Probabilistic Device Matching for Cross-User Advertising Profiles
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Solution Overview
Problem
Existing online advertising technologies struggle to effectively target users across multiple devices due to limitations in third-party cookie usage and device-specific user profiles, which restricts the ability to leverage holistic user behavior and demographic data.
Innovation Solution
A system and method for associating multiple Internet-enabled devices with a common user profile by analyzing source IP addresses and unique identifiers, calculating the probability of device ownership, and linking devices through probabilistic algorithms to create a unified user profile for targeted advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If third-party cookies are used for user identification across websites, then advertising targeting capability is improved, but device complexity and privacy concerns increase
Solution Approach 1:
The patent introduces an ad network server as an intermediary that centralizes cookie management. Instead of requiring complex browser configurations for third-party cookies, the ad network server acts as a mediator that receives requests from publisher servers, manages cookies centrally, and returns targeted ads. This simplifies the client-side complexity while maintaining advertising targeting capability.
2Adaptability or versatility
If device-specific user profiles are maintained, then device-level advertising is enabled, but user identity matching across devices deteriorates
Solution Approach 1:
The patent merges device-specific user profiles by introducing a user ID system that links multiple devices to a common user identity. The ad network server combines behavioral data from multiple devices associated with the same user ID, creating a holistic user profile that aggregates information across devices while maintaining device-level advertising capabilities.
3Measurement precision
If probabilistic device matching is implemented, then user identification accuracy across devices is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial matching by comparing only key identifying elements (user IDs, device identifiers, behavioral patterns) rather than performing exhaustive analysis of all device attributes. The probabilistic matching algorithm evaluates multiple possible matches and selects the most likely correspondence, performing sufficient analysis to achieve accurate identification without unnecessary computational overhead.
Data Source
AI summary
Systems and methods are disclosed for associating a plurality of Internet-enabled devices with a common user profile for targeting Internet content or advertising. One method includes: receiving, from a plurality of Internet-enabled devices, a plurality of requests for electronic content or advertising; extracting, from each of the plurality of requests, a source IP address and a unique identifier associated with the respective Internet-enabled device; for each source IP address for which requests were received over a predetermined time period from a number of Internet-enabled devices below a threshold number of devices, identifying each possible pair of devices from which requests were received; and for each possible pair of devices, calculating a probability that the pair of devices are owned or operated by a common user.