Targeted Ad Transmission via User Score Segmentation
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Solution Overview
Problem
Traditional targeted advertising approaches are often too broad, leading to increased costs due to the difficulty in effectively utilizing and processing large volumes of consumer data to identify the most likely audience for targeted content.
Innovation Solution
A system that analyzes user metadata, including browsing history, purchase history, social media posts, and location information, using machine learning and affinity prediction logic to determine user scores and provide targeted advertisements to users with a high likelihood of interest in specific digital content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If broad advertising campaigns are used to reach potential consumers, then the coverage and reach of the campaign is improved, but the cost of the campaign increases significantly
Solution Approach 1:
The patent segments the broad consumer market into specific target audiences based on demographic, psychographic, and behavioral data. By dividing the market into distinct segments with shared characteristics, the system enables advertisers to focus resources on the most relevant groups rather than casting a wide net, thereby reducing overall campaign costs while maintaining effective coverage.
Solution Approach 2:
The patent applies local quality by tailoring advertising content and delivery to specific target audience segments rather than using a uniform approach. Each segment receives customized advertising based on their unique characteristics, which improves the efficiency of ad spend by concentrating resources on areas (specific segments) most likely to convert, rather than uniformly distributing resources across all potential consumers.
2Measurement precision
If additional consumer data is collected to improve targeting accuracy, then the precision of targeted advertising is improved, but the complexity of data processing increases
Solution Approach 1:
The patent introduces an intermediary layer (the computing system with machine learning models) that processes raw consumer data and transforms it into actionable targeting insights. This intermediary handles the complexity of data processing internally, allowing the advertising system to achieve high targeting accuracy without exposing the complexity to advertisers or end users. The intermediary converts complex data relationships into simple targeting decisions.
Solution Approach 2:
The patent creates simplified representations (copies) of complex consumer data through user profiles and segment definitions. Instead of processing raw, complex data directly for each advertising decision, the system uses these simplified copies that capture essential characteristics, thereby reducing processing complexity while maintaining targeting accuracy.
3Ease of operation
If traditional broad advertising campaigns are used, then implementation simplicity is maintained, but the cost per acquisition increases
Solution Approach 1:
The patent enables self-service through automated audience selection and campaign optimization. The system automatically identifies target audiences, selects appropriate ad placements, and optimizes delivery based on performance data, reducing the manual effort required for campaign management. This automation maintains ease of operation for advertisers while significantly improving cost efficiency through precise targeting.
4Loss of information
If large volumes of consumer data are processed manually, then data analysis thoroughness is improved, but processing time and resources increase
Solution Approach 1:
The patent replaces manual mechanical data processing with automated computational systems using machine learning algorithms. This substitution enables the system to process large volumes of consumer data thoroughly and efficiently, maintaining comprehensive analysis while dramatically reducing processing time through automated pattern recognition and analysis capabilities.
Data Source
AI summary
Machine readable instructions for providing targeted advertisements for a piece of digital content receive a set of user metadata for a group of users. The machine readable instructions select at least one user from the group of users. In addition, the machine readable instructions select a term from a category of terms related to the piece of digital content. Moreover, the machine readable instructions determine a first value corresponding to the at least one user. Also, the machine readable instructions determine a second for the group of users. In addition, the machine readable instructions determine a user score based at least in part on the first value and the second value. When the user score is within a particular range, the machine readable instructions provide an advertisement for the piece of digital content to an electronic device associated with the at least one user.