Personalized Query Guidance Using Dynamic Preference Prediction
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Solution Overview
Problem
The traditional car-buying process is complex, time-consuming, and frustrating for consumers due to limited and inaccurate filters that do not reflect user preferences, leading to overwhelming and inefficient search results.
Innovation Solution
A machine-learning-based system that predicts user preferences by analyzing search history and inventory data to provide personalized and dynamic search recommendations without the need for manual filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional filtering methods are used to search for cars, then users can apply multiple filters (price, make, model, year, features), but the filters are limited and do not accurately reflect user preferences, leading to overwhelming and irrelevant search results
Solution Approach 1:
The patent replaces traditional mechanical filtering systems with a machine learning-based predictive system. Instead of requiring users to manually apply multiple filters, the system uses trained models to automatically predict and apply relevant filters based on user behavior patterns, search history, and inventory data, thereby improving result accuracy while reducing operational complexity
Solution Approach 2:
The system enables self-service by automatically adjusting search results based on user preferences without requiring manual filter application. The machine learning model autonomously analyzes user behavior and dynamically modifies search parameters, allowing the system to serve itself in optimizing search results without additional user intervention
2Productivity
If users manually apply multiple filters to narrow down search results, then they can find relevant cars, but the process becomes time-consuming and frustrating
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive user behavior data and inventory information before actual search operations. This preliminary training enables the system to quickly predict user preferences and apply appropriate filters instantaneously during search, eliminating the time-consuming manual filtering process while maintaining high productivity
Solution Approach 2:
The patent substitutes the manual mechanical process of applying filters with an automated machine learning system that predicts and applies filters based on user behavior patterns. This substitution dramatically reduces the time required for searches while maintaining or improving search efficiency through intelligent automation
3Measurement precision
If the system provides personalized recommendations based on user behavior data and inventory information, then search accuracy improves, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex machine learning system into distinct modules: user behavior analysis components, inventory data processing components, model training components, and recommendation generation components. Each module handles specific tasks independently, which manages system complexity while maintaining high recommendation accuracy through specialized processing in each segment
4Adaptability or versatility
If the system dynamically adjusts recommendations in real-time based on available inventory, then user experience improves, but computational requirements and system complexity increase
Solution Approach 1:
The system implements dynamics by enabling real-time adjustment of search recommendations based on current inventory availability. The machine learning models continuously process new inventory data and user behavior patterns, dynamically modifying recommendations without requiring system reconfiguration. This dynamic capability enhances adaptability while the modular architecture manages computational complexity through efficient real-time processing
Data Source
AI summary
The present invention describes a computer-based system and method for training machine-learning models for delivering personalized query results. The method starts by collecting user behaviors, then categorizing each behavior and refining these categories based on additional data. The refined categories are used to create sequential, dynamic inputs that serve as both ground truth and training data for the machine-learning model. This process helps to strengthen the connections between the training data and the desired output, resulting in more accurate personalized query results. The system and method may be directed towards providing recommendations to the user, such as in the form of vehicle recommendations.