Email Data Deobfuscation Layer for Accurate Open Event Tracking
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Solution Overview
Problem
Email service providers obfuscate event data, making it difficult for email distributors to accurately track the performance of email communication networks and how successful email campaigns are at reaching a target audience, and the ability of email distributors to accurately track email campaigns to determine network performance and engagement metrics.
Innovation Solution
A deobfuscation layer that uses machine learning to identify and correct erroneous events data by comparing rates to historical baselines and predicting accurate values for obfuscated data, restoring the flow of reliable event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If email service providers obfuscate event data to protect their platform, then data security and privacy are improved, but measurement precision and reliability of event tracking deteriorate
Solution Approach 1:
The patent introduces an intermediary deobfuscation layer that sits between the email service provider and the email distributor. This intermediary component receives obfuscated event data, applies machine learning models to deobfuscate it, and returns corrected data. The intermediary preserves the protective function while restoring data accuracy for tracking purposes.
Solution Approach 2:
The patent replaces traditional mechanical data transmission with an intelligent system using machine learning models. Instead of directly passing raw event data or completely blocked data, the system uses ML algorithms to analyze patterns, predict true values, and deobfuscate data intelligently, substituting brute-force data access with sophisticated computational analysis.
2Stability of the object's composition
If email service providers restrict event data flow to control network performance, then network stability is improved, but productivity and campaign optimization capability deteriorate
Solution Approach 1:
The patent implements preliminary action by training machine learning models in advance using historical event data. These pre-trained models are ready to quickly deobfuscate incoming event data without requiring real-time complex computations. This preliminary preparation enables fast data processing while maintaining network stability controls.
Solution Approach 2:
The patent establishes a feedback loop where deobfuscated event data is used to improve future email campaign performance. The system continuously learns from corrected data about actual user engagement, delivery rates, and network performance, then uses this feedback to optimize future campaigns, creating a virtuous cycle of improvement.
3Measurement precision
If email service providers obfuscate event data to prevent tracking, then privacy protection is improved, but measurement precision and audience engagement understanding deteriorate
Solution Approach 1:
The patent replaces direct observation of user behavior with machine learning inference. Instead of requiring access to raw, potentially privacy-intrusive data, the system uses ML models to infer engagement metrics and audience behavior patterns from obfuscated data, maintaining privacy protections while recovering measurement precision through intelligent analysis.
Data Source
AI summary
The subject technology identifies obfuscated email events received from one or more internet service providers (ISPs). The data deobfuscation layer may identify email messages including obfuscated open events and locations by monitoring the open rates of email messages received by different operating systems, ISPs, and/or device types. The data deobfuscation layer may determine accurate campaign level metrics and/or user open probabilities for batches of email messages having obfuscated events. For example, one or more machine learning models may predict an email open rate for one or more email campaigns and identify the users having the highest probability of generating a true open event. The data deobfuscation layer may be used to improve the performance of email communication networks and/or increase engagement metrics for media campaigns.