A survey on deep learning for patent analysis
| Autor: | Renukswamy Chikkamath, Ralf Krestel, Julian Risch, Christoph Hewel |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Renewable Energy
Sustainability and the Environment Computer science Intersection (set theory) business.industry Process Chemistry and Technology Deep learning 05 social sciences Energy Engineering and Power Technology Bioengineering Listing (computer) Library and Information Sciences 050905 science studies Data science Field (computer science) Computer Science Applications Task (project management) Domain (software engineering) Fuel Technology Categorization Artificial intelligence 0509 other social sciences Architecture 050904 information & library sciences business |
| Zdroj: | World Patent Information. 65:102035 |
| ISSN: | 0172-2190 |
| DOI: | 10.1016/j.wpi.2021.102035 |
| Popis: | Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel approaches have been developed in the field of patent analysis. The goal of these approaches is to reduce costs by automating tasks that previously only domain experts could solve. In this article, we provide a comprehensive survey of the application of deep learning for patent analysis. We summarize the state-of-the-art techniques and describe how they are applied to various tasks in the patent domain. In a detailed discussion, we categorize 40 papers based on the dataset, the representation, and the deep learning architecture that were used, as well as the patent analysis task that was targeted. With our survey, we aim to foster future research at the intersection of patent analysis and deep learning and we conclude by listing promising paths for future work. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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