In-Vehicle Context Engine for Personalized Voice Responses
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Solution Overview
Problem
Existing voice assistant systems in vehicles fail to provide personalized responses due to the lack of contextual data, leading to inaccurate or unrelated outputs when users do not explicitly provide sentiment, mood, or environmental conditions with their verbal commands.
Innovation Solution
A context engine is integrated into the vehicle's computing system to analyze user prompts, sensor data, and user preference data, generating modified prompts that include additional context such as weather, location, and user sentiment, which are then processed by machine-learned models to provide personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice assistant systems process only explicit user commands without contextual data, then system complexity is reduced, but response accuracy and personalization deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing contextual data (sensor data, user preferences, environmental information) before the user issues a command. This pre-prepared context is then automatically integrated with the user's explicit command, eliminating the need for users to manually provide all necessary information while maintaining high response accuracy without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces a context engine as an intermediary component that bridges the gap between simple command processing and complex personalized responses. This mediator automatically retrieves relevant contextual data, integrates it with user commands, and presents enriched prompts to the voice assistant, thereby improving response accuracy without requiring fundamental changes to the core voice assistant architecture.
2Adaptability or versatility
If the system collects and processes multiple types of sensor data and user preferences, then personalization and user experience are improved, but energy consumption increases
Solution Approach 1:
The system applies local quality by selectively retrieving and processing only the specific contextual data relevant to each user command rather than continuously processing all available sensor data. The context engine queries user preferences and sensor data locally based on the specific needs of each interaction, minimizing unnecessary data processing and associated energy consumption while maintaining high personalization capability.
3Adaptability or versatility
If the system integrates context engine and machine-learned models for personalized responses, then user experience quality is improved, but computing time and processing load increase
Solution Approach 1:
The system performs preliminary actions by pre-loading and caching user preference data and contextual information before interactions occur. Machine-learned models are pre-trained and stored in accessible memory, allowing the context engine to quickly retrieve and process relevant data during user interactions without requiring intensive real-time computation, thereby reducing computing time while maintaining personalization quality.
Data Source
AI summary
Methods, computing systems, and technology for personalizing a user experience in a vehicle. For example, a computing system may be configured to receive a user prompt from a user associated with a vehicle, the user prompt indicative of a statement or a question. The computing system may be configured to access sensor data associated with a surrounding environment of the vehicle. The computing system may be configured to generate, using a context engine, a modified user prompt based on the user prompt and the sensor data, wherein the modified user prompt supplements the user prompt with the context data, the context data providing one or more conditions associated with the user prompt. The computing system may be configured to generate, based on the modified user prompt, a user response, wherein the user response implements an action corresponding to the statement or the question.