Self-Learning Keyword Prioritization for Navigation Interaction
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Solution Overview
Problem
Current portable navigation devices lack interactive communication between users and systems, relying solely on voice commands for operation without providing dynamic information based on user interactions or external events.
Innovation Solution
A self-learning method for keyword-based human machine interaction that initializes and prioritizes keywords, updates their scores based on usage and relationships, and displays relevant information on a screen, allowing users to select keywords via voice or touch for enhanced interaction and information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice commands are used for operation, then hands are freed from physical controls, but interactive communication and dynamic information provision are not achieved
Solution Approach 1:
The system dynamically adjusts keyword priority scores based on user interaction patterns and usage frequency. Keywords that users interact with more frequently are automatically promoted to higher priority positions, enabling the system to adapt to individual user preferences and usage contexts over time, thereby achieving both hands-free operation and interactive communication capabilities
Solution Approach 2:
The system implements a feedback mechanism where user interactions with displayed keywords are monitored and used to update priority scores. The feedback loop continuously refines keyword ordering based on actual usage patterns, allowing the system to learn from user behavior and provide more relevant information proactively, thus resolving the contradiction between ease of operation and adaptability
2Adaptability or versatility
If keywords are displayed on screen for selection, then interactive communication is enabled, but device complexity increases
Solution Approach 1:
The system performs self-learning and self-adjustment by automatically calculating and updating keyword priority scores based on usage patterns. The self-service mechanism eliminates the need for manual configuration or complex user input to manage keyword ordering, reducing the operational complexity burden on users while maintaining high adaptability through automated learning
Solution Approach 2:
The system manages complexity by focusing on changing a single critical parameter - the priority score - rather than managing multiple complex parameters. By updating priority scores based on usage frequency and interaction patterns, the system achieves adaptive keyword ordering through a simple parameter modification approach, avoiding the need for complex multi-parameter management systems
3Productivity
If keyword priority is updated based on usage, then frequently needed information is prioritized, but calculation and processing time increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and recording user interactions with keywords in the background. Usage data is accumulated and processed periodically rather than in real-time for every display update, allowing the system to prepare and update priority scores ahead of time without impacting the immediate response time of information retrieval and display operations
Data Source
AI summary
A self-learning method for keyword based human machine interaction is disclosed. At least one of a plurality of keywords predetermined in a database is selected and a priority score of the selected keyword is updated and a weighted factor is generated for the selected keyword. A weighted score and the weighted factor of the selected keyword are transmitted to the keywords related to the selected keyword. The selected keyword is pushed to a keyword buffer and linkage strengths between the keywords in the keyword buffer are strengthened. When a keyword has been stored in the keyword buffer for over a predetermined reset time period, a reset operation is performed to remove the keyword from the keyword buffer.