Dynamic User Persona Detection for Objective-Guided SaaS Interfaces
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Solution Overview
Problem
Existing SaaS applications fail to optimize user experiences as users often log in without a clear objective, leading to sub-optimal interactions and reliance on support, due to a lack of personalized user interface adjustments based on historical user behavior.
Innovation Solution
A system that dynamically detects user personas by clustering similar user activities across historical sessions, mines sequential patterns, and adjusts the user interface to guide users towards their objectives using predictive rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user interface is customized based on user personas, then user experience is improved, but system complexity increases due to the need for clustering algorithms and predictive rule engines
Solution Approach 1:
The system performs preliminary actions by pre-processing user activity data during off-peak times to create user personas and mine sequential patterns. This allows the runtime system to simply match current user activity against pre-computed personas and predictive rules, significantly reducing runtime complexity while maintaining personalized user experience.
Solution Approach 2:
The patent introduces an intermediary layer consisting of user personas and predictive rules that mediate between raw user activity data and the user interface. This intermediary layer abstracts the complexity of real-time analysis, allowing the system to provide personalized interfaces without requiring complex real-time processing algorithms.
2Adaptability or versatility
If user activity data is analyzed in real-time, then personalized suggestions are provided, but processing time and computational resources increase
Solution Approach 1:
The system performs user persona creation and sequential pattern mining as preliminary actions during off-peak hours or in batches. This pre-processing approach allows the system to have user activity data ready for quick matching during user sessions, providing personalized suggestions with minimal real-time processing delay.
Solution Approach 2:
The patent segments the data processing into distinct phases: offline persona creation, offline pattern mining, and online activity matching. This segmentation allows computationally intensive operations to be performed offline while only requiring simple pattern matching operations in real-time, significantly reducing processing time during user interactions.
3Loss of information
If clustering algorithms are used to identify user personas, then user behavior understanding is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system applies partial action by focusing clustering algorithms on specific dimensions of user behavior that are most relevant to persona differentiation. Rather than analyzing all possible user activities equally, the system identifies and clusters based on key behavioral patterns, reducing computational complexity while maintaining effective user behavior understanding.
Solution Approach 2:
The patent implements self-service through iterative refinement of user personas based on accumulated data. The system automatically adjusts and refines persona characteristics as more user activity data becomes available, reducing the need for manual intervention and complex initial configuration while improving behavioral understanding over time.
Data Source
AI summary
A platform dynamically detects a user persona and facilitates a user objective in a user session. The platform obtains user activity of users across multiple historical sessions. The platform clusters similar user activity across the historical sessions to determine personas being used in the historical sessions. The platform mines sequential patterns in a set of user activity data associated with one of the personas and determines at least one predictive rule associated with that persona. Each predictive rule includes an initial activity among the set of user activity data and at least one subsequent activity in the set of user activity data.