Automated Personalized Message Composition System
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Solution Overview
Problem
Current electronic messaging technologies lack the ability to efficiently personalize messages, leading to slower message generation and less effective communication, as they do not account for individual user composition styles or specific occasions.
Innovation Solution
Implementing a system that uses machine learning to classify messages into categories based on features and determine user composition styles, allowing for the generation of personalized messages by modifying non-personalized ones with elements specific to the user's style for each category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional electronic messaging systems are used, then messages can be sent, but message generation is slow and lacks personalization
Solution Approach 1:
The system performs preliminary action by pre-processing and categorizing the user's message history before message generation is needed. It analyzes past messages to extract composition style characteristics and stores them for quick retrieval during message creation, enabling fast personalized message generation without real-time analysis delays
Solution Approach 2:
The system creates copies of the user's composition style by analyzing and replicating patterns from their historical messages. It generates multiple style variations that mirror the user's unique writing patterns, tone, and preferences, allowing personalized messages to be produced quickly by copying established style characteristics rather than creating them from scratch
2Reliability
If message personalization is implemented, then communication effectiveness improves, but system complexity increases
Solution Approach 1:
The system segments the message personalization process into distinct modular components: message categorization module, style analysis module, template selection module, and message generation module. Each component handles a specific aspect of personalization, making the overall system more manageable and maintainable while delivering comprehensive personalization
Solution Approach 2:
The system achieves universality by creating a multi-functional platform that can handle various message types (email, instant messages, text messages), multiple categories (personal, professional, casual, formal), and different composition styles all through a single unified system, reducing the need for separate specialized systems
3Measurement precision
If machine learning classification is used to categorize messages, then personalization accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary categorization of the user's message history using machine learning classification before message generation is needed. This pre-processing step builds trained classifiers and establishes message categories in advance, so that during actual message creation, the system can quickly retrieve pre-established categories without performing time-consuming real-time classification
Solution Approach 2:
The system implements dynamic adaptability where the message categories and style profiles are not static but can be updated and refined over time. The machine learning models continue to learn from new messages, adapting to changes in the user's writing style and message patterns, thereby maintaining high accuracy without requiring complete re-processing of historical data
Data Source
AI summary
Disclosed herein is an automated personalized message composition system, method and architecture. A composition style of a user is learned for each of a number of categories, such that each category has a corresponding composition style. The user's composition style determined for a given category can be used to personalize a non-personalized message for the user. A personalized message including elements of the user's composition style. The composition style elements from the user's composition style replacing a number of non-personalized elements in the non-personalized message.