International business machines corporation (20240232533). WIREFRAME GENERATION simplified abstract

From WikiPatents
Jump to navigation Jump to search

WIREFRAME GENERATION

Organization Name

international business machines corporation

Inventor(s)

Zhaoqi Wu of Shanghai (CN)

Yi Fang Chen of DaLian (CN)

Zhi Wang of Shanghai (CN)

Yi Qun Zhang of Shanghai (CN)

Yan Du of Beijing (CN)

Li Na Yuan of BEIJING (CN)

WIREFRAME GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240232533 titled 'WIREFRAME GENERATION

Simplified Explanation:

The method described in the abstract involves using two artificial intelligence models to create a wireframe based on text information related to requirements. The first AI model performs named entity recognition to extract entities and their relations from the text. These extracted entities and relations are then inputted into a second AI model to generate the wireframe, with the second model trained to minimize differences between the resultant relations and the extracted relations.

  • The method involves using two AI models to create a wireframe based on text information.
  • The first AI model performs named entity recognition to extract entities and relations from the text.
  • The extracted entities and relations are inputted into a second AI model to generate the wireframe.
  • The second AI model is trained to minimize differences between the resultant relations and the extracted relations.
  • The goal is to streamline the process of creating wireframes by leveraging AI technology.

Potential Applications:

This technology can be applied in various industries such as software development, web design, and product prototyping. It can streamline the process of creating wireframes by automating the extraction of entities and relations from text requirements.

Problems Solved:

This technology addresses the challenge of efficiently translating text requirements into visual wireframes. By using AI models for named entity recognition and wireframe generation, the process becomes more accurate and less time-consuming.

Benefits:

The benefits of this technology include increased efficiency in wireframe creation, improved accuracy in capturing requirements, and overall time savings in the design and development process.

Commercial Applications:

Title: AI-Powered Wireframe Generation Technology for Streamlined Design Processes

This technology can be commercially used by software development companies, web design agencies, and product design firms to enhance their design processes. By automating the wireframe creation process, businesses can save time and resources while improving the accuracy of their designs.

Prior Art:

Readers interested in exploring prior art related to this technology can start by researching AI models for named entity recognition and wireframe generation. Studies on natural language processing and design automation may also provide relevant background information.

Frequently Updated Research:

Researchers in the fields of artificial intelligence, natural language processing, and design automation are continuously exploring new methods and techniques to improve the efficiency and accuracy of wireframe generation processes. Stay updated on the latest advancements in these areas to leverage cutting-edge technologies for design tasks.

Questions about AI Technology:

1. How does the use of two AI models improve the accuracy of wireframe generation compared to traditional methods? 2. What are the potential limitations or challenges of using AI technology for wireframe creation tasks?


Original Abstract Submitted

a method of this disclosure may include performing a named entity recognition on text information related to requirements for a wireframe by a first artificial intelligence (ai) model, so as to extract entities and relations of the entities from the text information. the method may further comprise inputting the extracted entities and relations to a second ai model to generate the wireframe, wherein the second ai model is trained so that a difference between resultant relations of the entities of the generated wireframe and the extracted relations of the entities from the first ai model is decreased.