Intelligent Keyboard Context-Specific Conversation Suggestions
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Solution Overview
Problem
Current computing devices do not suggest entire phrases to start conversations based on the category of conversation and user intent, relying on word suggestions, auto-complete, and auto-correction but lacking in providing context-specific conversation starters.
Innovation Solution
An intelligent keyboard system that uses user-generated content, usage history, profile data, and artificial intelligence to provide context-specific conversation suggestions, ranking and tagging content for relevance and performance, and continuously updates based on user behavior and AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional word suggestions and auto-complete are used, then basic text input functionality is maintained, but context-specific conversation starters cannot be provided
Solution Approach 1:
The patent implements a nested architecture where multiple layers of content generation are integrated within the keyboard system. User-generated content, platform-generated content, and AI-generated content are nested within different modules that work together hierarchically, allowing complex conversation suggestion functionality to be built upon foundational text input capabilities without requiring complete system redesign
Solution Approach 2:
The keyboard system is designed to perform multiple functions: traditional text input, word suggestions, auto-complete, and context-specific conversation starters. The system adapts its functionality based on the detected conversation context, category, and user intent, making it a universal text input solution that handles both simple and complex communication needs
2Measurement precision
If comprehensive user data and AI models are integrated, then conversation suggestion relevance is improved, but system resource consumption increases
Solution Approach 1:
The system implements partial action by selectively generating content based on detected needs. Rather than continuously generating all types of content, the system activates specific content generation modules (user-generated, platform-generated, or AI-generated) only when appropriate for the current conversation context, reducing unnecessary processing while maintaining high relevance when needed
Solution Approach 2:
The system utilizes user-generated content from previous conversations and interactions as a self-service mechanism. By learning from and reusing patterns in user's own communication history, the system reduces dependency on resource-intensive AI generation for every suggestion, while still maintaining high relevance through personalized content
3Reliability
If real-time content ranking and tagging is implemented, then conversation suggestion quality is enhanced, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-ranking and tagging content from multiple sources before it is needed. User-generated content, platform-generated content, and AI-generated content are预先 processed, ranked by relevance, and tagged with metadata indicating their suitability for different conversation contexts. This pre-processing allows rapid retrieval and presentation of appropriate suggestions during actual conversation without real-time processing delays
Data Source
AI summary
An intelligent keyboard for mobile devices and other computer devices. This intelligent keyboard provides the user with suggestions of relevant words or phrases that can be used to start or continue a conversation on text message, email and/or various web applications. The intelligent keyboard provides conversation suggestions that are appropriate for given application contexts, categories, and conversation types. The intelligent keyboard uses user generated content from application users, usage history, profile data, dialogue data, platform generated content from the system managers/owners, content collected from various websites/integrations and natural language content generated by artificial intelligence. Content is ranked by preference, contextual suitability, and performance. Content is further tagged for application context. User behavior, user data and artificial intelligence models continuously update the system so that the relevance and performance of keyboard content is optimized.