Vehicle Function Recommendation via Conversation Nuance Analysis
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Solution Overview
Problem
Conventional conversation-based recommendation systems for vehicles lack accuracy in determining user intentions, often recommending tasks that do not match the user's actual intentions due to reliance on last or most frequent keywords.
Innovation Solution
An apparatus and method that analyze user conversations to extract intention information, nuance, and keywords, using a processor to determine and recommend tasks based on these factors, enhancing the accuracy of matching user intentions with vehicle functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword-based methods are used to determine user intention, then the system complexity is low, but the accuracy of intention recognition deteriorates
Solution Approach 1:
The patent segments the conversation analysis into multiple independent components: intention information extraction, nuance information extraction, and keyword extraction. Each component processes specific aspects of the conversation separately, then the results are integrated to determine the final user intention. This segmentation allows for more accurate analysis without requiring a monolithic complex system.
Solution Approach 2:
The patent adds nuance information as an additional dimension to the traditional keyword-based analysis. By incorporating sentiment polarity (positive, neutral, negative) as a separate analytical dimension alongside intention and keywords, the system achieves more accurate intention recognition without proportionally increasing overall complexity.
2Reliability
If only the last or most frequent keyword is used to predict user action, then the processing speed is fast, but the reliability of task recommendation deteriorates
Solution Approach 1:
The system performs preliminary extraction of intention information, nuance information, and keywords from the conversation before final task recommendation. By preparing and storing these extracted elements in advance during conversation processing, the system can quickly retrieve and integrate them for accurate task recommendation without requiring time-consuming analysis at the recommendation stage.
Solution Approach 2:
The patent incorporates feedback mechanisms where the extracted intention information, nuance information, and keywords are continuously refined based on their interrelationships. The system uses the extracted elements to validate and adjust task recommendations, ensuring higher reliability while maintaining efficient processing through iterative refinement rather than exhaustive analysis.
Data Source
AI summary
An apparatus for recommending a function of a vehicle includes an input module, a memory, an output module, and a processor. The processor obtains intention information indicating an action associated with each of a plurality of sentences, nuance information indicating a positive, neutral, or negative meaning included in each of the plurality of sentences, and one or more keywords for executing a function associated with the intention information among a plurality of functions embedded in the vehicle by analyzing each of the plurality included in the conversation, determines a task, associated with the function, to be recommended to at least some of the plurality of users, based on the intention information, the nuance information, and the one or more keywords, and outputs a message of recommending the task using the output module, when the end of the conversation is recognized.