Cross-Device User Profile Matching via IP Address Probability
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Solution Overview
Problem
Existing online advertising technologies struggle to effectively target users across multiple devices due to limitations in cookie-based identification methods, which are unable to leverage browsing history and behavioral data when users access the internet from various devices, leading to incomplete user profiles and reduced advertising effectiveness.
Innovation Solution
A system and method for associating multiple internet-enabled devices with a common user profile by receiving requests from devices, extracting source IP addresses and unique identifiers, and calculating the probability that devices are owned or operated by the same user, allowing for the creation of holistic user profiles across devices for targeted advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cookie-based identification methods are used to track users across websites, then advertising targeting capability is improved, but user identification accuracy across multiple devices deteriorates
Solution Approach 1:
The patent introduces IP address as an intermediary identifier to bridge the gap between cookie-based tracking and cross-device identification. By using IP address as a common denominator that can be extracted and compared across different devices, the system enables linking of multiple devices to a single user profile without relying solely on cookies that are device-specific.
Solution Approach 2:
The patent makes the user profile universal across multiple devices by allowing a single profile to be associated with multiple device identifiers (cookies, IP addresses, device IDs). This multi-functional approach enables the advertising system to track and target users regardless of which device they use, transforming the limitation of device-specific cookies into a strength through cross-device profiling.
2Productivity
If third party cookies are allowed across all websites to enable behavioral advertising, then advertising effectiveness is improved, but device complexity and data management burden increase
Solution Approach 1:
The patent extracts the essential identifying information (IP address, device identifiers) from complex cookie data structures and uses only these extracted elements for cross-device matching. This reduces the complexity of data management by focusing on key identifiers rather than managing entire cookie datasets across multiple devices.
Solution Approach 2:
The patent segments the user identification process into distinct components: device-level identification (cookies), network-level identification (IP addresses), and user-level profiling. This segmentation allows each layer to function independently, reducing the overall system complexity while maintaining advertising effectiveness.
3Adaptability or versatility
If users access the internet from multiple devices, then user behavior diversity is improved, but completeness of user profile deteriorates
Solution Approach 1:
The patent merges data from multiple devices into a single unified user profile by identifying common identifiers (IP addresses, device relationships) across devices. This combining process reconstructs the complete user behavior picture by aggregating information from all devices used by a user, preventing information loss despite multi-device usage.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors and updates device-to-profile associations based on observed browsing patterns and identifier correlations. This feedback loop ensures that as users switch between devices, the system adapts and maintains accurate profile completeness by learning from ongoing device usage patterns.
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.