| dc.contributor.author | Seebacher, Daniel | en_US |
| dc.contributor.author | Stein, Manuel | en_US |
| dc.contributor.author | Janetzko, Halldór | en_US |
| dc.contributor.author | Keim, Daniel A. | en_US |
| dc.contributor.editor | Natalia Andrienko and Michael Sedlmair | en_US |
| dc.date.accessioned | 2016-06-09T09:32:09Z | |
| dc.date.available | 2016-06-09T09:32:09Z | |
| dc.date.issued | 2016 | en_US |
| dc.identifier.isbn | 978-3-03868-016-1 | en_US |
| dc.identifier.issn | - | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/eurova.20161118 | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10 | |
| dc.description.abstract | Claiming intellectual property for an invention by patents is a common way to protect ideas and technological advancements. However, patents allow only the protection of new ideas. Assessing the novelty of filed patent applications is a very time-consuming, yet crucial manual task. Current patent retrieval systems do not make use of all available data and do not explain the similarity between patents. We support patent officials by an enhanced Visual Analytics multi-modal patent retrieval system. Including various similarity measurements and incorporating user feedback, we are able to achieve significantly better query results than state-of-the-art methods. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.3 [Computer Graphics] | en_US |
| dc.subject | Picture/Image Generation | en_US |
| dc.subject | Display algorithms H.3.3 [Information Systems] | en_US |
| dc.subject | Information Search and Retrieval | en_US |
| dc.subject | Relevance feedback | en_US |
| dc.title | Patent Retrieval: A Multi-Modal Visual Analytics Approach | en_US |
| dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | en_US |
| dc.description.sectionheaders | Multivariate Data Analysis | en_US |
| dc.identifier.doi | 10.2312/eurova.20161118 | en_US |
| dc.identifier.pages | 13-17 | en_US |
