Image-to-Markup Code Generation Using Iterative Layout Matching

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Solution Overview

Problem

Conventional techniques for generating markup language code from digital content require specialized knowledge and are computationally inefficient, often leading to inaccuracies and time-consuming manual processes, and are unavailable for casual users.

Innovation Solution

Employing machine learning through a multimodal machine-learning model to generate markup language code or other types of code that are executable by a processing device automatically and without user intervention from a digital image, markup language code or other types of markup code or other types of markup code or other types of markup code or other types of markup code or other types of markup code or other types of markup code or other types of markup code or other markup languages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional techniques are used to generate markup language code, then specialized knowledge and manual processes are required, but accuracy and operational efficiency deteriorate

Engineering Contradiction:

Improvecode generation accuracyVSAvoidoperational efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical coding processes with an automated machine learning system. The ML model automatically generates markup language code from digital images, eliminating the need for manual specialized knowledge while improving both accuracy through consistent automated processes and productivity through rapid code generation without human intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service code generation where the machine learning model autonomously converts digital images into executable markup language code without requiring human expertise. The automated process serves itself by taking an image as input and producing ready-to-use code as output, improving efficiency while maintaining accuracy through the model's trained capabilities.

Inventive Principle:

Principle #25Self-service

2Productivity

If conventional manual code generation techniques are used, then code can be produced, but time consumption and computational inefficiency increase

Engineering Contradiction:

Improvecode generation speedVSAvoidmanual process time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The machine learning model is pre-trained on extensive datasets of digital images and corresponding markup language code. This preliminary training action enables the model to rapidly generate accurate code during deployment without requiring manual intervention, significantly improving code generation speed while eliminating the time loss associated with manual coding processes.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent substitutes slow manual coding mechanics with fast automated machine learning inference. The ML system processes digital images and generates markup language code at machine speed, dramatically increasing productivity while eliminating the time-consuming nature of conventional manual code generation techniques.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated machine learning techniques are employed, then operational efficiency improves, but system complexity increases

Engineering Contradiction:

Improveautomated code generationVSAvoidmachine learning system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning system is designed as a universal multi-functional platform that handles various digital content types (images, videos, presentations) and generates multiple markup language formats. This universality consolidates what would otherwise require multiple separate tools and processes, improving automated code generation productivity while managing system complexity through a single integrated solution.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an intermediary processing layer that translates between digital content formats and markup language code through the machine learning model. This intermediary approach simplifies the overall system architecture by providing a standardized conversion interface, improving automated generation efficiency while containing complexity within the ML model rather than the surrounding system.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260023919A1Code generation from a digital image

Publication Date: 2026.01.22 ADOBE INC

AI summary

Code generation techniques from a digital image are described. In one or more examples, layout data is extracted from a digital image. The layout data describing a layout of elements included in the digital image. Markup language code is generated over one or more iterations of candidate markup code using a machine-learning model based on the digital image and the layout data and determining whether a similarity threshold is reached by comparing a candidate digital image generated using the candidate markup code with the digital image. The markup language code is output responsive to determining the similarity threshold is reached.