Vehicle Rental Preference Prediction for Owner-Specific Renter Filtering
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Solution Overview
Problem
Conventional vehicle-sharing platforms lack mechanisms for generating and enforcing personalized preferences of vehicle owners, and designing user interfaces for mobile devices with small screens is complex due to the need for dividing data and functionality into multiple layers, which decreases efficiency and usability.
Innovation Solution
A dynamic setup process in the vehicle-sharing platform that prompts vehicle owners with tailored questions based on previous answers, allowing customization of preferences and predicting eligible renters through a preference prediction engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional vehicle-sharing platforms use general trust mechanisms for all participants, then platform simplicity is maintained, but personalization and trust customization are lost
Solution Approach 1:
The system performs preliminary actions by pre-configuring a hierarchy of preference categories and generating tailored prompts before the actual rental interaction. The preference prediction engine proactively creates personalized rental criteria based on owner profiles, driving histories, and rental patterns, so that when a rental request comes in, the system can quickly evaluate against pre-established personalized criteria rather than requiring real-time complex decision-making.
Solution Approach 2:
The patent segments the complex trust evaluation process into distinct hierarchical categories (e.g., driving behavior, rental history, vehicle compatibility). Each category can be independently evaluated and weighted. The system divides the preference specification into multiple levels: overall trust thresholds, category-specific criteria, and individual prompt-based requirements. This segmentation allows the system to manage complexity through structured organization while maintaining personalization.
2Ease of operation
If the interface divides data and functionality into multiple layers for mobile devices, then mobile compatibility is achieved, but usability and efficiency decrease
Solution Approach 1:
The interface dynamically adapts its complexity based on the user's needs and the situation. The prompt generation system adjusts the number and type of questions asked based on the owner's profile completeness, the rental request characteristics, and the current context. This dynamic behavior allows the system to present simple interfaces for routine transactions while providing comprehensive personalized evaluation only when necessary, reducing perceived complexity for users.
Solution Approach 2:
The system performs self-service by automatically generating tailored prompts and evaluating rental requests based on pre-established criteria. The preference prediction engine autonomously creates personalized rental criteria without requiring manual configuration of every detail. The system self-adjusts the interaction complexity by automatically determining which prompts are necessary based on the data already available in owner profiles and rental histories.
3Measurement precision
If the system asks comprehensive questions to capture all owner preferences, then prediction accuracy improves, but interaction time increases
Solution Approach 1:
The system applies partial action by asking only the necessary questions based on the current context and available data. The prompt generation algorithm determines the minimum set of questions needed to achieve adequate prediction accuracy for the specific rental request. If an owner has a complete profile with extensive driving history and rental patterns, the system may ask fewer supplemental questions. Conversely, if data is missing or the request is atypical, the system asks additional targeted questions to improve accuracy.
Solution Approach 2:
The system uses feedback mechanisms where the preference prediction engine continuously refines its understanding of owner preferences based on actual rental outcomes and owner feedback. This feedback loop allows the system to improve prediction accuracy over time without requiring increasingly comprehensive initial questionnaires. The learning from past rentals enables the system to ask more targeted, high-impact questions that efficiently capture essential preferences.
Data Source
AI summary
Embodiments described herein generate a first set of prompts, the first set of prompts configured to prompt a vehicle owner for a first set of answers used to learn preferred vehicle renter characteristics; receive the first set of answers; generate, based upon the first set of answers, a second set of prompts, the second set of prompts configured to prompt the owner for a second set of answers used to learn additional preferred vehicle renter characteristics; receive the second set of answers; predict user preference value(s) of a profile of the owner based upon the second set of answers, wherein the user preference value(s) define criteria for sharing a vehicle associated with the profile with vehicle renters who satisfy the criteria; apply the criteria to potential vehicle renters; and cause an indication of the vehicle to be displayed only to the potential vehicle renters who satisfy the criteria.