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<h2 id="data-mining">Data mining</h2> <p>The main step in processing structured information is <a href="/facts/Data-mining/AH53q5ac">data-mining</a>,<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a> which emerged in the late 1980s. Data mining involves statistics, <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a>, and <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>.<a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a> Patent data mining extracts information from the structured data of the patent document.<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a> These structured data are bibliographic fields such as location, date or status. </p> <h3>Structured fields</h3> <table><tbody><tr><th>Structured data</th><th>Description</th><th>Business Intelligence use</th></tr><tr><td>Data</td><td>Patents contain identifying data including priority, publication data and the issue date.<ul><li>Priority data regroup priority number assigned for the first application, the corresponding date and priority country.</li><li>The publication data encompasses the publication number given when the patent is published, 18 months after filling and the publication date.</li><li>The issue date is the data the patent is granted, usually 3.5 years after filling depending on the patent office.</li></ul></td><td>Crossing dates and locations fields offer a global vision of a technology in time and space.</td></tr><tr><td>Assignee</td><td>Patent assignees are organizations or individuals - the owners of the patent.</td><td>The field can offer a ranking of the principal actors of the environment, thus allowing us to visualise potential competitors or partners.</td></tr><tr><td>Inventor</td><td>Inventors develop the invention/patent.</td><td>Inventors' field combined with the assignee field can create a social network and provide a method to follow field experts.</td></tr><tr><td>Classification</td><td>The classification can regroup inventions with similar technologies. The most commonly used is the <a href="/facts/International_Patent_Classification/zyosQlAR">International Patent Classification</a> (IPC). However, patent organizations have their own classification; for instance, the European Patent Office has framed the <a href="/facts/European_Classification/jRULFRc0">ECLA</a>.</td><td>Grouping patents by theme offers an overview of the corpus and the potential applications of studied technology.</td></tr><tr><td>Status</td><td>The legal status indicates whether an application is filed, approved, or rejected.</td><td>Patent family and legal status searching is important for litigation and competitive intelligence.</td></tr></tbody></table> <h3>Advantages</h3> <p>Data mining allows study of filing patterns of competitors and locates main patent filers within a specific area of technology. This approach can be helpful to monitor competitors' environments, moves and innovation trends and gives a macro view of a technology status. </p> <h2 id="text-mining">Text-mining</h2> <h3>Principle</h3> <p>Text mining is used to search through unstructured text documents.<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a><a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a> This technique is widely used on the Internet, it has had success in <a href="/facts/Bioinformatics/D5x2L8ee">bioinformatics</a> and now in the intellectual property environment.<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> </p><p>Text mining is based on a statistical analysis of word recurrence in a corpus.<a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> An algorithm extracts words and expressions from title, summary and claims and gathers them by <a href="/facts/Declension/SWppkx4X">declension</a>. "And" and "if" are labeled as non-information bearing words and are stored in the <a href="/facts/Stop_words/P0mMcMVr">stopword</a> list. Stoplists can be specialised in order to create an accurate analysis. Next, the algorithm ranks the words by weight, according to their frequency in the patent's corpus and the document frequency containing this word. The score for each word is calculated using a formula such as:<a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a><a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a> </p><p> W e i g h t = T e r m F r e q u e n c y D o c u m e n t F r e q u e n c y = F r e q u e n c y o f t h e w o r d o r e x p r e s s i o n i n t h e T e x t S e a N u m b e r o f d o c u m e n t s c o n t a i n i n g t h e e x p r e s s i o n o r w o r d {\displaystyle Weight={\frac {Term\ Frequency}{Document\ Frequency}}={\frac {Frequency\ of\ the\ word\ or\ expression\ in\ the\ Text\ Sea}{Number\ of\ documents\ containing\ the\ expression\ or\ word}}} </p><p>A frequently-used word in several documents has less weight than a word used frequently in a few patents. Words under a minimum weight are eliminated, leaving a list of pertinent words or descriptors. Each patent is associated to the descriptors found in the selected document. Further, in the process of clusterisation, these descriptors are used as subsets, in which the patent are regrouped or as tags to place the patents in predetermined categories, for example keywords from International Patent Classifications. </p><p>Four text parts can be processed with text-mining : </p> <ul><li>Title</li> <li>Abstract</li> <li>Claim</li> <li>Patent Full-Text</li></ul> <p>Software offer different combinations but title, abstract and claim are generally the most used, providing a good balance between interferences and relevancy. </p> <h3>Advantages</h3> <p>Text-mining can be used to narrow a search or quickly evaluate a patent corpus. For instance, if a query produces irrelevant documents, a multi-level clustering hierarchy identifies them in order to delete them and refine the search. Text-mining can also be used to create internal taxonomies specific to a corpus for possible mapping. </p> <h2 id="visualisations">Visualisations</h2> <p class="note">Further information: <a href="/facts/Patent_map/XvEvTPpX">Patent map</a></p> <p>Allying <a href="/facts/Patent_analysis/cqXDCUv4">patent analysis</a> and informatic tools offers an overview of the environment through value-added visualisations. As patents contain structured and unstructured information, visualisations fall in two categories. Structured data can be rendered with data mining in macrothematic maps and statistical analysis. Unstructured information can be shown in like clouds, cluster maps and 2D keyword maps. </p> <h3>Data mining visualisation</h3> <table><tbody><tr><th>Visualisation</th><th>Picture</th><th>Description</th><th>Business Intelligence use</th></tr><tr><td>Matrix chart</td><td>Picture</td><td>Graphic organizer used to summarize a multidimensional data set in a grid</td><td>Data comparison</td></tr><tr><td>Location map</td><td>Picture</td><td>Map with overlaid data values on geographic regions</td><td><ul><li>Spatial patterns</li><li>Find innovative jurisdictions</li></ul></td></tr><tr><td><a href="/facts/Bar_chart/EHCXtdeV">Bar chart</a></td><td>Picture</td><td>Graph with rectangular bars proportional to the values that they represent, useful for numerical comparisons.</td><td>Data evolution</td></tr><tr><td><a href="/facts/Line_graph/RWNxSy5j">Line graph</a></td><td>Picture</td><td>Graph used to summarize how two parameters are related and how they vary.</td><td>Data evolution and relationships</td></tr><tr><td><a href="/facts/Pie_chart/SmWhvWpM">Pie chart</a></td><td>Picture</td><td>Circular chart divided into sections, to illustrate proportions.</td><td>Data comparison</td></tr><tr><td><a href="/facts/Bubble_chart/opLXq8ne">Bubble chart</a></td><td>Picture</td><td>3-axis 2D chart which enables visualization similar to the <a href="/facts/Magic_quadrant/W9Et71IX">Magic quadrant</a> chart.</td><td><ul><li>Market maturity</li><li>Competitive analysis</li><li>Licensing opportunities</li></ul></td></tr></tbody></table> <h3>Text mining visualisation</h3> <table><tbody><tr><th>Visualisation</th><th>Description</th><th>Business Intelligence use</th></tr><tr><td><a href="/facts/Tree_(data_structure)/RcXz9noF">Tree list</a></td><td>Hierarchy list</td><td><ul><li>Evaluating relevance</li><li>Taxonomy</li><li>Concept relationships</li></ul></td></tr><tr><td><a href="/facts/Tag_cloud/cKJnqhFq">Tag cloud</a></td><td>Full text of concepts. The size of each word is determined by its frequency in the corpus</td><td><ul><li>Evaluating relevance</li><li>More visual than the tree list</li></ul></td></tr><tr><td><a href="http://www.infovis.net/imagenes/T1_N160_A853_Newsmaps.jpg">2D keyword map</a><a class="footnote-ref" id="fnref:20" href="#fn:20"><sup>20</sup></a></td><td>Tomographic map with quantitative representation of relief, usually using contour lines and colors. Distance on map is proportional to the difference between themes.<a class="footnote-ref" id="fnref:21" href="#fn:21"><sup>21</sup></a></td><td><ul><li>Landscape vision of thematics</li><li>Similarity vision with <a href="/facts/Service-oriented_modeling/dKBECNxo">SOM</a></li><li>Monitoring competitors</li></ul></td></tr><tr><td></td><td>2D hierarchical cluster map with quantitative and qualitative representation of document set association to topic, usually using quantized cells and colors. Size of topic cells may represent patent count per topic relative to overall document set. Density and distribution inside of a topic cell may be proportional to document count relative to association to the topic and strength of association, respectively.</td><td><ul><li>Landscape vision of thematics</li><li>Monitoring competitors or a technology space</li><li>Identifying trends in a defined patent set</li></ul></td></tr><tr><td></td><td>Text is decomposed into logical groupings and sub-groupings, then represented as a navigable hierarchy of those groupings by means of proportionate circle arcs.</td><td><ul><li>Landscape vision of thematics</li><li>Monitoring a technology space</li><li>Interactive navigation and granularity</li></ul></td></tr></tbody></table> <h3>Visualisation for both data-mining and text-mining</h3> <p>Mapping visualisations can be used for both text-mining and data-mining results. </p> <table><tbody><tr><th>Visualisation</th><th>Picture</th><th>Description</th><th>Business Intelligence use</th></tr><tr><td><a href="/facts/Tree_map/ws9ss8GK">Tree map</a></td><td>Picture</td><td>Visualization of hierarchical structures. Each data item, or row in the data set is represented by a rectangle, whose area is proportional to selected parameters.</td><td><ul><li>Landscape vision of hierarchical thematics</li><li>Position of competitors or technology by thematics</li></ul></td></tr><tr><td><a href="/facts/Network_mapping/uAk4chJ9">Network map</a></td><td>Picture</td><td>In a network diagram, entities are connected to each other in the form of a node and link diagram.</td><td><ul><li>Relationship visions</li><li>Monitoring similar competitors or technologies</li></ul></td></tr><tr><td>Citation map</td><td>Picture</td><td>In the citation map, the date of citation is visualized on the x axis and each individual citation takes an entry on the y axis. A strong vertical line indicates the filing date, showing which citations are cited by the patent as opposed to those which cite the patent.</td><td><ul><li>Qualitative and quantitative view of citation history and density</li></ul></td></tr></tbody></table> <h2 id="uses">Uses</h2> <p>What patent visualisation can highlight:<a class="footnote-ref" id="fnref:22" href="#fn:22"><sup>22</sup></a><a class="footnote-ref" id="fnref:23" href="#fn:23"><sup>23</sup></a> </p> <ul><li>Competitors</li> <li>Partners</li> <li>New innovations</li> <li>Technologic environment description<a class="footnote-ref" id="fnref:24" href="#fn:24"><sup>24</sup></a></li> <li><a href="/facts/Computer_network/3w5RM99p">Networks</a></li></ul> <p>Field application:<a class="footnote-ref" id="fnref:25" href="#fn:25"><sup>25</sup></a><a class="footnote-ref" id="fnref:26" href="#fn:26"><sup>26</sup></a> </p> <ul><li>R&D strategy management</li> <li><a href="/facts/Competitive_intelligence/4gHQIJzJ">Competitive intelligence</a></li> <li><a href="/facts/Licensing/xaP3U6ga">Licensing</a></li> <li>Strategy</li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>[1][dead link] <a href="http://www.wipo.int/export/sites/www/ipstats/en/statistics/patents/pdf/941e_2010.pdf" target="_blank">http://www.wipo.int/export/sites/www/ipstats/en/statistics/patents/pdf/941e_2010.pdf</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Kevin G. Rivette, David Kline, "Discovering new value in intellectual property", Harvard Business Review (January–February 2000) <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>"Thomson Reuters | Aureka | Intellectual Property". Archived from the original on 4 February 2013. <a href="https://archive.today/20130204105747/http://thomsonreuters.com/products_services/intellectual_property/ip_products/a-z/aureka/" target="_blank">https://archive.today/20130204105747/http://thomsonreuters.com/products_services/intellectual_property/ip_products/a-z/aureka/</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>"Patent Analysis, Mapping, and Visualization Tools - PIUG Space - Global Site". <a href="https://wiki.piug.org/display/PIUG/Patent+Analysis%2C+Mapping%2C+and+Visualization+Tools" target="_blank">https://wiki.piug.org/display/PIUG/Patent+Analysis%2C+Mapping%2C+and+Visualization+Tools</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>"Patent iNSIGHT Pro". Archived from the original on 2014-02-21. Retrieved 2014-02-07. <a href="https://web.archive.org/web/20140221090519/http://www.intellogist.com/wiki/Patent_iNSIGHT_Pro" target="_blank">https://web.archive.org/web/20140221090519/http://www.intellogist.com/wiki/Patent_iNSIGHT_Pro</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>Conduct patent portfolio analysis using comparative Topic Maps <a href="http://www.relecura.com/reports/Relecura_Whitepaper_-_Topic_Maps.pdf" target="_blank">http://www.relecura.com/reports/Relecura_Whitepaper_-_Topic_Maps.pdf</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Graphene Technology Insight Report <a href="http://www.patentinsightpro.com/techreports/1113/Tech%20Insight%20Report%20-%20Graphene.pdf" target="_blank">http://www.patentinsightpro.com/techreports/1113/Tech%20Insight%20Report%20-%20Graphene.pdf</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Daniel A Keim et IEEE Computer Society, "Information visualization and visual data mining," IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 8 (2002): 1--8. <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>Anthony J. Trippe, "Patinformatics: Tasks to tools," World Patent Information 25, n°. 3 (September 2003): 211-221. <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>Laura Ruotsalainen, "Data mining tools for technology and competitive intelligence" VTT Research Notes 2451(October 2008) <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>[2] Archived June 12, 2010, at the Wayback Machine <a href="http://www.data-mining-software.com/data_mining_history.htm" target="_blank">http://www.data-mining-software.com/data_mining_history.htm</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>"How Data Mining is Evolving". <a href="http://www.exforsys.com/tutorials/data-mining/how-data-mining-is-evolving.html" target="_blank">http://www.exforsys.com/tutorials/data-mining/how-data-mining-is-evolving.html</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>Sungjoo Lee, Byungun Yoon, et Yongtae Park, "An approach to discovering new technology opportunities: Keyword-based patent map approach," Technovation 29, n°. 6 (Juin): 481-497. <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>[3] Archived October 17, 2010, at the Wayback Machine <a href="http://comminfo.rutgers.edu/~msharp/text_mining.htm" target="_blank">http://comminfo.rutgers.edu/~msharp/text_mining.htm</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> <li id="fn:15"><p>Bonino, Dario; Ciaramella, Alberto; Corno, Fulvio (2010). "Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics". World Patent Information. 32: 30–38. doi:10.1016/j.wpi.2009.05.008. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></p></li> <li id="fn:16"><p>Sholom Weiss and al, Text Mining : Predictive Methods for Analyzing Unstructured Information, 1er ed. (Springer 2004). <a href="#fnref:16" class="footnote-back-ref">↩</a></p></li> <li id="fn:17"><p>Antoine Blanchard "La cartographie des brevets" La Recherche n°.398 (2006) : 82-83 <a href="#fnref:17" class="footnote-back-ref">↩</a></p></li> <li id="fn:18"><p>Gerard Salton et Christopher Buckley, "Term-weighting approaches in automatic text retrieval," Information Processing & Management 24, n°. 5 (1988): 513-523. <a href="#fnref:18" class="footnote-back-ref">↩</a></p></li> <li id="fn:19"><p>Y Kim, J Suh, et S Park, "Visualization of patent analysis for emerging technology," Expert Systems with Applications 34, no. 3 (4, 2008): 1804–1812. <a href="#fnref:19" class="footnote-back-ref">↩</a></p></li> <li id="fn:20"><p>"Newsmap". Archived from the original on July 8, 2010. 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