Vehicle Route Personalization Using Language Models and Feedback
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Solution Overview
Problem
Traditional navigation applications fail to customize vehicle routes based on operator preferences, such as pit stops or scenic views, and do not optimize routes for multi-segment sessions, leading to degraded experiences for vehicle operators.
Innovation Solution
Utilizing generative pre-trained transformer models to interact with vehicle operators, learn individualized route preferences, and generate personalized vehicle routes and sessions through a feedback loop, integrating with a route generation engine to provide real-time, customized navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional navigation applications use single-factor route selection (minimizing time), then route calculation is simple and fast, but the user experience is degraded and does not account for operator preferences
Solution Approach 1:
The patent transforms the route selection process from a single-parameter optimization (time) to a multi-parameter optimization problem by introducing operator preferences as additional parameters. The language model learns and incorporates multiple parameters including preferred pit stops, scenic views, route types, and other个性化 preferences to generate routes that satisfy multiple conditions simultaneously, thereby improving user experience without excessive complexity increase
Solution Approach 2:
The system implements a feedback mechanism where the language model continuously learns from operator interactions and route selection outcomes. By monitoring which routes operators accept or reject and what preferences they express, the system refines its understanding of individual operator preferences over time, enabling progressively better personalized route recommendations
2Productivity
If traditional navigation applications focus on single origin-destination routes, then route calculation is efficient, but multi-segment sessions are not optimized
Solution Approach 1:
The patent divides the operator session into multiple segments or stops, where each segment can have its own route optimization criteria. The language model learns preferences for different types of stops (dining, restroom, fuel, scenic views) and optimizes routes for each segment individually while considering the overall session context, enabling comprehensive session optimization without overwhelming computational complexity
Solution Approach 2:
The system performs preliminary learning of operator preferences during previous sessions or before the actual trip planning. By pre-learning preferences for pit stops, route types, and other preferences, the system is ready to quickly generate optimized multi-segment routes when needed, rather than calculating everything in real-time during the trip
3Ease of operation
If manual customization of routes is required, then route flexibility is high, but operator time and effort are consumed
Solution Approach 1:
The language model enables the navigation system to serve itself by automatically learning and applying operator preferences without requiring manual input for each trip. The system autonomously generates personalized routes by querying the learned preferences and translating them into optimized route recommendations, freeing operators from repetitive manual customization tasks while maintaining high route flexibility
Solution Approach 2:
The language model acts as an intermediary layer between the operator's implicit preferences and the route generation engine. Instead of operators directly configuring complex route parameters, they interact naturally through the language model which translates their preferences into technical route specifications, reducing the cognitive load and time required for route customization
Data Source
AI summary
This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.