Cloud Vehicle Profile Transfer via Confidence Score Recommendations
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Solution Overview
Problem
Current vehicle management systems lack the ability to learn and adapt user preferences and behaviors, leading to inefficient customization and settings application across different vehicles, especially in scenarios like car sharing or rental services.
Innovation Solution
A cloud-based system that processes user interactions with vehicles to generate confidence scores for recurring actions, allowing for automatic recommendation and implementation of settings, such as temperature, navigation, and entertainment, based on learned user behavior, and enables seamless transfer of user profiles across various vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual settings configuration is used for each vehicle, then customization flexibility is maintained, but user experience efficiency deteriorates due to repetitive setup across multiple vehicles
Solution Approach 1:
The system performs preliminary learning of user preferences and behaviors by monitoring interactions with vehicle settings over time. This preliminary action enables the system to automatically generate and apply customized settings profiles without requiring manual configuration each time the user accesses a vehicle, thereby improving settings application efficiency while managing system complexity through automated processes.
2Ease of operation
If automated settings application is implemented across vehicles, then user experience improves through personalization, but measurement precision deteriorates due to difficulty in accurately determining user intent
Solution Approach 1:
The system continuously monitors and learns from user interactions with vehicle settings, using this feedback to refine and update preference profiles over time. By implementing feedback loops that track user behavior patterns across multiple vehicles and sessions, the system improves its ability to accurately determine user intent, thereby enhancing both personalization quality and preference detection accuracy simultaneously.
3Adaptability or versatility
If user profiles are transferred across multiple vehicles, then adaptability improves for car sharing scenarios, but loss of information increases due to potential incompatibility between different vehicle types
Solution Approach 1:
The system applies local quality by customizing the application of user profiles based on the specific vehicle type and available settings. When transferring profiles across different vehicle types, the system selectively applies compatible settings while adapting or omitting those that are vehicle-specific, thereby maintaining cross-vehicle profile compatibility while minimizing information loss through intelligent setting mapping and adaptation.
Data Source
AI summary
Methods and systems are provided for processing information associated with vehicles via one or more servers of a cloud system. One example method includes establishing a communication link between a computing device associated with a vehicle and a server. The communication link is over a wireless network and the communication link is established in association with a user account. The communication link is established for one or more sessions The method further includes receiving, at the server, a plurality of actions associated with inputs to the vehicle. The plurality of actions are received during the one or more sessions, and generating, by the server, a recommendation to program a setting at the vehicle. One or more of the plurality of actions at the vehicle during the one or more sessions are processed to determine a confidence score associated with generating the recommendation to program the setting. Generation of the recommendation occurs upon reaching or exceeding a predefined threshold. The server then sends to the user account the recommendation to enable programming of the setting for the vehicle.