No-Code Interface for Machine Learning Application Development

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Solution Overview

Problem

Application development, especially with machine-learning models, is complex and costly, requiring extensive knowledge of development environments, languages, and machine-learning optimization, which hinders efficient and user-friendly development processes.

Innovation Solution

A no-code or minimal-code application development interface that facilitates application development by allowing developers to customize feature values of application features through a user-friendly interface, utilizing underlying machine-learning models to perform application functions and customize outputs for individual users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional application development methods are used with machine-learning models, then application functions can be performed, but the complexity and cost of development increase significantly

Engineering Contradiction:

Improveapplication function performanceVSAvoiddevelopment complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary layer (the application development system with pre-configured machine-learning model templates) between the developer and the complex machine-learning infrastructure. This intermediary abstracts the complexity by providing standardized interfaces and automated configuration, allowing developers to build applications without deep machine-learning expertise while still accessing powerful model capabilities.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual, mechanical configuration processes with automated systems. Instead of manually configuring machine-learning models, the system automatically selects, configures, and optimizes models based on application requirements, substituting complex manual engineering work with automated computational processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional application development methods are used with machine-learning models, then application functions can be performed, but the cost of development increases

Engineering Contradiction:

Improveapplication function performanceVSAvoiddevelopment cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent uses pre-configured templates and reusable machine-learning model configurations that can be copied and adapted across multiple applications. Instead of building machine-learning integrations from scratch for each application, developers can replicate proven configurations, significantly reducing development time and cost while maintaining performance.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent creates a universal application development platform that can handle multiple machine-learning model types and application scenarios through a single interface. This multi-functional system eliminates the need for separate development processes for different model types, reducing overall development costs while maintaining access to diverse machine-learning capabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If traditional application development methods are used, then functional applications can be created, but extensive technical expertise is required

Engineering Contradiction:

Improveapplication creation capabilityVSAvoidease of development

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent segments the complex application development process into distinct, manageable components with standardized interfaces. By breaking down the development workflow into discrete steps (data input, model selection, configuration, deployment) with clear interfaces, the system makes the process more accessible to developers without extensive machine-learning expertise while maintaining productivity.

Inventive Principle:

Principle #1Segmentation

4Adaptability or versatility

If machine-learning models are integrated into applications, then customized user outputs can be provided, but the development process becomes more complex

Engineering Contradiction:

Improvecustomization capabilityVSAvoiddevelopment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent enables customization through parameter adjustment rather than structural changes. The system allows developers to customize user outputs by modifying model parameters and configuration settings through a simplified interface, maintaining adaptability while avoiding the complexity of custom model development. This approach lets users tune model behavior without understanding the underlying complex algorithms.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250117193A1Systems and methods for providing a user interface that facilitates application development

Publication Date: 2025.04.10 SHAPIRO DMITRY

AI summary

Systems and methods for providing a user interface that facilitates application development. The applications may utilize one or more underlying machine-learning models to perform application functions. Exemplary implementations may: effectuate presentation of an application development interface to developers through client computing platforms associated with the developers; receive, from client computing devices, input information indicating feature values entered and/or selected by the developers via the user interface fields; responsive to receipt of first input information, configure a first application in accordance with the feature values included in a first feature values set; provide the configured application for user by one or more users; and/or other exemplary implementations.