18178791. MACHINE LEARNING-BASED LAYOUT GENERATION simplified abstract (Adobe Inc.)

MACHINE LEARNING-BASED LAYOUT GENERATION

Organization Name

Adobe Inc.

Inventor(s)

Sukriti Verma of Pittsburgh PA (US)

Venkata naveen kumar Yadav Marri of San Jose CA (US)

Ritiz Tambi of San Francisco CA (US)

Pranav Vineet Aggarwal of Santa Clara CA (US)

Peter O'donovan of Seattle WA (US)

Midhun Harikumar of Sunnyvale CA (US)

Ajinkya Kale of San Jose CA (US)

MACHINE LEARNING-BASED LAYOUT GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18178791 titled 'MACHINE LEARNING-BASED LAYOUT GENERATION

The abstract describes a method for machine learning-based generation of recommended layouts using a set of design elements and a generative layout model.

• The method involves receiving a set of design elements for layout recommendation.
• It determines the number of each type of design element in the set.
• A trained generative layout model is used to generate a set of recommended layouts based on the design elements.
• The generated layouts are then output for use.

Potential Applications: This technology could be applied in graphic design software to assist users in creating visually appealing layouts efficiently.

Problems Solved: This technology addresses the challenge of designing layouts by automating the process based on input design elements.

Benefits: The technology streamlines the layout design process, saving time and effort for designers while ensuring visually pleasing results.

Commercial Applications: Title: Machine Learning-Based Layout Recommendation Technology This technology could be utilized in graphic design software, marketing tools, and website development platforms to enhance layout design capabilities and user experience.

Questions about Machine Learning-Based Layout Recommendation Technology: 1. How does this technology improve the efficiency of layout design? 2. What are the key factors that influence the recommended layouts generated by the system?

Original Abstract Submitted

Embodiments are disclosed for machine learning-based generation of recommended layouts. The method includes receiving a set of design elements for performing generative layout recommendation. A number of each type of design element from the set of design elements is determined. A set of recommended layouts are generated using a trained generative layout model and the number and type of design elements. The set of recommended layouts are output.