Invented by Timothy P. Barber, Kount Inc
The Kount Inc invention works as followsOnline entities are often interested in obtaining information about their audiences. Online transactions, including data about the transactions made by audience members, are collected to determine information about them and their activities. Online transactions are subjected to one or more audience analyses to determine which online transactions were performed by an audience member. “With an accurate assignment to the online transaction, the audience and the associated transactions can be classified as legitimate or illegitimate.
Background for Authenticating users to accurately measure online audience
The present invention relates in general to measuring online viewership, and more specifically to authentication of users.
Online audience measurements are used in a wide range of fields, such as Internet advertising, information consumption and the determination of audience for specific online media, like a website or video, an article, a blog, an advertisement, a file available for download, etc. Advertising entities, for example, want to know how many unique visitors are associated with an ad that is associated with online media. Non-advertising organizations also want to know how many unique visitors they have associated with their media. Advertising entities can use accurate audience measurements to “get what they paid for” Non-advertising entities and advertising entities can both use the same accurate measurement of audience to market their popularity.
In television and print media, there are established statistical methods for measuring the audience size. Online entities estimate audience size by counting the unique machines that visit a web page on a particular day. This method is sufficient for generic traffic measurements at short intervals. However, a census that accurately counts the number of unique users (legitimate visitors) who visit any online media on a regular basis over time can be incredibly difficult. One of the obstacles to a census is the fact that some users have multiple accounts on the same site and others use different devices from different locations. Some of the other obstacles include malicious users who may use automated programs to create a large number of false user accounts, fake interaction or hijack another users’ system and direct them directly to a website to inflate unique visitors. “Nefarious users can use a variety of methods to commit fraud, or negatively impact the experience and participation of genuine users.
An audience analytics server is configured in a way to store multiple online transactions that are received from different online entities. Each online transaction represents a particular activity that an online audience member performed. It includes transaction data such as the description of the activity, and any attributes associated with it. An online transaction, for example, may show that a TV was purchased at a certain date using a particular credit card on a specific device.
Each of the many online transactions are assigned to one of a number of sets of transactions based on the characteristics of the transaction data. The sets are subjected to one or more audience analyses to refine and modify the assignment of the transactions to the sets, and to determine the associations between the sets.
The audience analysis process can analyze the associations between sets in order to attribute a set collection to a member of an audience. The set collection attributed to a member of the audience contains all activities performed by that member.
The audience analysis process can analyze further the sets of collections attributed to more than two audience members in order to find correlations. Correlations can show relationships between audience members, or whether a collection of sets is only representative of one audience member.
The analysis of a set or number of transactions attributed to a member is done to determine if they are legitimate or not. Audience measurements and monitoring services classify the audience member, and any associated transactions.
Analysis on legitimate user behavior can be reported to establish the number of users interacting with an online entity or performing certain activities. Audience analysis can be used to analyze fraudulent behavior and produce a list of the fraudulent audience members. This will help in reducing online fraud. Other analyses can be done based on the transaction data.
Overview of Audience Measuring
The disclosure facilitates accurate measurements of online audiences based on the online activities performed by members. Total audience members include legitimate users as well as various illegitimate entities, such as malicious users who use hijacked identities or accounts of legitimate users, hijacked computers systems, and automated computer programs known as bots. Since the legitimacy of an individual audience member can’t be determined a posteriori, or more generally from a single activity, all activities are recorded before conducting an online measurement. These records are then analyzed by combining the audience analysis techniques described in this document to attribute each record to a legitimate or illegitimate member of an audience. The true online audience is the multitude of audience members who are legitimately identified by the audience analysis process.
Furthermore with a large number of records already attributed to audience members (or new members), additional incoming records can be examined and easily attributed to the correct audience member in light of the previous analysis.” The actions and online identities of illegitimate audiences can be reported by processing multiple records and performing audience analysis routines over time.
The present disclosure envisages a discrete collection of transaction data obtained for a given online activity, as a record. The data collected in the transaction information includes information about the device that performed the transaction, the location (physical or electronic), the active online accounts near or during the transaction and even the activity itself. The transaction information is a collection of data which can be divided into two categories: unique identifiers (unique IDs) and contextual information. Unique identifiers are characteristics that are associated with a transaction. They often refer to one audience member. Contextual information is a way of providing additional details about an activity.
Some examples of unique IDs associated with online accounts or the activity performed include registered user account names and passwords, email address, credit card or bank account numbers (including shipping and billing addresses), online payment accounts (or hashes thereof), as well as a cookie value. Unique IDs can be associated with online accounts and the activities performed. Examples include user names, passwords, email addresses, credit card numbers or bank account details, shipping or billing address, online payment accounts or hashes of these.
Examples of contextual information are domain names, timestamps and Uniform Resource Locators (URLs) as well as keywords that describe the activity. Other examples include device types, Internet Protocol (IP) addresses, networks available (wired or wireless), nicknames, dates and locations of devices, and application preferences. Contextual data may include other device capabilities, such as GPS, connection speed, connection strength, audio or video recording, and other sensors. Although contextual information is not irreducible in general, a script can collect vast amounts of contextual data for a device or transaction that could be combined into a fingerprint to serve as an unique ID. A combination of hardware, software, or contextual information can be used as a unique identification without any other information.
In some embodiments, IP addresses may be used both as contextual information and/or unique ID. An IP address that is associated with transactions made on a home network, for example, will be more unique than one associated with a public or business wireless network. Different IP visions can also carry additional information which may be used to determine a unique identification or as a unique identity. IPv6 addresses, for example, may contain information that identifies a device connected to a private network (e.g. home, business or wireless) via a router, network switching device, etc. IP addresses can be broken down into ID components, which are unique to the device and may not change across networks, or contextual components, such as the ISP, location, etc. (e.g., via a ?whois? look-up).
Conceptual Representation of the Online Audience”.
FIG. The diagram 1 illustrates an application of audience measurement based on online transaction records. The transactions 100 represent the online activities of various audience members and include different amounts of transaction information 101.
As well, members of an audience online are shown following an audience measurement over a range of transactions 100. The online audience is shown to include legitimate users 110A and B, as well as illegitimate ones such a bot 113 and user 110C.
The transactions 100 can be obtained from various online entities, such as content providers and payment systems that are Internet-enabled, social networking websites and forums and advertisers who participate in online audience measurement services. The transactions 100 are a broad range of activities that audience members engage in online, such as online purchases or downloads of media or viewing of online accounts. ), etc. In FIG. In FIG. Below are some more detailed examples.
Also shown are snapshots from sets 105 during the audience analysis. Each transaction 100 is initially attributed to a particular set 105 based on one or more filtering criteria. New sets receive an audience ID. In one embodiment, filtering parameters can be based on the unique IDs that are associated with many transactions 100. As an example, FIG. “Sets 105A – 105E are assigned to the sets 105A – 105BC – 105D & 100E based upon the MAC addresses of the devices that the audience members used to execute the transactions.
Click here to view the patent on Google Patents.