Machine Learning Models for Interface Experience Metrics
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Solution Overview
Problem
Existing solutions are inadequate in determining user interaction goals in interactive computing environments, leading to inaccurate user experience metrics and limited ability to modify interfaces effectively to suit individual user needs.
Innovation Solution
Applying machine-learning models to interaction data to identify interaction goals and compute interface experience metrics, which enables modifying the interface to improve user experience by customizing layout and feature prominence based on user interaction patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surveys are used to evaluate user experience, then user opinions can be collected, but response rates are low and data reliability is poor
Solution Approach 1:
The patent replaces the mechanical survey system with an automated machine learning-based analysis system that processes user interaction data (clicks, scrolls, time spent) to objectively measure user experience, eliminating the need for manual survey participation while improving measurement accuracy through behavioral analytics
Solution Approach 2:
The system enables self-service measurement by automatically collecting and analyzing user interaction data without requiring user initiation or participation, allowing the system to autonomously evaluate user experience metrics from observed behavioral patterns
2Measurement precision
If user interactions are analyzed in real-time, then accurate user experience metrics can be computed, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using historical interaction data, so that during real-time operation, the system only needs to execute pre-computed analysis algorithms, significantly reducing online computational complexity while maintaining high measurement precision
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw interaction data and experience metrics, where the models process and interpret complex interaction patterns, simplifying the computational task and enabling accurate metric computation without directly analyzing all raw data points
3Ease of operation
If the interface is customized for individual users, then user experience is improved, but interface complexity increases
Solution Approach 1:
The patent implements self-service customization where the system automatically analyzes user interaction patterns and applies interface modifications without requiring user configuration input, enabling personalized interfaces while keeping the user-facing system simple and intuitive
Solution Approach 2:
The patent uses feedback loops where user interactions are continuously monitored, experience metrics are computed, and interface modifications are automatically applied based on the metrics, creating a dynamic system that adapts to user needs while maintaining operational simplicity through automated decision-making
Data Source
AI summary
A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.