Automated Ad Targeting System with Privacy-Preserving User Segmentation

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Solution Overview

Problem

Existing online advertising campaigns lack real-time optimization and often rely on post-campaign data analysis, which can lead to inefficiencies in targeting the most effective user segments, as advertisers must manually select criteria for user classification without knowing the most effective categories beforehand.

Innovation Solution

An automated system that classifies users into categories based on online activities and dynamically adjusts advertising campaigns by correlating page link analysis histories with advertisement delivery histories to identify high-effectiveness categories, allowing for real-time optimization of ad targeting without prior sponsor-identified characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If advertisers manually select criteria for user classification based on intuition and past experiences, then the advertising campaign can be executed with simple targeting, but the effectiveness of ad targeting is reduced because the most effective user segments cannot be identified

Engineering Contradiction:

Improvemanual targeting setupVSAvoidad effectiveness measurement

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system implements automated feedback loops where ad performance data is continuously collected, analyzed, and used to refine user segmentation. Effectiveness metrics from delivered ads are fed back into the system to automatically identify high-response user categories, replacing manual intuition-based targeting with data-driven automated optimization

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The advertising system performs self-optimization by automatically analyzing ad performance data and identifying effective user segments without requiring continuous manual intervention. The system serves itself by autonomously adjusting targeting parameters based on measured effectiveness, freeing advertisers from manual optimization while improving precision

Inventive Principle:

Principle #25Self-service

2Quantity of substance

If post-campaign data analysis is used to evaluate ad effectiveness, then comprehensive data can be collected, but real-time optimization is lost and advertising budget efficiency is reduced

Engineering Contradiction:

Improvedata volumeVSAvoidoptimization delay

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The system performs preliminary actions by continuously collecting and pre-processing ad performance data during the campaign in real-time. User responses and engagement metrics are captured and analyzed as ads are delivered, enabling immediate optimization decisions rather than waiting for post-campaign analysis

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system maintains continuous data collection and analysis throughout the advertising campaign, ensuring uninterrupted optimization capability. The useful action of measuring effectiveness and adjusting targeting operates continuously rather than in discrete post-campaign batches, maximizing budget efficiency through real-time adaptations

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If automated systems analyze individual user browsing histories to optimize ad targeting, then ad effectiveness is improved through precise targeting, but user privacy is compromised through collection of detailed personal information

Engineering Contradiction:

Improvetargeting precisionVSAvoidprivacy intrusion

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system segments users into categories based on aggregated browsing patterns and behaviors rather than tracking individual user histories. By analyzing population-level data trends and grouping users with similar characteristics, the system achieves effective targeting without collecting or storing personally identifiable information

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary layer of aggregation and anonymization between individual user data and ad targeting decisions. Browsing histories are processed through privacy-preserving mechanisms that convert detailed personal information into generalized user category labels, allowing precise targeting while protecting individual privacy

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11526905B1Systems and methods for preserving privacy

Publication Date: 2022.12.13 GOOGLE LLC

AI summary

A system collects information from different sources regarding online activities of users and information regarding presentation of additional content. The user online activity information can include an indication of a web page visited (e.g., URL), a time when the web page was visited, and an anonymized identifier for a user device. Additional content service information can include an additional content identifier, a time the additional content was served, and an anonymized identifier for a user device to which the additional content was served. An optimizing engine uses this information to correlate additional content presentation to user online activity while preserving privacy of users. The system can use the correlation information to perform various statistical analyses, including determining the effects of presentation of particular additional content on user online activity, while preserving the privacy of individual users and preventing the information from being linked to a particular user.