Text-Guided Digital Content Generation With Asset Interactions

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Solution Overview

Problem

Conventional digital content creation techniques separate the creation of digital content from its underlying message, leading to inaccuracies and computational inefficiencies.

Innovation Solution

Utilize a text-based input to generate asset recommendation data using a machine-learning model, enabling the selection of assets and interactions that align with the underlying message, thereby improving accuracy and efficiency in digital content generation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If conventional digital content creation techniques are used to create supporting digital content and then fit them together, then digital content can be created, but inaccuracies are introduced and computational inefficiencies occur

Engineering Contradiction:

Improveaccuracy of digital content alignment with messageVSAvoidcomputational efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent inverts the conventional content creation workflow by starting with the underlying message and generating supporting digital content (charts, tables, graphics) from it, rather than creating content first and then fitting it to a message. This inversion ensures that all generated content is inherently aligned with the message, eliminating inaccuracies while improving computational efficiency through automated generation.

Inventive Principle:

Principle #13The other way round (Inversion)

2Stability of the object's composition

If conventional techniques create supporting digital content separately and then fit them together, then content can be assembled, but the cohesiveness with the underlying message deteriorates

Engineering Contradiction:

Improvecohesiveness of digital content with messageVSAvoidease of content creation

Core Design Contradiction:

Stability of the object's compositionVSEase of manufacture

Solution Approach 1:

The patent merges the message with the generation process of supporting digital content by using the message as direct input to generate charts, tables, and graphics. This integration ensures that all content elements are cohesively aligned with the underlying message while maintaining ease of creation through automated generation from the message.

Inventive Principle:

Principle #5Merging (Combining)

3Productivity

If conventional digital content creation is used, then content can be produced, but computational resource consumption increases

Engineering Contradiction:

Improvedigital content generation efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent applies preliminary action by using the underlying message to pre-determine and generate all supporting digital content elements before final assembly. This approach eliminates the need for iterative fitting and adjustment of content to message, thereby reducing computational resource consumption while improving generation efficiency.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250278437A1Digital content generation from a text-based input

Publication Date: 2025.09.04 ADOBE INC

AI summary

Digital content generation techniques are described that are performed using a text-based input. A text-based input is received and asset recommendation data is generated based on the text-based input using a machine-learning model, e.g., a large language model (LLM). A selection of a plurality of assets is received from the asset recommendation data and a selection is also received of at least one interaction from a plurality of interactions for the plurality of assets. The digital content is generated as having the interaction between the selection of the plurality of assets.