USER VERIFICATION SYSTEMS AND METHODS

• US 20200137073A1
• Filed: 10/25/2019
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Grant

• Current Assignee: Intertrust Technologies Corporation (Fidelio Acquisition Co LLC)
• Original Assignee: Intertrust Technologies Corporation (Fidelio Acquisition Co LLC)
• Inventors: Palan, Vivek, Nilsson, Jarl
• Granted Patent: US 11,539,713 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/2423 Interactive query statement specification based on a database schema
• CPC Class Codes: G06F 2221/2133 Verifying human interaction, e.g., Captcha
• CPC Class Codes: H04L 2463/144 Detection or countermeasures against botnets
• CPC Class Codes: H04L 63/123 received data contents, e.g. message integrity
• CPC Class Codes: H04L 63/1475 Passive attacks, e.g. eavesdropping or listening without modification of the traffic monitored
• CPC Class Codes: H04L 9/3247 involving digital signatures

Abstract

This disclosure relates to systems and methods for verifying the presentation of content to a target audience using generated metrics indicative of a likelihood that the content was presented to actual human individuals within the target audience. In some instances, such a metric may be associated with a probability model estimating that a user (e.g., a user of a device) is human and not a bot and/or other automated service. Metrics consistent with aspects of the disclosed embodiments may be generated based, at least in part, on user information received from a user and/or associated devices and/or associated services. Consistent with various disclosed embodiments, metrics indicative of whether a user is human, content distribution decisions and user agency decisions may use such metrics

Claims

1. A method for managing the distribution of content performed by a user verification system comprising a processor and a non-transitory computer readable storage medium storing instructions that, when executed by the processor, cause the processor to perform a method comprising:

receiving one or more first signals from a first device, the one or more first signals being associated with a user of the first device;

generating, based on the received one or more first signals, a Turing score associated with the user of the first device, the Turing score indicating a relative likelihood that the user of the first device is a human user;

receiving a content item for distribution, the content item being associated with at content item least one content parameter specifying a threshold Turing score associated with the content item;

determining that the generated Turing score meets the threshold Turing score specified in the at least one content parameter; and

transmitting, based on the determination, the content item to the first device.

2. The method of claim 1, wherein the one or more first signals comprise at least one user input provided by the user to the first device.

3. The method of claim 2, wherein the one or more first signals comprise information generated based on the user'"'"'s response to an interactive query presented to the user on the first device.

4. The method of claim 1, wherein the one or more first signals comprise information generated by monitoring a user'"'"'s interaction with the first device.

5. The method of claim 1, wherein the one or more first signals comprise one or more identification messages received by the first device from one or more other devices.

6. The method of claim 5, wherein the one or more identification messages comprise signed messages.

7. The method of claim 6, wherein the one or more identification messages comprise messages signed by one or more private keys associated with the one or more other devices.

8. The method of claim 5, wherein the one or more identification messages are received by the first device when the first device is proximate to the one or more other devices.

9. The method of claim 1, wherein the method further comprises:

receiving one or more second signals from a second device, the one or more second signals being associated with the user of the first device,

wherein generating the Turing score is further based on the received one or more second signals.

10. The method of claim 1, wherein generating the Turing score comprises comparing the received one or more signals to panel signal data to identify a relative deviation of the received one or more signals from the panel signal data.