In-Vehicle AI Activity Routing Based on Occupant State

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Solution Overview

Problem

Existing vehicle systems lack the ability to dynamically suggest personalized activities or detours based on the occupant's current state and route conditions, failing to enhance the user experience or optimize travel time effectively.

Innovation Solution

An AI system integrated into vehicles that monitors the occupant's state and route conditions, suggesting personalized activities or detours through real-time data analysis, biometric monitoring, and machine learning algorithms to enhance the driving experience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional navigation systems are used to provide route guidance, then basic transportation function is maintained, but the system cannot provide personalized activity suggestions or optimize travel experience based on occupant state

Engineering Contradiction:
Improvepersonalized activity recommendation capabilityVSAvoidsystem complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the navigation functionality into modular components: biometric monitoring module, AI model execution module, activity recommendation module, and route adjustment module. Each module performs a specific function, allowing the system to provide personalized recommendations without requiring complete system redesign.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The navigation system is enhanced to perform multiple functions beyond basic route guidance, including biometric monitoring, AI-based activity recommendation, entertainment suggestion, and dynamic route adjustment. This multi-functionality allows a single system to address diverse user needs while maintaining core navigation capabilities.

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Ease of operation

If real-time biometric monitoring and AI processing are implemented, then personalized activity suggestions are provided, but computational resources and energy consumption increase

Engineering Contradiction:
Improveuser experience qualityVSAvoidcomputational energy consumption
Core Design Contradiction:
Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system executes AI models to generate activity recommendations in advance during periods of lower computational demand, such as when the vehicle is stationary or during predictable idle periods. This preliminary processing reduces real-time computational requirements and energy consumption during critical navigation phases.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The biometric monitoring operates continuously at low power consumption levels, while intensive AI processing is triggered only when meaningful state changes are detected or during designated processing windows. This continuous monitoring with periodic intensive analysis maintains user experience quality while managing energy consumption efficiently.

Inventive Principle:
Principle #20Continuity of useful action

3Loss of time

If the system dynamically adjusts routes based on real-time analysis, then travel time optimization is achieved, but system reliability and safety may be compromised due to frequent changes

Engineering Contradiction:
Improvetravel time efficiencyVSAvoidroute stability
Core Design Contradiction:
Loss of timeVSReliability

Solution Approach 1:

The route adjustment mechanism implements dynamic threshold-based triggering, where route changes are only recommended when accumulated time savings exceed a predetermined threshold or when significant state changes are detected. This dynamic approach balances travel time optimization with route stability, preventing excessive or premature route changes that could compromise reliability.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system continuously monitors route performance, user response to recommendations, and changing conditions, using this feedback to adjust future route change decisions. If frequent route changes are detected, the system adapts by increasing the threshold for future changes, thereby maintaining reliability while preserving time optimization benefits.

Inventive Principle:
Principle #23Feedback

Data Source

PatentUS20260079014A1Route-based activity recommendation using artificial intelligence
Publication Date: 2026.03.19 TOYOTA MOTOR NORTH AMERICA INC
  • US20260079014A1 patent drawing
  • US20260079014A1 patent drawing
  • US20260079014A1 patent drawing

AI summary

An example operation includes one or more of monitoring a state of one or more of an occupant of a vehicle and a route on which the vehicle is currently travelling, identifying a current location of the vehicle on the route, executing an artificial intelligence (AI) model on the state and the current location of the vehicle to determine an activity for the occupant and an alternate location of the activity, presenting the alternate location of the activity for the occupant via a display device of the vehicle while the vehicle is travelling along the route, and routing the vehicle to the alternate location.