Electronic Message Formatting via Machine Learning Preferences
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Solution Overview
Problem
Electronic messages often fail to reflect the recipient's formatting preferences, leading to inconsistent and poorly formatted messages due to varying personal preferences among recipients.
Innovation Solution
A networked environment with a messaging application, processing application, recommendation application, and formatting application that automatically reformats messages based on the recipient's formatting preferences using a sequence-to-sequence model and attributes like relationship, message type, and frequency, allowing for personalized formatting recommendations and reformatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If messages are formatted according to sender's preferences, then sender's formatting consistency is improved, but recipient's formatting preferences are not satisfied
Solution Approach 1:
The system pre-processes messages by extracting formatting elements and generating tokens before transmission. The formatting application analyzes message attributes and applies appropriate formatting based on recipient preferences in advance, so the formatted message is ready upon delivery without requiring real-time adjustment.
Solution Approach 2:
The patent introduces a formatting application as an intermediary component between the messaging system and the recipient. This intermediary analyzes message attributes, determines appropriate formatting based on recipient preferences, and transforms the message content accordingly, acting as a mediator that adapts sender's message to recipient's preferences.
2Adaptability or versatility
If manual formatting adjustment is performed for each recipient, then recipient's formatting preferences are satisfied, but time and effort are significantly increased
Solution Approach 1:
The formatting application operates autonomously to analyze message attributes, determine recipient preferences, and apply appropriate formatting without requiring manual intervention from the sender. The system serves itself by automatically detecting formatting elements, generating tokens, and transforming messages according to learned recipient preferences.
Solution Approach 2:
The patent replaces the manual mechanical process of formatting adjustment with an automated machine learning-based system. The formatting application uses trained models to automatically analyze messages and apply formatting, substituting human manual effort with computational processes that operate rapidly and consistently.
3Stability of the object's composition
If automated formatting is implemented, then formatting consistency is improved, but system complexity increases
Solution Approach 1:
The patent segments the formatting process into distinct functional components: a formatting application that extracts formatting elements, a token generation component that creates structured representations, and a transformation component that applies formatting based on recipient preferences. This segmentation allows each component to perform its specific function independently, managing complexity through modular design.
Data Source
AI summary
Disclosed herein are examples of systems and methods for formatting electronic messages using machine learning. An electronic message can be obtained, and a processed message can be generated based at least in part on the electronic message. At least one attribute for the processed message can be determined. A formatting specification can be generated based at least in part on the at least one attribute. A reformatted message can be generated based at least in part on the formatting specification.