Predictive Navigation GUI for Reducing User Confusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional website navigation methods overwhelm users with extensive resources, leading to confusion and reduced purchasing interest due to the need for significant computing effort.
Innovation Solution
A system and method that utilizes machine learning models to detect trigger events, generate search queries, determine target objects, and provide a graphical user interface (GUI) with a recommended navigation path based on website, historical user, and preference data, enhancing user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional website navigation methods are used with extensive resources, then comprehensive information is provided, but user confusion increases and purchasing interest decreases
Solution Approach 1:
The patent introduces an intermediary system comprising machine learning models, trigger event detectors, and GUI generation components that mediate between the user and the extensive website resources. This intermediary processes user interactions, predicts intent, and selectively presents relevant information, thereby maintaining comprehensive information availability while eliminating user confusion through curated presentation.
Solution Approach 2:
The system dynamically changes presentation parameters of website information based on detected trigger events and predicted user intent. By adjusting which information is displayed, how it is organized, and what navigation options are prioritized, the system maintains comprehensive underlying resources while adapting the user-facing presentation to reduce confusion and enhance navigation ease.
2Adaptability or versatility
If extensive website resources are made available, then comprehensive shopping options are provided, but compute requirements increase and buyer interest decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing website resources and user data through machine learning models before actual user queries. Trigger events are detected in advance, and navigation paths are pre-computed based on historical user data and preferences. This preliminary processing reduces the compute energy required during actual user interactions while maintaining comprehensive shopping options.
Solution Approach 2:
The patent replaces traditional mechanical search and navigation systems with machine learning-based predictive systems. Instead of relying on users to manually search through extensive resources or on conventional search algorithms to process queries in real-time, the system uses trained machine learning models to predict user intent and generate optimized navigation paths, significantly reducing compute energy consumption during user sessions.
3Loss of information
If traditional search boxes are used, then user queries can be entered, but navigation time increases and user interest decreases
Solution Approach 1:
The system performs preliminary analysis of user behavior, website structure, and resource relationships before users execute searches. By pre-computing navigation paths and predicting user intent based on trigger events and historical data, the system prepares optimized routes in advance, allowing users to maintain full search query capability while dramatically reducing the time required to reach target information.
Data Source
AI summary
Systems and methods for generating directional information, including detecting an occurrence of a first trigger event, upon detecting the occurrence of the first trigger event, generating a first search query based on a predicted object, determining a target object based on the first search query, determining a recommended navigation path to the target object, generating a first graphical user interface (GUI) based on the recommended navigation path, and causing to display the first GUI.