User Targeting via Machine Learning Configuration Profiles
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Solution Overview
Problem
Existing advertising methods lack personalization and often target undefined groups of users, leading to low efficiency and user annoyance, as well as potential legal risks due to profiling.
Innovation Solution
A system and method that use machine learning models to identify target users by analyzing configuration profiles of computing systems and online content, ensuring that advertising is directed towards users who meet specific search requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional advertising methods are used to reach undefined groups of users, then advertising coverage is broad, but advertising efficiency is low and user annoyance increases
Solution Approach 1:
The patent segments the undefined user group into specific target user groups based on configuration profile characteristics. By dividing the broad user base into segments with similar hardware/software configurations, the system enables targeted advertising that reaches only relevant users, thereby improving advertising efficiency while reducing annoyance to users who are not part of the target group.
Solution Approach 2:
The system performs preliminary analysis of user configuration profiles before delivering advertising content. By pre-processing and categorizing user data according to their computing system characteristics, the system prepares targeted advertising campaigns in advance, ensuring that only relevant advertisements are presented to each user segment, thus improving efficiency and reducing unnecessary user annoyance.
2Adaptability or versatility
If configuration profiles are analyzed to identify target users, then advertising personalization is improved, but system complexity increases
Solution Approach 1:
The patent creates simplified copies or representations of user configuration profiles that capture essential characteristics without requiring complete analysis of all system parameters. By working with these condensed profile representations, the system achieves effective advertising personalization while avoiding the complexity of processing entire configuration datasets.
Solution Approach 2:
The system develops a universal advertising delivery mechanism that can handle multiple types of configuration profiles and user characteristics through a single integrated approach. This multi-functional system reduces overall complexity by providing a unified method for personalization across different user segments, rather than requiring separate specialized systems for each type of analysis.
3Measurement precision
If machine learning models are used to analyze user profiles, then targeting precision is improved, but computational resources required increase
Solution Approach 1:
The patent extracts only the most relevant features from user configuration profiles that are necessary for accurate targeting, rather than processing all available data. By selectively taking out and analyzing only the critical characteristics needed for advertising purposes, the system maintains high targeting precision while significantly reducing the computational resources required for machine learning analysis.
Data Source
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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.