Patent Grant
11727432
The present disclosure relates generally to monitoring media and, more particularly, to methods and apparatus to correct audience measurement data.
Traditionally, audience measurement entities determine audience engagement levels for media based on registered panel members. That is, an audience measurement entity enrolls people who consent to being monitored into a panel. The audience measurement entity then monitors those panel members to determine media (e.g., television programs or radio programs, movies, DVDs, advertisements, streaming media, websites, etc.) exposed to those panel members. In this manner, the audience measurement entity can determine exposure metrics for different media based on the collected media measurement data.
Techniques for monitoring user access to Internet resources such as web pages, advertisements and/or other Internet-accessible media have evolved significantly over the years. Some known systems perform such monitoring primarily through server logs. In particular, entities serving media on the Internet can use known techniques to log the number of requests received for their media (e.g., content and/or advertisements) at their server.
Techniques for monitoring user access to Internet-accessible media such as web pages, advertisements, content and/or other media have evolved significantly over the years. At one point in the past, such monitoring was done primarily through server logs. In particular, entities serving media on the Internet would log the number of requests received for their media at their server. Basing Internet usage research on server logs is problematic for several reasons. For example, server logs can be tampered with either directly or via zombie programs which repeatedly request media from the server to increase the server log counts. Secondly, media is sometimes retrieved once, cached locally and then repeatedly viewed from the local cache without involving the server in the repeat viewings. Server logs cannot track these repeat views of cached media. Thus, server logs are susceptible to both over-counting and under-counting errors.
The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637, fundamentally changed the way Internet monitoring is performed and overcame the limitations of the server side log monitoring techniques described above. For example, Blumenau disclosed a technique wherein Internet media to be tracked is tagged with beacon instructions. In particular, monitoring instructions are associated with the hypertext markup language (HTML) of the media to be tracked. When a client requests the media, both the media and the beacon instructions are downloaded to the client. The beacon instructions are, thus, executed whenever the media is accessed, be it from a server or from a cache.
The beacon instructions cause monitoring data reflecting information about the access to the media to be sent from the client that downloaded the media to a monitoring entity. Typically, the monitoring entity is an audience measurement entity (AME) that did not provide the media to the client and who is a trusted (e.g., neutral) third party for providing accurate usage statistics (e.g., The Nielsen Company, LLC). Advantageously, because the beaconing instructions are associated with the media and executed by the client browser whenever the media is accessed, the monitoring information is provided to the AME irrespective of whether the client is a panelist of the AME.
Audience measurement entities and/or other businesses often desire to link demographics to the monitoring information. To address this issue, the AME establishes a panel of users who have agreed to provide their demographic information and to have their Internet browsing activities monitored. When an individual joins the panel, they provide detailed information concerning their identity and demographics (e.g., gender, age, ethnicity, income, home location, occupation, etc.) to the AME. The audience measurement entity sets a cookie on the panelist computer that enables the audience measurement entity to identify the panelist whenever the panelist accesses tagged media and, thus, sends monitoring information to the audience measurement entity.
Since most of the clients providing monitoring information from the tagged media are not panelists and, thus, are unknown to the audience measurement entity, it is necessary to use statistical methods to impute demographic information based on the data collected for panelists to the larger population of users providing data for the tagged media. However, panel sizes of audience measurement entities remain small compared to the general population of users. Thus, a problem is presented as to how to increase panel sizes while ensuring the demographics data of the panel is accurate.
There are many database proprietors operating on the Internet. These database proprietors provide services to large numbers of subscribers. In exchange for the provision of the service, the subscribers register with the proprietor. As part of this registration, the subscribers provide detailed demographic information. Examples of such database proprietors include social network providers, email providers, etc. such as Facebook, Myspace, Twitter, Yahoo!, Google, etc. These database proprietors set cookies or other device/user identifiers on the client devices of their subscribers to enable the database proprietor to recognize the user when they visit their website.
The protocols of the Internet make cookies inaccessible outside of the domain (e.g., Internet domain, domain name, etc.) on which they were set. Thus, a cookie set in, for example, the amazon.com domain is accessible to servers in the amazon.com domain, but not to servers outside that domain. Therefore, although an audience measurement entity might find it advantageous to access the cookies set by the database proprietors, they are unable to do so.
The inventions disclosed in Mainak et al., U.S. Pat. No. 8,370,489, which is incorporated by reference herein in its entirety, enable an audience measurement entity to leverage the existing databases of database proprietors to collect more extensive Internet usage and demographic data by extending the beaconing process to encompass partnered database proprietors and by using such partners as interim data collectors. The inventions disclosed in Mainak et al. accomplish this task by structuring the AME to respond to beacon requests from clients (who may not be a member of an audience member panel and, thus, may be unknown to the audience member entity) and redirect the client from the audience measurement entity to a database proprietor such as a social network site partnered with the audience member entity. The redirection initiates a communication session between the client accessing the tagged media and the database proprietor. The database proprietor (e.g., Facebook) can access any cookie it has set on the client to thereby identify the client based on the internal records of the database proprietor. In the event the client corresponds to a subscriber of the database proprietor, the database proprietor logs an impression in association with the demographics data associated with the client and subsequently forwards logged impressions to the audience measurement company. In the event the client does not correspond to a subscriber of the database proprietor, the database proprietor may redirect the client to the audience measurement entity and/or another database proprietor. The audience measurement entity may respond to the redirection from the first database proprietor by redirecting the client to a second, different database proprietor that is partnered with the audience measurement entity. That second database proprietor may then attempt to identify the client as explained above. This process of redirecting the client from database proprietor to database proprietor can be performed any number of times until the client is identified and the content exposure logged, or until all database partners have been contacted without a successful identification of the client. The redirections all occur automatically so the user of the client is not involved in the various communication sessions and may not even know they are occurring.
Periodically or aperiodically, the partnered database proprietors provide their logs and demographic information to the audience measurement entity which then compiles the collected data into statistical reports accurately identifying the demographics of persons accessing the tagged media. Because the identification of clients is done with reference to enormous databases of users far beyond the quantity of persons present in a conventional audience measurement panel, the data developed from this process is extremely accurate, reliable and detailed.
Significantly, because the audience measurement entity remains the first leg of the data collection process (e.g., receives the request generated by the beacon instructions from the client), the audience measurement entity is able to obscure the source of the media access being logged as well as the identity of the media itself from the database proprietors (thereby protecting the privacy of the media sources), without compromising the ability of the database proprietors to log impressions for their subscribers. Further, when cookies are used as device/user identifiers, the Internet security cookie protocols are complied with because the only servers that access a given cookie are associated with the Internet domain (e.g., Facebook.com) that set that cookie.
Examples disclosed in Mainak et al. (U.S. Pat. No. 8,370,489) can be used to determine any type of media impressions or exposures (e.g., content impressions, advertisement impressions, content exposure, and/or advertisement exposure) using demographic information, which is distributed across different databases (e.g., different website owners, service providers, etc.) on the Internet. Not only do such disclosed examples enable more accurate correlation of Internet advertisement exposure to demographics, but they also effectively extend panel sizes and compositions beyond persons participating in the panel of an audience measurement entity and/or a ratings entity to persons registered in other Internet databases such as the databases of social media sites such as Facebook, Twitter, Google, etc. Such extension effectively leverages the media tagging capabilities of the ratings entity and the use of databases of non-ratings entities such as social media and other websites to create an enormous, demographically accurate panel that results in accurate, reliable measurements of exposures to Internet media such as advertising and/or programming.
Traditionally, audience measurement entities (also referred to herein as “ratings entities”) determine demographic reach for advertising and media programming based on registered panel members. That is, an audience measurement entity enrolls people that consent to being monitored into a panel. During enrollment, the audience measurement entity receives demographic information from the enrolling people so that subsequent correlations may be made between advertisement/media exposures to those panelists and different demographic markets. Unlike traditional techniques in which audience measurement entities rely solely on their own panel member data to collect demographics-based audience measurements, example methods, apparatus, and/or articles of manufacture disclosed herein enable an audience measurement entity to share demographic information with other entities that operate based on user registration models. As used herein, a user registration model is a model in which users subscribe to services of those entities by creating an account and providing demographic-related information about themselves. Sharing of demographic information associated with registered users of database proprietors enables an audience measurement entity to extend or supplement their panel data with substantially reliable demographics information from external sources (e.g., database proprietors), thus extending the coverage, accuracy, and/or completeness of audience measurement entity's demographics-based audience measurements. Such access also enables the audience measurement entity to monitor persons who would not otherwise have joined an audience measurement panel. Any entity having a network-accessible database identifying demographics of a set of individuals may cooperate with the audience measurement entity. Such entities may be referred to as “database proprietors” and include entities such as Facebook, Google, Yahoo!, MSN, Twitter, Apple iTunes, Experian, etc.
To increase the likelihood that measured viewership is accurately attributed to the correct demographics, examples disclosed herein use demographic information located in the audience measurement entity's records as well as demographic information located at one or more database proprietors that maintain records or profiles of users having accounts therewith. In this manner, examples disclosed herein may be used to supplement demographic information maintained by a ratings entity (e.g., an AME such as The Nielsen Company of Schaumburg, Ill., United States of America, that collects media exposure measurements and/or demographics) with demographic information from one or more different database proprietors.
The use of demographic information from disparate data sources (e.g., high-quality demographic information from the panels of an audience measurement company and/or registered user data of web service providers) results in improved reporting effectiveness of metrics for both online and offline advertising campaigns. Example techniques disclosed herein use online registration data to identify demographics of users and use server impression counts, tagging (also referred to herein as beaconing), and/or other techniques to track quantities of impressions attributable to those users. Online web service providers such as social networking sites (e.g., Facebook) and multi-service providers (e.g., Yahoo!, Google, Experian, etc.) (collectively and individually referred to herein as database proprietors) maintain detailed demographic information (e.g., age, gender, geographic location, race, income level, education level, religion, etc.) collected via user registration processes. As used herein, an impression is defined to be an event in which a home or individual is exposed to corresponding media (e.g., content and/or an advertisement). Thus, an impression represents a home or an individual having been exposed to media (e.g., an advertisement, content, a group of advertisements and/or a collection of content). In Internet advertising, a quantity of impressions or impression count is the total number of times media (e.g., content, an advertisement or advertisement campaign) has been accessed by a web population (e.g., the number of times the media is accessed). As used herein, a demographic impression is defined to be an impression that is associated with a characteristic (e.g., a demographic characteristic) of the person exposed to the media.
Although such techniques for collecting media impressions are based on highly accurate demographic information, in some instances impressions collected by a database proprietor (e.g., Facebook, Yahoo, Google, etc.) may be inaccurate and/or incomplete when the database proprietor does not have complete coverage of device/user identifiers (e.g., cookies) at all of the client devices that visit a site of the database proprietor. As used herein in this context, coverage represents the extent to which a database proprietor has set device/user identifiers in client devices that visit the site of the database proprietor. For example, if only 50% of client devices that visit the site of the database proprietor have a cookie set therein by the database proprietor, then the database proprietor has 50% coverage of client devices that visit its site. A client device may not have a cookie set by the database proprietor in its web browser if, for example, a user doesn't have an account with the database proprietor or if the user has an account with the database proprietor but has cleared the cookie cache and deleted the database proprietor's cookie before or at the time of a media exposure. In such instances, the database proprietor would not be able to detect the media exposure and, thus, would not report any audience or impressions for that exposure. As a result, the database proprietor would underestimate the reach and gross rating points (GRPs) of a campaign.
As used herein, reach is a measure indicative of the demographic coverage achieved by media such as content or an ad campaign (e.g., demographic group(s) and/or demographic population(s) exposed to the media). For example, an ad campaign reaching a broader demographic base will have a larger reach than an ad campaign that reached a more limited demographic base. The reach metric may be measured by tracking media impressions for known users (e.g., panelists or non-panelists) for which an AME stores demographic information or can obtain demographic information (e.g., via a database proprietor).
In illustrated examples disclosed herein, media exposure is measured in terms of online Gross Rating Points. A Gross Rating Point (GRP) is a unit of measurement of audience size that has traditionally been used in the television ratings context. It is used to measure exposure to one or more media (e.g., programs, advertisements, etc.) without regard to multiple exposures of the same media to individuals. In terms of television (TV) advertisements, one GRP is equal to 1% of TV households. While GRPs have traditionally been used as a measure of television viewership, examples disclosed herein may be used in connection with generating online GRPs for online media to provide a standardized metric that can be used across the Internet to accurately reflect online advertisement exposure. Such standardized online GRP measurements can provide greater certainty to advertisers that their online advertisement money is well spent. It can also facilitate cross-medium comparisons such as viewership of TV advertisements and online advertisements, exposure to radio advertisements and online media, etc. Because examples disclosed herein may be used to correct impressions that associate exposure measurements with corresponding demographics of users, the information processed using examples disclosed herein may also be used by advertisers to more accurately identify markets reached by their advertisements and/or to target particular markets with future advertisements.
Examples disclosed herein may be implemented by an audience measurement entity (AME) (e.g., any entity interested in measuring or tracking audience exposures to advertisements, content, and/or any other media) in cooperation with any number of database proprietors such as online web services providers. Such database proprietors/online web services providers may be social network sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), credit reporting sites (e.g., Experian) and/or any other web service(s) site that maintains user registration records.
Example processes to adjust impressions collected by database proprietors having limited coverage of cookies are disclosed herein. In some examples, profile correction (e.g., a current Decision Tree (DT) model) is applied to impression data collected by database proprietors. In the illustrated examples, using a panel of registered audience members, an AME calculates missing-adjustment (M-A) factors for each website and demographic group using, for example, three months of historical data. However, any other amount of historical data may be used. In some examples, the AME panel of registered audience members may be a cross-platform home television/computer panel (e.g., a TVPC panel). In other examples, the AME panel of registered users may be a computer panel or internet-device panel without corresponding to a television audience panel. In other examples, the AME panel may be a cross-platform radio/internet panel, and/or a panel focusing on other mediums.
In examples disclosed herein, the AME determines missing-audience (M-A) factors using historical impression data and subsequently applies the M-A factors to impression data collected by a database proprietor in order to compensate the database proprietor impressions for the database proprietor's non-coverage due to not having database proprietor device/user identifiers set on some client devices. In examples disclosed herein, the AME panel includes audience members recruited by the AME. When recruited into the AME panel, the AME collects demographic information from the enrolled panelist audience members so that the AME can correlate demographics with exposures to online media. In some examples, the AME also uses the AME panel demographics data to correlate the demographics with media exposures (e.g., television exposures, radio exposures, etc.). In some examples, the AME uses particular techniques when collecting panelist demographic information to ensure that the demographic information is highly accurate. In this manner, demographic-based impression data generated by the AME can accurately reflect demographics that correspond to impressions for particular media.
Example methods and computer readable instructions disclosed herein may be used to determine a missing-audience factor based on a first quantity of impressions corresponding to first client devices that do not have database proprietor identifiers for use by a database proprietor to identify subscribers registered with the database proprietor, and based on a second quantity of impressions corresponding to second client devices that do have the database proprietor identifiers for use by the database proprietor to identify subscribers registered with the database proprietor. In such examples, the first quantity of impressions is representative of accesses to media via the first client devices, and the second quantity of impressions is representative of access to the media via the second client devices. In such examples, a coverage-corrected unique audience size may be determined based on the missing-audience factor and a unique audience size of database proprietor subscribers exposed to the media. In such examples, the unique audience size is determined based on a quantity of impressions logged by the database proprietor. In such examples, the coverage-corrected unique audience size corresponds to the quantity of impressions logged by the database proprietor and a quantity of impressions not logged by the database proprietor. In some examples, the missing-audience factor and the coverage-corrected unique audience size are determined by an audience measurement entity separate from the database proprietor. In some examples, the database proprietor is at least one of a social network service provider or an email service provider.
In some examples, a coverage-corrected impression count is determined based on the coverage-corrected unique audience size and an impressions frequency. In such some examples, the coverage-corrected impression count is representative of the quantity of impressions logged by the database proprietor and the quantity of impressions not logged by the database proprietor. In some examples, the impressions frequency is the quantity of impressions logged by the database proprietor divided by the unique audience size of database proprietor subscribers.
In some examples, the missing-audience factor is determined by dividing the first quantity of impressions corresponding to the first client devices that do not have the database proprietor identifiers by the second quantity of impressions corresponding to the second client devices that do have the database proprietor identifiers. In some examples, the missing-audience factor and the coverage-corrected unique audience size are determined based on impressions logged by the database proprietor for a particular demographic group identified by the database proprietor.
In some examples, the impressions are collected by responding to beacon requests from client devices by redirecting the client devices to communicate with the database proprietor to enable the database proprietor to record the impressions. In some such examples, the client devices are instructed to provide an identifier (e.g., a device/user identifier 227 of
Example apparatus disclosed herein may include a missing-audience factor determiner to determine a missing-audience factor based on a first quantity of impressions corresponding to first client devices that do not have database proprietor identifiers for use by a database proprietor to identify subscribers registered with the database proprietor, and based on a second quantity of impressions corresponding to second client devices that do have the database proprietor identifiers for use by the database proprietor to identify subscribers registered with the database proprietor. The first quantity of impressions is representative of accesses to media via the first client devices. The second quantity of impressions is representative of accesses to the media via the second client devices. Disclosed example apparatus may also include a unique audience size corrector to determine a coverage-corrected unique audience size based on the missing-audience factor and a unique audience size of database proprietor subscribers exposed to the media. In such examples, the unique audience size is determined based on a quantity of impressions logged by the database proprietor. In such examples, the coverage-corrected unique audience size corresponds to the quantity of impressions logged by the database proprietor and a quantity of impressions not logged by the database proprietor. In some examples, the missing-audience factor determiner and the unique audience corrector are operated by an audience measurement entity separate from the database proprietor. In some examples, the database proprietor is at least one of a social network service provider or an email service provider.
Some example apparatus also include an impressions corrector to determine a coverage-corrected impression count based on the coverage-corrected unique audience size and an impressions frequency. In such some examples, the coverage-corrected impression count is representative of the quantity of impressions logged by the database proprietor and the quantity of impressions not logged by the database proprietor. In some examples, the impressions frequency is the quantity of impressions logged by the database proprietor divided by the unique audience size of database proprietor subscribers.
In some example apparatus, the missing-audience factor determiner determines the missing-audience factor by dividing the first quantity of impressions corresponding to the first client devices that do not have the database proprietor identifiers by the second quantity of impressions corresponding to the second client devices that do have the database proprietor identifiers. In some example apparatus, the missing-audience factor determiner determines the missing-audience factor and the unique audience corrector determines the coverage-corrected unique audience size based on impressions logged by the database proprietor for a particular demographic group identified by the database proprietor.
Some example apparatus also include an impressions collector to collect the impressions by responding to beacon requests from client devices to redirect the client devices to communicate with the database proprietor to enable the database proprietor to record the impressions. In such some examples, the impressions collector instructs the client devices to provide an identifier (e.g., a device/user identifier 227 of
In the illustrated example, the client devices 102 employ web browsers and/or applications (e.g., apps) to access media, some of which include instructions that cause the client devices 102 to report media monitoring information to one or more of the impression collection entities 104. That is, when a client device 102 of the illustrated example accesses media, a web browser and/or application of the client device 102 executes instructions in the media to send a beacon request or impression request 108 to one or more impression collection entities 104 via, for example, the Internet 110. The beacon requests 108 of the illustrated example include information about accesses to media at the corresponding client device 102 generating the beacon requests. Such beacon requests allow monitoring entities, such as the impression collection entities 104, to collect impressions for different media accessed via the client devices 102. In this manner, the impression collection entities 104 can generate large impression quantities for different media (e.g., different content and/or advertisement campaigns).
The impression collection entities 104 of the illustrated example include an example audience measurement entity (AME) 114 and an example database proprietor (DP) 116. In the illustrated example, the AME 114 does not provide the media to the client devices 102 and is a trusted (e.g., neutral) third party (e.g., The Nielsen Company, LLC) for providing accurate media access statistics. In the illustrated example, the database proprietor 116 is one of many database proprietors that operates on the Internet to provide services to large numbers of subscribers. Such services may be email services, social networking services, news media services, cloud storage services, streaming music services, streaming video services, online retail shopping services, credit monitoring services, etc. Example database proprietors include social network sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), credit services (e.g., Experian), and/or any other web service(s) site that maintains user registration records. In examples disclosed herein, the database proprietor 116 maintains user account records corresponding to users registered for Internet-based services provided by the database proprietors. That is, in exchange for the provision of services, subscribers register with the database proprietor 116. As part of this registration, the subscribers provide detailed demographic information to the database proprietor 116. Demographic information may include, for example, gender, age, ethnicity, income, home location, education level, occupation, etc. In the illustrated example, the database proprietor 116 sets a device/user identifier (e.g., an identifier described below in connection with
In the illustrated example, when the database proprietor 116 receives a beacon/impression request 108 from a client device 102, the database proprietor 116 requests the client device 102 to provide the device/user identifier that the database proprietor 116 had previously set for the client device 102. The database proprietor 116 uses the device/user identifier corresponding to the client device 102 to identify demographic information in its user account records corresponding to the subscriber of the client device 102. In this manner, the database proprietor 116 can generate demographic impressions by associating demographic information with an audience impression for the media accessed at the client device 102. As explained above, a demographic impression is an impression that is associated with a characteristic (e.g., a demographic characteristic) of the person exposed to the media.
In the illustrated example, the AME 114 establishes a panel of users who have agreed to provide their demographic information and to have their Internet browsing activities monitored. When an individual joins the AME panel, the person provides detailed information concerning the person's identity and demographics (e.g., gender, age, ethnicity, income, home location, occupation, etc.) to the AME 114. The AME 114 sets a device/user identifier (e.g., an identifier described below in connection with
In the illustrated example, when the AME 114 receives a beacon request 108 from a client device 102, the AME 114 requests the client device 102 to provide the AME 114 with the device/user identifier the AME 114 previously set for the client device 102. The AME 114 uses the device/user identifier corresponding to the client device 102 to identify demographic information in its user AME panelist records corresponding to the panelist of the client device 102. In this manner, the AME 114 can generate demographic impressions by associating demographic information with an audience impression for the media accessed at the client device 102.
In the illustrated example, three of the client devices 102a, 102b, 102c have AME identifiers (IDs) (AME device/user IDs) that identify corresponding panelists of the AME 114 and also have DP IDs (DP device/user IDs) that identify corresponding subscribers of the database proprietor 116. In this manner, when the client devices 102a, 102b, 102c corresponding to both AME panelists and DP subscribers send beacon requests 108 to the impression collection entities 104, both the AME 114 and the database proprietor 116 can log demographic impressions. (Although for simplicity of illustration, the signaling is not shown in
In the illustrated example, the client device 102 accesses media 206 that is tagged with the beacon instructions 208. The beacon instructions 208 cause the client device 102 to send a beacon/impression request 212 to an AME impressions collector 218 when the client device 102 accesses the media 206. For example, a web browser and/or app of the client device 102 executes the beacon instructions 208 in the media 206 which instruct the browser and/or app to generate and send the beacon/impression request 212. In the illustrated example, the client device 102 sends the beacon/impression request 212 using an HTTP (hypertext transfer protocol) request addressed to the URL (uniform resource locator) of the AME impressions collector 218 at, for example, a first internet domain of the AME 114. The beacon/impression request 212 of the illustrated example includes a media identifier 213 (e.g., an identifier that can be used to identify content, an advertisement, and/or any other media) corresponding to the media 206. In some examples, the beacon/impression request 212 also includes a site identifier (e.g., a URL) of the website that served the media 206 to the client device 102 and/or a host website ID (e.g., www.acme.com) of the website that displays or presents the media 206. In the illustrated example, the beacon/impression request 212 includes a device/user identifier 214. In the illustrated example, the device/user identifier 214 that the client device 102 provides to the AME impressions collector 218 in the beacon impression request 212 is an AME ID because it corresponds to an identifier that the AME 114 uses to identify a panelist corresponding to the client device 102. In other examples, the client device 102 may not send the device/user identifier 214 until the client device 102 receives a request for the same from a server of the AME 114 in response to, for example, the AME impressions collector 218 receiving the beacon/impression request 212.
In some examples, the device/user identifier 214 may be a device identifier (e.g., an international mobile equipment identity (IMEI), a mobile equipment identifier (MEID), a media access control (MAC) address, etc.), a web browser unique identifier (e.g., a cookie), a user identifier (e.g., a user name, a login ID, etc.), an Adobe Flash® client identifier, identification information stored in an HTML5 datastore, and/or any other identifier that the AME 114 stores in association with demographic information about users of the client devices 102. In this manner, when the AME 114 receives the device/user identifier 214, the AME 114 can obtain demographic information corresponding to a user of the client device 102 based on the device/user identifier 214 that the AME 114 receives from the client device 102. In some examples, the device/user identifier 214 may be encrypted (e.g., hashed) at the client device 102 so that only an intended final recipient of the device/user identifier 214 can decrypt the hashed identifier 214. For example, if the device/user identifier 214 is a cookie that is set in the client device 102 by the AME 114, the device/user identifier 214 can be hashed so that only the AME 114 can decrypt the device/user identifier 214. If the device/user identifier 214 is an IMEI number, the client device 102 can hash the device/user identifier 214 so that only a wireless carrier (e.g., the database proprietor 116) can decrypt the hashed identifier 214 to recover the IMEI for use in accessing demographic information corresponding to the user of the client device 102. By hashing the device/user identifier 214, an intermediate party (e.g., an intermediate server or entity on the Internet) receiving the beacon request cannot directly identify a user of the client device 102.
In response to receiving the beacon/impression request 212, the AME impressions collector 218 logs an impression for the media 206 by storing the media identifier 213 contained in the beacon/impression request 212. In the illustrated example of
In some examples, the beacon/impression request 212 may not include the device/user identifier 214 if, for example, the user of the client device 102 is not an AME panelist. In such examples, the AME impressions collector 218 logs impressions regardless of whether the client device 102 provides the device/user identifier 214 in the beacon/impression request 212 (or in response to a request for the identifier 214). When the client device 102 does not provide the device/user identifier 214, the AME impressions collector 218 will still benefit from logging an impression for the media 206 even though it will not have corresponding demographics. For example, the AME 114 may still use the logged impression to generate a total impressions count and/or a frequency of impressions (e.g., an impressions frequency) for the media 206. Additionally or alternatively, the AME 114 may obtain demographics information from the database proprietor 116 for the logged impression if the client device 102 corresponds to a subscriber of the database proprietor 116.
In the illustrated example of
In the illustrated example of
Although only a single database proprietor 116 is shown in
In some examples, prior to sending the beacon response 222 to the client device 102, the AME impressions collector 218 replaces site IDs (e.g., URLs) of media provider(s) that served the media 206 with modified site IDs (e.g., substitute site IDs) which are discernable only by the AME 114 to identify the media provider(s). In some examples, the AME impressions collector 218 may also replace a host website ID (e.g., www.acme.com) with a modified host site ID (e.g., a substitute host site ID) which is discernable only by the AME 114 as corresponding to the host website via which the media 206 is presented. In some examples, the AME impressions collector 218 also replaces the media identifier 213 with a modified media identifier 213 corresponding to the media 206. In this way, the media provider of the media 206, the host website that presents the media 206, and/or the media identifier 213 are obscured from the database proprietor 116, but the database proprietor 116 can still log impressions based on the modified values which can later be deciphered by the AME 114 after the AME 114 receives logged impressions from the database proprietor 116. In some examples, the AME impressions collector 218 does not send site IDs, host site IDS, the media identifier 213 or modified versions thereof in the beacon response 222. In such examples, the client device 102 provides the original, non-modified versions of the media identifier 213, site IDs, host IDs, etc. to the database proprietor 116.
In the illustrated example, the AME impression collector 218 maintains a modified ID mapping table 228 that maps original site IDs with modified (or substitute) site IDs, original host site IDs with modified host site IDs, and/or maps modified media identifiers to the media identifiers such as the media identifier 213 to obfuscate or hide such information from database proprietors such as the database proprietor 116. Also in the illustrated example, the AME impressions collector 218 encrypts all of the information received in the beacon/impression request 212 and the modified information to prevent any intercepting parties from decoding the information. The AME impressions collector 218 of the illustrated example sends the encrypted information in the beacon response 222 to the client device 102 so that the client device 102 can send the encrypted information to the database proprietor 116 in the beacon/impression request 226. In the illustrated example, the AME impressions collector 218 uses an encryption that can be decrypted by the database proprietor 116 site specified in the HTTP “302 Found” re-direct message.
Periodically or aperiodically, the impression data collected by the database proprietor 116 is provided to a DP impressions collector 232 of the AME 114 as, for example, batch data. As discussed above, the client devices 102d, 102e of
Additional examples that may be used to implement the beacon instruction processes of
In the example of
The example missing-audience factor determiner 234 of
In the illustrated example, the missing-audience factor determiner 234 determines M-A factors for different demographic groups based on historical impressions (e.g., the example historical impressions of the example combined impressions table 300 of
The example unique audience corrector 236 is provided to correct unique audience sizes or quantities by applying the M-A factor (determined by the missing-audience factor determiner 234) to total unique audience sizes corresponding to total impressions collected by the AME 114. The example impressions corrector 238 is provided to correct an impressions count by applying the M-A factor (determined by the missing-audience factor determiner 234) to the total number of impressions collected by the AME 114.
Although the coverage corrector 202 is shown as being located in the AME 114, the coverage corrector 202 may alternatively be located anywhere including at the database proprietor 116 or at any other suitable location separate from the AME 114 and the database proprietor 116. In addition, although the AME impressions collector 218, the modified ID map 228, and the DP impressions collector 232 are shown separate from the coverage corrector 202, one or more of the AME impressions collector 218, the modified ID map 228, and the DP impressions collector 232 may be implemented in the coverage corrector 202.
While an example manner of implementing the example coverage corrector 202, the example impressions collector 218, the example modified ID map 228, the example DP impressions collector 232, the example missing-audience factor determiner 234, the example unique audience corrector 236, and the example impressions corrector 238 is illustrated in
In the illustrated example of
The non-coverage corresponding to the impressions (IMP #) 23-28 of
In the simplified example impressions table 300 of
In the illustrated example of
In the illustrated example of
As used herein, a unique audience measure (e.g., the database proprietor unique audience size (DP_UA) 402 of
The database proprietor unique audience size (DP_UA) 402 of
As used herein, total impressions (e.g., the database proprietor impressions (DP_IMP) 404 of
As used herein, impressions frequency (e.g., the database proprietor frequency (DP_FREQ) 408 of
M-A Factor(DEMO GP)=(DP non-coverage quantity)(DEMO GP)/(DP coverage quantity)(DEMO GP) Equation 1
In Equation 1 above, the “DP non-coverage quantity” is a quantity of unique audience members in a demographic group (DEMO GP) that do not have DP device and/or user identifiers on their client devices. That is, the “DP non-coverage quantity” is a number of unique audience members (e.g., a unique audience count) for a demographic group (DEMO GP) for which impressions were logged by the AME 114 for client devices 102 (
For example, the M-A factor 502 for the demographic group F<50 is 67%, which the missing-audience factor determiner 234 determines based on impression records (IMP #) 3, 7, 11, 26, and 27 of
CCUA=DP_UA+(M-A factor×DP_UA) Equation 2
In Equation 2 above, the unique audience corrector 236 determines the coverage-corrected unique audience (CCUA) 602 by adding the database proprietor unique audience (DP_UA) value to the product of the M-A factor and the database proprietor unique audience (DP_UA) value. For example, the unique audience corrector 236 calculates the coverage-corrected unique audience (CCUA) 602 for the demographic group F<50 by multiplying 67% (the M-A factor of
In the illustrated example of
CCI=CCUA×DP_FREQ Equation 3
In Equation 3 above, the impressions corrector 238 determines the coverage-corrected impressions (CCI) 604 by multiplying the coverage-corrected unique audience (CCUA) value by the database proprietor frequency (DP_FREQ). For example, the impressions corrector 238 calculates the coverage-corrected impressions (CCI) 604 for the demographic group F<50 by multiplying 139,597 (the CCUA for the demographic group F<50) by 3.1 (the DP_FREQ of
As mentioned above, the example process of
The example flow diagram of
The example M-A factors development phase 702 of
The example missing-audience factor determiner 234 selects a demographic group (block 708). For example, the missing-audience factor determiner 234 selects one of the demographic groups of
The example missing-audience factor determiner 234 determines a M-A factor 502 (
In the non-coverage correction phase 704 of the illustrated example, the DP impressions collector 232 obtains database proprietor aggregate demographic impression-based data (block 716). For example, the DP impressions collector 232 obtains the database proprietor unique audience (DP_UA) values 402 (
The example coverage corrector 202 selects a demographic group (block 718). For example, the coverage corrector 202 selects one of the demographic groups of
The coverage corrector 202 determines whether there is another demographic group for which to determine a coverage-corrected unique audience (CCUA) value 602 and/or a coverage-corrected impressions (CCI) value 604 (block 724). If there is another demographic group for which to determine a coverage-corrected unique audience (CCUA) value 602 and/or a coverage-corrected impressions (CCI) value 604, control returns to block 718 at which another demographic group is selected. Otherwise, if there is not another demographic group for which to determine a coverage-corrected unique audience (CCUA) 602 and/or a coverage-corrected impressions (CCI) 604, the example process of
The processor platform 800 of the illustrated example includes a processor 812. The processor 812 of the illustrated example is hardware. For example, the processor 812 can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer.
The processor 812 of the illustrated example includes a local memory 813 (e.g., a cache). The processor 812 of the illustrated example is in communication with a main memory including a volatile memory 814 and a non-volatile memory 816 via a bus 818. The volatile memory 814 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory 816 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 814, 816 is controlled by a memory controller.
The processor platform 800 of the illustrated example also includes an interface circuit 820. The interface circuit 820 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface.
In the illustrated example, one or more input devices 822 are connected to the interface circuit 820. The input device(s) 822 permit(s) a user to enter data and commands into the processor 812. The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system.
One or more output devices 824 are also connected to the interface circuit 820 of the illustrated example. The output devices 824 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a light emitting diode (LED), a printer and/or speakers). The interface circuit 820 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor.
The interface circuit 820 of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network 826 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.).
The processor platform 800 of the illustrated example also includes one or more mass storage devices 828 for storing software and/or data. Examples of such mass storage devices 828 include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.
Coded instructions 832 include the machine readable instructions of
From the foregoing, it will be appreciated that above disclosed methods, apparatus and articles of manufacture are useful to enhance the operations of a computer to improve the accuracy of impression-based data such as unique audience and impression counts so that computers and processing systems therein can be relied upon to produce audience analysis information with higher accuracies. In some examples, computer operations can be made more efficient based on the above equations for determining M-A factors, coverage-corrected unique audience (CCUA), and coverage-corrected impressions (CCI). That is, through the use of these processes, computers can operate more efficiently by using fewer processor cycles to relatively quickly identify parameter values needed to determine coverage-corrected data and applying those parameter values through the above equations to determine the coverage-corrected data. Such coverage-corrected data is useful in subsequent processing for identifying exposure performances of different media so that media providers, advertisers, product manufacturers, and/or service providers can make more informed decisions on how to spend advertising dollars and/or media production and distribution dollars. Furthermore, example methods, apparatus, and/or articles of manufacture disclosed herein overcome the technical problem of counting impressions of media on media devices which do not have a user/device identifier such as cookies and/or other identifiers. Example methods, apparatus, and/or articles of manufacture disclosed herein solve this problem without forcing such devices to store user/device identifiers and without requiring follow-up network communications with the client device. This solution, thus, avoids creating additional network traffic and further avoids the requirement to store additional data such as identifiers at client devices.
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.
This patent arises from a continuation of U.S. patent application Ser. No. 16/196,481 filed on Nov. 20, 2018, which is a continuation of U.S. patent application Ser. No. 14/502,409 filed on Sep. 30, 2014, now U.S. Pat. No. 10,147,114, and claims priority to U.S. Provisional Patent Application Ser. No. 61/923,967 filed on Jan. 6, 2014, all of which are hereby incorporated herein by reference in their entireties. Priority to U.S. patent application Ser. No. 16/196,481, U.S. patent application Ser. No. 14/502,409, and U.S. Provisional Patent Application Ser. No. 61/923,967 is claimed.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3406516 | Yates | Oct 1968 | A |
| 5584050 | Lyons | Dec 1996 | A |
| 5594934 | Lu et al. | Jan 1997 | A |
| 5675510 | Coffey et al. | Oct 1997 | A |
| 5796952 | Davis et al. | Aug 1998 | A |
| 5832520 | Miller | Nov 1998 | A |
| 5848396 | Gerace | Dec 1998 | A |
| 5870740 | Rose et al. | Feb 1999 | A |
| 5948061 | Merriman et al. | Sep 1999 | A |
| 6035339 | Agraharam et al. | Mar 2000 | A |
| 6052730 | Felciano et al. | Apr 2000 | A |
| 6055573 | Gardenswartz et al. | Apr 2000 | A |
| 6098093 | Bayeh et al. | Aug 2000 | A |
| 6102406 | Miles et al. | Aug 2000 | A |
| 6108637 | Blumenau | Aug 2000 | A |
| 6138155 | Davis et al. | Oct 2000 | A |
| 6141694 | Gardner | Oct 2000 | A |
| 6223215 | Hunt et al. | Apr 2001 | B1 |
| 6279036 | Himmel et al. | Aug 2001 | B1 |
| 6286140 | Ivanyi | Sep 2001 | B1 |
| 6415323 | McCanne et al. | Jul 2002 | B1 |
| 6434614 | Blumenau | Aug 2002 | B1 |
| 6457010 | Eldering et al. | Sep 2002 | B1 |
| 6460079 | Blumenau | Oct 2002 | B1 |
| 6529952 | Blumenau | Mar 2003 | B1 |
| 6606657 | Zilberstein et al. | Aug 2003 | B1 |
| 6658410 | Sakamaki et al. | Dec 2003 | B1 |
| 6704787 | Umbreit | Mar 2004 | B1 |
| 6714917 | Eldering et al. | Mar 2004 | B1 |
| 6839680 | Liu et al. | Jan 2005 | B1 |
| 6877007 | Hentzel et al. | Apr 2005 | B1 |
| 6993590 | Gauthier et al. | Jan 2006 | B1 |
| 7039699 | Narin et al. | May 2006 | B1 |
| 7092926 | Cerrato | Aug 2006 | B2 |
| 7139723 | Conkwright et al. | Nov 2006 | B2 |
| 7146329 | Conkwright et al. | Dec 2006 | B2 |
| 7150030 | Eldering et al. | Dec 2006 | B1 |
| 7152074 | Dertinger et al. | Dec 2006 | B2 |
| 7159023 | Tufts | Jan 2007 | B2 |
| 7181412 | Fulgoni et al. | Feb 2007 | B1 |
| 7260837 | Abraham et al. | Aug 2007 | B2 |
| 7272617 | Bayer et al. | Sep 2007 | B1 |
| 7302447 | Dettinger et al. | Nov 2007 | B2 |
| 7343417 | Baum | Mar 2008 | B2 |
| 7363643 | Drake et al. | Apr 2008 | B2 |
| 7386473 | Blumenau | Jun 2008 | B2 |
| 7406516 | Davis et al. | Jul 2008 | B2 |
| 7546370 | Acharya et al. | Jun 2009 | B1 |
| 7590568 | Blumenau | Sep 2009 | B2 |
| 7613635 | Blumenau | Nov 2009 | B2 |
| 7627291 | James-Roxby | Dec 2009 | B1 |
| 7634786 | Knee et al. | Dec 2009 | B2 |
| 7644156 | Blumenau | Jan 2010 | B2 |
| 7647418 | Ash et al. | Jan 2010 | B2 |
| 7650407 | Blumenau | Jan 2010 | B2 |
| 7653724 | Blumenau | Jan 2010 | B2 |
| 7716326 | Blumenau | May 2010 | B2 |
| 7720963 | Blumenau | May 2010 | B2 |
| 7720964 | Blumenau | May 2010 | B2 |
| 7756974 | Blumenau | Jul 2010 | B2 |
| 7788216 | Li et al. | Aug 2010 | B2 |
| 7882054 | Levitan | Feb 2011 | B2 |
| 7882242 | Chen | Feb 2011 | B2 |
| 7890451 | Cancel et al. | Feb 2011 | B2 |
| 7925694 | Harris | Apr 2011 | B2 |
| 7941525 | Yavilevich | May 2011 | B1 |
| 7949565 | Eldering et al. | May 2011 | B1 |
| 7949569 | Maes | May 2011 | B2 |
| 7949639 | Hunt et al. | May 2011 | B2 |
| 7958234 | Thomas et al. | Jun 2011 | B2 |
| 7962603 | Morimoto | Jun 2011 | B1 |
| 7979544 | Cancel et al. | Jul 2011 | B2 |
| 8006259 | Drake et al. | Aug 2011 | B2 |
| 8046255 | Bistriceanu et al. | Oct 2011 | B2 |
| 8060601 | Brown et al. | Nov 2011 | B1 |
| 8087041 | Fu et al. | Dec 2011 | B2 |
| 8131763 | Tuscano et al. | Mar 2012 | B2 |
| 8131861 | Butler et al. | Mar 2012 | B2 |
| 8151194 | Chan et al. | Apr 2012 | B1 |
| 8151291 | Ramaswamy | Apr 2012 | B2 |
| 8180112 | Kurtz et al. | May 2012 | B2 |
| 8229780 | Davidow et al. | Jul 2012 | B2 |
| 8235814 | Willis et al. | Aug 2012 | B2 |
| 8266687 | Baldry | Sep 2012 | B2 |
| 8271886 | Lee et al. | Sep 2012 | B2 |
| 8280683 | Finkler | Oct 2012 | B2 |
| 8302120 | Ramaswamy | Oct 2012 | B2 |
| 8307006 | Hannan et al. | Nov 2012 | B2 |
| 8370489 | Mazumdar et al. | Feb 2013 | B2 |
| 8412648 | Karypis et al. | Apr 2013 | B2 |
| 8453173 | Anderson et al. | May 2013 | B1 |
| 8484511 | Tidwell | Jul 2013 | B2 |
| 8495198 | Sim et al. | Jul 2013 | B2 |
| 8504411 | Subasic et al. | Aug 2013 | B1 |
| 8504507 | Srinivasaiah | Aug 2013 | B1 |
| 8514907 | Wright et al. | Aug 2013 | B2 |
| 8543454 | Fleischman et al. | Sep 2013 | B2 |
| 8549552 | Ramaswamy et al. | Oct 2013 | B2 |
| 8600796 | Sterne et al. | Dec 2013 | B1 |
| 8631122 | Kadam et al. | Jan 2014 | B2 |
| 8688524 | Ramalingam et al. | Apr 2014 | B1 |
| 8700457 | Craft | Apr 2014 | B2 |
| 8713168 | Heffernan et al. | Apr 2014 | B2 |
| 8751461 | Abraham et al. | Jun 2014 | B2 |
| 8775332 | Morris et al. | Jul 2014 | B1 |
| 8831362 | Steffens | Sep 2014 | B1 |
| 8843626 | Mazumdar et al. | Sep 2014 | B2 |
| 8874652 | Pecjak | Oct 2014 | B1 |
| 8898689 | Georgakis | Nov 2014 | B2 |
| 8909771 | Heath | Dec 2014 | B2 |
| 8910195 | Barney et al. | Dec 2014 | B1 |
| 8930701 | Burbank et al. | Jan 2015 | B2 |
| 8954536 | Kalus et al. | Feb 2015 | B2 |
| 8973023 | Rao | Mar 2015 | B1 |
| 8984547 | Lambert et al. | Mar 2015 | B2 |
| 9055122 | Grecco et al. | Jun 2015 | B2 |
| 9083853 | Shkedi | Jul 2015 | B2 |
| 9117217 | Wilson et al. | Aug 2015 | B2 |
| 9215288 | Seth et al. | Dec 2015 | B2 |
| 9237138 | Bosworth et al. | Jan 2016 | B2 |
| 9269049 | McCann et al. | Feb 2016 | B2 |
| 9301007 | Ramaswamy | Mar 2016 | B2 |
| 9380122 | Pecjak | Jun 2016 | B1 |
| 9430746 | Pecjak | Aug 2016 | B2 |
| 9438939 | Wright et al. | Sep 2016 | B2 |
| 9519914 | Splaine et al. | Dec 2016 | B2 |
| 9948530 | Niederstrasser | Apr 2018 | B1 |
| 10147114 | Toupet et al. | Dec 2018 | B2 |
| 10963907 | Toupet et al. | Mar 2021 | B2 |
| 11068927 | Toupet et al. | Jul 2021 | B2 |
| 20020099609 | Nascenzi et al. | Jul 2002 | A1 |
| 20030006911 | Smith et al. | Jan 2003 | A1 |
| 20030037131 | Verma | Feb 2003 | A1 |
| 20030046385 | Vincent | Mar 2003 | A1 |
| 20030065770 | Davis et al. | Apr 2003 | A1 |
| 20030105604 | Ash et al. | Jun 2003 | A1 |
| 20030177488 | Smith et al. | Sep 2003 | A1 |
| 20030220901 | Carr et al. | Nov 2003 | A1 |
| 20040088212 | Hill | May 2004 | A1 |
| 20040107125 | Guheen et al. | Jun 2004 | A1 |
| 20040167763 | Liebman | Aug 2004 | A1 |
| 20040167928 | Anderson et al. | Aug 2004 | A1 |
| 20040186840 | Dertinger et al. | Sep 2004 | A1 |
| 20050166233 | Beyda et al. | Jul 2005 | A1 |
| 20050223093 | Hanson et al. | Oct 2005 | A1 |
| 20050267799 | Chan et al. | Dec 2005 | A1 |
| 20060074953 | Dettinger et al. | Apr 2006 | A1 |
| 20060089754 | Mortenson | Apr 2006 | A1 |
| 20060178996 | Matsushima et al. | Aug 2006 | A1 |
| 20060271641 | Stavrakos et al. | Nov 2006 | A1 |
| 20060294259 | Matefi et al. | Dec 2006 | A1 |
| 20070043769 | Kasahara et al. | Feb 2007 | A1 |
| 20070106787 | Blumenau | May 2007 | A1 |
| 20070106792 | Blumenau | May 2007 | A1 |
| 20070112714 | Fairweather | May 2007 | A1 |
| 20070156532 | Nyhan et al. | Jul 2007 | A1 |
| 20070198327 | Yazdani et al. | Aug 2007 | A1 |
| 20070260603 | Tuscano et al. | Nov 2007 | A1 |
| 20070271518 | Tischer et al. | Nov 2007 | A1 |
| 20070271580 | Tischer et al. | Nov 2007 | A1 |
| 20080004958 | Ralph et al. | Jan 2008 | A1 |
| 20080086356 | Glassman et al. | Apr 2008 | A1 |
| 20080091639 | Davis et al. | Apr 2008 | A1 |
| 20080126420 | Wright et al. | May 2008 | A1 |
| 20080201427 | Chen | Aug 2008 | A1 |
| 20080201472 | Bistriceanu et al. | Aug 2008 | A1 |
| 20080222201 | Chen et al. | Sep 2008 | A1 |
| 20080235243 | Lee et al. | Sep 2008 | A1 |
| 20080248815 | Busch | Oct 2008 | A1 |
| 20080276179 | Borenstein et al. | Nov 2008 | A1 |
| 20080300965 | Doe | Dec 2008 | A1 |
| 20090030780 | York et al. | Jan 2009 | A1 |
| 20090055241 | Chen et al. | Feb 2009 | A1 |
| 20090070443 | Vanderhook et al. | Mar 2009 | A1 |
| 20090070797 | Ramaswamy et al. | Mar 2009 | A1 |
| 20090076899 | Gbodimowo | Mar 2009 | A1 |
| 20090171762 | Alkove et al. | Jul 2009 | A1 |
| 20090217315 | Malik et al. | Aug 2009 | A1 |
| 20090292587 | Fitzgerald | Nov 2009 | A1 |
| 20090293001 | Lu et al. | Nov 2009 | A1 |
| 20090307084 | Monighetti et al. | Dec 2009 | A1 |
| 20090327026 | Bistriceanu et al. | Dec 2009 | A1 |
| 20100010866 | Bal et al. | Jan 2010 | A1 |
| 20100070621 | Urdan et al. | Mar 2010 | A1 |
| 20100088152 | Bennett | Apr 2010 | A1 |
| 20100088373 | Pinkham | Apr 2010 | A1 |
| 20100121676 | Jackson | May 2010 | A1 |
| 20100153175 | Pearson et al. | Jun 2010 | A1 |
| 20100153544 | Krassner et al. | Jun 2010 | A1 |
| 20100161385 | Karypis et al. | Jun 2010 | A1 |
| 20100191723 | Perez et al. | Jul 2010 | A1 |
| 20100205057 | Hook et al. | Aug 2010 | A1 |
| 20100241745 | Offen et al. | Sep 2010 | A1 |
| 20100262498 | Nolet et al. | Oct 2010 | A1 |
| 20100268540 | Arshi et al. | Oct 2010 | A1 |
| 20100268573 | Jain et al. | Oct 2010 | A1 |
| 20100281178 | Sullivan | Nov 2010 | A1 |
| 20100299604 | Blumenau | Nov 2010 | A1 |
| 20100312854 | Hyman | Dec 2010 | A1 |
| 20100313009 | Combet et al. | Dec 2010 | A1 |
| 20100325051 | Etchegoyen | Dec 2010 | A1 |
| 20110016482 | Tidwell et al. | Jan 2011 | A1 |
| 20110041062 | Singer et al. | Feb 2011 | A1 |
| 20110087519 | Fordyce, III et al. | Apr 2011 | A1 |
| 20110093327 | Fordyce, III et al. | Apr 2011 | A1 |
| 20110106620 | Setiawan et al. | May 2011 | A1 |
| 20110131596 | Amsterdam et al. | Jun 2011 | A1 |
| 20110137733 | Baird et al. | Jun 2011 | A1 |
| 20110153391 | Tenbrock | Jun 2011 | A1 |
| 20110157475 | Wright et al. | Jun 2011 | A1 |
| 20110191184 | Blackhurst et al. | Aug 2011 | A1 |
| 20110191664 | Sheleheda et al. | Aug 2011 | A1 |
| 20110191831 | Chan et al. | Aug 2011 | A1 |
| 20110196735 | von Sydow et al. | Aug 2011 | A1 |
| 20110202500 | Warn et al. | Aug 2011 | A1 |
| 20110208860 | Sim et al. | Aug 2011 | A1 |
| 20110231240 | Schoen et al. | Sep 2011 | A1 |
| 20110246297 | Buchalter et al. | Oct 2011 | A1 |
| 20110246306 | Blackhurst et al. | Oct 2011 | A1 |
| 20110246641 | Pugh | Oct 2011 | A1 |
| 20110282730 | Tarmas | Nov 2011 | A1 |
| 20110288907 | Harvey et al. | Nov 2011 | A1 |
| 20120005213 | Hannan et al. | Jan 2012 | A1 |
| 20120030037 | Carriero | Feb 2012 | A1 |
| 20120042005 | Papakostas et al. | Feb 2012 | A1 |
| 20120072469 | Perez et al. | Mar 2012 | A1 |
| 20120109709 | Fordyce, III et al. | May 2012 | A1 |
| 20120109882 | Bouse et al. | May 2012 | A1 |
| 20120110027 | Falcon | May 2012 | A1 |
| 20120110071 | Zhou et al. | May 2012 | A1 |
| 20120143713 | Dittus et al. | Jun 2012 | A1 |
| 20120151079 | Besehanic et al. | Jun 2012 | A1 |
| 20120151322 | Lindsay et al. | Jun 2012 | A1 |
| 20120158490 | Neumeyer et al. | Jun 2012 | A1 |
| 20120158954 | Heffernan et al. | Jun 2012 | A1 |
| 20120166520 | Lindsay et al. | Jun 2012 | A1 |
| 20120173701 | Tenbrock | Jul 2012 | A1 |
| 20120185274 | Hu | Jul 2012 | A1 |
| 20120192214 | Hunn et al. | Jul 2012 | A1 |
| 20120206331 | Gandhi | Aug 2012 | A1 |
| 20120209920 | Neystadt et al. | Aug 2012 | A1 |
| 20120215621 | Heffernan | Aug 2012 | A1 |
| 20120239407 | Lynch et al. | Sep 2012 | A1 |
| 20120239809 | Mazumdar et al. | Sep 2012 | A1 |
| 20120265606 | Patnode | Oct 2012 | A1 |
| 20120302222 | Williamson et al. | Nov 2012 | A1 |
| 20120310729 | Daito et al. | Dec 2012 | A1 |
| 20120311017 | Sze et al. | Dec 2012 | A1 |
| 20130007794 | Besehanic et al. | Jan 2013 | A1 |
| 20130013308 | Cao et al. | Jan 2013 | A1 |
| 20130014144 | Bhatia et al. | Jan 2013 | A1 |
| 20130019262 | Bhatia et al. | Jan 2013 | A1 |
| 20130046615 | Liyanage | Feb 2013 | A1 |
| 20130060629 | Rangsikitpho et al. | Mar 2013 | A1 |
| 20130066713 | Umeda | Mar 2013 | A1 |
| 20130080263 | Goldman et al. | Mar 2013 | A1 |
| 20130097311 | Mazumdar et al. | Apr 2013 | A1 |
| 20130097312 | Mazumdar et al. | Apr 2013 | A1 |
| 20130117103 | Shimizu et al. | May 2013 | A1 |
| 20130124628 | Weerasinghe | May 2013 | A1 |
| 20130138506 | Zhu et al. | May 2013 | A1 |
| 20130138743 | Amento et al. | May 2013 | A1 |
| 20130145022 | Srivastava et al. | Jun 2013 | A1 |
| 20130159499 | Besehanic | Jun 2013 | A1 |
| 20130198125 | Oliver et al. | Aug 2013 | A1 |
| 20130204694 | Banister et al. | Aug 2013 | A1 |
| 20130212188 | Duterque et al. | Aug 2013 | A1 |
| 20130246389 | Osann, Jr. | Sep 2013 | A1 |
| 20130246609 | Topchy et al. | Sep 2013 | A1 |
| 20130282898 | Kalus et al. | Oct 2013 | A1 |
| 20130290070 | Abraham et al. | Oct 2013 | A1 |
| 20130297411 | van Datta et al. | Nov 2013 | A1 |
| 20130325588 | Kalyanam et al. | Dec 2013 | A1 |
| 20130331971 | Bida et al. | Dec 2013 | A1 |
| 20130332604 | Seth et al. | Dec 2013 | A1 |
| 20140033317 | Barber | Jan 2014 | A1 |
| 20140058836 | Wiseman et al. | Feb 2014 | A1 |
| 20140075004 | Van Dusen et al. | Mar 2014 | A1 |
| 20140108130 | Vos et al. | Apr 2014 | A1 |
| 20140122703 | Pugh et al. | May 2014 | A1 |
| 20140156761 | Heffernan et al. | Jun 2014 | A1 |
| 20140279074 | Chen et al. | Sep 2014 | A1 |
| 20140280891 | Doe | Sep 2014 | A1 |
| 20140298025 | Burbank et al. | Oct 2014 | A1 |
| 20140324544 | Donato et al. | Oct 2014 | A1 |
| 20140324545 | Splaine et al. | Oct 2014 | A1 |
| 20140337104 | Splaine et al. | Nov 2014 | A1 |
| 20150019322 | Alla et al. | Jan 2015 | A1 |
| 20150019327 | Mazumdar et al. | Jan 2015 | A1 |
| 20150046579 | Perez et al. | Feb 2015 | A1 |
| 20150186403 | Srivastava et al. | Jul 2015 | A1 |
| 20150189500 | Bosworth et al. | Jul 2015 | A1 |
| 20150193813 | Toupet et al. | Jul 2015 | A1 |
| 20150193816 | Toupet et al. | Jul 2015 | A1 |
| 20150262207 | Rao et al. | Sep 2015 | A1 |
| 20150278858 | Lyons et al. | Oct 2015 | A1 |
| 20150324837 | Shimizu | Nov 2015 | A1 |
| 20190034962 | Toupet et al. | Jan 2019 | A1 |
| 20190087853 | Toupet et al. | Mar 2019 | A1 |
| 20200273049 | Pugh | Aug 2020 | A1 |
| 20210192565 | Toupet et al. | Jun 2021 | A1 |
| Number | Date | Country |
|---|---|---|
| 2013205736 | May 2013 | AU |
| 2757278 | Oct 2010 | CA |
| 1898662 | Jan 2007 | CN |
| 101077014 | Nov 2007 | CN |
| 101222348 | Jul 2008 | CN |
| 101505247 | Aug 2009 | CN |
| 101536503 | Sep 2009 | CN |
| 104520839 | Apr 2015 | CN |
| 104541513 | Apr 2015 | CN |
| 104584564 | Apr 2015 | CN |
| 2176639 | Dec 1986 | GB |
| 07262167 | Oct 1995 | JP |
| 2001282982 | Oct 2001 | JP |
| 2001357192 | Dec 2001 | JP |
| 2002163562 | Jun 2002 | JP |
| 2002373152 | Dec 2002 | JP |
| 2004504674 | Feb 2004 | JP |
| 2006127320 | May 2006 | JP |
| 2006127321 | May 2006 | JP |
| 2010039845 | Feb 2010 | JP |
| 2010257448 | Nov 2010 | JP |
| 20020037980 | May 2002 | KR |
| 20090020558 | Feb 2009 | KR |
| 20100094021 | Aug 2010 | KR |
| 20110017895 | Feb 2011 | KR |
| 20110023293 | Mar 2011 | KR |
| 20120091411 | Aug 2012 | KR |
| 20120123148 | Nov 2012 | KR |
| 9617467 | Jun 1996 | WO |
| 9628904 | Sep 1996 | WO |
| 9641495 | Dec 1996 | WO |
| 0041115 | Jul 2000 | WO |
| 0152168 | Jul 2001 | WO |
| 0207054 | Jan 2002 | WO |
| 03027860 | Apr 2003 | WO |
| 2005013072 | Feb 2005 | WO |
| 2005024689 | Mar 2005 | WO |
| 2008150575 | Dec 2008 | WO |
| 2010088372 | Aug 2010 | WO |
| 2010104285 | Sep 2010 | WO |
| 2011097624 | Aug 2011 | WO |
| 2012019643 | Feb 2012 | WO |
| 2012040371 | Mar 2012 | WO |
| WO-2012087954 | Jun 2012 | WO |
| 2012128895 | Sep 2012 | WO |
| 2012170902 | Dec 2012 | WO |
| 2012177866 | Dec 2012 | WO |
| 2013122907 | Aug 2013 | WO |
| 2013188429 | Dec 2013 | WO |
| 2014059319 | Apr 2014 | WO |
| 2014176343 | Oct 2014 | WO |
| 2014179218 | Nov 2014 | WO |
| 2014182764 | Nov 2014 | WO |
| 2015005957 | Jan 2015 | WO |
| 2015023589 | Feb 2015 | WO |
| 2015102794 | Jul 2015 | WO |
| 2015102795 | Jul 2015 | WO |
| 2015102796 | Jul 2015 | WO |
| 2015102803 | Jul 2015 | WO |
| Entry |
|---|
| Nekovee et al, An Adaptive Method for Dynamic Audience Size Estimation in Multicast, B. Stiller et al. (Eds.): NGC/ICQT 2003, LNCS 2816, pp. 23-33, 2003, Springer-Verlag Berlin Heidelberg 2003 (Year: 2003). |
| Koehler et al, “A Method for Measuring Online Audiences”, Google Inc (2013), pp. 1-24, https://research.google/pubs/pub41089/ (Year: 2013). |
| United States Patent and Trademark Office, “Final Office Action,” dated Nov. 18, 2022 in connection with U.S. Appl. No. 17/191,479, 37 pages. |
| TUTORIALSPOINT,“Microprocessor—Overview,” retrieved from <https://www.tutorialspoint.com/microprocessor/microprocessor_overview.htm> on Nov. 22, 2022, 2 pages. |
| Jeffries-Fox, “A Primer in Internet Audience Measurement,” Digital PR Guidebook: 178-184. Rockville: Access Intelligence LLC. (2007/2008) (Year: 2007), 12 pages. |
| Bernstein et al., “Quantifying the Invisible Audience in Social Networks,” Managing Social Media, p. 21-30, 2013 (Year: 2013), 10 pages. |
| Taboga, “Linear combinations of normal random variables,” from “Lectures on probability and statistics,” [http ://www.statlect.com/normal_distribution_linear_combinations. Htm], 2010, 4 pages. |
| Goerg et al., “How Many Millenials Visit YouTube? Estimating Unobserved Events From Incomplete Panel Data Conditioned on Demographic Covariates,” Google Inc, Apr. 27, 2015, 27 pages. |
| Goerg et al., “How Many People Visit YouTube? Imputing Missing Events in Panels With Excess Zeros,” Google Inc, 2015, 6 pages. |
| Patent Cooperation Treaty, “International Search Report,” issued in connection with International Application No. PCT/US2014/068168, dated Mar. 2, 2015, 3 pages. |
| Patent Cooperation Treaty, “Written Opinion of the International Searching Authority,” issued in connection with International Application No. PCT/US2014/068168, dated Mar. 2, 2015, 5 pages. |
| Patent Cooperation Treaty, “International Preliminary Report on Patentability,” issued in connection with International Application No. PCT/US2014/068168, dated Jul. 12, 2016, 1 page. |
| Braverman, “Are the Online Marketing Efforts of TV Shows and Programs Worthwhile?,” Harris Interactive, Mar. 30, 2011, 5 pages. |
| Danaher, “A Canonical Expansion Model for Multivariate Media Exposure Distributions: A Generalization of the ‘Duplication of Viewing Law,’” Journal of Marketing Research, vol. XXVIII, Aug. 1991, 7 pages. |
| Enoch et al., “Cracking the Cross-Media Code: How to Use Single-Source Measures to Examine Media Cannibalization and Convergence,” Journal of Advertising Research, Jun. 2010, 13 pages. |
| Headen et al., “The Duplication of Viewing Law and Television Media Schedule Evaluation,” Journal and Marketing Research, vol. XVI, Aug. 1979, 9 pages. |
| Huang et al., “Modeling the Audience's Banner Ad Exposure for Internet Advertising Planning,” Journal of Advertising Research, vol. 35, No. 2, Summer 2006, 15 pages. |
| Nielsen, “How Teens Use Media: A Nielsen report on the myths and realities of teen media trends,” Jun. 2009, 17 pages. |
| Arbitron Inc. and Edison Research, “The Infinite Dial 2011: Navigating Digital Platforms,” 2011, 83 pages. |
| Rust et al, “A Comparative Study of Television Duplication Models,” Journal for Advertising, vol. 10, No. 3, 1981, 6 pages. |
| Edwards, “Apple Wants More Advertisers to Use Its iPhone Tracking System,” Business Insider, Jun. 13, 2013, [http://www.businessinsider.com/apples-idfa-and-ifa-tracking-system-2013-- 6] retrieved on Jul. 24, 2014, 2 pages. |
| Facebook for Business, “Measuring Conversions on Facebook, Across Devices and in Mobile Apps,” Aug. 14, 2014, [https://www.facebook.com/business/news/cross-device-measurement] retrieved on Aug. 14, 2014, 3 pages. |
| Adam et al., “Privacy Preserving Integration of Health Care Data,” AMIA 2007 Symposium Proceedings, 2007, 6 pages. |
| Albanesius, “Facebook Issues Fix for Several Tracking Cookies,” internet article, [www.pcmag.com], Sep. 28, 2011, 2 pages. |
| Protalinski, “Facebook denies cookie tracking allegations,” internet article, [www.zdnet.com], Sep. 25, 2011, 2 pages. |
| Protalinski, “Facebook fixes cookie behavior after logging out,” internet article, [www.zdnet.com], Sep. 27, 2011, 2 pages. |
| Protalinski, “US congressmen ask FTC to investigate Facebook cookies,” internet article, [www.zdnet.com], Sep. 28, 2011, 2 pages. |
| Fliptop, “Fliptop Person API Documentation,” [https://developer.fliptop.com/documentation], retrieved on May 7, 2013, 6 pages. |
| Fliptop, “Get, Keep and Grow Customers with Fliptop's Customer Intelligence Application,” [www.fliptop.com/features#social_matching], retrieved on May 7, 2013, 3 pages. |
| Fliptop, “What is Fliptop?,” [www.fliptop.com/about_us], retrieved on May 7, 2013, 1 page. |
| “JavaScript and Ajax Forum,” WebmasterWorld, Sep. 28, 2005, [http://www.webmasterworld.com/forum91/4465.htm] retrieved on Jun. 29, 2011, 4 pages. |
| Launder, “Media Journal: Nielsen to Test Online-TV Viewing Tool,” The Wall Street Journal, Apr. 30, 2013, 2 pages. |
| Wikipedia, “Mental poker,” Jan. 12, 2010, [http://en.wikipedia.org/wiki/Mental_poker], retrieved on Sep. 21, 2010, 5 pages. |
| Nielsen, “Nielsen Unveils New Online Advertising Measurement,” Sep. 27, 2010, [http://nielsen.com/us/en/insights/pressroom/2010/nielsen_unveils_newonli-neadvertisingmeasurement.html], retrieved on May 31, 2012, 3 pages. |
| Cubrilovic, “Logging out of Facebook is not enough,” [www.nikcub.appspot.com] Sep. 25, 2011, 3 pages. |
| Rapleaf, “Fast. Simple. Secure.,” [www.rapleaf.com/why-rapleaf/], retrieved on May 7, 2013, 3 pages. |
| Rapleaf, “Frequently Asked Questions,” [www.rapleaf.com/about-us/faq/#where], retrieved on May 7, 2013, 3 pages. |
| Rapleaf, “The Consumer Data Marketplace,” [www.rapleaf.com/under-the-hood/], retrieved on May 7, 2013, 2 pages. |
| Sharma, “Nielsen Gets Digital to Track Online TV Viewers,” All Things, Apr. 30, 2013, 3 pages. |
| Coffey, “Internet Audience Measurement: A Practitioner's View,” Journal of Interactive Advertising, vol. 1, No. 2, Spring 2001, 8 pages. |
| Vega, “Nielsen Introduces New Ad Measurement Product,” The New York Times, Sep. 27, 2010, 7 pages. |
| Vranica, “Nielsen Testing a New Web-Ad Metric,” The Wall Street Journal, Sep. 23, 2010, 2 pages. |
| Hothorn et al., “Unbiased Recursive Partitioning: A Conditional Inference Framework,” Journal of Computational and Geographical Statistics, vol. 15, No. 3, 2006, pp. 651-674, 21 pages. |
| Pouttu-Clarke, “J2EE patterns: Cross Domain Cookie Provider,” The Server Side, [http://www.theserverside.com/discussions/thread/31258.html], Jan. 19, 2005, 12 pages. |
| Interactive Advertising Bureau, “Interactive Audience Measurement and Advertising Campaign Reporting and Audit Guidelines,” Version 6.0b, United States Version, Sep. 2004, 20 pages. |
| United States Patent and Trademark Office, “Final Office Action,” issued in connection with U.S. Appl. No. 14/502,409, dated Mar. 9, 2017, 17 pages. |
| United States Patent and Trademark Office, “Advisory Action,” issued in connection with U.S. Appl. No. 14/502,409, dated May 17, 2017, 5 pages. |
| United States Patent and Trademark Office, “Final Office Action,” issued in connection with U.S. Appl. No. 14/502,409, dated Nov. 16, 2017, 13 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 14/502,409, dated Jun. 20, 2017, 13 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 14/502,409, dated Nov. 16, 2016, 22 pages. |
| United States Patent and Trademark Office, “Notice of Allowance and Fee(s) Due,” issued in connection with U.S. Appl. No. 14/502,409, dated Jul. 20, 2018, 15 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 14/528,495, dated Nov. 28, 2016, 34 pages. |
| United States Patent and Trademark Office, “Final Office Action,” issued in connection with U.S. Appl. No. 14/528,495, dated Mar. 23, 2017, 49 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 14/528,495, dated Oct. 6, 2017, 34 pages. |
| United States Patent and Trademark Office, “Final Office Action,” issued in connection with U.S. Appl. No. 14/528,495, dated Mar. 29, 2018, 40 pages. |
| United States Patent and Trademark Office, “Advisory Action,” issued in connection with U.S. Appl. No. 14/528,495, dated Jul. 23, 2018, 3 pages. |
| United States Patent and Trademark Office, “Notice of Panel Decision from Pre-Appeal Brief Review,” issued in connection with U.S. Appl. No. 14/528,495, dated Aug. 13, 2018, 2 pages. |
| Patent Cooperation Treaty, “International Search Report,” issued in connection with International Application No. PCT/US2014/068165, dated Feb. 25, 2015, 3 pages. |
| Patent Cooperation Treaty, “Written Opinion of the International Searching Authority,” issued in connection with International Application No. PCT/US2014/068165, dated Feb. 25, 2015, 5 pages. |
| Patent Cooperation Treaty, “International Preliminary Report on Patentability,” issued in connection with International Application No. PCT/US2014/068165, dated Jul. 12, 2016, 6 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 16/146,912, dated May 18, 2020, 20 pages. |
| United States Patent and Trademark Office, “Notice of Allowance and Fee(s) Due,” issued in connection with U.S. Appl. No. 16/146,912, dated Nov. 18, 2020, 10 pages. |
| United States Patent and Trademark Office, “Notice of Allowability,” issued in connection with U.S. Appl. No. 16/146,912, dated Dec. 24, 2020, 2 pages. |
| Liu et al., “Broadcast Audience Estimation,” 2000 IEEE InfoCom, p. 952-960, 2000, 9 pages. |
| United States Patent and Trademark Office, “Notice of Abandonment,” issued in connection with U.S. Appl. No. 14/528,495, dated Apr. 8, 2019, 2 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 16/196,481, dated Aug. 24, 2020, 19 pages. |
| United States Patent and Trademark Office, “Notice of Allowance and Fee(s) Due,” issued in connection with U.S. Appl. No. 16/196,481, dated Mar. 15, 2021, 19 pages. |
| United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 17/191,479, dated Apr. 29, 2022, 34 pages. |
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| 20210342880 A1 | Nov 2021 | US |
| Number | Date | Country | |
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| 61923967 | Jan 2014 | US |
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| Child | 17378463 | US | |
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| Child | 16196481 | US |
