Emotion-Aware Navigation Routes Using Contextual Machine Learning
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Solution Overview
Problem
Existing navigation systems fail to consider individual user emotions and stress situations when determining navigation routes, leading to suboptimal user experiences.
Innovation Solution
A computer-implemented method using a machine learning model to generate an emotion map layer that predicts user emotions based on contextual data, integrating these predictions into route planning to optimize for positive emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional navigation route calculation methods are used, then the route is determined based on standard criteria (fastest, shortest, consumption-optimized), but user emotions and stress situations are disregarded leading to suboptimal user experience
Solution Approach 1:
The system performs preliminary analysis of route segments before the user travels, using machine learning models to predict emotional responses based on historical data and contextual factors. Emotion weightings are pre-calculated for each road segment and stored in an emotion map layer, enabling the navigation system to optimize routes based on predicted user emotions without requiring real-time emotional sensing during travel.
Solution Approach 2:
The patent introduces an emotion map layer as an intermediary data structure between the road graph and the route calculation algorithm. This emotion map layer contains emotion weightings for each road segment, serving as a mediator that translates contextual factors (traffic, weather, road characteristics) into emotional predictions, which then influence the final route optimization without directly modifying the core navigation algorithms.
2Ease of operation
If route optimization considers multiple factors including emotions, then user satisfaction improves, but the complexity of route calculation increases
Solution Approach 1:
The navigation system segments the overall route into individual road segments (edges in the road graph), each with its own set of contextual factors and emotion weightings. This segmentation allows the complex emotion optimization problem to be broken down into manageable units, where each segment can be evaluated independently using the machine learning model, simplifying the overall calculation process while maintaining comprehensive emotion consideration.
Solution Approach 2:
The patent modifies the traditional route calculation parameters by introducing emotion weightings as additional parameters in the road graph. Instead of optimizing solely based on distance, time, or consumption, the system incorporates emotional predictions as weighted parameters. This parameter change approach allows the existing route calculation algorithms to be extended for emotion optimization without fundamentally redesigning the core calculation framework.
Data Source
AI summary
A computer-implemented method is provided for determining a navigation route between a start point and a target point. The method includes setting the start and target points, and then generating a road map with a road graph having plural edges between the star and target points representing traversable route portions. The generated road map also has nodes that interconnect the adjacent edges. The road map is generated from geospatial data retrievably stored in a database. The method proceeds by generating an emotion map layer by a machine learning model that is trained to predict, based on static and/or dynamic contextual input data for each of the edges, the likely emotions of a driver as they traverse the relevant edge and generate emotion weightings in the emotion map layer. The method then calculates the navigation route by the emotion map layer with the emotion weightings obtained in the previous step.