Adaptive User Interface Personalization via Machine Learning

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Solution Overview

Problem

Existing social networking systems lack personalization in user interfaces, failing to adapt to individual user preferences and trends, leading to lower conversion rates and user frustration due to manual and limited customization processes.

Innovation Solution

The implementation of machine learning models to test and optimize user interfaces based on user activity data, analyzing responses from test groups with different attributes to determine optimal interface features and rules, enabling automatic personalization and adaptation to user preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual user interface customization is provided, then users can select from default styles, but the personalization process becomes manual and tedious

Engineering Contradiction:

Improveinterface personalizationVSAvoidcustomization process

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system automatically performs user interface personalization by analyzing user behavior data and applying machine learning models to determine optimal interface configurations, eliminating the need for manual user intervention in the customization process

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical customization processes with automated machine learning-based systems that analyze user data and generate personalized interface configurations automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If users must explicitly select preferences, then interface options are limited, but the process becomes tiresome to complete

Engineering Contradiction:

Improveinterface flexibilityVSAvoidpreference selection time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of user behavior data and pre-determines optimal interface configurations before users need them, so that personalization is already in place when users interact with the system

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors user interactions and behavior patterns, using this feedback to automatically adjust and refine interface personalization without requiring explicit user input

Inventive Principle:

Principle #23Feedback

3Productivity

If A/B testing is used to test interface features, then new features can be evaluated, but the tests lack consideration of users' personal style and are not adaptive

Engineering Contradiction:

Improveinterface testing efficiencyVSAvoidadaptive personalization

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent applies different interface features and testing approaches to different user segments based on their specific attributes, behaviors, and preferences, rather than applying uniform A/B tests to all users

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts interface testing and feature allocation based on real-time analysis of user behavior patterns and changing preferences, making the testing process adaptive rather than static

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS10402039B2Adaptive user interface using machine learning model

Publication Date: 2019.09.03 LOT NETWORK INC

AI summary

Techniques for social networking systems and methods for testing and applying user interfaces are disclosed herein. The method includes steps of presenting a user interface including a new user interface feature to a group of test users, collecting response data from the test users experiencing the user interface, performing analytics on the response data, and determining at least one interface rule of applying user interface features for a user depending on one or more user attributes of the user based on the analytics using a machine learning model.