User Interface Structural Clustering for Event Analysis
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Solution Overview
Problem
Existing user interface analysis methods fail to accurately cluster user interface event data based on structure, leading to inaccurate statistics and user experience evaluation, as they do not account for structural similarities and variations in user interactions across different entities and resources.
Innovation Solution
A system that receives user interface event data, assigns it to clusters based on structural similarities using hash comparisons and similarity scores, and generates user interface state groups based on common attributes like URL patterns and titles, enabling accurate identification of logical user interface states and providing aggregate statistics for improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user interface event data is analyzed without structural clustering, then analysis simplicity is maintained, but measurement precision of user interface states deteriorates
Solution Approach 1:
The patent segments user interface event data into distinct clusters based on structural similarities. Each cluster represents a specific user interface state, allowing precise identification and analysis of different interface configurations. The segmentation process divides the overall event data into manageable groups that can be analyzed independently, improving measurement precision without overwhelming system complexity.
Solution Approach 2:
The patent transforms user interface event data into structural representations by changing parameters such as element hierarchy, attribute values, and spatial relationships. This parameter transformation enables the system to identify structural similarities and variations, leading to accurate clustering of user interface states while maintaining a systematic approach that controls complexity.
2Measurement precision
If structural clustering is implemented, then user interface state identification accuracy is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary structural analysis of user interface event data to identify common patterns and characteristics before full clustering is executed. By pre-processing the data to extract key structural features and similarities, the system reduces the computational burden during actual clustering operations, thereby improving accuracy while minimizing additional processing time.
3Measurement precision
If detailed structural analysis is performed on all event data, then clustering accuracy is improved, but computational resources are excessively consumed
Solution Approach 1:
The patent applies local quality analysis by focusing computational resources on specific structural features that are most relevant to user interface state identification. Instead of uniformly analyzing all aspects of every event data point, the system identifies and analyzes only the critical structural elements that differentiate user interface states, thereby maintaining high clustering accuracy while reducing overall computational resource consumption.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering user interface event data for analysis and retrieval are disclosed. In one aspect, a system includes a data store and computer(s) that interact with the data store and execute instructions that cause the computer(s) to receive, for a user interface event, event data specifying a structure of a user interface presented during the user session. The event is assigned to a respective cluster based on a comparison of the structure of the user interface specified by the event data to a user interface structure that represents the respective cluster. For each cluster, a user interface attribute indicative of a user interface state of user interfaces specified by the event data in the cluster is determined. User interface state groups are generated based on the user interface attribute for each cluster.