Interface Experience Metric for Online Platform Adaptation

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Solution Overview

Problem

Existing solutions have limited capability to customize interactive computing environments based on user experiences, as experience is latent and difficult to measure directly, leading to subjective and unreliable evaluations that do not provide detailed insights for modifying interfaces to better suit user needs.

Innovation Solution

Applying machine-learning models to interaction data to compute an interface experience metric, which evaluates the quality of user experiences by predicting next states and calculating value differentials, allowing for objective modifications to interface elements based on user interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If surveys are used to evaluate user experience, then user opinions can be collected, but the response rates are low and the data is subjective and unreliable

Engineering Contradiction:

Improveuser experience evaluation accuracyVSAvoidresponse rate

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces the mechanical survey system with an automated machine learning-based analysis system that processes interaction data. Instead of relying on users to complete surveys, the system automatically evaluates user experience by analyzing objective interaction patterns, thereby eliminating the low response rate and subjectivity problems while maintaining measurement precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces interaction data as an intermediary between the user and the evaluation system. Rather than directly asking users for feedback, the system uses interaction data (clicks, navigation patterns, time spent) as a mediator to objectively infer user experience quality, providing reliable measurements without requiring user participation in surveys.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If surveys are used to collect user feedback, then some experience data can be obtained, but the data reflects subjective opinions rather than actual interactions

Engineering Contradiction:

Improvedetailed interaction insightsVSAvoidexperience evaluation reliability

Core Design Contradiction:

Loss of informationVSMeasurement precision

Solution Approach 1:

The patent substitutes subjective user reports with objective machine learning analysis of interaction data. The system replaces the mechanical process of users recalling and reporting their experience with an automated system that directly analyzes actual interaction patterns, eliminating the gap between perceived and actual user experience while preserving detailed interaction insights.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a digital copy of user interactions through comprehensive tracking of clickstream data, navigation patterns, and interface engagements. This copy serves as a faithful representation of actual user behavior, allowing the system to analyze real interactions rather than relying on users' subjective recollections, thereby maintaining both information detail and measurement precision.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If the interactive environment is customized for different users, then user experience quality improves, but the system complexity increases

Engineering Contradiction:

Improveinterface customization capabilityVSAvoidsystem evaluation and modification complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a self-service system where the interface automatically adapts to user needs without requiring manual configuration or complex intervention. The machine learning model continuously analyzes interaction data and autonomously modifies interface elements, allowing the system to self-optimize for different users while managing complexity through automation rather than manual processes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent creates a dynamic interface that continuously adapts based on real-time interaction analysis. Rather than requiring complex static customization rules, the system uses dynamic machine learning models that automatically adjust interface elements based on current user behavior patterns, enabling high adaptability while managing system complexity through continuous automated optimization rather than pre-configured complexity.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12124948B2Machine-learning models applied to interaction data for facilitating experience-based modifications to interface elements in online environments

Publication Date: 2024.10.22 ADOBE INC

AI summary

In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities. The computing system transmits the interface experience metric to an online platform, which can cause interface elements of the online platform to be modified based on the interface experience metric.