US20250111409
2025-04-03
Physics
G06Q30/0269
The multi-stage content analysis system is designed to enhance online advertising by analyzing user communications to select personalized promotions. It operates in two main stages: the first stage evaluates a user's single communication to identify potential targets for promotions, and the second stage examines the user's communication history to construct a detailed profile. This profile is then used to select and present a relevant promotion. The system aims to improve promotional response rates significantly by leveraging detailed profile tags rather than relying on traditional demographic data or cookies.
Current internet advertising systems struggle with low click-through rates due to limited user information, often below 0.1%. Traditional tracking methods like cookies are less effective with the diverse range of devices users employ. While membership sites collect some user data, it is often insufficient or inaccurate. A rich source of user information exists in their electronic communication history, which can indicate personal interests and preferences. Existing systems have not effectively utilized this data, highlighting the need for a more sophisticated approach, such as a multi-stage analysis system.
The invention comprises three major components: a first analysis stage that identifies potential promotion targets from individual communications, a second analysis stage that builds user profiles from communication histories, and a promotion selector that uses these profiles to choose suitable promotions. The user profiles are enriched with detailed tags that reflect affiliations, beliefs, or life cycle stages, such as religious or political affiliations or stages like student or retiree. These tags provide a more nuanced understanding of users than traditional demographic categories.
To generate user profile tags, the system compares words and phrases from communication histories against a database of keywords associated with potential tags. It calculates frequency counts for these words to determine tag relevance scores. A naïve Bayes classifier may be employed to map communication histories into profile tags based on these scores. Additionally, a third analysis stage may refine these tags by accessing external databases if the user remains a potential target after the second stage.
Machine learning enhances the system's performance across all stages and the promotion selector. A machine learning engine uses training data on user responses to promotions to refine tag associations and improve response rates. This includes adjusting parameters within classifiers like the naïve Bayes model. By continuously learning from user interactions, the system adapts its profiling and promotion selection processes for better accuracy and effectiveness.