Personalized Email Strategy Generation With Dynamic Feature Hierarchy
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Solution Overview
Problem
Existing systems for personalized email generation struggle with scalability, inefficiency in processing large volumes of multi-dimensional data, and inability to dynamically adapt to real-time changes in recipient behavior, leading to generic content and reduced engagement rates.
Innovation Solution
A system that utilizes a dynamic feature hierarchy and continuously fine-tuned machine learning model to process multi-dimensional data, prioritize relevant features, and generate personalized email strategies in real-time, considering factors like engagement potential, relevance, and saliency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional mail merge techniques or rudimentary templating systems are used, then device complexity is reduced, but manufacturing precision (email personalization quality) deteriorates
Solution Approach 1:
The patent replaces traditional mechanical mail merge systems with an AI-based generative language model that uses natural language processing to create personalized emails. The system processes multi-dimensional data (account data, recipient data, seller data) through machine learning models to generate customized content, substituting the mechanical templating approach with intelligent content generation that maintains both ease of use and high personalization quality
2Manufacturing precision
If manual crafting of individualized content is performed, then manufacturing precision (email personalization quality) is improved, but productivity (email generation speed) deteriorates
Solution Approach 1:
The patent substitutes manual content crafting with an automated AI system that processes multi-dimensional data through machine learning models. The generative language model analyzes account data, recipient data, and seller data to automatically generate personalized email content, eliminating the need for manual intervention while maintaining high personalization quality and significantly increasing generation speed
Solution Approach 2:
The system dynamically adjusts content generation parameters based on the analyzed data. The machine learning model modifies email content, tone, structure, and timing parameters according to recipient characteristics, account data, and seller information, enabling automated high-quality personalization without manual effort
3Ease of manufacture
If basic templating systems are used, then device complexity is reduced, but adaptability (email customization capability) deteriorates
Solution Approach 1:
The patent implements dynamic parameter adjustment where the AI model modifies multiple email parameters (content, tone, structure, timing) based on real-time analysis of multi-dimensional data. The system adapts to different scenarios, recipients, and contexts automatically, providing high customization capability without increasing operational complexity
Solution Approach 2:
The system replaces rigid mechanical templates with a flexible AI-based generation system that can adapt to any scenario. The generative language model processes complex data relationships and generates customized content dynamically, enabling versatility across different email types and recipient profiles while maintaining ease of use
4Adaptability or versatility
If real-time data processing is performed, then adaptability (email relevance) is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary data processing and feature extraction before email generation. The system pre-processes multi-dimensional data, extracts relevant features, and prepares them for the machine learning model, enabling efficient real-time email generation with high relevance without excessive processing delays
Data Source
AI summary
A method for efficiently generating a plurality of personalized email strategies and corresponding personalized emails for campaign recipients is disclosed. Multi-dimensional data comprising account data, recipient data, and seller data is received from one or more data sources. The received multi-dimensional data is processed to extract relevant features. A dynamic feature hierarchy is generated using the extracted features. A pre-trained machine learning model is fine-tuned using the dynamic feature hierarchy to generate email strategies, wherein model parameters are adjusted based on the hierarchy during fine-tuning. A plurality of email strategies is generated for each recipient by applying the fine-tuned model's recommendations. An email strategy is selected from the plurality of strategies based on one or more factors. A personalized email corresponding to the selected email strategy is generated. The personalized email and the selected email strategy used to generate it are displayed to a user.