Mobile Client Keyword Vector Learning for Privacy-Compliant Targeting

The patent introduces user profile data as an intermediary layer between personal information and advertising delivery. Instead of directly using personal information for targeting, the system processes personal data into anonymized user profiles that contain behavioral patterns and preferences. These profiles serve as mediators that enable targeted advertising while preventing direct exposure of sensitive personal information, thus resolving the contradiction between ad effectiveness and privacy protection.

The patent extracts and separates identifiable personal information from user profile data through anonymization processes. By removing directly identifiable elements while retaining behavioral patterns and preferences, the system maintains the utility of user profiles for targeted content delivery while reducing data security risks. The extracted anonymized data can be processed and stored without the same security concerns as raw personal information.

The patent transforms user data from identifiable personal information into anonymized profile parameters that retain targeting utility while meeting regulatory requirements. By changing the parameter representation from direct personal data to aggregated behavioral patterns, the system achieves both precise message targeting and compliance with privacy regulations such as the Graham-Leach-Bliley Act and EU data protection policies.