Graph-Based User Interaction Data Analysis System
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Solution Overview
Problem
Current systems for user interaction data analysis lack effective visualization and optimization tools, making it difficult to derive insights from aggregated user interaction data across various platforms and time frames.
Innovation Solution
An interactive, graph-based user interaction data analysis system that generates a force-directed graph with nodes representing content items and edges indicating user transitions, allowing operators to select and manipulate nodes and edges to view associated metrics and optimize user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user interaction data is aggregated across many users and platforms, then the quantity and comprehensiveness of data increases, but the complexity of analyzing and visualizing the data increases
Solution Approach 1:
The system segments the complex user interaction data into distinct visual elements: nodes represent content items, edges represent user transitions, and different visual properties encode various metrics (time spent, visit frequency, source/destination). This segmentation allows the system to handle large volumes of aggregated data by breaking it down into manageable, visually distinguishable components that can be processed and displayed efficiently.
Solution Approach 2:
The patent transforms multi-dimensional data (users, platforms, time frames, metrics) into a two-dimensional force-directed graph visualization. By mapping complex relationships onto spatial positions, edge weights, and node properties in a 2D plane, the system makes aggregated data from multiple dimensions perceptually accessible and easier to analyze at a glance.
2Measurement precision
If detailed user interaction metrics are collected and displayed, then the precision of analysis improves, but the complexity of the interface and ease of operation decreases
Solution Approach 1:
Different visual properties are assigned to different elements based on their local significance: node size encodes visit frequency, edge width encodes transition volume, color encoding source/destination platforms, and position encodes temporal relationships. This local differentiation allows precise metrics to be conveyed through intuitive visual cues without requiring complex interfaces or extensive data fields.
Solution Approach 2:
The system uses color coding to represent different platforms, time frames, and interaction types. By encoding multiple dimensions of precision data through color variations rather than text labels or multiple controls, the interface remains simple and intuitive while maintaining high measurement precision for user interaction analysis.
3Difficulty of detecting and measuring
If force-directed graph visualization is used to represent user transitions, then the effectiveness of pattern recognition improves, but the computational complexity increases
Solution Approach 1:
The force-directed layout algorithm automatically positions nodes and edges based on simulated physical forces (repulsion between nodes, attraction along edges), requiring minimal manual intervention. The system self-adjusts the visualization to optimize pattern recognition by computing optimal spatial arrangements through iterative force simulation, reducing the need for complex manual configuration while maintaining high pattern detection effectiveness.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables comprehensive visualization and analysis of user interaction patterns, facilitating optimization of user interactions by providing actionable insights into user behavior across different platforms and time frames.
Implementation Method 1
locations of the two or more nodes of the graph on the interactive user interface are automatically determined based on at least one of repulsive forces associated with each of the two or more nodes or contractive forces associated with each of the at least one edge
Data Source
AI summary
An interactive, graph-based user interaction data analysis system is disclosed. The system is configured to provide analysis and graphical visualizations of user interaction data to a system operator. In various embodiments, interactive visualizations and analyzes provided by the system may be based on user interaction data aggregated across particular groups of users, across particular time frames, and/or from particular computer-based platforms and/or applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns and/or ways to optimize for desired user interactions, among others. In an embodiment, the system allows an operator to analyze and investigate user interactions with content provided via one or more computer-based platforms, software applications, and/or software application editions.