Machine Learning User Targeting via Configuration Profiles
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Solution Overview
Problem
Existing advertising methods lack personalization, often annoying users who do not need the advertised products, leading to low efficiency and legal risks due to intrusive profiling, and fail to accurately target users' actual demands.
Innovation Solution
A system and method that uses machine learning to identify target users by analyzing configuration profiles and online content of computing systems and social networking sites to match specific search requirements, providing personalized notifications about relevant products or services based on actual user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional advertising methods are used to reach undefined groups of people, then advertising coverage is broad, but advertising efficiency is low and user satisfaction deteriorates due to lack of personalization and intrusive profiling
Solution Approach 1:
The patent segments the undefined user group into specific target user groups based on configuration profile characteristics. Instead of advertising to everyone, the system divides users into segments with similar hardware/software configurations and targets them with relevant advertising, thereby improving efficiency while maintaining user satisfaction through personalization.
Solution Approach 2:
The patent changes the parameters used for advertising targeting from probabilistic user profiles to actual configuration profile parameters (hardware specs, software versions, etc.). This parameter change enables precise matching of advertising to user needs based on their actual system characteristics rather than guessed preferences.
2Measurement precision
If configuration profiles and online content are analyzed using machine learning to accurately identify target users, then advertising personalization is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by collecting and storing configuration profiles and online content data before the actual advertising targeting process. Configuration profiles including hardware specifications, software installations, and user-generated online content are gathered in advance and structured for efficient machine learning analysis, reducing complexity during the actual identification phase.
3Measurement precision
If machine learning models analyze configuration profiles and online content to identify target users, then advertising relevance to actual user needs is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent performs preliminary analysis by pre-processing configuration profiles and online content data, structuring it in ways that optimize machine learning model performance. Configuration profiles are organized with key characteristics highlighted, and online content is pre-tagged and categorized, enabling faster and more accurate demand matching during actual advertising campaigns.
Data Source
AI summary
System and methods are provided for searching users that meet one or more search requirements. Configuration profiles are obtained of computing systems operated by sample users that have at least one determined characteristic. A machine learning model is generated that associates the determined characteristic of the sample users with the configuration profiles of the computing systems of the sample users. Identifying at least one target user that matches the at least one determined characteristic specified in a search query based on analysis of the configuration profile of the computing system of said target user by the machine learning model.