Vehicle Personalization via Swarm Intelligence Association Rules
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Solution Overview
Problem
Existing methods for personalizing motor vehicles are limited in transferring comfort settings between different vehicle types, as settings are typically specific to the interior design of each vehicle type, making it difficult for new users to easily personalize vehicles of a different type.
Innovation Solution
A method utilizing swarm intelligence to collect and analyze user data from multiple users of the same vehicle type, forming association rules between personal data and configuration data, allowing for the inference and adjustment of settings for new users, including user-specific correction data for personal preferences, enabling seamless personalization across different vehicle types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If comfort settings are transferred between motor vehicles, then personalization for new users is improved, but transferability is limited to the same vehicle type due to different interior designs
Solution Approach 1:
The patent segments the personalization problem by creating vehicle-type-specific association rules. Instead of attempting universal transferability across all vehicle types, the system divides users into groups based on their vehicle type and personal characteristics, forming separate association rules for each segment. This allows accurate personalization within each vehicle type while acknowledging the limitations of cross-type transferability.
Solution Approach 2:
The system performs preliminary actions by collecting personal characteristics and comfort settings from multiple users of the same vehicle type in advance. These data are used to pre-form association rules that can automatically suggest appropriate settings for new users. This preliminary data collection and rule formation enables rapid personalization without requiring new users to manually adjust all settings.
2Extent of automation
If association rules are formed from collected user data, then automatic personalization is improved, but data privacy and security requirements increase
Solution Approach 1:
The patent applies local quality by processing and storing association rules locally within the motor vehicle rather than maintaining centralized databases of user data. The server device receives anonymized personal characteristics to form association rules, but the actual personalization data remains in the vehicle's memory. This distributed approach maintains automation benefits while reducing privacy and security risks associated with centralized data storage.
3Measurement precision
If configuration data is collected from multiple users, then personalization accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system applies partial action by collecting only the most relevant personal characteristics and comfort setting data needed to form accurate association rules, rather than attempting to collect and process all possible user data. This selective data collection approach maintains personalization accuracy while avoiding the excessive complexity that would result from comprehensive data gathering and processing.
Data Source
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AI summary
The invention relates to a method for setting user data (NN) of a new user (38) in a motor vehicle (40, 42) of a specific vehicle type (T1, T2) in order to personalize the motor vehicle (40, 42) for the new user (38). The new user (38) is to receive technical support when setting the new user's user data (NN), i.e. the new user's personal data and desired device configuration data. The personal data (P) of many different users (22, 24) of the vehicle type (T1, T2) and their set configuration data (K) is collected from the users. An assignment rule (34, 36) between personal data (P) and configuration data (K) is formed from the collected personal data (P) and the collected configuration data (K). Only a part (NP) of the user data (NN) required from the new user is detected from the new user (38).