Probabilistic ID Graphs for Cross-Device Audience Targeting
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Solution Overview
Problem
Existing techniques for identifying users across multiple channels and devices are limited in effectively reaching target audiences for messaging campaigns, as they struggle to combine user data from diverse sources without violating privacy laws or expectations.
Innovation Solution
A system for extending audience reach by linking user attribute records through a data management platform that ingests, classifies, and stores user data from various sources, using ID graphs and probabilistic ID linking to create a unified view of users across devices, enabling targeted messaging campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user data from multiple devices and channels is combined to identify users across platforms, then audience reach and campaign effectiveness are improved, but privacy violations and data security risks increase
Solution Approach 1:
The patent introduces a probabilistic ID linking system that acts as an intermediary between user data from multiple devices and the advertising campaign system. Instead of directly combining raw user data which would violate privacy, the system uses probabilistic matching algorithms to link device identifiers to user profiles through intermediate steps that preserve anonymity. The system matches devices to users based on behavioral patterns, contextual signals, and demographic probabilities rather than direct identification, thus extending audience reach while maintaining privacy constraints.
2Productivity
If traditional user identification methods are used across multiple devices, then user tracking is simplified, but the ability to reach users on unknown devices is limited
Solution Approach 1:
The patent transforms the approach to user identification by changing the parameters used for matching. Instead of relying on fixed, deterministic identifiers like logged-in user accounts, the system employs probabilistic parameters including behavioral patterns, contextual signals, temporal patterns, and demographic probabilities. This parameter transformation enables the system to adapt to unknown devices and contexts while maintaining tracking efficiency through automated probabilistic matching algorithms that can operate without direct user identification.
3Measurement precision
If comprehensive user data collection is implemented across all channels, then targeting precision is improved, but data management complexity increases
Solution Approach 1:
The patent segments the comprehensive user data collection process into distinct modular components: device identification layer, probabilistic matching layer, profile construction layer, and campaign targeting layer. Each layer processes specific types of data independently and passes refined outputs to the next layer. This segmentation allows the system to collect comprehensive data across all channels while managing complexity through modular architecture, where each component can be developed, maintained, and optimized independently.
Data Source
AI summary
A server receives incoming data records comprising an ID value and respective user attributes corresponding to a user activity that originates at a user device. Characteristics of the activity and/or characteristics of the user device are considered in order to assign a probabilistic confidence value, which is in turn used to generate links from an incoming data record to other previously-received data records, and in so doing, generates a probabilistic link between one set of user attributes from the incoming data record and another set of user attributes from previously-received data records. A messaging campaign specification that describes target user attributes is used to identify a matching set of target audience member records. The determination of a match or not is based on the probabilistic confidence value and a threshold can be varied to extend audience reach. The identified set of target audience member records are transmitted over a network.