Gesture-Based Vehicle Recommendation for Adaptive Search
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Solution Overview
Problem
Consumers face frustration and inefficiency in vehicle purchasing due to rigid, predefined search categories on current websites, leading to prolonged research and disengagement.
Innovation Solution
A system that utilizes user gestures on vehicle images to determine preferences and recommend vehicles based on machine learning algorithms, such as convolutional neural networks, to provide personalized vehicle recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rigid predefined search categories are used, then search structure is simple and easy to implement, but user engagement decreases and research time increases
Solution Approach 1:
The patent transforms static predefined search categories into dynamic gesture-based interactions. Users can perform gestures (e.g., circling, pointing) on vehicle images to dynamically express preferences, and the system adapts by updating search criteria in real-time based on gesture patterns, thereby reducing research time while maintaining ease of use.
Solution Approach 2:
The patent replaces traditional mechanical dropdown menus and filter checkboxes with gesture-based interaction on vehicle images. Machine learning models analyze gesture data (position, type, sequence) to infer preferences, substituting rigid mechanical UI elements with intuitive visual gestures that reduce research time without sacrificing operational simplicity.
2Ease of operation
If rigid predefined search categories are used, then search interface is simple, but adaptability to user preferences decreases
Solution Approach 1:
The patent enables the search interface to serve itself by automatically learning user preferences from gesture interactions. The machine learning system processes gesture data to identify preferred vehicle features, automatically updating search criteria without requiring users to manually configure complex filters, thus maintaining interface simplicity while dramatically improving adaptability to individual preferences.
Solution Approach 2:
The patent implements a feedback loop where user gestures on vehicle images are captured, analyzed by machine learning models, and used to refine search results in real-time. The system continuously adapts to user preferences by learning from interaction patterns, maintaining a simple interface while achieving high adaptability through iterative feedback between user actions and system responses.
3Loss of information
If multiple websites are visited for research, then information completeness improves, but time consumption increases
Solution Approach 1:
The patent merges the functionality of multiple vehicle research websites into a single unified platform. By collecting and analyzing gesture interactions across diverse vehicle images from multiple sources, the system consolidates information gathering in one interface, providing comprehensive vehicle information without requiring users to navigate multiple websites, thus improving information completeness while reducing research time.
Data Source
AI summary
According to certain aspects of the disclosure, a computer-implemented method may be used for providing a vehicle recommendation based on user gestures. The method may include displaying an image of a vehicle to a user and receiving at least one gesture from the user performed on the image of the vehicle. Additionally, the method may include assigning a value to the at least one gesture from the user and determining a feature of the vehicle based on the at least one gesture from the user. Additionally, the method may include receiving gesture information related to the at least one gesture and determining a vehicle preference of the user based on the value, the feature of the vehicle, and the gesture information. Additionally, the method may include identifying an available vehicle based on the vehicle preference of the user and displaying the available vehicle to the user.