression information collector 208 of . In a first example of misidentification correction, the misidentification correction matrix is ​​applied to correct impressions to belong to demographic groups without affecting audience counts. Such examples can, for example, be applied to correct impressions of Internet-based streaming media (e.g., streaming video and / or streaming audio). For example, corrected impression information for Internet-based streaming media can be combined with impression information for another type of device, and the media can be accessed on the other type of device in addition or alternatively. For example, after the episode is presented (e.g., broadcast) on television, audience members can access the TV series with the help of a computing device for delayed or time-shifted streaming playback. Impressions of streaming media accessed with the help of a computing device can be added to the Live+7 ratings metric, which measures the sum of viewing impressions (or corresponding audience size) of the initial presentation and impressions (or corresponding audience size) occurring on the day of the initially scheduled broadcast presentation and within 7 days after the initially scheduled broadcast presentation.

[0204] Figure 2 The exemplary impression information collector 208 of the present invention collects impression information from the database owner 104a and collects impression information from client devices (e.g. Figure 1 The impression volume information obtained from the database owner 104a includes an aggregate number of impressions associated with each demographic group from the database owner 104a.

[0205] The example database owner 104a may provide impression information (e.g., the number of impressions identified by the database owner 104a as being associated with an identified person) for each media item of interest (e.g., a media item being monitored by the audience measurement entity 108). Additionally or alternatively, the example database owner 104a provides impression information for various types of devices to the impression information collector 208. Table 15 below illustrates example impression information for a tablet device obtained from the example database owner 104a by the impression information collector 208. The example impression information collector 208 may collect similar data for other types of mobile devices (e.g., smartphones, portable media players, etc.).

[0206]

[0207]

[0208] Exemplary impression information for a tablet device

[0209] Table 15

[0210] In some examples, the impressions are processed through a decision tree before being provided to the impression information collector 208. In some examples, the decision tree is used to determine the delineation between the identified demographic group i and / or the actual viewer demographic group j. Examples of processing impressions are disclosed in U.S. Patent Application Serial No. 13 / 209,292 filed on August 12, 2011 and in U.S. Provisional Patent Application Serial No. 61 / 923,959 filed on January 6, 2014. The entire contents of U.S. Patent Application Serial No. 13 / 209,292 and U.S. Provisional Patent Application Serial No. 61 / 923,959 are incorporated herein by reference.

[0211] Figure 2 The exemplary misidentification corrector 206 adjusts or compensates for impression information obtained from the database owner 104a for misidentification errors. Figure 3A 2 shows an exemplary calculation performed by the false positive corrector 206 to adjust the impression information. Figure 2 and Figure 3A In the example of , the false positive corrector 206 uses the device sharing matrix generated by the example sharing matrix generator 204 to adjust the demographic information provided by the database owner 104a (eg, impression counts per demographic group per device type and / or media category, etc.).

[0212] Using the database owner data obtained by the impression information collector 208 (e.g. Figure 1 impression information 102a, 102b), Figure 2 The misidentification corrector 206 computes the dot product of the n×n misidentification correction matrix 302 for device types and / or media categories and the n×1 database owner data 304 (e.g., an impression count matrix) for the device types and / or media categories. The result of the dot product is an n×1 misidentification adjusted data matrix 306 having an adjusted number of impressions for the device types and / or media categories.

[0213] Table 16 below shows an exemplary misidentification adjusted impression calculated by misidentification corrector 206 using the exemplary misidentification correction matrix of Table 14 and the impression information of Table 15. Table 16 includes the following information based on the above reference: Figure 3A Adjusted impressions calculated for the dot product in question. The unadjusted impressions from Table 15 above are also shown in Table 16 for ease of comparison.

[0214]

[0215]

[0216] Misidentification of adjusted and unadjusted impressions

[0217] Table 16

[0218] Therefore, as shown in Table 16 above, the adjusted impressions compensate for the misidentification errors of the impression information 102a, 102b received from the exemplary database owner 104a.

[0219] Misidentification Correction Example 2

[0220] In a second example of misidentification correction, the misidentification correction matrix of Table 14 above is applied to correct misidentification of impressions and audiences to demographic groups. As described above, misidentification of impressions to an incorrect demographic group can occur when the database owner 104a attributes an impression to a person in a first demographic group when in fact the impression is correctly attributed to a second person in a second demographic group (e.g., due to a person in the first demographic group being logged into the database owner on a device during the time that the second person in the second demographic group viewed the media that gave the impression). Exemplary misidentification correction of impressions and audiences can be used, for example, when media can be accessed from different types of computing devices and audiences recorded or measured for those computing devices can be de-duplicated. For example, duplication of recorded audience members occurs when an audience member accesses the same media multiple times from the same device and / or different devices. Multiple visits to the same media by the same audience member result in audience duplication because the same audience member is counted twice or more in the audience size count based on multiple impressions recorded for the audience member's multiple visits to the same media. Such audience duplication can result in an inflated representation of the true audience size that is exposed to or accesses a particular media. Therefore, deduplication can be used to more accurately count the audience size to which impressions of media can be attributed.

[0221] Figure 2 The exemplary impression information collector 208 collects impression information from the database owner 104a and collects volume information for impressions occurring at the client device 106. Exemplary impression information obtained from the database owner 104a includes an aggregate number of impressions by demographic group generated by the database owner 104a and / or the size of the audience from each demographic group.

[0222] The exemplary database owner 104a may provide impression information (e.g., impression counts, impression counts by demographic group, etc.) and / or audience information (e.g., audience size, audience size by demographic group, etc.) for each media item of interest (e.g., media items being monitored by the audience measurement entity 108). Additionally or alternatively, the exemplary database owner 104a provides impression and / or audience information for various types of devices to the impression information collector 208. In some examples, the impression information collector 208 also collects impressions and / or audience information for media impressions that occur on computer platforms (e.g., non-mobile device platforms, such as desktop computers and / or laptop computers). Table 17 below shows exemplary impression and audience information (e.g., uncorrected impression counts and audience size) for tablet devices obtained by the impression information collector 208 from the exemplary database owner 104a. The exemplary impression information collector 208 may collect similar data for other types of mobile devices (e.g., smartphones, portable media players, etc.) and / or computer platforms. Exemplary Table 17 below is similar to Exemplary Table 15 above, except that Table 17 below also includes audience size and frequency information (eg, from database owner 104a).

[0223]

[0224]

[0225] Example impression and audience information for tablet devices obtained from an example database owner

[0226] Table 17

[0227] In some examples, the database owner total impressions and audience size are processed through a decision tree before being provided to the impression information collector 208. Examples of processing impressions and specific audiences are disclosed in U.S. Non-Provisional Patent Application No. 13 / 209,292 filed on August 12, 2011 and in U.S. Provisional Patent Application No. 61 / 923,959 filed on January 6, 2014. The entire contents of U.S. Non-Provisional Patent Application No. 13 / 209,292 and U.S. Provisional Patent Application No. 61 / 923,959 are incorporated herein by reference.

[0228] Figure 2 The exemplary misidentification corrector 206 adjusts or compensates for the impression information obtained from the database owner 104a for the misidentification error. Figure 3AAlso shown is an exemplary calculation performed by the false positive corrector 206 to adjust impression and / or audience information. In this example, the false positive corrector 206 uses the device sharing matrix generated by the exemplary sharing matrix generator 204 to adjust the demographic information provided by the database owner 104a (e.g., impression counts per demographic group per device type and / or media category, audience size per demographic group per device type and / or media category, etc.).

[0229] Using database owner data (e.g., impression counts and / or audience size information) obtained by impression information collector 208, Figure 2 The false positive corrector 206 computes the dot product of the n×n false positive correction matrix 302 and / or the n×1 database owner data 304 (e.g., impression count matrix, audience size matrix) for device type and / or media category. The result of the dot product is an n×1 false positive adjusted data matrix 306 having adjusted impression counts or adjusted audience size.

[0230] Table 18 below shows an exemplary misidentification-adjusted data matrix calculated by misidentification corrector 206 using the misidentification correction matrix of Table 14 above and the exemplary impression count and / or audience size data of Table 17 above. Table 18 below includes the audience calculated based on the dot product discussed above, and the impression count information calculated by misidentification corrector 206 using the adjusted audience size. In this example, misidentification corrector 206 determines misidentification-adjusted impressions (e.g., 118,492 for the demographic group of F45-49) by dividing the misidentification-adjusted audience size (e.g., 12,216 for the demographic group of F45-49) by the frequency corresponding to the demographic group of Table 17 above (e.g., 9.7 for the demographic group of F45-49). The unadjusted audience size and unadjusted impression count are also shown in Table 18 for ease of comparison.

[0231]

[0232]

[0233] Misidentification of adjusted impression counts and audience size, and unadjusted impression counts and audience size for comparison

[0234] Table 18

[0235] Thus, as shown in Table 18 above, the misidentification adjusted impression counts and misidentification adjusted audience size compensate for misidentification errors in the impression count and audience size information received from the example database owner 104a. The total misidentification adjusted audience size is substantially equal to (e.g., equal to, except for rounding errors) the total unadjusted audience size reported by the database owner 104a. The example misidentification adjusted audience size and / or misidentification adjusted impression counts of this example are corrected for non-coverage errors as described in the examples below.

[0236] Alpha factor for uncovered

[0237] In some examples, such as compensating for errors in impression count information corresponding to media associated with ratings (e.g., episodes of television programs available for streaming), Figure 2 The non-coverage calculator 210 calculates a non-coverage factor for an audience using an "alpha factor". As used herein, the term "alpha factor" refers to the ratio of B / A, where B is defined as the probability that a person (e.g., a person in a demographic group of interest) accesses a media item of interest (e.g., an episode of a television program via streaming video) using a device type of interest that is not covered by the database owner (e.g., on a mobile device and / or on a specific type of mobile device, such as a tablet, smartphone, or portable media player). For example, if the database owner does not access any identifier or information on the device type of interest (e.g., a person in a demographic group of interest), the database owner may access the media item of interest (e.g., an episode of a television program via streaming video). Figure 1 If a person accesses a media item of interest on a device other than a mobile device (such as a standard device for accessing the media item of interest, e.g., a television set in the case of a television program) and / or one or more device / user identifiers 124), the database owner may not have covered the device type, which the database owner may use to associate with registered user information (e.g., demographics). As used herein, in the alpha factor ratio B / A, A is defined as the probability that a person accesses the media item of interest on a device other than a mobile device (such as a standard device for accessing the media item of interest, e.g., a television set in the case of a television program). For example, for episodes of a television program in a particular media category, the designated type may be television and the first device type may be a computing device (e.g., a mobile device and / or a more specific type of mobile device, such as a smartphone, tablet computer, and / or portable media player) on which episodes of the television program may be accessed via streaming video. Such television programs are often made available via streaming video after an initial or premiere presentation on television. Thus, a person may access episodes of a television program on a television and / or on a computing device via streaming media.

[0238] Figure 2The exemplary coverage calculator 210 can generate different alpha factors for different demographic groups, different media categories, different mobile device types, mobile and non-mobile devices, different geographic regions, different stations, different time periods, and / or any other factors identified based on a survey calibration data source.

[0239] exist Figure 2 In the example of , the exemplary non-coverage calculator 210 calculates probability B (e.g., probability that a person views a media item of interest using a device type of interest) as the proportion of the selected demographic group (e.g., based on responses from a survey or another calibration data source) that accesses the media item of interest on the selected device type to the total number of people in the selected demographic group (e.g., based on responses from a survey or another calibration data source). For example, if 40 people in the M18-24 demographic group responded to the survey that they accessed media in the 'comedy' media category on a tablet (out of 100 people in the M18-24 demographic group represented in the survey), then probability B is 40% or 0.40. Similarly, the exemplary non-coverage calculator 210 calculates probability A (e.g., probability that a person views a media item of interest using a selected other device type) as the proportion of the selected demographic group (e.g., based on responses from a survey or another calibration data source) that accesses the media item of interest on the other device type to the total number of people in the selected demographic group (e.g., based on a survey or another calibration data source). For example, if 20 people in the M18-24 demographic group responded to a survey that they accessed media in the 'comedy' media category on television (out of 100 people in the M18-24 demographic group represented in the survey), then probability A is 20% or 0.20. Equations 2 and 3 below show exemplary models for calculating probability A and probability B, respectively. The alpha factor for the formation of the 'comedy' media category on tablets for the M18-24 demographic group is 0.40 / 0.20=2.

[0240] Equation 2A = (number of people in age and gender group X who watch media category Y on TV) / (total number of people in age and gender group X)

[0241] Equation 3B = (Number of people in age and gender group X who watch media category Y on device type of interest) / (Total number of people in age and gender group X)

[0242] In an example where the non-coverage calculator 210 determines an alpha factor for determining non-coverage for the database owner 104a, the exemplary non-coverage corrector 212 corrects the impression information by multiplying the alpha factor for the demographic group by the distribution percentage of the demographic group for the media item of interest. For example, if the 35-39 year old female group (e.g., F35-39 in Table 19 below) represents 2.9% of the impressions for a particular episode of a television program, and for the media category that classifies the television program, the alpha factor for the 35-39 year old female group is 3.8, then the newly calculated percentage is approximately 11.2%. However, the formed percentage (i.e., 11.2%) is normalized so that for the episodes of the television program, the percentages for all demographic groups total 100%. The exemplary non-coverage corrector 212 multiplies the normalized percentage by the number of impressions that are not associated to the user by the database owner 104a to determine the number of impressions attributed to the 35-39 year old female group (F35-39). In some examples, the number of impressions that are not associated to a user by the database owner 104a is determined from the difference between: 1) the number of impressions identified by the AME 108 and 2) the number of impressions that are associated to a user by the database owner 104a. Additionally or alternatively, the database owner 104a monitors and reports the number of impressions that the database owner 104a is unable to associate to a user, while also monitoring the number of impressions corresponding to a demographic group (e.g., impressions that the database owner is able to associate to a user).

[0243] Table 19 below shows exemplary data generated by the uncovered corrector 212 to correct impression information using the alpha factor. In the example of Table 19, AME 108 counts 2,000 media impressions that the database owner 104a cannot associate to a demographic group (e.g., labeled "Demos" in the first column of Table 19 below).

[0244]

[0245]

[0246] Uncovered impressions

[0247] Table 19

[0248] As shown in the example of Table 19 above, for each demographic group (Demos), the non-coverage corrector 212 calculates the adjusted percentage (Adj%) as the product of the alpha factor (α=B / A) and the measured percentage. The non-coverage corrector 212 normalizes the adjusted percentage (Adj%) to a sum of 100% (e.g., dividing the adjusted percentage (Adj%) by the total adjusted percentage (e.g., 160.9%)) to obtain the normalized percentage (Norm%). The non-coverage corrector 212 multiplies the normalized percentage (Norm%) by the number of impressions that are not associated with the demographic group (Demos) by the database owner 104a to obtain the number of uncovered impressions (non-covered impressions) attributed to each demographic group (Demos). The exemplary impressions (non-covered impressions) determined by the non-coverage corrector 212 can be added to the misidentified adjusted impressions to determine the misidentified and non-covered adjusted impressions.

[0249] Non-coverage factor for non-coverage correction

[0250] As an alternative to using the exemplary α factor disclosed above (α=B / A) to compensate the impression information for uncovered errors, Figure 2 The exemplary non-coverage calculator 210 can calculate a non-coverage factor for each exemplary demographic group. The non-coverage factor reflects the portion of the impression that is not attributed to a person by the database owner 104a.

[0251] To calculate the non-coverage factor for a demographic group and a particular device type, the exemplary non-coverage calculator 210 determines from a survey calibration data source a large number or portion of persons in the demographic group (e.g., respondents to a survey) who indicate that they would not be recognized by the database owner 104a when using a particular device type as a portion of persons in the demographic group who own and access the particular device type. For example, the non-coverage calculator 210 may determine that a respondent would not be recognized by the database owner 104a when using a particular device type if the respondent indicates that no one in the respondent's home uses the particular device type to access the database owner 104a, that the respondent is not registered with the database owner 104a, and / or that the respondent takes any other action or inaction that prevents the database owner 104a from recognizing the person when the person accesses media on the particular device type.

[0252] Figure 2The exemplary non-coverage calculator 210 creates a table of non-coverage factors for each demographic group and device type, where the non-coverage factor is calculated as: non-coverage factor = (uncovered portion of respondents in the demographic group for the device type) / (total number of respondents in the demographic group who accessed devices of the device type). An exemplary non-coverage factor table for a tablet is shown in Table 20 below. In some examples, the exemplary non-coverage calculator 210 makes similar tables for other device types. As shown in Table 20, 20% of the 21-24 year old female demographic group is not covered (e.g., not identifiable) by the database owner 104a on the tablet. In other words, 10% of the 21-24 year old females who access the tablet will not be recognized by the database owner 104a. Similarly, 10% of the 18-20 year old male demographic group is not covered by the database owner 104a on the tablet.

[0253]

[0254]

[0255] Exemplary non-coverage factors for tablets

[0256] Table 20

[0257] The non-coverage factor of Table 20 above can be used as an alternative to the alpha factor (α=B / A) for performing non-coverage adjustments for impression information. For example, the frequency observed by the database owner 104a (e.g., average impressions per audience member during a period) can be used to calculate the audience corresponding to the impressions observed by the database owner 104a. The exemplary non-coverage calculator 210 then adjusts the audience by the non-coverage factor (e.g., adjusted audience=audience / (1-non-coverage factor)) and uses the frequency to convert the adjusted audience into non-coverage adjusted impressions.

[0258] Although the above example describes a non-coverage factor for one database owner, the example non-coverage calculator 210 may additionally or alternatively calculate non-coverage factors for multiple database owners. For example, if two database owners are used, the example survey calibration data source may include data specified to determine whether the respondent accessed any of the database owners 104a-b via one or more device types. Figure 2 The exemplary non-coverage calculator 210 then determines that the non-coverage error is limited to those persons and / or impressions that are not identifiable to any of the database owners 104a-b. Figure 2 In the example of , an exemplary person is considered covered if at least one of the database owners 104a - b can identify the person on the mobile device.

[0259] Uncovered Error Compensation and Scaling Example 1

[0260] A first example of compensating for uncovered errors includes multiplying an alpha factor (α=B / A) for a demographic group by the misidentification-adjusted impression count corresponding to the same demographic group. For example, the alpha factor can be used to calculate the uncovered impression count based on the misidentification-adjusted impression count of the first misidentification compensation example described above with reference to Table 16 and / or Table 18.

[0261] Figure 3B It shows that Figure 2 The uncovering corrector 212 of FIG. 1 performs exemplary calculations to compensate for the uncovering error. Figure 3B In the example of Figure 3A In the example of FIG. 1 , the misidentification adjusted data 306 is calculated by the misidentification corrector 206. The uncovering corrector 212 applies the uncovering factors 308 (e.g., a set of alpha factors, a set of scalars) corresponding to the same device type (and / or media category) to the misidentification adjusted data 306 for the device type (and / or media category) to determine the misidentification and uncovering adjusted data 310 for the device type (and / or media category).

[0262] For example, Table 21 below shows an exemplary calculation for determining the amount of uncovered impressions per demographic group using the total number of corresponding misidentification-adjusted impression counts from Table 18 above. The uncovered corrector 212 adjusts the misidentification-adjusted impression counts (e.g., 210,945 for the M25-29 demographic group from Table 18 above) using an alpha factor (α=B / A) for the M25-29 demographic group (e.g., 1.68 from Table 19 above). Table 21 below shows an example of misidentification- and uncovered-adjusted data 310 (e.g., misidentification- and uncovered-adjusted impression counts). In the following example, AME 108 has identified 1,126,462 total impressions that the database owner 104 has not associated with a demographic group.

[0263]

[0264]

[0265]

[0266] Example Misidentification and Non-coverage Adjusted Impression Data

[0267] Table 21

[0268] In Table 21 above, exemplary misidentification-adjusted impressions (Misatt.-adjusted Imp. Count) are obtained from the misidentification correction example described above with reference to Table 18. The exemplary measured percentages (Meas %) of Table 21 are determined based on the misidentification-adjusted impression counts (Misatt.-adjusted Imp. Count) for each demographic group by determining the percentage of the misidentification-adjusted impression count (e.g., 182,026 for the F30-34 demographic group) relative to the total misidentification-adjusted impression count (e.g., 3,034,551).

[0269] Table 21 includes a set of exemplary alpha factors (α=B / A) for various exemplary demographic groups, which can be calculated as described above with reference to exemplary Table 19. The alpha factors are determined, for example, with the aid of the results of a calibration survey (e.g., the survey discussed above) and Equations 2 and 3 disclosed above. Using the alpha factors, the exemplary non-coverage corrector 212 calculates an adjusted percentage (Adj.%) for each exemplary demographic group of Table 21 by multiplying the measured percentage (Meas.%) by the corresponding alpha factor. The exemplary non-coverage corrector 212 also normalizes the adjusted percentage (Adj.%) for each exemplary demographic group of Table 21 to obtain a normalized percentage (Norm.%).

[0270] The exemplary non-coverage corrector 212 determines the non-covered impression count (Non-Covered Imp. Count) by multiplying the normalized percentage (Norm. %) for each exemplary demographic group of Table 21 by the total non-covered impression count (e.g., 1,126,426). For example, the non-covered impression count (Non-CoveredImp. Count) for the F30-34 demographic group is calculated as 3.85%*1,126,426=43,388. The exemplary non-coverage corrector 212 can then add the non-covered impression count (Non-Covered Imp. Count) to the misidentification-adjusted impression count (Misatt.-adjusted Imp. Count) for each exemplary demographic group of Table 21 to determine the misidentification and non-coverage adjusted impression count (Misatt. and Non-Cov.-Adj.Imp. Count).

[0271] Uncovered Error Compensation and Scaling Example 2

[0272] A second example of compensating for uncovered errors includes using the uncovered factors of Table 20 above. For example, when the alpha factor is not available for a particular type of media and / or a particular device type (e.g., the probability of viewing the media and / or the probability of viewing on the device type is not available), the exemplary uncovered method disclosed above in conjunction with the uncovered factor can be used instead of the alpha factor of the uncovered error compensation example disclosed above in conjunction with the alpha factor.

[0273] In this example, Figure 2 The non-coverage corrector 212 corrects the impression information obtained from the database owner 104a using the non-coverage factor. For example, the non-coverage corrector 212 may determine the adjusted number of impressions as: (reported impression count) / (1-(non-coverage factor for the demographic group)).

[0274] use Figure 3B For example, the uncovered corrector 212 obtains Figure 3A , the example non-coverage corrector 212 calculates the misidentification-adjusted data 306 calculated by the misidentification corrector 206. The non-coverage corrector 212 applies non-coverage factors 308 corresponding to the same device type (e.g., a set of scalars rather than a set of alpha factors) to the misidentification-adjusted data 306 for the device type to determine the misidentification- and non-coverage-adjusted data 310 for the device type. For example, the non-coverage corrector 212 adjusts the misidentification-adjusted impression count (e.g., 210,945 for the M25-29 demographic group of Table 18 above) using the 10.0% non-coverage factor of Table 20 for the M25-29 demographic group to determine the misidentification- and non-coverage-adjusted impression count as 19,046. The example non-coverage corrector 212 also determines the misidentification- and non-coverage-adjusted audience size as the quotient of the misidentification- and non-coverage-adjusted impression count divided by the frequencies calculated or obtained from the database owner (e.g., the frequencies from Table 17 above). Table 22 shows an example of misidentification and non-reach adjusted data 310 (eg, misidentification and non-reach adjusted impressions and audience) for a tablet device type.

[0275]

[0276]

[0277] Example misidentification and nonreach adjustment impression counts and audience size for tablet device type

[0278] Table 22

[0279] In the example of Table 22 above, the exemplary non-coverage corrector 212 determines the misidentified and non-coverage adjusted audience size for each demographic group by dividing the corresponding misidentified and non-coverage adjusted impression count by the corresponding frequency of Table 17 (eg, from database owner 104a).

[0280] Figure 2 The example impression information adjuster 214 adjusts the compensated impression count and the audience size to align the number of impressions observed by the audience measurement entity 108 . Figure 3C An exemplary process for adjusting compensated (eg, misidentification and non-coverage adjusted) impression counts and / or audience size 310 based on observed census data (eg, impression counts) is shown.

[0281] Although compensation for impressions and special audience sizes is described above with reference to the examples of misidentification and / or non-coverage described in Tables 15-22, Figure 2 The exemplary misidentification corrector 206 and / or exemplary non-coverage corrector 212 may additionally or alternatively use the same technique to compensate for duration unit misidentification and / or non-coverage. Table 23 below shows an example of applying the misidentification matrix of Table 14 above to duration units associated with impressions and special audience sizes of the example of Table 18 above.

[0282]

[0283]

[0284] Example misidentification and non-coverage adjusted impression counts, duration units, and special audience sizes, and unadjusted impression counts, duration units, and special audience sizes for tablet device types

[0285] Table 23

[0286] As shown in Table 23 above, applying the false positive correction matrix of Table 14 to the unadjusted duration units (Unadj.Duration Units) of Table 23 results in a redistribution of duration units among the demographic groups (Demo.Group). The adjustments for false positives and special audience sizes are the same as in the example described above with reference to Table 18. The example non-coverage corrector 212 can then correct the false positive adjusted duration units using the non-coverage correction techniques described above with reference to the impressions of Tables 19-21.

[0287] Examples of impression scaling, duration unit scaling, and / or audience scaling

[0288] Figure 2 The example impression information adjuster 214 adjusts the compensated impressions to align with the number of impressions observed by the audience measurement entity 108 . Figure 3C An exemplary process for adjusting compensated (eg, misidentification and non-coverage adjusted) impressions based on observed census data (eg, impression volume counts) is shown.

[0289] Using the exemplary misidentified and uncovered adjusted impression counts of the example of Table 21 above, the impression information adjuster 214 scales 312 the misidentified and uncovered adjusted impression counts 310 to match (e.g., be equal to) the number of impressions observed from the tablet (e.g., as determined by Figure 1 The exemplary impression information adjuster 214 may scale the impression count for a primary website (e.g., a website on which an advertisement or other media is hosted) and / or may scale the impressions of media (advertisements or other media) placed on a primary site.

[0290] To scale 312 the exemplary compensated impression information for the demographic group, the exemplary impression information adjuster 214 multiplies the total number of impressions (e.g., recognized impressions and unrecognized impressions) observed from the database owner (e.g., 6,385,686 in this example) by the compensated impression count attributed to the M21-24 demographic group (e.g., 158,067 in Table 21 above) as a fraction (e.g., a percentage) of the total compensated impressions (e.g., 4,161,011 in Table 21 above). For example, the impression information adjuster 214 determines the scaled compensated impressions for the 21-24 year old male demographic group as (6,385,686)*(123,750 / 4,161,011)=189,913. Table 24 below shows exemplary scaled compensated impression counts for a tablet.

[0291]

[0292]

[0293] Exemplary scaled compensated impression count for tablets

[0294] Table 24

[0295] In some other examples, the impression information adjuster 214 calculates impression information for the media based on the PDF scaled impressions of the site hosting the media. For example, to scale impressions for media placed on a primary website, the exemplary impression information adjuster 214 determines the difference between a) the number of impressions for the device type identified by the audience measurement entity 108 for the primary site (census count of impressions) and b) the impressions adjusted for misidentification and non-coverage for the device type for the demographic group of interest and the devices of interest (or all devices). The difference is the number of impressions explained by the scaling. The exemplary impression information adjuster 214 determines the product of the difference and the partial impressions attributable to the demographic group of interest for the device type of interest. In other words, the impression information adjuster 214 applies the probability distribution function for the demographic group of the primary site to the number of impressions required to add to scale the impressions adjusted for misidentification and non-coverage. The product (e.g., partial impressions for the demographic group) is added to the impressions adjusted for misidentification and non-coverage for the demographic group. Therefore, in this example, the impression information adjuster 214 determines the scaled impressions as: scaled impressions = (misidentification and non-coverage adjusted impressions for the selected demographic group and the selected device type) + (census impressions for media on the primary site for the selected device type - total misidentification and non-coverage adjusted impressions for all demographic groups for the selected device type) * (scaled impressions for the primary site for the selected demographic group and the selected device type / total scaled impressions for the primary site for all demographic groups and the selected device type).

[0296] Table 25 below shows an exemplary scaling of impressions as adjusted for scaled media impressions (not impressions of the primary site) using the probability distribution function described above, and using the exemplary misidentification and non-coverage adjusted impressions of Table 19 above. Table 25 is based on 6,385,687 total census impressions for tablets on the primary site for the exemplary media.

[0297]

[0298]

[0299] Example Scaling

[0300] Table 25

[0301] In the example of Table 25 above, the exemplary impression information adjuster 214 scales impressions for the F21-24 demographic group and tablet device type to scaled impressions = (false positives and non-coverage adjusted impression count for device type and demographic group) + (total observed tablet impression count for all demographic groups - total false positives and non-coverage adjusted tablet impression count for all demographic groups) * (scaled primary site impression count for demographic group / total scaled primary site impression count for all demographic groups) = 123,750 + (6,385,686 - 4,161,015) * (1,132,301 / 37,873,074) = 190,262. The exemplary scaling of the above example can be performed to scale impressions for different demographic groups to other census impression counts, such as the primary site's impression count (e.g., if the primary site exclusively presents media of interest).

[0302] Although the examples disclosed herein are described with reference to watching and video media and / or combined audio / video media, the examples disclosed herein may also be used to measure listeners of audio-only media. For example, the media categories, survey calibration data, and / or second device types used to calculate the alpha factor may be tailored for audio media. For example, the second device type used to calculate the "A" term (e.g., Equation 2 above) may be modified to refer to (the number of people in age and gender group X who listen to media category Y on the radio) / (the total number of people in age and gender group X).

[0303] The exemplary scaling techniques described above with reference to Tables 24 and 25 may be used to scale the misidentification and / or non-coverage adjusted duration units to duration unit counts observed for the primary site (eg, census duration counts).

[0304] Although the above examples disclose performing both misidentification correction and non-coverage correction, misidentification correction may be performed on impression counts and / or audience size without performing non-coverage correction. Alternatively, non-coverage correction may be performed on impression counts and / or audience size without performing misidentification correction.

[0305] Despite Figure 2 An exemplary manner of implementing an exemplary impression data compensator 200 is shown in Figure 2One or more of the elements, processes and / or devices shown in can be combined, divided, rearranged, omitted, eliminated and / or implemented in any other manner. In addition, the exemplary calibration data collector 202, the exemplary sharing matrix generator 204, the exemplary error identification corrector 206, the exemplary impression information collector 208, the exemplary non-coverage calculator 210, the exemplary non-coverage corrector 212, the exemplary impression information adjuster 214, the exemplary household distribution generator 216, the exemplary aggregate distribution generator 218, the exemplary matrix corrector 220, the exemplary matrix normalizer 222, the exemplary co-viewing matrix generator 224 and / or more generally, the exemplary impression data compensator 200 can be implemented using hardware, software, firmware, and / or any combination of hardware, software and / or firmware. Thus, for example, any of the exemplary calibration data collector 202, the exemplary sharing matrix generator 204, the exemplary false positive corrector 206, the exemplary impression information collector 208, the exemplary non-coverage calculator 210, the exemplary non-coverage corrector 212, the exemplary impression information adjuster 214, the exemplary household distribution generator 216, the exemplary aggregate distribution generator 218, the exemplary matrix corrector 220, the exemplary matrix normalizer 222, the exemplary co-viewing matrix generator 224 and / or more generally, the exemplary impression data compensator 200 may be implemented using one or more analog or digital circuits, one or more logic circuits, one or more programmable processors, one or more application specific integrated circuits (ASICs), one or more programmable logic devices (PLDs) and / or one or more field programmable devices (FPLDs), etc. When reading any of the device or system claims of this patent covering pure software and / or firmware implementations, at least one of the exemplary calibration data collector 202, exemplary sharing matrix generator 204, exemplary false positive corrector 206, exemplary impression information collector 208, exemplary non-coverage calculator 210, exemplary non-coverage corrector 212, exemplary impression information adjuster 214, exemplary household distribution generator 216, exemplary aggregate distribution generator 218, exemplary matrix corrector 220, exemplary matrix normalizer 222, and / or exemplary co-viewing matrix generator 224 is hereby expressly defined to include a tangible computer-readable storage device or storage disk, such as a memory, digital versatile disk (DVD), compact disk (CD), Blu-ray disk, etc. storing software and / or firmware. In addition, Figure 2 The exemplary impression data compensator 200 may include in addition to Figure 2 other than or in lieu of those shown in Figure 2 One or more of the elements, processes, and / or devices shown in the drawings, and / or may include more than one of any or all of the illustrated elements, processes, and devices.

[0306] exist Figures 4 to 13 The diagram shows a method for implementing Figure 2 Flowchart of exemplary machine readable instructions of impression data compensator 200. In this example, the machine readable instructions include a program executed by a processor, such as described below in conjunction with Fig.14 The processor 1412 shown in the exemplary processor platform 1400 discussed. These programs may be embedded in software stored on a tangible computer-readable storage medium such as a CD-ROM, floppy disk, hard drive, digital versatile disk (DVD), Blu-ray disk, or memory associated with the processor 1412, but all programs and / or portions thereof may alternatively be executed by devices other than the processor 1412 and / or embodied in firmware or dedicated hardware. In addition, although reference is made to Figures 4 to 13 Although the exemplary process is described with reference to the flowchart shown in , many other methods of implementing the exemplary impression data compensator 200 may be alternatively 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.

[0307] As mentioned above, Figures 4 to 13 The exemplary processes of may 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 disk (CD), digital versatile disk (DVD), cache, random access memory (RAM), and / or any other storage device or storage disk in which information is stored for any duration (e.g., for an extended period of time, permanently, temporarily, temporarily buffered, and / or cached information). As used herein, the term "tangible computer readable storage medium" is expressly defined to include any type of computer readable storage medium and / or storage disk and to exclude propagating signals and transmission media. As used herein, "tangible computer readable storage medium" and "tangible machine readable storage medium" are used interchangeably. Additionally or alternatively, Figures 4 to 13The exemplary processes of can be implemented using coded instructions (e.g., computer and / or machine readable instructions) stored on a non-volatile computer and / or machine readable medium, such as a hard drive, flash memory, read-only memory, compact disk, digital versatile disk, cache, random access memory, and / or any other storage device or storage disk, wherein information is stored for any duration (e.g., an extended period of time, permanently, temporarily, temporarily buffered, and / or cached information). As used herein, the term "non-volatile computer readable medium" is expressly defined to include any type of computer readable storage medium and / or storage disk and to exclude propagating signals and transmission media. As used herein, when the phrase "at least" is used as a transition word in the preamble of a claim, it is open-ended, as is the term "comprising".

[0308] Figure 4 is a flow chart representing example machine readable instructions 400 that may be executed to implement Figure 2 An exemplary impression data compensator 200 for compensating impression data.

[0309] Figure 2 The exemplary shared matrix generator 204 of FIG. 4 generates a false positive correction matrix and / or a co-viewing matrix (block 402). For example, the shared matrix generator 204 generates a false positive correction matrix and / or a co-viewing matrix for the image data collected by the calibration data collector 202 ( Figure 2 ) from database owner 104a ( Figure 1 ) for each device type represented in the impression information (e.g., impression) obtained by the exemplary sharing matrix generator 204. The exemplary sharing matrix generator 204 may calculate the false positive correction matrix for the device type by calculating the probabilities described above in Equation 1 and / or by calculating and aggregating the family sharing matrix. Additionally or alternatively, the exemplary sharing matrix generator 204 may calculate the inclusion co-viewing probability κ for each device type and / or media category ij The co-viewing probability κ can also be calculated using an exemplary survey calibration data source (e.g., a survey of randomly selected persons and / or households). ij , to determine, for example, the incidence of co-viewing in a representative household. Figure 5 Example instructions that may be used to implement block 402 are described.

[0310] Figure 2The exemplary non-cover calculator 210 determines the probability that the non-covered audience accesses the media on the device type of interest (block 404). The exemplary non-cover calculator 210 also determines a non-cover factor (block 405). For example, the non-cover calculator 210 may determine an alpha factor (e.g., the exemplary alpha factor α=B / A described above with reference to Table 17) that is used to calculate the demographic distribution for impressions that are not associated with demographic information by the database owner 104a. Fig. 9 Example instructions that may be executed to implement blocks 404 and 405 are described.

[0311] Figure 2 The exemplary impression information collector 208 of obtains an impression count from a volumetric (e.g., census) measurement of impressions (block 406). For example, the impression information collector 208 determines that at the audience measurement entity 108 ( Figure 1 ) for each media item of interest (e.g., monitored media) and / or for each device type. Exemplary volumetric data is shown in Table 16 above. The exemplary impression information collector 208 also obtains demographic information corresponding to the impressions (and / or a subset of impressions) (block 408). For example, the impression information collector 208 receives a count of impressions associated with each demographic group by the database owner 104a for each device type and / or for all device types.

[0312] Figure 2 The exemplary misidentification corrector 206 selects a media category (e.g., comedy, drama, feature film, etc.) (block 410). The exemplary misidentification corrector 206 also selects a device type (e.g., smartphone, tablet, portable media player) (block 412). The exemplary misidentification corrector 206 adjusts impressions, duration units, and / or special audience sizes (obtained from the database owner 104a) based on a misidentification correction matrix for the selected media category and the selected device type (block 414). For example, the misidentification corrector 206 may determine a dot product of the misidentification correction matrix 302 and the database owner impression data 304, such as Figure 3A In some examples, the misidentification corrector 206 calculates the specific audience size of the misidentification correction based on the impression of the misidentification correction and the frequency determined by the database owner 104a. Figure 7 Example instructions that may be executed to implement block 414 are described.

[0313] Figure 2 The exemplary non-coverage corrector 212 of FIG. 4 also adjusts the impression, duration unit, and / or special audience size based on the non-coverage factor for the selected media category and the selected device type (block 416). For example, the non-coverage corrector 212 may Figure 3BThe misidentification adjusted impressions 306 are divided by the coverage percentage (e.g., 1-non-coverage percentage) to obtain misidentification and non-coverage adjusted impression information 310. In some examples, the misidentification corrector 206 calculates the misidentification and non-coverage corrected audience based on the misidentification and non-coverage corrected impressions and the frequency determined by the database owner 104a. Figure 8 Example instructions that may be executed to implement block 416 are described.

[0314] The example false positive corrector 206 determines whether there are additional device types for which impression information is to be compensated (block 418). If there are additional device types for which impression information is to be compensated (block 418), control returns to block 412 to select another device type. When there are no more device types for which impression information is to be compensated (block 418), the example false positive corrector 206 determines whether there are additional media categories for which impression information is to be compensated (block 420). If there are additional media categories for which impression information is to be compensated (block 420), control returns to block 410 to select another media category.

[0315] When there are no more media categories for which impression information is to be compensated (block 420), Figure 2 The example impression information adjuster 214 calculates media ratings based on the adjusted impression information (block 422). For example, the impression information adjuster 214 may scale the misidentified and uncovered adjusted impressions to match impressions identified by the audience measurement entity 108. Additionally or alternatively, the example impression information adjuster 214 may combine device types to determine impression information for multiple or all mobile device types. Figure 4 The example instructions 400 then end.

[0316] Figure 5 is a flow chart representing example machine readable instructions 500 that may be executed to implement Figure 2 The exemplary instructions 500 may be used to calculate the misidentification correction matrix and / or the co-viewing matrix. Figure 2 The exemplary shared matrix generator 204 performs to implement Figure 4 Frame 402.

[0317] Figure 2 The exemplary shared matrix generator 204 of FIG. 1 is derived from a survey calibration data source (e.g., from Figure 2The exemplary aggregate distribution generator 218 selects a media category (block 504). The exemplary aggregate distribution generator 218 selects a device type (block 506). In Figure 5 In the example of , the media categories and / or device types are selected from the media categories and / or device types represented in the survey calibration data. For all demographic groups i and j, the example aggregate distribution generator 218 determines the probability γ of the following event ij :(1) The persons in the identified demographic group i are identified by the database owner (e.g. Figure 1 The database owner 104a) is identified as a viewer of the media, and (2) persons in the actual viewer demographic group j are actual viewers (block 508). For example, the aggregate distribution generator 218 may use the exemplary Tables 1-13 above to generate the false positive correction matrix shown in Table 14 above. Figure 6 Example instructions that may be executed to implement block 508 are described.

[0318] For all pairs of demographic groups i and j, the exemplary aggregate distribution generator 218 determines the probability κ of the following event: ij : (1) People in the identified demographic group i are identified by the database owner as viewers of the media, and (2) there are also impressions for people in demographic group j who are actual viewers (block 510). The probability κ for the pair of demographic groups i and j is ij The collection of can be aggregated into a co-viewing matrix. Figure 8 Example instructions that may be executed to implement block 510 are described.

[0319] The example aggregated distribution generator 218 determines whether there are any additional device types for which the survey calibration data from the survey calibration data source should be processed (block 512). If there are additional device types (block 512), control returns to block 506. If there are no additional device types (block 512), the example aggregated distribution generator 218 determines whether there are additional media categories for which the survey calibration data from the survey calibration data source should be processed (block 514). If there are additional media categories, control returns to block 504. Otherwise, if there are no additional media categories (block 514), the example instructions 500 end and control returns, for example, to Figure 4 Frame 404.

[0320] Figure 6is a flow chart representing exemplary machine readable instructions 600 that may be executed to determine a false positive probability γ. ij The exemplary instructions 600 may be executed to implement Figure 5 Determine the probability of the following event γ ij Block 508: (1) The identified persons in demographic group i are identified by the database owner (e.g. Figure 1 The exemplary household distribution generator 216 redistributes the audience of registered users of the database owner 104a among the household members (block 602). For example, the exemplary household distribution generator 216 may generate an exemplary redistributed audience matrix for the selected media category, as described above in conjunction with Table 5 and / or Table 6. The exemplary aggregate distribution generator 218 aggregates the redistributed audience among the household for the identified demographic group i and the actual viewer demographic group j (block 604). For example, the aggregate distribution generator 218 may generate an exemplary redistributed audience matrix for the selected media category for multiple households, as described above in conjunction with Table 8.

[0321] The exemplary matrix corrector 220 generates a probability P(L) that accounts for the first person in the identified demographic group i and the person in the actual viewer demographic group j living in the same household. ij (Block 606). For example, the example matrix corrector 220 may apply the NPM data to the survey response data to generate the NPM indicators, as described above in conjunction with Tables 9-11. The example matrix corrector 220 applies the NPM indicators to the database owner impressions redistributed among households to account for the probability P(L) that the first person in the identified demographic group i lives in the same household as a person in the actual viewer demographic group j. ij (Block 608). For example, as described above in connection with Table 12, the matrix corrector 220 may determine each i, j cell value in Table 12 by multiplying the redistributed audience value in the i, j cell of Table 8 above by the corresponding NPM metric in the i, j cell value of Table 11 above. In this manner, the matrix corrector 220 applies the NPM metric to the redistributed impressions collected by the database owner 104a in different households to account for the probability P(L) that the first person in the identified demographic group i lives in the same household as a person in the actual viewer demographic group j ij .

[0322] The exemplary matrix normalizer 222 normalizes the probabilities for each identified demographic group i (block 610). For example, the matrix normalizer 222 normalizes each column in the false positive matrix of Table 14 (e.g., each demographic group identified by the database owner 104a) so that the totals of each column equal the same number (e.g., 1.00 or 100%). Each column is normalized so that the number of impressions after correcting for false positives equals the total number of impressions detected by the database owner 104a, and the proportion of impressions associated with each identified demographic group i by the database owner 104a is maintained. Figure 6 The exemplary instruction 600 ends, and control returns to, for example, Figure 5 Frame 510.

[0323] Figure 7 is a flow chart representing example machine readable instructions 700 that may be executed to implement Figure 2 The exemplary impression data compensator 200 for calculating the misidentification correction matrix and / or the co-viewing matrix is ​​provided in accordance with the exemplary instructions 700 described above with reference to Figure 5 Implementation of the description Figure 4 An alternative example of the exemplary instructions of block 402 of .

[0324] Figure 2 The exemplary shared matrix generator 204 of FIG. 1 is derived from a survey calibration data source (e.g., from Figure 2 The exemplary aggregate distribution generator 218 selects a media category (block 704). The exemplary aggregate distribution generator 218 selects a device type (block 706). In Figure 7 In the example of , the media categories and / or device types are selected from the media categories and / or device types represented in the survey calibration data. For all pairs of demographic groups i and j, the example aggregate distribution generator 218 calculates the probability P(L) that a person in the identified demographic group i lives in the same household as a person in the actual viewer demographic group j. ij (eg, based on a survey calibration data source) (block 708) For example, the aggregate distribution generator 218 may determine the incidence of co-viewing for persons in demographic groups i and j who live in the same household.

[0325] For all pairs of demographic groups i and j, the exemplary aggregate distribution generator 218 determines the probability P(D|L) that a person in demographic group i and a person in actual viewer demographic group j who live in the same household both access a mobile device of the selected device type. ij(Frame 710).

[0326] For all pairs of demographic groups i and j, the exemplary aggregate distribution generator 218 determines the probability P(S) that a person in the identified demographic group i (who is a database owner user) who lives in the same household and has access to a mobile device of the selected device type shares the same mobile device used to view media of the selected media category with a person in the actual viewer demographic group j. x |D) ij (Block 712) For example, the example aggregate distribution generator 218 may determine the incidence of persons in demographic groups i and j both accessing media of the selected media category on a device type selected from the survey calibration data.

[0327] For all pairs of demographic groups i and j, the example aggregate distribution generator 218 determines the shared probability γ for the selected pair of demographic groups i, j for the false positive correction matrix associated with the selected device type and the selected media category. ij (Block 714). For example, the aggregate distribution generator 218 may calculate the sharing probability γ using the exemplary equation 1 described above. ij .

[0328] For all pairs of demographic groups i and j, the exemplary co-viewing matrix generator 224 determines the probability κ of the following event: ij : (1) People in identified demographic group i are identified by the database owner as viewers of the media, and (2) there are also impressions for people in actual viewer demographic group j (block 716). The probability κ for the pair of demographic groups i and j is ij The collection of can be aggregated into a co-viewing matrix. Figure 8 Example instructions for implementing block 716 are described.

[0329] The example aggregated distribution generator 218 determines whether there are any additional device types for which the survey calibration data from the survey calibration data source should be processed (block 718). If there are additional device types (block 718), control returns to block 706. If there are no additional device types (block 718), the example aggregated distribution generator 218 determines whether there are additional media categories for which the survey calibration data from the survey calibration data source should be processed (block 720). If there are additional media categories, control returns to block 704. Otherwise, if there are no additional media categories (block 720), the example instructions 700 end and control returns, for example, to Figure 4 Frame 404.

[0330] Figure 8is a flow chart of exemplary machine readable instructions 800 that may be executed to calculate a co-viewing matrix. For example, the instructions 800 may be Figure 2 The exemplary co-viewing matrix generator 224 performs to implement Figure 5 Block 510 and / or Figure 7 Frame 716.

[0331] For all pairs of demographic groups i and j, the exemplary co-viewing matrix generator 224 determines the probability P(C) that a person in the identified demographic group i, who lives in the same household and has access to a mobile device of the selected device type, and a person in the actual viewer demographic group j, concurrently access (e.g., view) media of the selected media category using the same mobile device. x |D) ij (Block 802). In other words, assuming that the persons live in the same household and have access to the same mobile device, the example co-viewing matrix generator 224 determines the probability or incidence of co-viewing for the selected device type, media category, and demographic groups i and j. The example co-viewing matrix generator 224 determines the probability K for the co-viewing matrix for the selected device type and the selected media category ij (Block 804). For example, the example co-viewing matrix generator 224 may convert the probability κ ij Calculate κ ij =P(L) ij ×P(D|L) ij ×P(C x |D) ij . Example probability κ ij represents the probability of co-viewing for the selected pair of demographic groups i and j, the selected device type, and the selected media category.

[0332] Example instruction 800 ends, and control returns to, for example, Figure 5 Box 512 and / or Figure 7 Frame 718.

[0333] Fig. 9 is a flow chart representing example machine readable instructions 900 that may be executed to implement Figure 2 The exemplary impression data compensator 200 for calculating demographics (or non-coverage factors) associated with media audiences not covered by a database owner. The exemplary instructions 900 may be Figure 2 The exemplary uncovered calculator 210 performs to implement Figure 4 Frame 404 and frame 405 of FIG.

[0334] Figure 2The exemplary uncovered calculator 210 of FIG. 900 obtains survey calibration data from a survey calibration data source (block 902). For example, the uncovered calculator 210 may obtain survey calibration data (e.g., from Figure 2 The survey calibration data collector 202 indicates the number of persons who are not registered with the database owner 104a and / or who are registered with the database owner 104a but are not logged into the database owner 104a on a particular type of device.

[0335] The example non-coverage calculator 210 selects a device type (block 904). The example non-coverage calculator 210 selects a demographic group (block 906). The example non-coverage calculator 210 selects a media category (block 908). Fig. 9 In the example of , the media categories, demographic groups, and / or device types are selected from the media categories, demographic groups, and / or device types represented in the survey calibration data. For example, the non-coverage calculator 210 can generate different alpha factors for different combinations of demographic groups, media categories, and device types (e.g., the exemplary alpha factor α=B / A of Table 17) based on the survey calibration data obtained from the survey calibration data source.

[0336] The example uncovered calculator 210 determines a probability B based on survey calibration data (e.g., data from a survey calibration data source), where B is the probability that a person in the selected demographic group views a media item in the selected media category on the selected device type (block 910). Fig. 9 In the example of , the non-coverage calculator 210 determines the probability B based on weights associated with the selected device type, the selected demographic group, and / or the selected media category determined from the survey calibration data.

[0337] The example non-coverage calculator 210 also determines a probability A based on the survey calibration data, where A is a probability that a person in the selected demographic group views a media item in the selected media category on another device type than the selected device type (e.g., a standard device type associated with the media item) (block 912). Fig. 9 In the example of , the non-coverage calculator 210 determines the probability A based on a weight associated with another device type, a selected demographic group, and / or a selected media category determined from the survey calibration data. Fig. 9 In the example of , probabilities B and A are determined for media that is accessible via both the selected device type and another device type.

[0338] For example, for episodes of a television program in a particular media category, the other device type may be a television, and the selected device type may be a computing device (e.g., a mobile device and / or a more specific type of mobile device, such as a smartphone, tablet computer, and / or portable media player) on which the episodes of the television program may be accessed via streaming video. Such television programs are often made available via streaming video after an initial or premiere presentation on the television. Thus, a person may access episodes of the television program on a television (e.g., the other device type) and / or on a computing device (e.g., the selected device type) via streaming media.

[0339] The example uncovered calculator 210 determines an alpha factor (e.g., α=B / A of Table 17 above) for the selected demographic group, the selected media category, and / or the selected device type (block 914). Fig. 9 In the example of , each combination of demographic group, media category, and device type has a separate alpha factor. However, in other examples, the alpha factor may be the same for each demographic group, each media category, and / or each device type. Additionally or alternatively, the example non-coverage calculator 210 determines the alpha factor using a combination of factors including, for example, geographic region, site, time period, and / or other factors.

[0340] The example no-cover calculator 210 determines whether there are additional media categories (block 916). If there are additional media categories (block 916), control returns to block 908 to select another media category. When there are no more media categories (block 916), the example no-cover calculator 210 determines whether there are additional demographic groups (block 918). If there are additional demographic groups (block 918), control returns to block 906 to select another demographic group. When there are no more demographic groups for the selected device type (block 918), the example no-cover calculator 210 determines whether there are additional device types (block 920). If there are additional device types (block 920), control returns to block 904 to select another device type. When there are no additional device types for the selected media category (block 920), Fig. 9 The exemplary instruction 900 ends, and control returns to, for example, Figure 4 406 of the present invention.

[0341] Fig.10 is a flow chart representing example machine readable instructions 1000 that may be executed to implement Figure 2 Example impression data compensator 200 for adjusting impression and / or duration units based on a misidentification correction matrix. Example instructions 1000 may be Figure 2 The exemplary false positive corrector 206 performs to implement Figure 4 Frame 414.

[0342] Figure 2 The exemplary misidentification corrector 206 of 1002 determines misidentification-adjusted impressions by calculating the dot product of: a) a misidentification correction matrix corresponding to a selected media category and a selected device type, and b) an impression count identified by a database owner for each identified demographic group i (block 1002). The result of the dot product is the misidentification-adjusted impression for each demographic group. For example, the misidentification corrector 206 may calculate the dot product of the misidentification correction matrix of Table 14 and the impression information of Table 15 to obtain the misidentification-adjusted impressions.

[0343] Figure 2 The example false positive correction 206 determines the duration unit for the false positive adjustment by computing the dot product of: a) the false positive correction matrix corresponding to the selected media category and the selected device type, and b) the duration unit identified by the database owner for each identified demographic group i (block 1004). The example 1000 ends and control returns to, for example, Figure 4 Frame 416.

[0344] Fig.11 is a flow chart representing example machine readable instructions 1100 that may be executed to implement Figure 2 Example impression data compensator 200 for adjusting impressions based on an uncovered factor for a database owner. Example instructions 1100 may be Figure 2 The exemplary uncovered corrector 212 performs to implement Figure 4 Frame 416.

[0345] Figure 2 The exemplary non-coverage corrector 212 of the embodiment selects a demographic group (block 1102). The selected demographic group can be the identified demographic group i and / or the actual viewer demographic group j discussed above with reference to Tables 1-14. The non-coverage corrector 212 determines the non-coverage impressions (block 1104) by multiplying the misidentification adjusted impressions by an alpha factor associated with the selected demographic group, the selected device type, and / or the selected media category (e.g., α=B / A of Table 17 above). For example, the non-coverage corrector 212 can determine the non-coverage impressions that are applicable to the selected demographic group (e.g., selected in block 1102), the selected device type (e.g., in Figure 4 ) and / or a selected media category (e.g., in Figure 4 410) of the α factor (e.g., Figure 4 In block 404 and / or Figure 6At block 1104, the example uncover corrector 212 multiplies the determined alpha factor by the misidentification adjusted impression to determine the uncover impression.

[0346] The example non-coverage corrector 212 determines whether there are additional demographic groups (block 1106). If there are additional demographic groups (block 1106), control returns to block 1102 to select another demographic group. When there are no more demographic groups to adjust (block 1106), the example non-coverage corrector 212 scales the non-coverage impressions for the demographic groups (e.g., all demographic groups in the same group calculation) so that the sum of the non-coverage impressions for all demographic groups is equal to the number of observed non-coverage impressions (block 1108). For example, as described above with reference to Table 17, the exemplary uncovered corrector 212 may 1) calculate an adjusted percentage (Adj% of Table 17) for each demographic group (Demos) as the product of an alpha factor (α=B / A) and a measured percentage (Meas% of Table 17); 2) normalize the adjusted percentage (Adj% of Table 17) to sum to 100%; and 3) multiply the normalized percentage (Norm% of Table 17) by uncovered impressions (e.g., the number of impressions not associated by the database owner 104a to a demographic group (Demos of Table 17)) to obtain a scaled number of uncovered impressions (Uncovered Impressions of Table 17) attributed to each demographic group (Demos of Table 17).

[0347] Figure 2 The exemplary non-coverage corrector 212 selects a demographic group (block 1110). The demographic group selected in block 1110 is the demographic group previously selected in the iteration of block 1102. In the illustrated example, blocks 1110, 1112, and 1114 are performed to process the scaled non-coverage impressions for all demographic groups determined at blocks 1102, 1104, 1106, and 1108. The non-coverage corrector 212 determines the misidentification and non-coverage adjusted impressions for the selected demographic group by adding the scaled non-coverage impressions (e.g., determined in block 1108) to the misidentification adjusted impressions for the demographic group (block 1112).

[0348] The example uncovered corrector 212 determines if there are additional demographic groups (block 1114). If there are additional demographic groups (block 1114), control returns to block 1110 to select another demographic group. When there are no more demographic groups to adjust (block 1114), the example instructions 1100 end and control returns to, for example, Figure 4 Frame 418.

[0349] Although the above examples are described with reference to impressions, these examples can be additionally or alternatively applied to special audiences and / or duration units. For example, the redistribution audience matrix can be applied to the audience of the media of interest, rather than to the impression described above. In some examples, only one impression needs to count the personnel in the audience of the media, and does not count personnel in the audience of the media when he or she does not have an impression. Therefore, for example, in the example of Table 3, after the redistribution of impressions, the audience for 12-17 year old males will be reduced by 1, because 12-17 year old males are reported by database owner 104a to access the media, but after redistribution, it is determined that there is no impression of the media for the comedy category on the tablet computer, thereby reducing the audience of 12-17 year old males by 1. On the contrary, the audience for 35-44 year old females will increase by 1, because 35-44 year old females are not associated to any impression by database owner 104a and do not form an audience, but after the redistribution of the audience, it can be determined that 35-44 year old females actually do visit the media of the category of interest on the tablet computer.

[0350] Fig.12 is a flow chart representing example machine readable instructions 1200 that may be executed to implement Figure 2 The exemplary impression data compensator 200 for calculating demographics (or non-coverage factors) associated with media audiences not covered by a database owner. The exemplary instructions 1200 may be Figure 2 The exemplary uncovered calculator 210 performs to implement Figure 4 Frame 404 and frame 405 of FIG.

[0351] The example non-coverage calculator 210 obtains data from a calibration survey (block 1202). For example, the non-coverage calculator 210 may obtain survey calibration data indicating the number of respondents who are not registered with the database owner 104a and / or who are registered with the database owner 104a but are not logged into the database owner 104a on a particular type of device.

[0352] The example non-coverage calculator 210 selects a device type (block 1204) and selects a demographic group (block 1206). The example non-coverage calculator 210 determines a non-coverage factor for the selected demographic group and the selected device type as a portion of respondents to the calibration survey in the selected demographic group that would not be recognized by the database owner 104a when viewing media on the selected device type (block 1208).

[0353] The example non-coverage calculator 210 determines whether there are additional demographic groups (block 1210). If there are additional demographic groups (block 1210), control returns to block 1206 to select another demographic group. When there are no more demographic groups for the selected device type (block 1210), the example non-coverage calculator 210 determines whether there are additional device types (block 1212). If there are additional device types (block 1212), control returns to block 1204 to select another device type. When there are no additional device types (block 1212), Fig.12 The exemplary instruction 1200 ends, and control returns to the calling function or procedure, such as Figure 4 The example instructions 400 continue to execute block 406.

[0354] Fig.13 is a flow chart representing example machine readable instructions 1300 that may be executed to implement Figure 2 Example impression data compensator 200 for adjusting impression counts and / or audience size based on an uncovered factor for a database owner. Example instructions 1300 may be Figure 2 The exemplary uncovered corrector 212 performs to implement Figure 4 Frame 416.

[0355] Figure 2 The exemplary non-coverage corrector 212 selects a demographic group (block 1302). The exemplary non-coverage corrector 212 adjusts the specific audience size (using Fig.10 The instruction determined by ) is divided by 1-(the non-coverage factor for the selected demographic group and the selected device type) to determine the special audience size for misidentification and non-coverage error adjustment (block 1304).

[0356] The example non-cover corrector 212 determines a misidentified and non-cover adjusted impression count by multiplying the misidentified and non-cover adjusted special audience size by the frequency determined from the impression information for the selected demographic group and the selected device type (block 1306).

[0357] The exemplary uncovered corrector 212 adjusts the duration units (using Fig.10 The duration units for the false positive and non-coverage error adjustment are determined by dividing the duration units for the false positive and non-coverage error adjustment by 1-(non-coverage factor for the selected demographic group and the selected device type) (block 1308).

[0358] The example uncovered corrector 212 determines if there are additional demographic groups (block 1310). If there are additional demographic groups (block 1310), control returns to block 1302 to select another demographic group. When there are no more demographic groups to adjust (block 1308), the example instructions 1300 end and control returns to the calling function or process, such as, for example. Figure 4 The example instructions 400 continue to execute block 418.

[0359] In some instances, data selection may be performed based on art type. In some examples, the art type may be provided by a media publisher 120 (e.g., a television network provider, a media provider, etc.). In other examples, the art type may not be available from the provider of the media. In such other examples, when the art type is not readily available, the selection may be performed using, for example, the following in conjunction with Figures 14 to 20 Public techniques to predict art genres.

[0360] In cross-platform audience measurement, when a television (and / or radio) program is broadcast via a television (and / or radio) station (e.g., a local television station, a local radio station) and the television (and / or radio) program is provided via a delayed media service within a certain amount of time after the broadcast, AME (e.g., Figure 1 AME 108) monitors the audience of television (and / or radio) programs. The exemplary delayed media service can be accessed on a mobile device or a fixed computing device with the help of on-demand services, streaming service applications, applications, web pages, etc. The exemplary delayed media service can also be provided on a digital video recorder (DVR), a cable box, an Internet media delivery set-top box, a smart TV, etc. In this way, AME performs cross-platform audience measurement by collecting impressions for television (and / or radio) audiences and delayed audiences. In some examples, impressions are analyzed to generate media ratings. In some examples, art types are used to determine adjustment factors for calibrating mobile census data. In such examples, calibrated mobile census data is used to generate ratings. Art types are media categories based on similarities such as form, style and / or theme. Exemplary art types include comedy, drama, sports, news, children's programs, etc.

[0361] In some examples, impressions are collected based on data encoded in the media. In some examples, the encoded data does not include art type information. For example, programs broadcast by local broadcasters do not have art type information and the encoded data for local broadcasts does not include program names or identifiers. In addition, art type information or program names are not provided by broadcasters or media owners in a timely manner to generate overnight ratings. Overnight ratings are often used to quickly evaluate the success of broadcast programs based on impression counts and audience size. In some instances, the success of broadcast programs reflected in overnight ratings can be used by media networks (e.g., television networks) to sell advertising slots corresponding to delayed viewing access to the same media available through applications or web pages of, for example, media networks (e.g., ABC Television Broadcasting Company) or third parties (e.g., Hulu.com) that have the right to provide media through delayed viewing services. The examples disclosed herein can be used to generate models that predict the art type of local television (and / or radio) impressions. Although the examples are described below with respect to television, the examples below can be implemented alternatively using radio or other media types.

[0362] Art Genre Predictive Modeling

[0363] The above examples of misidentification and / or non-coverage correction can include generating multiple misidentification correction tables (e.g., such as Table 14 above). In some examples, misidentification correction matrices are generated for different device types and / or art types for different media. The following examples can be used to predict the media art type of an impression for which art type information is not provided from the impression. By assigning art types to impressions, the examples disclosed herein select an appropriate misidentification correction matrix and / or non-coverage factor (alpha factor of Table 19 above and / or Table 21 above) to compensate for misidentification errors and / or non-coverage errors in impression counts and / or audience sizes as disclosed above.

[0364] The examples of generating art type prediction models disclosed below use a home media meter installed locally at a client computer or television to collect information about accessing or tuning into media to facilitate generating an art type prediction model. However, the examples disclosed above generate misidentification correction factors without relying on panelist metering software installed locally at a client computer to collect database owner registration status data and / or collect data about media accessed by family members. The use of home media meters installed locally at a client computer or television in art type prediction modeling and art type assignment is limited to collecting impressions and / or assigning art types to impressions. Such locally installed home meters are not used to generate misidentification correction matrices or non-coverage factors as disclosed herein. The art type prediction modeling and art type assignment disclosed below can be used to correct impressions for misidentification and / or non-coverage as disclosed above.

[0365] Fig.14 An exemplary system 1400 is shown that generates an art type prediction model and predicts an art type and / or assigns an art type to non-art type impression data 1402 (e.g., impression data that does not include art type information). In the illustrated example, a home media meter 1404 (e.g., a set-top meter, a personal people meter, etc.) sends local television impression data 1402 to an audience measurement entity (AME) 108. Fig.14 An exemplary AME 108 may be Figure 1 AME 108.

[0366] In the illustrated example, the impression data 1402 generated from local broadcast television is not encoded with the artist type information because the local television broadcaster has not yet encoded the media when it is broadcast. In the illustrated example, in order to monitor the cross-platform audience, in addition to collecting the local television impression data 1402 for the television audience, an exemplary collector 1406 is provided in the network-enabled media device 1410 to collect impressions of the media accessed using the streaming service via the network-enabled media device 1410. In the illustrated example, the collector 1406 is implemented using instructions incorporated into a web page, application, or application (e.g., an on-demand application, a streaming application, a DVR access application, etc.) executed by the network-enabled media access device 1410 (e.g., a computer, television, tablet, smartphone, e-book reader, etc.).

[0367] Examples of using the exemplary collector 1406 to collect impressions are disclosed in U.S. Patent No. 8,370,489, issued on February 5, 2013, U.S. patent application serial number 14 / 127,414, filed on December 18, 2013, U.S. patent application serial number 14 / 329,779, filed on July 11, 2014, U.S. provisional application serial number 61 / 952,726, filed on March 13, 2014, U.S. provisional application serial number 62 / 014,659, filed on June 19, 2014, and U.S. provisional application serial number 62 / 023,675, filed on July 11, 2014, the entire contents of which are incorporated herein by reference. The example media meter 1404 and / or the example collector 1406 communicate with the example AME 108 via an example network 1412 (e.g., the Internet, a local area network, a wide area network, a cellular data network, etc.) via a wired connection and / or a wireless connection (e.g., a cable / DSL / satellite modem, a base station, etc.).

[0368] exist Fig.14In the illustrated example of AME 108, AME 108 processes non-art type impression data 1402 to determine an art type for an impression recorded at AME 108 based on the non-art type impression data 1402. In the illustrated example, AME 108 includes a collection database 1414, an art type prediction modeler 1416, an art type predictor 1418, a historical art type database 1420, and a prediction database 1422. The example collection database 1414 is provided to store impressions recorded based on the example non-art type impression data 1402 received and / or retrieved from the example media meter 1404 and / or the example collector 1406.

[0369] The exemplary art genre prediction modeler 1416 generates one or more art genre prediction models 1428 based on historical art genre data stored in the exemplary historical art genre database 1420. In the illustrated example, the historical art genre data includes historical program records 1430 that, for example, identify the art genre of the program, the time slot of the program, the day category of the program, and the duration of the program. The exemplary historical program records 1430 are based on information (e.g., program identifier, one or more timestamps, station identifier, affiliate identifier, etc.) provided by the local broadcaster 1432 after the program is broadcast. For example, the local broadcaster 1432 may provide records 1430 for every quarter hour of the local broadcast program for a week after the local broadcast.

[0370] The example art type predictor 1418 uses one or more art type prediction models 1428 generated by the example art type prediction modeler 1416 to predict art type information and assign predicted art types to impressions recorded in the collection database 1414 based on the non-art type impression data 1402. In this manner, the example art type predictor 1418 generates predicted art type impression records 1424 (e.g., non-art type impressions recorded in the collection database 1414 combined with predicted art type information). Fig.14 In the illustrated example of , predicted art type impression records 1424 are stored in prediction database 1422. In some examples, predicted art type impression records 1424 are used as uncorrected impressions that can compensate for misidentification and non-coverage to determine the demographic group to which the predicted art type impression belongs, as discussed above.

[0371] Fig.15 Shows Fig.14 An exemplary implementation of the art type predictor 1418 is based on Fig.14The non-art type impression data 1402 determines the art type information for the non-art type impression record 1501 recorded in the collection database 1414. In the illustrated example, the art type predictor 1418 receives and / or retrieves the non-art type impression record 1501 from the collection database 1414 and generates a predicted art type impression record 1424, which is stored in the prediction database 1422 and / or reported in the prediction report 1426. Fig.15 In the illustrated example of FIG. 1 , the art type predictor 1418 includes a data classifier 1500 and an art type analyzer 1502. The exemplary data classifier 1500 is configured to transform the exemplary non-art type impression record 1501 into classified impression data 1504. Fig.16 The classified impression data 1504 is described in more detail.

[0372] The example art type analyzer 1502 is configured to apply one or more art type prediction models 1428 generated by the example art type prediction modeler 1416 to the example classified impression data 1504. The example art type analyzer 1502 applies the one or more art type prediction models 1428 to the classified impression data 1504. The example art type analyzer 1502 then assigns a predicted art type to the classified impression data 1504 to generate an example predicted art type impression record 1424. The example art type analyzer 1502 stores the example predicted art type impression data 1424 in the example prediction database 1422 and / or includes the example predicted art type impression data 1424 on the example prediction report 1426.

[0373] Fig.16 An exemplary approach is shown, in which Fig.15 The data classifier 1500 classifies the non-artistic type impression data record 1501, and the non-artistic type impression data record 1501 will be Fig.15The art type analyzer 1502 is used to predict the art type. In the illustrated example, the non-art type impression data record 1501 includes an exemplary market identifier (ID) 1600, an exemplary station ID 1602, an exemplary member ID 1604, an exemplary start timestamp 1606, an exemplary end timestamp 1608, an exemplary program ID 1610, and an exemplary source ID 1612. Any one or more of these fields may be omitted and one or more additional fields may exist. For example, the non-art type impression data record 1501 corresponding to a national program may include the program ID 1610 and the source ID 1612. Additionally, the non-art type impression data record 1501 corresponding to a local broadcast program may omit the program ID 1610 and the source ID 1612. The exemplary market ID 1600 identifies the market (e.g., geographic location, municipality, etc.) in which the corresponding television program is broadcast. In some examples, the market ID 1600 is provided by AME 108 ( Fig.14 ) or any other suitable entity. Station ID 1602 identifies a local station that broadcasts a television program. In some examples, station ID 1602 is assigned by AME 108 ( Fig.14 ) or any other suitable entity assigned an alphanumeric number. In some examples, station ID 1602 is a call letter used by a local television station to identify itself (e.g., WXTV, KVEA, WSNS, etc.).

[0374] Exemplary affiliate ID 1604 identifies a network affiliate of a local television station (e.g., ABC, Telemundo TV, Fox Broadcasting Company, etc.). In some examples, affiliate ID 1604 is a number assigned by AME 108 that represents a network affiliate (e.g., 100 for ABC, 101 for NBC, etc.). In some examples, affiliate ID 1604 may be an alphabetical representation of the name of a network affiliate (e.g., "ABC" for ABC, "TMD" for Telemundo, etc.). Exemplary start timestamp 1606 includes a start date and a start time for a program associated with non-artistic type impression record 1501. Exemplary end timestamp 1608 includes an end date and an end time for a program associated with non-artistic type impression record 1501. Exemplary program ID 1610, if included in non-artistic type impression record 1501, identifies a television program associated with non-artistic type impression record 1501 (e.g., "Arrested Development," "News Radio," "Sabado Gigante," etc.). In some examples, program ID 1610 is a numeric or alphanumeric identifier that is uniquely associated with the title and / or other information of the corresponding television program in a reference database.Exemplary source ID 1612 identifies the source of the television program (eg, production company, etc.).

[0375] exist Fig.16 In the illustrated example of , the classified impression data 1504 includes an exemplary market ID 1600, an exemplary station ID 1602, an exemplary member ID 1604, an exemplary day category 1614, an exemplary time period 1616, and an exemplary duration 1618. The exemplary data classifier 1500 uses the start timestamp 1606 and the end timestamp 1608 of the non-art type impression record 1501 to determine the exemplary day category 1614, the exemplary time period 1616, and the exemplary duration 1618 of the classified impression data 1504. In the illustrated example, the duration 1618 of the classified impression data 1504 is calculated by the data classifier 1500 as the time difference between the start time marked in the start timestamp 1606 and the end time marked in the end timestamp 1608.

[0376] In the illustrated example, the data classifier 1500 determines an exemplary day category 1614 by using the start date in the exemplary start timestamp 1606 and the end date in the exemplary end timestamp 1608. In some examples, the day category 1614 may have a value for a day of the week (e.g., Monday, Tuesday, Wednesday, etc.). In some examples, the day category 1614 may be a reduced set of days of the week. For example, the reduced set may include values ​​for "weekend," "Saturday," or "Sunday." In some examples, when the start date marked in the exemplary start timestamp 1606 and the end date marked in the exemplary end timestamp 1608 are different, the data classifier 1500 may assign a day to the day category 1614 based on the amount of time that the program was broadcast on that day. For example, if the start timestamp 1606 is "7 / 25 / 2014 23:30" (e.g., 30 minutes on Friday) and the end timestamp 1608 is "7 / 26 / 2014 1:00" (e.g., 60 minutes on Saturday), "Saturday" would be assigned to the day category 1614. In such examples, the data classifier 1500 assigns days with a larger portion of broadcast programming to the day category 1614.

[0377] The example data classifier 1500 uses the start time indicated in the example start timestamp 1606 and the end time indicated in the example end timestamp 1608 to determine an example time period 1616 of the example classified impression data 1504. Fig.17 As further discussed, a time period is a time segment or partition of a day (e.g., nighttime, weekend mornings, weekday daytime, evening time, prime time, weekend daytime, etc.) during which different types of television programs are broadcast, advertisements are purchased, and / or audience ratings are measured. In some examples, Fig.16The time period 1616 may be a time period including the start time indicated in the start timestamp 1606 and the end time indicated in the end timestamp 1608 .

[0378] Despite Fig.15 The implementation is shown in Fig.14 The exemplary manner of the art type predictor 1418, however Fig.15 One or more of the elements, processes, and / or devices shown in the example data classifier 1500, the example art type analyzer 1502, and / or more generally, may be combined, divided, rearranged, omitted, eliminated, and / or implemented in any other manner. Fig.14 The exemplary art type predictor 1418 may be implemented by hardware, software, firmware, and / or any combination of hardware, software, and / or firmware. Thus, for example, any of the exemplary data classifier 1500, the exemplary art type analyzer 1502, and / or more generally, the exemplary art type predictor 1418 may be implemented using one or more analog or digital circuits, logic circuits, one or more programmable processors, one or more application specific integrated circuits (ASICs), one or more programmable logic devices (PLDs), and / or one or more 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 exemplary data classifier 1500, the exemplary art type analyzer 1502, and / or the exemplary art type predictor 1418 is hereby expressly defined to include a tangible 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. storing software and / or firmware. Additionally, the exemplary art type predictor 1418 may include, in addition to Fig.15 other than or in lieu of those shown in Fig.15 One or more of the elements, processes, and / or devices shown in the drawings, and / or may include more than one of any or all of the illustrated elements, processes, and devices.

[0379] Fig.17 Shown is a depiction of Fig.14 and Fig.15 The time period used by the art type predictor 1418 (e.g., Fig.16 The time periods that can be indicated in the exemplary time periods 1616 of Fig.161614 of the exemplary day categories 1614 of FIG. 1615 ). In the illustrated example, each day 1700 of the week is divided into a plurality of time periods 1702. In the illustrated example, Monday through Friday are divided into the same time period segments or partitions (e.g., time periods having the same duration and delimited by the same time period start time and time period end time). Alternatively, each day 1700 of the week or any combination thereof may be divided into different time periods. In the illustrated example, the day categories 1704 are divided into three categories of "weekdays", "Saturdays", and "Sundays". Alternatively, the day categories 1704 may include separate categories for each day of the week (e.g., a "Monday" category, a "Tuesday" category, etc.). In some examples, the day categories may have different day groupings based on similar characteristics between different days grouped together (e.g., temporal proximity, audience viewing habits, etc.). For example, the day categories 1704 may include an "early week" category (e.g., including Monday and Tuesday), a "mid-week" category (e.g., including Wednesday and Thursday), a "late week" category (e.g., including Friday), and a "weekend" category (e.g., including Saturday and Sunday).

[0380] Fig.18 Shows Fig.14 An example of an art type prediction modeler 1416 that constructs an art type prediction modeler 1416 that will be Fig.14 The art type predictive modeler 1418 of the art type predictor 1416 is used to generate one or more art type prediction models 1428. In the illustrated example, the art type prediction modeler 1416 includes an exemplary art type prediction model builder 1802, an exemplary art type prediction model evaluator 1804, and an exemplary art type prediction model comparator 1806. The exemplary art type prediction model builder 1802 uses a data set from the historical art type database 1420 to generate one or more candidate models using different modeling techniques. Exemplary modeling techniques that can be used to implement the art type prediction modeler 1416 include logistic regression, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor algorithm, etc. The exemplary model builder 1802 retrieves an exemplary training data set 1808 from the historical art type database 1420. In the illustrated example, the training data set 1808 is used to establish the input of one or more art type prediction models 1428 (e.g., in Fig.15The exemplary training data set 1808 includes the classified impression data (e.g., the exemplary market identifier 1600, the exemplary station ID 1602, the exemplary member ID 1604, the exemplary day category 1614, the exemplary time period 1616, and the exemplary duration 1618) and the assigned art type. Using the training data set 1808, the exemplary art type prediction model builder 1802 generates one or more candidate models 1800. The candidate models 1800 are for the art type analyzer 1502 ( Fig.15 ) is used to assign art type to non-art type impression record 1501 ( Fig.14 ) is evaluated for suitability (e.g., accuracy) of an art type prediction model. In some examples, after generating the exemplary candidate model 1800, the art type prediction model builder 1802 calculates a correct discrimination rate (CCR) using the training data set 1808. In some examples, the CCR is the percentage of impressions in the training data set 1808 that are correctly predicted by the candidate model.

[0381] exist Fig.18 In the illustrated example, the art type prediction model evaluator 1804 receives and / or retrieves the candidate model 1800 generated by the art type prediction model builder 1802. The art type prediction model evaluator 1804 also retrieves the test data set 1810 from the historical art type database 1420. The exemplary art type prediction model evaluator 1804 validates the candidate model 1800 using the test data set 1810. In some examples, the art type prediction model evaluator 1804 calculates the CCR for the candidate model using the test data set 1810. In some such examples, the art type prediction model evaluator 1804 calculates the CCR for the art type of interest included in the test data set 1810.

[0382] exist Fig.18 In the example shown, the art type prediction model comparator 1806 uses the AME 108 ( Fig.14 ) selects one or more of the candidate models 1800 as one or more art type prediction models 1428 based on selection criteria defined by the art type prediction model comparator 1806. In some examples, the selection criteria include the highest CCR, the shortest processing time, the smallest resource requirements, etc. In some examples, the art type prediction model comparator 1806 selects the candidate model 1800 with the highest CCR.

[0383] Despite Fig.18 The implementation is shown in Fig.14 The exemplary manner of the art type prediction modeler 1416, however Fig.18One or more of the elements, processes, and / or devices shown in the can be combined, divided, rearranged, omitted, eliminated, and / or implemented in any other manner. In addition, the exemplary art type prediction model builder 1802, the exemplary art type prediction model evaluator 1804, the exemplary art type prediction model comparator 1806 and / or more generally, Fig.14 The exemplary art type prediction modeler 1416 may be implemented by hardware, software, firmware, and / or any combination of hardware, software, and / or firmware. Thus, for example, any of the exemplary art type prediction model builder 1802, the exemplary art type prediction model evaluator 1804, the exemplary art type prediction model comparator 1806, and / or more generally, the exemplary art type prediction modeler 1416 may be implemented using one or more analog or digital circuits, logic circuits, one or more programmable processors, one or more application specific integrated circuits (ASICs), one or more programmable logic devices (PLDs), and / or one or more 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 exemplary art type prediction model constructor 1802, the exemplary art type prediction model evaluator 1804, the exemplary art type prediction model comparator 1806, and / or the exemplary art type prediction modeler 1416 is hereby expressly defined to include a tangible computer-readable storage medium or storage disk, such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing software and / or firmware. In addition, the exemplary art type prediction modeler 1416 may include, in addition to Fig.18 other than or in lieu of those shown in Fig.18 One or more of the elements, processes, and / or devices shown in the drawings, and / or may include more than one of any or all of the illustrated elements, processes, and devices.

[0384] exist Fig.19 The diagram shows a method for implementing Fig.14 and Fig.18 A flowchart of exemplary machine readable instructions for an exemplary art genre prediction modeler 1416. Fig. 20 The diagram shows a method for implementing Fig.14 and Fig.15 1418 of an exemplary art type predictor 1418. In these examples, the machine-readable instructions include one or more programs for execution by a processor, such as described below with reference to Fig.21The processor 2112 shown in the exemplary processor platform 2100 discussed above. The one or more programs may be embodied in software stored on a tangible computer-readable storage medium such as a CD-ROM, floppy disk, hard drive, digital versatile disk (DVD), Blu-ray disk, or memory associated with the processor 2112, but all of the one or more programs and / or portions thereof may alternatively be executed by a device other than the processor 2112 and / or embodied in firmware or dedicated hardware. In addition, although reference is made to Fig.19 and Fig. 20 Although the exemplary one or more procedures are described with reference to the flowchart shown in , many other methods of implementing the exemplary art type predictor 1418 and / or the exemplary art type prediction modeler 1416 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.

[0385] As mentioned above, Fig.19 and / or Fig. 20 The exemplary processes of may be implemented using coded instructions (e.g., computer and / or machine readable instructions) stored on a tangible computer readable storage medium such as a hard drive, flash memory, read only memory (ROM), compact disk (CD), digital versatile disk (DVD), cache, random access memory (RAM), and / or any other storage device or storage disk in which information is stored for any duration (e.g., for an extended period of time, permanently, temporarily, temporarily buffered, and / or cached information). Additionally or alternatively, Fig.19 and / or Fig. 20 The exemplary processes may be implemented using encoded instructions (e.g., computer and / or machine readable instructions) stored on a non-volatile computer and / or machine readable medium, such as a hard drive, flash memory, read-only memory, compact disk, digital versatile disk, cache, random access memory, and / or any other storage device or storage disk in which information is stored for any duration (e.g., for an extended period of time, permanently, temporarily, temporarily buffered, and / or cache of information).

[0386] Fig.19 is a flowchart representing example machine readable instructions 1900 that may be executed to implement Fig.14 and Fig.18 Construct one or more art type prediction models (e.g. Fig.18 The exemplary art type prediction modeler 1416 of one or more art type prediction models 1428 of the art type prediction model is used to predict the art type for the non-art type impression data 1402 ( Fig.14) recorded art type of non-art type impressions. Initially, at block 1902, the art type prediction model builder 1802 ( Fig.18 ) to include in the training dataset (e.g. Fig.18 The example art type prediction model builder 1802 selects the variables or parameters (e.g., market identifier, station ID, member ID, day category, time period, and duration) in the impressions in the training data set 1808 of the example art type prediction model builder 1802. In some examples, the art type prediction model builder 1802 selects the variables by performing statistical significance tests on the variables (e.g., calculating p-values, performing chi-square tests, etc.). At block 1903, the example art type prediction model builder 1802 excludes impressions from the training data set 1808 based on the selection criteria (e.g., excluding impressions associated with art types that are not of interest, excluding impressions with a duration of contact or visit below a threshold, etc.). For example, the historical art type database 1420 ( Fig.14 ) some records in 1430( Fig.14 ) may be classified in the "unclassified" art type. In some instances, it may not be desirable for art type prediction model 1428 to predict the "unclassified" art type for an impression. In such examples, to prevent art type prediction model 1428 from classifying non-art type impression 1402 ( Fig.14 ) is assigned to the “uncategorized” art type, and the records 1430 classified in the “uncategorized” art type in the historical art type database 1420 are excluded from the training data set 1808.

[0387] At block 1904, the exemplary art genre prediction model builder 1802 constructs the candidate model 1800 ( Fig.18 ). Exemplary modeling techniques that can be used to generate the candidate model 1800 include logistic regression, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor algorithm, etc. For example, the art type prediction model builder 1802 can use the k-nearest neighbor algorithm to construct a model, where k is equal to 9. In some examples, the art type prediction model builder 1802 calculates the CCR of the candidate model 1800 using the training data set 1808. At block 1906, the art type prediction model evaluator 1804 ( Fig.18 ) evaluates the candidate models 1800 generated in block 1904. In some examples, the art genre prediction model evaluator 1804 selects the candidate models 1800 from the historical art genre database 1420 ( Fig.14 ) retrieve one or more test data sets 1810 ( Fig.18), and uses the one or more test data sets 1810 to calculate one or more CCRs for the candidate model 1800. In some such examples, the art genre prediction model evaluator 1804 calculates a separate one or more CCRs for each art genre represented in the one or more test data sets 1810.

[0388] At block 1908, the art type prediction model evaluator 1804 determines whether the one or more CCRs generated at block 1906 are acceptable. If the one or more CCRs are acceptable, program control proceeds to block 1912. Otherwise, if the one or more CCRs are unacceptable, program control proceeds to block 1910. At block 1910, the art type prediction model builder 1802 refines or adjusts the candidate model 1800 generated at block 1904. In some examples, the art type prediction model builder 1802 adjusts model parameters (e.g., selects a different value for k, etc.), adjusts input variables (e.g., uses different day categories, adds and / or removes variables, etc.), and / or adjusts the training data set 1808 (e.g., increases the size of the training data set 1808, changes the composition of the training data set 1808 to include impressions that were previously excluded based on, for example, the type of art type, etc.).

[0389] At block 1912, the art type prediction model evaluator 1804 determines whether another candidate model 1800 is to be generated. In some examples, the art type prediction model evaluator 1804 uses a CCR threshold to determine whether another candidate model 1800 is to be constructed. In some examples, the art type prediction model evaluator 1804 evaluates the candidate model 1800 based on processing speed and / or computing resource requirements. If another candidate model 1800 is to be constructed, program control returns to block 1902. Otherwise, if another candidate model 1800 is not to be constructed, program control proceeds to block 1914. At block 1914, the art type prediction model comparator 1806 ( Fig.18 ) compares the candidate models 1800 generated by the art type prediction model builder 1802. In some examples, the art type prediction model comparator 1806 compares the CCRs of the candidate models 1800. In some such examples, for the art type of interest, the art type prediction model comparator 1806 may compare the individual CCRs corresponding to the candidate models 1800. For example, a first candidate model may have an overall CCR of 70% (e.g., an average of the CCRs for the art type of interest, etc.), but has a CCR of 30% for the "family" art type. A second candidate model may have an overall CCR of 65%, but has a CCR of 57% for the "family" art type.

[0390] At box 1916, the art type prediction model comparator 1806 selects a candidate art type prediction model 1800 as the art type prediction model 1428 to be used by the art type prediction model predictor 1418. In some examples, the art type prediction model comparator 1806 selects the candidate model 1800 with the highest overall CCR. For example, from the above example, the art type prediction model comparator 1806 would select the first candidate model. In some examples, the art type prediction model comparator 1806 selects the candidate model 1800 with the highest CCR on the specific art type of interest. For example, from the above example, if the "family" art type is of particular interest, the art type prediction model comparator 1806 would select the second candidate model. This is because the "family" art type CCR of the second candidate model is higher than the "family" art type CCR of the first candidate model. In some examples, the art type prediction model comparator 1806 selects the candidate model 1800 based on performance factors (e.g., processing power required to predict the art type, processing speed required to predict the art type, etc.). Then Fig.19 The exemplary procedure 1900 ends.

[0391] Fig. 20 is a flow chart representing example machine readable instructions 2000 that may be executed to implement Fig.14 and Fig.15 An exemplary art genre predictor 1418 for predicting the art genre of impressions collected for local television programs that are not encoded with art genre information (e.g., Fig.14 At block 2002, the exemplary data classifier 1500 ( Fig.15 and Fig.16 ) from the collection database 1414 ( Fig.14 and Fig.15 ) Retrieve non-artistic impression data (e.g. based on Fig.14 and Fig.15 In block 2004, the data classifier 1500 transforms the non-artistic impression record 1501 into classified impression data 1504 ( Fig.15 In some examples, data classifier 1500 uses a timestamp value (eg, Fig.16 The start timestamp 1606 and end timestamp 1608 of the art genre prediction model (e.g. Fig.14 and Fig.18The art type prediction model 1428) is used to classify the non-art type impression records 1501 to be input into the art type prediction model 1428. For example, the data classifier 1500 can generate a day category value 1614 ( Fig.16 ), time period value 1616( Fig.16 ), and / or duration value 1618 ( Fig.16 ).

[0392] At block 2006, the art type analyzer 1502 ( Fig.15 ) will be used by the art type prediction modeler 1416 ( Fig.14 and Fig.18 ) is applied to the classified impression data 1504 to predict art type information. In some examples, the art type prediction may include an accuracy rating. In some such examples, the accuracy rating may be based on one or more CCRs of the art type prediction model 1428 calculated by the art type prediction modeler 1416. At block 2008, the art type analyzer 1502 associates the art type prediction with the non-art type impression data 1402 to create a predicted art type impression 1424 ( Fig.14 and Fig.15 In some examples, the art type analyzer 1502 stores the predicted art type impression 1424 in the prediction database 1422 ( Fig.14 and Fig.15 Alternatively or additionally, the art type analyzer 1502 causes the predicted art type impression 1424 to be included in the prediction report 1426 ( Fig.14 and Fig.15 ). Then Fig. 20 The exemplary procedure 2000 ends.

[0393] Fig.21 is a block diagram of an exemplary processor platform 1200 that can execute Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 , Fig.10 , Fig.11 , Fig.12 and / or Fig.13to implement the exemplary calibration data collector 202, the exemplary sharing matrix generator 204, the exemplary misidentification corrector 206, the exemplary impression information collector 208, the exemplary non-coverage calculator 210, the exemplary non-coverage corrector 212, the exemplary impression information adjuster 214, the exemplary household distribution generator 216, the exemplary aggregate distribution generator 218, the exemplary matrix corrector 220, the exemplary matrix normalizer 222, the exemplary co-viewing matrix generator 224, and / or more generally, Figure 2 The processor platform 2100 can be, for example, a server, a personal computer, a mobile device (e.g., a mobile phone, a smart phone, a tablet computer, such as an iPad, etc.). TM ), Internet appliance, or any other type of computing device.

[0394] The processor platform 2100 of the illustrated example includes a processor 2112. The processor 2112 of the illustrated example is hardware. For example, the processor 2112 can be implemented by one or more integrated circuits, logic circuits, microprocessors, or controllers from any desired family or manufacturer. Fig.21 The exemplary processor 2112 may implement an exemplary calibration data collector 202, an exemplary sharing matrix generator 204, an exemplary error identification corrector 206, an exemplary impression information collector 208, an exemplary non-coverage calculator 210, an exemplary non-coverage corrector 212, an exemplary impression information adjuster 214, an exemplary household distribution generator 216, an exemplary aggregate distribution generator 218, an exemplary matrix corrector 220, an exemplary matrix normalizer 222, and / or an exemplary co-viewing matrix generator 224.

[0395] The processor 2112 of the illustrated example includes a local memory 2113 (e.g., cache). The processor 2112 of the illustrated example communicates with a main memory via a bus 2118, and the main memory includes a volatile memory 2114 and a non-volatile memory 2116. The volatile memory 2114 can be implemented by a synchronous dynamic random access memory (SDRAM), a dynamic random access memory (DRAM), a RAMBUS dynamic random access memory (RDRAM), and / or any other type of random access memory device. The non-volatile memory 2116 can be implemented by a flash memory and / or any other desired type of storage device. Access to the main memory 2114, 2116 is controlled by a memory controller.

[0396] The processor platform 2100 of the illustrated example also includes an interface circuit 2120. The interface circuit 2120 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and / or a PCI express interface.

[0397] In the illustrated example, one or more input devices 2122 are connected to the interface circuit 2120. The one or more input devices 2122 allow a user to enter data and commands into the processor 2112. The one or more input devices may be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, buttons, a mouse, a touch screen, a trackpad, a trackball, a dot, and / or a voice recognition system.

[0398] One or more output devices 2124 are also connected to the interface circuit 2120 of the illustrated example. The output device 2124 can be implemented by, for example, a display device (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touch screen, a touch output device, a light emitting diode (LED), a printer, and / or a speaker). The interface circuit 2120 of the illustrated example therefore typically includes a graphics driver card, a graphics driver chip, or a graphics driver processor.

[0399] The interface circuitry 2120 of the illustrated example also includes communication devices such as transmitters, receivers, transceivers, modems and / or network interface cards to facilitate data exchange with an external machine (e.g., any type of computing device) via a network 2126 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, a coaxial cable, a mobile telephone system, etc.).

[0400] The processor platform 2100 of the illustrated example also includes one or more mass storage devices 2128 for storing software and / or data. Fig.21 An exemplary mass storage device 2128 may implement Figure 1 AME media image storage 134. Examples of such mass storage devices 2128 include floppy disk drives, hard drive disks, optical disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.

[0401] Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 , Fig.10 , Fig.11 , Fig.12 and / or Fig.13 The encoded instructions 2132 may be stored in the mass storage device 2128, the volatile memory 2114, the non-volatile memory 2116, and / or a removable tangible computer-readable storage medium (such as a CD or DVD).

[0402] Fig. 22is a block diagram of an exemplary processor platform 1200 that can execute Fig.19 and / or Fig. 20 to implement the exemplary collection database 1414, the exemplary art type prediction modeler 1416, the exemplary art type predictor 1418, the exemplary historical art type database 1420, the exemplary prediction database 1422, the exemplary data classifier 1500, the exemplary art type analyzer 1502, the exemplary art type prediction model builder 1802, the exemplary art type prediction model evaluator 1804, the exemplary art type prediction model comparator 1806, and / or more generally, Figure 1 The processor platform 2200 can be, for example, a server, a personal computer, a mobile device (e.g., a mobile phone, a smart phone, a tablet computer, such as an iPad, etc.). TM ), Internet appliance, or any other type of computing device.

[0403] The processor platform 2200 of the illustrated example includes a processor 2212. The processor 2212 of the illustrated example is hardware. For example, the processor 2212 can be implemented by one or more integrated circuits, logic circuits, microprocessors, or controllers from any desired family or manufacturer. Fig. 22 The exemplary processor 2212 may implement the exemplary art type prediction modeler 1416, the exemplary art type predictor 1418, the exemplary data classifier 1500, the exemplary art type analyzer 1502, the exemplary art type prediction model constructor 1802, the exemplary art type prediction model evaluator 1804, and / or the exemplary art type prediction model comparator 1806.

[0404] The processor 2212 of the illustrated example includes a local memory 2213 (e.g., cache). The processor 2212 of the illustrated example communicates with a main memory via a bus 2218, and the main memory includes a volatile memory 2214 and a non-volatile memory 2216. The volatile memory 2214 can be implemented by a synchronous dynamic random access memory (SDRAM), a dynamic random access memory (DRAM), a RAMBUS dynamic random access memory (RDRAM), and / or any other type of random access memory device. The non-volatile memory 2216 can be implemented by a flash memory and / or any other desired type of storage device. Access to the main memory 2214, 2216 is controlled by a memory controller.

[0405] The processor platform 2200 of the illustrated example also includes an interface circuit 2220. The interface circuit 2220 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and / or a PCI express interface.

[0406] In the illustrated example, one or more input devices 2222 are connected to the interface circuit 2220. The one or more input devices 2222 allow a user to enter data and commands into the processor 2212. The one or more input devices may be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, buttons, a mouse, a touch screen, a trackpad, a trackball, a dot, and / or a voice recognition system.

[0407] One or more output devices 2224 are also connected to the interface circuit 2220 of the illustrated example. The output device 2224 can be implemented by, for example, a display device (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touch screen, a touch output device, a light emitting diode (LED), a printer, and / or a speaker). The interface circuit 2220 of the illustrated example therefore typically includes a graphics driver card, a graphics driver chip, or a graphics driver processor.

[0408] The interface circuitry 2220 of the illustrated example also includes communication devices such as transmitters, receivers, transceivers, modems and / or network interface cards to facilitate data exchange with an external machine (e.g., any type of computing device) via a network 2226 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, a coaxial cable, a mobile telephone system, etc.).

[0409] The processor platform 2200 of the illustrated example also includes one or more mass storage devices 2228 for storing software and / or data. Fig. 22 An exemplary mass storage device 2228 may implement the AME media impression memory 134, the exemplary collection database 1414, the exemplary historical art type database 1420, and / or the exemplary prediction database 1422. Examples of such mass storage devices 2228 include floppy disk drives, hard drive disks, optical disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.

[0410] Fig.19 and / or Fig. 20 The coded instructions may be stored in the mass storage device 2228, the volatile memory 2214, the non-volatile memory 2216, and / or a removable tangible computer-readable storage medium (such as a CD or DVD).

[0411] From the foregoing, it will be appreciated that methods, apparatus, and articles of manufacture have been disclosed for enhancing the operation of a computer to improve the accuracy of impression-based data, such as special audiences, impression counts, and duration units, so that the computers and processing systems herein may be relied upon to generate audience analytics information with a higher degree of accuracy. In some examples, based on the above-described equations and techniques for determining the size of a special audience for misidentification correction and / or non-coverage correction, impression counts for misidentification correction and / or non-coverage correction, and / or duration units for misidentification correction and / or non-coverage correction, the computer operation may be made more efficient. That is, by using these processes, the computer may operate more efficiently by relatively quickly determining parameters and applying those parameters to the data for determining misidentification correction and / or non-coverage correction through the techniques disclosed above. For example, using the exemplary processes disclosed herein, a computer can more efficiently and effectively correct misidentification errors (e.g., applying the misidentification correction matrix of Table 14 above to impressions, special audience sizes, and / or duration units) and / or correct non-coverage errors (e.g., applying non-coverage factors or alpha factors to impressions, special audience sizes, and / or duration units) in development or test data recorded by AME 108 and database owners 104a-b without using a large amount of network communication bandwidth (e.g., saving network communication bandwidth) and without using a large amount of computer processing resources (e.g., saving computer processing resources) to continuously communicate with individual online users to request survey responses about their online media access behavior, and without relying on such continuous survey responses from such online users. Survey responses from online users may be inaccurate due to the user's inability or unwillingness to re-collect online media access. Survey responses may also be incomplete, which may require additional processor resources to identify and supplement incomplete survey responses. Therefore, the examples disclosed herein more efficiently and effectively determine data for misidentification correction. Such misidentification-corrected data may be used in subsequent processing to identify the engagement performance of different media so that media providers, advertisers, product manufacturers, and / or service providers may make more informed decisions about how to spend advertising dollars and / or media production and distribution dollars.

[0412] 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

1. A device for compensating for the impression of a media for a misidentification error, the device comprising: An impression collector circuit, the impression collector circuit being used for: receiving a first request from a computing device over a first network connection in an Internet domain, the first request indicating access to media at the computing device; sending, over a second network connection, a second request for demographic information corresponding to a plurality of requests received from the computing device at the Internet domain, the plurality of requests including the first request; as well as recording in a database an impression of the media for the computing device based on the first request and the demographic information, the demographic information including information associating the impression of the media with a second demographic group; an aggregated distribution generator circuit configured to generate an aggregated audience distribution comprising a first audience distribution for a first household aggregated with a second audience distribution for a second household, the first audience distribution comprising a distribution of family members of the first household across demographic groups of family members registered with a database owner, the distribution based on survey responses of the first household; a matrix normalizer circuit for normalizing the aggregate audience distribution to generate a false positive correction matrix, the false positive correction matrix including server-based false positive errors corresponding to a determination by the database owner's server that an impression of the media corresponds to the second demographic group when the impression of the media is attributed to a first demographic group different from the second demographic group; as well as and a misidentification corrector circuit for causing a computer to correct the server-based misidentification errors in the impressions by reallocating impressions from the second demographic group to the first demographic group using the misidentification correction matrix, thereby improving the functioning of the computer, the reallocation being used to generate more accurate impression information than that recorded in the database.

2. The apparatus of claim 1 further comprising a matrix corrector circuit for generating correction indicators to correct the aggregate audience distribution for at least one of oversampling and undersampling associated with a survey calibration data source, the survey calibration data source indicating the family members' access to the media and the family members' registration status with the database owner.

3. The device as claimed in claim 2, wherein: The correction indicator is based on a second probability that a first person in the first demographic group lives with a second person in the second demographic group.

4. The device as claimed in claim 3, wherein: The matrix corrector circuit is used to generate the correction index in the following manner: determining a first number of first person pairs based on the first survey calibration data, the first number of the first person pairs correspondingly being the number of the first person pairs including a first person in the first demographic group and a second person in the second demographic group living together; determining a second number of second person pairs based on second survey calibration data, the second number of second person pairs correspondingly being the number of second person pairs including a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; as well as A ratio of the first amount to the second amount is determined.

5. The device according to claim 1, wherein: The aggregate audience distribution describes the number of persons in the second demographic group who will be attributed to the first demographic group, and the matrix normalizer circuit is used to normalize the aggregate audience distribution by scaling the corresponding number of persons in the second demographic group so that the total number of persons for the second demographic group is a specified value.

6. The apparatus of claim 1, further comprising a home profile generator circuit, the home profile generator circuit being configured to: determining a sharing mode for the first family, the sharing mode indicating the family members who access a media category and other family members who do not access the media category; and A probability density function of device sharing probabilities for the family members in the first household is determined based on the sharing pattern for the media category, the probability density function indicating corresponding probabilities of the family members accessing the media category, the first audience distribution being based on the device sharing probabilities.

7. The device according to claim 6, wherein: The aggregate distribution generator circuit is configured to aggregate the device sharing probabilities for the family members when the first household has two or more of the registered family members in a same second demographic group.

8. The apparatus of claim 1 further comprising a household distribution generator circuit for generating the first audience distribution by distributing portion numbers into the demographic groups, the portion numbers totaling a count of those of the household members who are registered with the database owner.

9. The device of claim 1, wherein: The false positive corrector circuit is for redistributing the impressions by determining a product of the false positive correction matrix and an impression matrix indicating the number of impressions corresponding to the respective demographic groups as determined by the database owner.

10. The device of claim 9, wherein: The false positive corrector circuit is used to reallocate the impressions so that the total number of reallocated impressions is the same as the total number of impressions in the impression matrix.

11. A non-transitory computer-readable storage medium comprising computer-readable instructions that, when executed, cause at least a processor to: accessing a first request from a computing device over a first network communication in an Internet domain, the first request indicating access to media at the computing device; sending, via a second network communication, a second request for demographic information corresponding to a plurality of requests received from the computing device at the Internet domain, the plurality of requests including the first request; recording in a database an impression of the media for the computing device based on the first request and the demographic information, the demographic information including information associating the impression of the media with a second demographic group; generating an aggregate audience distribution comprising a first audience distribution for a first household aggregated with a second audience distribution for a second household, the first audience distribution comprising a distribution of family members of the first household among demographic groups of the family members registered with a database owner, the distribution based on survey responses of the first household; normalizing the aggregate audience distribution to generate a false positive correction matrix, the false positive correction matrix including probabilities of server-based false positive errors resulting from a determination by the database owner's server that an impression of the media corresponds to the second demographic group when the impression of the media is attributed to a first demographic group different from the second demographic group; as well as The function of the processor is improved by correcting the server-based misidentification errors in the impressions by reallocating impressions from the second demographic group to the first demographic group using the misidentification correction matrix, wherein the reallocation causes the computer to generate more accurate impression information than that recorded in the database.

12. The storage medium according to claim 11, wherein: The instructions are also for causing the processor to generate a correction indicator to correct the aggregate audience distribution for at least one of oversampling and undersampling associated with a survey calibration data source, the survey calibration data source indicating the family member's access to the media and the family member's registration status with the database owner.

13. The storage medium according to claim 12, wherein: The correction indicator is based on a second probability that a first person in the first demographic group lives with a second person in the second demographic group.

14. The storage medium according to claim 12, wherein: The instruction is used to enable the processor to generate the correction indicator in the following manner: determining a first number of first person pairs based on the first survey calibration data, the first number of the first person pairs correspondingly being the number of the first person pairs including a first person in the first demographic group and a second person in the second demographic group living together; determining a second number of second person pairs based on second survey calibration data, the second number of second person pairs correspondingly being the number of second person pairs including a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; as well as A ratio of the first amount to the second amount is determined.

15. The storage medium according to claim 11, wherein: The aggregate audience distribution describes the number of people in the second demographic group who will be attributed to the first demographic group, and the instructions are used to cause the processor to normalize the aggregate audience distribution by scaling the corresponding number of people in the second demographic group so that the total number of people for the second demographic group is a specified value.

16. The storage medium according to claim 11, wherein: The instructions are also used to cause the processor to: determining a sharing mode for the first family, the sharing mode indicating the family members who access a media category and other family members who do not access the media category; and A probability density function of device sharing probabilities for the family members in the first household is determined based on the sharing pattern for the media category, the probability density function indicating corresponding probabilities of the family members accessing the media category, the first audience distribution being based on the device sharing probabilities.

17. The storage medium according to claim 16, wherein: The instructions are further configured to cause the processor to aggregate the device sharing probabilities for the family members when the first household has two or more of the registered family members in a same second demographic group.

18. The storage medium according to claim 11, wherein: The instructions are for causing the processor to generate the first audience distribution by distributing portion numbers into the demographic groups, the portion numbers totaling a count of those of the family members that are registered with the database owner.

19. The storage medium according to claim 11, wherein: The instructions are for causing the processor to redistribute the impressions by determining a product of the false positive correction matrix and an impression matrix indicating a number of impressions corresponding to respective ones of the demographic groups as determined by the database owner.

20. The storage medium of claim 19, wherein: The instructions are for causing the processor to reallocate the impressions so that a total number of reallocated impressions is the same as a total number of impressions in the impression matrix.

21. An apparatus comprising: at least one memory; instructions in said device; as well as a processor circuit configured to execute the instructions to: accessing a first request from a computing device over a first network connection in an Internet domain, the first request indicating access to media at the computing device; sending, over a second network connection, a second request for demographic information corresponding to a plurality of requests received from the computing device at the Internet domain, the plurality of requests including the first request; recording in a database an impression of the media for the computing device based on the first request and the demographic information, the demographic information including information associating the impression of the media with a second demographic group; generating an aggregate audience distribution comprising a first audience distribution for a first household aggregated with a second audience distribution for a second household, the first audience distribution comprising a distribution of family members of the first household among demographic groups of family members registered with a database owner, the distribution based on survey responses of the first household; normalizing the aggregate audience distribution to generate a false positive correction matrix, the false positive correction matrix including probabilities of server-based false positive errors resulting from a determination by the database owner's server that an impression of the media corresponds to the second demographic group when the impression of the media is attributed to a first demographic group different from the second demographic group; as well as The function of the processor circuit is improved by correcting the server-based misidentification errors in the impressions by reallocating impressions from the second demographic group to the first demographic group using the misidentification correction matrix, wherein the reallocation is used to enable the computer to generate more accurate impression information than that recorded in the database.

22. The device of claim 21, wherein: The processor circuit is used to execute the instructions to generate a correction indicator to correct the aggregate audience distribution for at least one of oversampling and undersampling associated with a survey calibration data source, wherein the survey calibration data source indicates the family member's access to the media and the family member's registration status with the database owner.

23. The device of claim 22, wherein: The correction indicator is based on a second probability that a first person in the first demographic group lives with a second person in the second demographic group.

24. The device of claim 22, wherein: The processor circuit is configured to execute the instructions to generate the correction indicator in the following manner: determining a first number of first person pairs based on the first survey calibration data, the first number of the first person pairs correspondingly being the number of the first person pairs including a first person in the first demographic group and a second person in the second demographic group living together; determining a second number of second person pairs based on second survey calibration data, the second number of second person pairs correspondingly being the number of second person pairs including a third person in the first demographic group and a fourth person in the second demographic group living together, the second survey calibration data having a higher accuracy than the first survey calibration data; as well as A ratio of the first amount to the second amount is determined.

25. The apparatus of claim 21, wherein: The aggregate audience distribution describes the number of persons in the second demographic group who would be attributed to the first demographic group, and the processor circuit is used to execute the instructions to normalize the aggregate audience distribution by scaling the corresponding number of persons in the second demographic group so that the total number of persons for the second demographic group is a specified value.

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