Context-Specific Text Prediction Using User Message Profiles
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Solution Overview
Problem
Existing text prediction technologies in electronic messaging devices rely on dictionaries or language statistics, which do not account for individual user preferences and message histories, leading to inefficient message composition due to the need for multiple key presses and lack of personalized suggestions.
Innovation Solution
A user message profile is maintained, including usage data from past communications, personal information, and file system data, to generate customized text predictions based on the user's history with specific contacts, allowing for on-the-fly prediction of words and phrases while composing messages or replies, using a data structure like a tree or dictionary to provide ordered lists of predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dictionary-based or statistical text prediction is used, then text input speed is improved, but personalization and context-specific accuracy deteriorate
Solution Approach 1:
The system pre-collects and stores usage data from the user's message history, contact list, scheduler, and file system before text prediction is needed. This preliminary action creates a personalized user message profile that enables accurate, context-specific predictions without sacrificing input speed.
Solution Approach 2:
The system continuously updates the user message profile based on actual user messaging patterns and feedback. By analyzing the user's real communication behavior with specific contacts and updating the profile dynamically, the system improves prediction accuracy over time while maintaining personalization.
2Loss of time
If generic text prediction is used, then message composition time is reduced, but context-specific accuracy deteriorates
Solution Approach 1:
Instead of using a single generic prediction model, the system creates localized prediction profiles for each contact based on the user's specific communication patterns with that contact. This local quality approach ensures predictions are accurate to the specific context and recipient while maintaining fast composition times.
Solution Approach 2:
The system dynamically changes prediction parameters based on the user message profile, including contact-specific word frequencies, message patterns, and contextual information from the scheduler and file system. These parameter changes enable context-specific accuracy without sacrificing overall composition speed.
3Measurement precision
If personalized text prediction is implemented, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses a universal user message profile data structure that serves multiple functions: storing contact information, message history, scheduler data, and file system references. This multi-functional approach enables personalized predictions without requiring separate complex systems for each data type.
Solution Approach 2:
The system automatically collects, processes, and updates the user message profile without requiring manual configuration or intervention. It self-adapts to the user's messaging patterns by continuously analyzing communication data, which reduces the complexity burden on the user while maintaining high prediction accuracy.
Data Source
AI summary
A method for an electronic messager to predict text while composing a reply message, including receiving an incoming message for a user, parsing the incoming message to identify questions, phone numbers and special phrases therein, and presenting, by an electronic messager having a memory and a processor, possible responses that the user may choose from while the user replies to the incoming message, based on the questions, phone numbers and special phrases identified by the parsing.