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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.09724 |
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| _version_ | 1866908534639165440 |
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| author | Kim, Wonyoung Seo, Sujeong Lee, Juhyun |
| author_facet | Kim, Wonyoung Seo, Sujeong Lee, Juhyun |
| contents | Technology opportunities are critical information that serve as a foundation for advancements in technology, industry, and innovation. This paper proposes a framework based on the temporal relationships between technologies to identify emerging technology opportunities. The proposed framework begins by extracting text from a patent dataset, followed by mapping text-based topics to discover inter-technology relationships. Technology opportunities are then identified by tracking changes in these topics over time. To enhance efficiency, the framework leverages a large language model to extract topics and employs a prompt for a chat-based language model to support the discovery of technology opportunities. The framework was evaluated using an artificial intelligence patent dataset provided by the United States Patent and Trademark Office. The experimental results suggest that artificial intelligence technology is evolving into forms that facilitate everyday accessibility. This approach demonstrates the potential of the proposed framework to identify future technology opportunities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiTTO-LLM: Framework for Discovering Topic-based Technology Opportunities via Large Language Model Kim, Wonyoung Seo, Sujeong Lee, Juhyun Computation and Language Artificial Intelligence Machine Learning 68T09 Technology opportunities are critical information that serve as a foundation for advancements in technology, industry, and innovation. This paper proposes a framework based on the temporal relationships between technologies to identify emerging technology opportunities. The proposed framework begins by extracting text from a patent dataset, followed by mapping text-based topics to discover inter-technology relationships. Technology opportunities are then identified by tracking changes in these topics over time. To enhance efficiency, the framework leverages a large language model to extract topics and employs a prompt for a chat-based language model to support the discovery of technology opportunities. The framework was evaluated using an artificial intelligence patent dataset provided by the United States Patent and Trademark Office. The experimental results suggest that artificial intelligence technology is evolving into forms that facilitate everyday accessibility. This approach demonstrates the potential of the proposed framework to identify future technology opportunities. |
| title | DiTTO-LLM: Framework for Discovering Topic-based Technology Opportunities via Large Language Model |
| topic | Computation and Language Artificial Intelligence Machine Learning 68T09 |
| url | https://arxiv.org/abs/2509.09724 |