Route Planning System Using Machine Learning Preference Weights
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Solution Overview
Problem
Conventional route planning applications fail to optimize routes based on user preferences, requiring significant user effort and resources to research and configure routes that account for trade-offs such as road conditions and personal preferences.
Innovation Solution
A system that tracks individual user preferences using machine learning models based on sensor data, generates route scores by combining preference weights, and selects routes that meet threshold values compared to a reference route, thereby providing optimized routes efficiently without excessive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional route generation algorithms are used to minimize total distance or time, then route optimization for basic metrics is improved, but user preference optimization deteriorates
Solution Approach 1:
The patent transforms user preferences into quantifiable parameters (preference weights) that can be integrated into the route scoring algorithm. Each route component is evaluated against multiple preference criteria, and the results are combined into a comprehensive route score that reflects both objective metrics and subjective user preferences.
Solution Approach 2:
The patent creates a composite route evaluation system that combines multiple different types of data (sensor data, user feedback, route characteristics) into a unified route score. This composite approach allows simultaneous optimization across diverse criteria including distance, time, and personalized user preferences.
2Ease of operation
If basic configurability is provided in route planning applications, then ease of operation is improved, but comprehensive preference optimization deteriorates
Solution Approach 1:
The system automatically tracks and learns user preferences through sensor data and usage patterns without requiring manual configuration. The machine learning model continuously updates preference weights based on observed user behavior, enabling the system to serve itself by automatically adapting to user needs rather than requiring users to explicitly configure preferences.
Solution Approach 2:
The system incorporates feedback loops where user interactions with routes (selections, modifications, completions) are fed back into the machine learning model to refine preference weights. This continuous feedback mechanism allows the system to improve its understanding of user preferences over time while maintaining ease of operation.
3Manufacturing precision
If users manually research road conditions and set route waypoints to achieve optimal routes, then route optimization for user preferences is improved, but loss of time and resources deteriorates
Solution Approach 1:
The system performs preliminary analysis of multiple potential routes and pre-calculates their scores against learned user preferences before presenting options to the user. By anticipating user needs and pre-evaluating routes based on historical preference data, the system eliminates the need for users to manually research and configure routes.
Solution Approach 2:
The patent replaces manual user actions (researching road conditions, setting waypoints) with automated computational processes. Machine learning models and algorithms automatically evaluate route characteristics and calculate optimized routes, substituting the mechanical process of manual configuration with intelligent automated systems.
4Adaptability or versatility
If multiple route options with detailed preferences are provided to users, then user preference satisfaction is improved, but device complexity and computational resources deteriorates
Solution Approach 1:
The patent divides the route evaluation process into segmentable components (route components) that can be independently scored and then combined. This segmentation allows the system to efficiently process complex evaluations by breaking them into manageable parts, reducing computational overhead while maintaining comprehensive preference analysis.
Data Source
AI summary
In various implementations, routing factors are identified based on a routing request associated with a user, where the routing factors include route preferences of the user. Routes are generated based on the routing request. Preference weights are determined for the route preferences, where the preference weights correspond to machine learning models based on sensor data provided by one or more sensors in association with the user. Route scores are determined for the routes based on the preference weights. A suggested route is provided to a user device associated with the user, where the suggested route corresponds to a selected route of the routes and is provided based on the route score of the selected route.