In-Vehicle Assistant Suggestions With Local Personalization
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Solution Overview
Problem
Existing in-vehicle computing systems face inefficiencies and security concerns due to the need for remote suggestion systems, leading to unnecessary resource consumption, latency, and data transmission risks.
Innovation Solution
Implementing a local suggestion system on the in-vehicle computing device that generates personalized candidate suggestions based on contextual signals, stores them for quick retrieval, and presents them to the user, reducing the need for remote data transmission and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a remote suggestion system is used to generate suggestions, then the system can provide suggestion functionality, but computational resources and network resources are unnecessarily consumed
Solution Approach 1:
The suggestion generation functionality is extracted from the remote server and implanted into the local in-vehicle computing device. The device now generates suggestions locally using onboard computational resources, eliminating the need to transmit requests and receive responses over the network, thereby reducing network resource consumption and latency.
Solution Approach 2:
The in-vehicle computing device serves itself by generating suggestions using its own computational resources rather than relying on external remote servers. The device processes contextual signals, retrieves relevant information from local data sources, and generates suggestions independently, making the system self-sufficient and reducing external resource dependencies.
2Productivity
If a remote suggestion system is used, then suggestion functionality is provided, but latency is introduced due to transmission time
Solution Approach 1:
The suggestion generation process is extracted from the remote network environment and relocated to the local in-vehicle computing device. This eliminates network transmission delays for both sending contextual signals and receiving generated suggestions, significantly reducing overall latency in the suggestion delivery pipeline.
3Productivity
If user data is transmitted to a remote system, then suggestion generation can occur, but security of user data is compromised
Solution Approach 1:
User data processing is extracted from the remote server environment and moved to the local in-vehicle computing device. Contextual signals and user information remain within the vehicle's computing system throughout the suggestion generation process, eliminating exposure to network transmission risks and remote server security vulnerabilities.
Solution Approach 2:
The system creates a secure, isolated computational environment within the in-vehicle computing device where user data is processed in a protected manner. By keeping data processing local and avoiding external network communication for sensitive operations, the system maintains an inert security environment that protects user information from external threats.
Data Source
AI summary
Implementations described herein relate to generating candidate suggestion(s) that are personalized to a given user locally at an in-vehicle computing device of a vehicle of the given user. For example, implementations can identify an occurrence of a given suggestion state, generate the candidate suggestion(s) that are personalized to the user for the given suggestion state, store the candidate suggestion(s) in on-device storage of the in-vehicle computing device in association with the given suggestion state, and cause a given one of the candidate suggestion(s) to be provided for presentation to the given user. Accordingly, upon a subsequent occurrence of the given suggestion state, implementations can obtain the candidate suggestion(s) stored in association with the given suggestion state, and cause the given one of the candidate suggestion(s), or an additional one of the candidate suggestion(s), to be provided for presentation to the given user.