PATopics: An automatic framework to extract useful information from pharmaceutical patents documents
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910568996143104 |
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| author | Cecilio, Pablo Perreira, Antônio Viegas, Juliana Santos Rosa Cunha, Washington Viegas, Felipe Tuler, Elisa Vicentini, Fabiana Testa Moura de Carvalho Rocha, Leonardo |
| author_facet | Cecilio, Pablo Perreira, Antônio Viegas, Juliana Santos Rosa Cunha, Washington Viegas, Felipe Tuler, Elisa Vicentini, Fabiana Testa Moura de Carvalho Rocha, Leonardo |
| contents | Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08905 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PATopics: An automatic framework to extract useful information from pharmaceutical patents documents Cecilio, Pablo Perreira, Antônio Viegas, Juliana Santos Rosa Cunha, Washington Viegas, Felipe Tuler, Elisa Vicentini, Fabiana Testa Moura de Carvalho Rocha, Leonardo Digital Libraries Information Retrieval Machine Learning Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario. |
| title | PATopics: An automatic framework to extract useful information from pharmaceutical patents documents |
| topic | Digital Libraries Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2408.08905 |