User Experience Prediction via Iterative Pattern Feedback Loop
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Solution Overview
Problem
User experience (UX) designers face challenges in comprehensively understanding and enhancing user interactions with touchpoints, as existing methods lack effectiveness in analyzing and adjusting user experiences across multiple interactions.
Innovation Solution
A system and method that utilize a feedback loop to analyze user interactions by identifying patterns through a modeling pipeline, filtering, and evaluating them to adjust user experience parameters, such as touchpoints and interaction data, to improve UX prediction and satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to analyze user interactions, then analysis can be performed, but the effectiveness in comprehensively understanding and enhancing user experiences across multiple interactions is insufficient
Solution Approach 1:
The patent implements a feedback loop that iteratively analyzes user interaction patterns, evaluates results against effectiveness criteria, and refines the analysis approach. This continuous feedback mechanism enables comprehensive understanding of user experiences across multiple interactions while systematically improving analysis effectiveness without overwhelming complexity
Solution Approach 2:
The system performs preliminary pattern identification and filtering operations before detailed evaluation. By pre-processing interaction data to identify recurring patterns and filter relevant information in advance, the system prepares comprehensive analysis inputs that improve subsequent evaluation effectiveness while managing computational complexity
2Loss of information
If multiple user interactions are analyzed comprehensively, then user experience understanding improves, but the complexity of analyzing and adjusting experiences across multiple interactions increases
Solution Approach 1:
The patent segments user interactions into distinct patterns that can be independently identified and analyzed. By dividing complex multi-interaction data into recognizable pattern units, the system maintains complete information about user experiences while reducing the complexity of analyzing multiple interactions through structured pattern recognition
Solution Approach 2:
The system develops universal pattern recognition capabilities that can identify and analyze multiple types of user interactions through a unified approach. This multi-functional pattern analysis framework enables comprehensive understanding across diverse interaction types without requiring separate complex analysis procedures for each interaction type
Data Source
AI summary
The present disclosure is directed to systems and methods for predicting an outcome of a user journey. For example, a method may include: identifying a plurality of patterns based on a plurality of user interactions of a plurality of users with a plurality of touchpoints; applying a parameter to filter the plurality of patterns; evaluating the filtered plurality of patterns based an evaluation criterion; and applying a feedback loop based on the evaluation of the filtered patterns to modify the parameter or adjust a user experience.