Summary of Automotive Innovation Landscaping Using Llm, by Raju Gorain and Omkar Salunke
Automotive innovation landscaping using LLM
by Raju Gorain, Omkar Salunke
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: A novel approach to automating patent landscaping, utilizing Large Language Models (LLMs), is proposed in this paper. The method, based on prompt engineering, extracts essential information from patent databases, including the problem addressed, technology utilized, and innovation areas within the vehicle ecosystem. This enables Research and Development teams to efficiently categorize patents and extract state-of-the-art inventive concepts. The authors demonstrate their approach by creating a comprehensive landscape of fuel cell technology using open-source patent data, providing valuable insights for future research and development in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine having a superpower that helps scientists and engineers discover new ideas and trends in car technology faster and more efficiently. This paper talks about how to use special computers called Large Language Models (LLMs) to analyze patents and find important information like what problems they solve, which technologies are used, and where innovation is happening. The authors show how this approach can help create a big picture of the current state of fuel cell technology, giving scientists valuable insights for future research and development. |
Keywords
» Artificial intelligence » Prompt
