Cluster Profile Generation for Anonymous Consumer Identification
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Solution Overview
Problem
Existing systems face challenges in identifying and profiling consumers who are not registered or are pseudonymous, especially in scenarios where device sharing and multiple device usage are prevalent, leading to reduced efficacy in targeting and advertising systems.
Innovation Solution
A system that uses deterministic and probabilistic methods to estimate consumer identities, employing unique identifiers and co-occurrence patterns to associate devices and software agents, and generates cluster profiles by intersecting facts from member profiles to provide richer, anonymous consumer profiles for advertising and content selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deterministic and probabilistic methods are used to estimate consumer identities across multiple devices, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments consumer identification into two distinct approaches: deterministic methods (using explicit user identifiers like logged-in accounts) and probabilistic methods (using device fingerprinting and behavioral patterns). This segmentation allows the system to apply the appropriate level of complexity based on the identification scenario, improving overall accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces cluster profiles as an intermediary layer between individual device profiles and advertising systems. These cluster profiles aggregate and synthesize information from multiple devices associated with the same consumer, acting as a mediator that simplifies the complex task of cross-device identification by providing a unified consumer view without requiring direct complex analysis of all individual device data.
2Loss of information
If cluster profiles are generated by intersecting facts from member profiles, then profile comprehensiveness is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing individual member profiles with their facts and attributes before cluster formation. When a cluster is created, the system can efficiently intersect facts from pre-processed member profiles rather than processing raw data in real-time. This preliminary preparation significantly reduces the data processing time required for cluster profile generation while maintaining comprehensive profile information.
3Productivity
If anonymous consumer profiling is implemented, then advertising targeting effectiveness is improved, but consumer privacy protection becomes more challenging
Solution Approach 1:
The patent creates simplified copies of consumer profiles at the cluster level that contain aggregated and synthesized information rather than raw personal data. These cluster profiles serve as anonymized representations that maintain advertising targeting effectiveness by preserving behavioral patterns and preferences while removing direct personal identifiers. The copying approach allows effective targeting while reducing privacy risks through information abstraction.
Data Source
AI summary
A system for generating a cluster profile is provided. The system may include a server and a database. The server may be configured to receive event information from a plurality of consumer devices. The database may store a plurality of member profiles. The server may be configured to retrieve the member profiles from the database and may determine a subset of member profiles to associate with a cluster; the server may calculate an intersection of the facts from the subset of member profiles and may generate a cluster profile based on the intersection of the facts from the subset of member profiles.