Context-Based Operation Determination for Vehicle Command Recognition
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Solution Overview
Problem
Existing vehicle systems require unnecessary and additional user input for command recognition and operation, leading to inefficiencies in providing recommended operations, especially in dynamic contexts such as changing user preferences and environments.
Innovation Solution
A context-based operation determination apparatus that utilizes a processor to analyze context information, including time and location, to determine a recommended operation by comparing data from a context awareness database and selection history, generating a recommendation index, and verifying the operation through a selection history database, ultimately providing a final operation with reduced user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional vehicle systems require additional user input for command recognition, then command accuracy can be maintained, but user convenience and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing context information (location, time, weather, traffic) and building user preference profiles in advance. This allows the system to predict user intentions before explicit commands are given, reducing the need for additional user input while maintaining accurate command recognition through pre-established context understanding
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user responses to recommended operations and adjusting context analysis accordingly. User feedback on recommended operations (acceptance or rejection) is fed back into the system to refine context interpretation and improve future recommendations, enabling accurate command recognition with minimal user input
2Reliability
If the system provides detailed recommended operations, then operational appropriateness improves, but system complexity increases
Solution Approach 1:
The system segments the complex operation determination process into distinct modules: context information collection, context analysis, recommendation generation, verification against user profiles, and output presentation. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while still providing comprehensive and appropriate operational recommendations
Solution Approach 2:
The system implements a universal context analysis framework that handles multiple types of operations (navigation, climate control, entertainment, communication) through a single integrated architecture. The same context information and analysis methods are applied across different operation types, reducing redundancy and simplifying system design while maintaining operational appropriateness across diverse functions
3Measurement precision
If the system processes extensive context information, then recommendation quality improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing by pre-collecting and organizing context information (location, time, weather, traffic) and pre-building user preference profiles during off-peak periods. This preliminary preparation allows the system to quickly retrieve and analyze relevant data when recommendations are needed, improving recommendation quality without significantly increasing real-time processing time
Solution Approach 2:
The system applies local quality by focusing context analysis on only the most relevant factors for each specific operation type and user situation. Rather than processing all available context information uniformly, the system selectively analyzes pertinent data points based on the current operational context and user profile, maintaining high recommendation quality while reducing overall processing time
Data Source
AI summary
A context-based operation determination apparatus includes a context information obtainer configured to obtain context information; a context awareness database constructed using data related to a previously performed action; a processor configured to determine a recommendation index that corresponds to a recommended operation and the recommended operation using the context information, and data obtained from the context awareness database; and a selection history database constructed using the final operation, the recommendation index, and an actual operation in a state where the final operation is presented, where the processor verifies the recommended operation using the selection history database.