Machine-Learned UI Generation for Relevance-Based Screen Layout

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Solution Overview

Problem

Statically defined user interfaces in software applications do not adapt to individual user behavior, leading to irrelevant information display and inefficient use of screen space.

Innovation Solution

Utilize machine learning models to analyze user input queries and historical interaction data to generate prioritization scores for user interface components, dynamically determining their inclusion and order based on user relevance, and transmit these definitions to client devices for rendering.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If user interfaces are statically defined for different portions of a workflow or according to user interface configuration, then the interface structure is simple and easy to implement, but the interface cannot adapt to individual user behavior and displays irrelevant information

Engineering Contradiction:

Improveinterface adaptabilityVSAvoidinterface generation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic user interface generation by transitioning from static, pre-defined interfaces to dynamically generated interfaces that adapt to individual user behavior. The system uses machine learning models to analyze user interactions and generate customized interface definitions in real-time, allowing the interface to change and adapt based on user needs while maintaining manageable complexity through automated generation processes.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system enables self-service by allowing the user interface to automatically adapt to user behavior without requiring manual reconfiguration. The machine learning model continuously learns from user interactions and autonomously generates optimized interface definitions, eliminating the need for users to manually adjust interface configurations while improving adaptability.

Inventive Principle:

Principle #25Self-service

2Productivity

If user interfaces are statically defined, then the implementation is straightforward and maintenance is easy, but screen space is not efficiently utilized and relevant information is not prioritized

Engineering Contradiction:

Improveinformation retrieval efficiencyVSAvoidtime to locate relevant information

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent applies local quality by prioritizing different interface components based on their relevance to the current user context and task. Instead of treating all interface elements equally, the system assigns different importance levels to different components, displaying the most relevant information prominently and allowing users to quickly locate needed information without scanning the entire interface.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically changes interface parameters such as component visibility, positioning, and prominence based on user behavior analysis. The machine learning model adjusts these parameters in real-time to optimize information retrieval efficiency, ensuring that relevant information is always displayed in optimal locations while reducing the time users spend searching for needed functionality.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If machine learning models are used to generate prioritization scores for interface components, then the interface becomes highly adaptive to user needs, but the computational resources and processing time increase

Engineering Contradiction:

Improveinterface personalizationVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent implements partial action by applying machine learning-based prioritization selectively to the most critical interface components rather than analyzing every element. The system identifies key components that have the greatest impact on user experience and focuses computational resources on optimizing these, achieving effective personalization while reducing overall computational overhead.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12541379B2Dynamically generating user interfaces based on machine learning models

Publication Date: 2026.02.03 INTUIT INC

AI summary

Certain aspects of the present disclosure provide techniques for rendering visual artifacts in virtual worlds using machine learning models. An example method generally includes identifying, based on a machine learning model and a streaming natural language input, an intent associated with the streaming natural language input; generating, based on the identified intent associated with the streaming natural language input, one or more virtual objects for rendering in a virtual environment displayed on one or more displays of an electronic device; and rendering the generated one or more virtual objects in the virtual environment.