Personalized Information Service Using Vehicle Context Data
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Solution Overview
Problem
Existing methods for providing personalized information based on user interest on social network services do not effectively utilize real-time vehicle operation data to identify and serve relevant information during a user's time of interest.
Innovation Solution
An apparatus and method that includes a vehicle terminal and a communication device to collect and analyze user operation data, assigning weight values to context information based on gaze and operation data to determine the user's field of interest, and providing personalized information by matching this data with stored context information, using algorithms like Euclidean distance and K-means clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized information is provided based on general user interest data from social network services, then information can be provided to users, but the information lacks timeliness and relevance to real-time user needs during vehicle travel
Solution Approach 1:
The system performs preliminary actions by collecting and storing context information (user operation data, gaze information, vehicle status) before the user actually needs information. This accumulated data is then used to provide timely and relevant personalized information when the user expresses interest during vehicle travel, resolving the contradiction between general information provision and real-time relevance.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user operations, gaze direction, and vehicle status to detect real-time interest patterns. This feedback loop enables the system to adjust and provide personalized information dynamically based on current user needs, improving both relevance and timeliness simultaneously.
2Measurement precision
If the system collects and analyzes multiple types of real-time data (gaze information, operation data, vehicle status), then the accuracy of interest identification improves, but the system complexity increases
Solution Approach 1:
The system segments the complex data collection and analysis task into distinct functional modules: gaze information collection module, operation data collection module, vehicle status monitoring module, and interest identification module. Each module handles specific data types independently, making the overall complex system more manageable and maintainable while achieving high measurement precision through integrated analysis.
Solution Approach 2:
The system employs a universal data processing framework that can handle multiple types of data (gaze, operation, vehicle status) through a common analysis architecture. This multi-functional approach allows the system to maintain high accuracy in interest identification across different data sources while avoiding the need for separate specialized systems for each data type, thus managing complexity effectively.
3Productivity
If the system processes and analyzes real-time vehicle operation data and context information, then personalized information can be provided at the time of interest, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction in advance, pre-processing vehicle operation data, gaze information, and context data to identify interest patterns before the user actually needs information. This preliminary action reduces the computational burden during real-time information provision, improving productivity while managing energy consumption by performing heavy computations offline or in advance.
Solution Approach 2:
The system applies partial action by processing only the most relevant features and data points necessary for interest identification, rather than analyzing all available data in full detail. This selective processing approach maintains adequate productivity for timely information provision while significantly reducing computational resource consumption and energy usage by focusing calculations on critical data elements.
Data Source
AI summary
An apparatus for servicing personalized information based on user interest includes: a communication device that performs communication with a vehicle terminal of a vehicle; storage that stores context information for respective fields of interest of users; and a processor that extracts a field of interest of the user of the vehicle based on similarity obtained by matching context information received from the vehicle terminal at time of interest and the stored context information for the respective fields of interest. In particular, the processor provides a cluster of similar interest information to the vehicle terminal, based on the extracted field of interest.