Cloud-Based IME Shared Vocabulary Typing History
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Solution Overview
Problem
Existing input method editors (IMEs) for East Asian languages in social communication tools do not adapt well to vocabulary used in social networks, leading to inefficient typing as they do not share or learn from community-specific terms and typing history, resulting in incorrect conversions and the need for constant personal dictionary updates.
Innovation Solution
A cloud-based system that shares vocabulary and typing history among users, utilizing a shared dictionary and web dictionaries to provide accurate prediction and conversion resources for IMEs, allowing users to access and update dictionaries collectively for improved typing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If each user maintains their own personal dictionary in IME, then the system can adapt to individual typing habits, but it prevents sharing of community-specific vocabulary and requires constant manual updates
Solution Approach 1:
The patent merges individual user dictionaries with a cloud-based shared dictionary, combining personal typing history with community vocabulary. This allows users to benefit from both their personal preferences and shared community knowledge without manual intervention, resolving the contradiction between personal adaptation and community sharing.
Solution Approach 2:
The system automatically synchronizes dictionaries between users and the cloud without requiring manual intervention. The IME service autonomously updates shared vocabulary based on community usage patterns, eliminating the need for users to manually add or update terms while maintaining both personal and community adaptability.
2Reliability
If IME uses standard dictionaries for conversion, then conversion accuracy for common words is maintained, but it fails to recognize new or community-specific terms
Solution Approach 1:
The patent implements a dynamic dictionary system that automatically evolves with community usage. Instead of using a static standard dictionary, the system continuously learns from new terms and updates its vocabulary model, enabling it to adapt to emerging slang, proper nouns, and community-specific terminology while maintaining accuracy for established words.
Solution Approach 2:
The system incorporates feedback loops where user corrections and usage patterns feed back into the shared dictionary. When users correct conversion errors or introduce new terms, the system learns from this feedback and updates the shared vocabulary, improving both conversion accuracy and adaptability to new language usage.
3Adaptability or versatility
If users manually add new terms to their personal vocabulary, then the IME can recognize new words, but it creates tedious work and does not propagate to other users
Solution Approach 1:
The system automatically performs vocabulary expansion by monitoring community usage patterns and automatically adding new terms to the shared dictionary without requiring user action. This self-service approach eliminates the tedious manual addition process while ensuring vocabulary expansion propagates to all users through cloud synchronization.
Solution Approach 2:
The shared dictionary serves multiple functions simultaneously: it stores community vocabulary, enables automatic term propagation, provides conversion references, and adapts to evolving language usage. This multi-functional approach consolidates what would otherwise require multiple separate operations into a single unified system.
Data Source
AI summary
An input method editor (IME) is associated with a local user. Memory stores local data and a processor, coupled to the memory, is configured to receive input from a local, first user, obtain shared data associated with at least a remote, second user from a remote server and generate prediction candidates and conversion candidates based on the input provided by the local, first user and correlation of the input and the obtained shared data.