Session Analysis System for User Behavior Visualization
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Solution Overview
Problem
Current systems lack effective methods to analyze and improve user experiences in online activities by understanding user behavior through session data, particularly in complex web-based activities involving multiple steps and dependencies.
Innovation Solution
A session analysis system that generates graphical representations of session data, allowing users to control display parameters and analyze user behavior by segmenting and aggregating data, identifying temporal relationships, and clustering sessions for better understanding and improvement of user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If session data is collected and analyzed to understand user behavior, then user experience improvement is enabled, but system complexity increases
Solution Approach 1:
The patent segments session data into discrete events with specific attributes (timestamp, event type, duration, success/failure status). By dividing complex user sessions into standardized event units, the system can analyze behavior patterns without requiring complex proprietary data structures, thus improving reliability of analysis while controlling system complexity through standardized segmentation.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes raw session data and transforms it into meaningful metrics and visualizations. This intermediary layer (comprising event tracking, aggregation, and visualization components) mediates between raw data collection and user experience insights, enabling reliable analysis without exposing the full complexity of the data processing pipeline to end users.
2Measurement precision
If detailed session data is tracked for each user activity, then behavior analysis precision is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining event types and their attributes during data collection. Events are categorized and tagged with relevant attributes (duration, success status, event type) at the point of generation rather than requiring complex post-processing analysis. This preliminary structuring enables precise behavior analysis while minimizing processing time during query and analysis phases.
3Loss of information
If multiple event attributes are tracked per session, then analysis completeness is improved, but information processing complexity increases
Solution Approach 1:
The patent changes parameters by standardizing event attributes into consistent categories (timestamp, event type, duration, success status) that can be uniformly processed. By transforming diverse user interactions into standardized parameter sets, the system achieves complete information capture across different event types while simplifying processing complexity through parameter uniformity and consistency.
Data Source
AI summary
A method and system analyze session data. In some embodiments, the method accesses session data, identifies multiple sessions contained in the session data, and identifies multiple events that occurred within each of the multiple sessions. The method determines a temporal relationship between the multiple events in each session and analyzes the multiple sessions to aggregate events associated with the multiple sessions. The method then generates a graphical representation of the aggregated events.