Language Model Route Personalization for Multi-Stop Vehicle Sessions
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Solution Overview
Problem
Existing 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, degrading the experience of vehicle operators.
Innovation Solution
A navigation application utilizing a generative pre-trained transformer model 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 for real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation applications use single-factor route selection to minimize travel time, then route calculation speed is improved, but route personalization and operator experience deteriorate
Solution Approach 1:
The patent segments the route selection process into multiple independent factors: travel time, operator preferences, activity patterns, and session optimization. Each factor is evaluated separately and then integrated to produce a personalized route recommendation, allowing the system to maintain calculation efficiency while incorporating diverse personalization dimensions.
Solution Approach 2:
The system dynamically changes the parameters used for route selection based on operator profiles and session contexts. Instead of using a fixed single parameter (time), the system adjusts multiple parameters including preferred stops, scenic route preferences, and session-based optimization goals, transforming the route selection from a static to a dynamic multi-parameter process.
2Loss of time
If navigation applications focus solely on minimizing origin-to-destination time, then route efficiency is improved, but overall operator session optimization deteriorates
Solution Approach 1:
The patent implements preliminary action by learning operator preferences and activity patterns in advance through feedback loops and historical data analysis. This pre-learning enables the system to proactively optimize entire operator sessions rather than reactively optimizing individual routes, improving overall session efficiency while maintaining reasonable travel times.
Solution Approach 2:
The system employs feedback mechanisms to continuously learn from operator responses and session outcomes. By analyzing operator acceptance or rejection of route recommendations and incorporating this feedback into future recommendations, the system progressively optimizes operator sessions, transforming from simple time-minimization to comprehensive session efficiency improvement.
3Device complexity
If navigation applications provide generic route recommendations, then system complexity is reduced, but operator experience and satisfaction deteriorate
Solution Approach 1:
The patent implements self-service by enabling the system to automatically learn and adapt to individual operator preferences without requiring manual configuration. The feedback loop allows the system to self-improve its personalization capabilities over time, providing customized operator experiences while avoiding the complexity of manual setup and configuration interfaces.
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.