Vehicle Interface Predictive Menu Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users of vehicles face inefficiencies in configuring settings due to the need to navigate extensive menus, which can be time-consuming and distracting, especially when interacting with the vehicle while in transit.
Innovation Solution
A vehicle user interface that employs machine learning and predictive behavior analysis, using input systems like cameras, microphones, and sensors to anticipate user intentions and preferences, generating a customized interface with filtered menu options based on past interactions, allowing for voice prompts and gesture recognition to streamline configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extensive menus are provided for vehicle configuration, then user customization capability is improved, but user interaction time and complexity increase
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns, preferences, and historical interactions before the user actually needs to configure settings. It pre-generates personalized menu recommendations and predictions about what settings the user will want to adjust, so that when the user interacts with the interface, the most relevant options are already presented, eliminating the need to search through extensive menus.
Solution Approach 2:
The patent replaces the traditional mechanical menu navigation system with an intelligent predictive system using machine learning algorithms. Instead of requiring users to manually navigate through hierarchical menus using buttons or touch inputs, the system uses sensors, cameras, and microphones to detect user intent and automatically presents relevant configuration options, substituting physical interaction with intelligent prediction and automation.
2Adaptability or versatility
If extensive menus are provided for vehicle configuration, then user customization capability is improved, but interface complexity increases
Solution Approach 1:
The system applies local quality by providing different levels of interface complexity to different users based on their individual characteristics, preferences, and behavior patterns. Rather than presenting the same extensive menu structure to everyone, the interface dynamically adapts its complexity locally for each user, showing simplified personalized options to some users while providing access to full functionality when needed, thus maintaining customization capability while reducing perceived complexity.
Solution Approach 2:
The patent extracts and separates the essential configuration functions from the extensive menu structure. It identifies and extracts the most frequently used and important settings based on user behavior analysis, presenting these extracted functions as simplified direct controls or prominent options, while still maintaining access to the full menu system for less frequently used features. This extraction reduces interface complexity by removing unnecessary navigational layers.
3Reliability
If traditional button pressing or knob turning is used for interaction, then device reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system replaces mechanical interaction methods (button pressing, knob turning) with non-contact or minimal-contact interaction methods. It uses sensors, cameras, and microphones to detect user intent through gestures, voice commands, or even gaze direction, and translates these into configuration actions. This substitution maintains reliability by still executing proven configuration functions while dramatically improving ease of operation, especially when the vehicle is in motion and manual dexterity may be reduced.
4Productivity
If predictive options are presented to reduce interaction time, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically analyzing user behavior patterns and generating predictive recommendations without requiring explicit user input or programming. The machine learning models continuously learn from user interactions and automatically adapt the interface to predict what settings the user will want to configure next. This self-learning capability improves configuration speed while managing system complexity through automated adaptation rather than requiring complex manual configuration of the predictive system itself.
Data Source
AI summary
Embodiments of the present disclosure relate to a vehicle user interface. The vehicle user interface may receive user input from an input system. It may present user selectable options or prompt user action via an output system. The vehicle user interface may transmit, via a communication interface, to a computing system a series of user inputs received from at least the first input system, wherein the computing system is configured to extract at least one feature from the series of user inputs and generate a prediction model based on the at least one feature. At least one predicted option may be identified based on the prediction model. The vehicle user interface may instruct the first output system to present the at least one predicted option.