Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining
To file a patent or examine a submitted patent, one must perform a prior-art search that includes both patent and non-patent literature. Unlike patent literature, non-patent literature is not standardized and lacks a unified search system, thus necessitating separate searches for patents and non-pat...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Korea Institute of Intellectual Property
2024-06-01
|
| Series: | Journal of Intellectual Property |
| Subjects: | |
| Online Access: | https://jip.or.kr/1902-06/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849729493554429952 |
|---|---|
| author | Seongwon Kim Donghee Yoo Suwon Lee |
| author_facet | Seongwon Kim Donghee Yoo Suwon Lee |
| author_sort | Seongwon Kim |
| collection | DOAJ |
| description | To file a patent or examine a submitted patent, one must perform a prior-art search that includes both patent and non-patent literature. Unlike patent literature, non-patent literature is not standardized and lacks a unified search system, thus necessitating separate searches for patents and non-patents. This renders the process particularly challenging for the latter. Hence, classification methods used in patent literature are applied to non-patent literature in this study, thus enabling a search system that operates in the same manner as patent-literature searches. The proposal includes the application of machine-learning techniques to recommend or automatically assign patent-classification codes to non-patent literature. For example, a process is reviewed in which international patent classificationcodes are automatically assigned to scholarly papers using machine-learning algorithms. Based on analyzing methods that leverage text-similarity and text-classification algorithms, the automatic classification of non-patent literature through patent-literature text mining is shown to be effective and thus warrants further research. Building a database of non-patent literature coded with patent classifications can result in a more efficient prior-art search process by allowing searches under a unified classification system for both patent and non-patent literatures. |
| format | Article |
| id | doaj-art-cf0c585de3154588b8120daee015fc9b |
| institution | DOAJ |
| issn | 1975-5945 2733-8487 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Korea Institute of Intellectual Property |
| record_format | Article |
| series | Journal of Intellectual Property |
| spelling | doaj-art-cf0c585de3154588b8120daee015fc9b2025-08-20T03:09:12ZengKorea Institute of Intellectual PropertyJournal of Intellectual Property1975-59452733-84872024-06-0119211714110.34122/jip.2024.19.2.117Automatic Classification of Non-Patent Literature via Patent-Literature Text MiningSeongwon Kim0Donghee Yoo1Suwon Lee2https://orcid.org/0000-0003-2603-1385Master’s Student, Department of Intellectual Property Convergence, Gyeongsang National University, Republic of KoreaProfessor, Department of Management Information Systems, Gyeongsang National University, Republic of KoreaProfessor, Department of Computer Science, Gyeongsang National University, Republic of KoreaTo file a patent or examine a submitted patent, one must perform a prior-art search that includes both patent and non-patent literature. Unlike patent literature, non-patent literature is not standardized and lacks a unified search system, thus necessitating separate searches for patents and non-patents. This renders the process particularly challenging for the latter. Hence, classification methods used in patent literature are applied to non-patent literature in this study, thus enabling a search system that operates in the same manner as patent-literature searches. The proposal includes the application of machine-learning techniques to recommend or automatically assign patent-classification codes to non-patent literature. For example, a process is reviewed in which international patent classificationcodes are automatically assigned to scholarly papers using machine-learning algorithms. Based on analyzing methods that leverage text-similarity and text-classification algorithms, the automatic classification of non-patent literature through patent-literature text mining is shown to be effective and thus warrants further research. Building a database of non-patent literature coded with patent classifications can result in a more efficient prior-art search process by allowing searches under a unified classification system for both patent and non-patent literatures.https://jip.or.kr/1902-06/patent literaturenon-patent literaturetext miningautomatic classificationtext similaritytext classification |
| spellingShingle | Seongwon Kim Donghee Yoo Suwon Lee Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining Journal of Intellectual Property patent literature non-patent literature text mining automatic classification text similarity text classification |
| title | Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining |
| title_full | Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining |
| title_fullStr | Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining |
| title_full_unstemmed | Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining |
| title_short | Automatic Classification of Non-Patent Literature via Patent-Literature Text Mining |
| title_sort | automatic classification of non patent literature via patent literature text mining |
| topic | patent literature non-patent literature text mining automatic classification text similarity text classification |
| url | https://jip.or.kr/1902-06/ |
| work_keys_str_mv | AT seongwonkim automaticclassificationofnonpatentliteratureviapatentliteraturetextmining AT dongheeyoo automaticclassificationofnonpatentliteratureviapatentliteraturetextmining AT suwonlee automaticclassificationofnonpatentliteratureviapatentliteraturetextmining |