Language Model Route Personalization for Vehicle Operators

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Solution Overview

Problem

Traditional navigation systems for vehicle operators primarily focus on minimizing time between origin and destination, failing to account for operator preferences, such as desired stops, scenic views, or optimal route customization for multi-segment sessions, leading to suboptimal experiences and manual adjustments.

Innovation Solution

A navigation application utilizing pre-trained language models to interact with vehicle operators, learn individualized route preferences, and generate personalized vehicle routes and sessions by communicating with a route generation engine, incorporating preferences for ride duration, stops, scenic routes, and revenue optimization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If traditional navigation applications minimize time from origin to destination, then travel time is reduced, but operator preferences and activity patterns are not accounted for

Engineering Contradiction:

Improvetravel timeVSAvoidoperator preference customization

Core Design Contradiction:

Loss of timeVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by collecting operator interaction data and learning activity patterns before route calculation. The language model is pre-trained with navigation-related instructions and continuously learns from operator interactions to understand preferences for stops, dining, restroom breaks, and scenic views, enabling personalized route optimization that anticipates operator needs before they are explicitly stated.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring operator interactions with the navigation application and using this data to refine route recommendations. The language model learns from operator responses, adjustments, and usage patterns to improve personalization of routes, ensuring that future recommendations better align with operator preferences while maintaining efficient travel times.

Inventive Principle:

Principle #23Feedback

2Productivity

If navigation applications focus solely on minimizing time duration, then route calculation is simple and fast, but holistic optimization for multi-segment sessions is not achieved

Engineering Contradiction:

Improveroute calculation efficiencyVSAvoidmulti-segment session optimization

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system segments the operator session into multiple components including origin-destination routes, intermediate stops, dining breaks, restroom stops, and scenic view opportunities. The language model analyzes each segment independently while considering their collective impact on the overall session, enabling holistic optimization that balances travel efficiency with operator needs across the entire multi-segment journey.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adds another dimension to route optimization by incorporating operator preferences and activity patterns alongside traditional time-minimization criteria. The language model evaluates routes based on multiple dimensions including travel time, operator preferences for specific types of stops, scenic views, and overall session optimization, transforming the single-objective problem into a multi-dimensional optimization task.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Loss of energy

If traditional navigation systems use simple route selection algorithms, then computational resources are conserved, but personalized route recommendations cannot be provided

Engineering Contradiction:

Improvecomputational resource consumptionVSAvoidpersonalization capability

Core Design Contradiction:

Loss of energyVSAdaptability or versatility

Solution Approach 1:

The system introduces a language model as an intermediary between the operator interaction data and the route generation engine. This intermediary layer processes and understands operator preferences, activity patterns, and natural language inputs, translating them into meaningful parameters for route optimization. The language model efficiently captures complex preferences without requiring computationally intensive custom algorithms for each routing decision.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes parameters by using the language model to extract and represent operator preferences as structured parameters that can be efficiently processed by the route generation engine. Instead of implementing complex personalization algorithms, the system transforms unstructured operator interactions into standardized preference parameters that guide route selection, reducing computational complexity while maintaining personalization capability.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11874127B1Language models and machine learning frameworks for optimizing vehicle navigation routes and vehicle operator sessions

Publication Date: 2024.01.16 MARROW IP LLC

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.