patent visualisation

The main step in processing structured information is data-mining,[http://www.data-mining-software.com/data_mining_history.htm] {{webarchive|url=https://web.archive.org/web/20100612094951/http://www.data-mining-software.com/data_mining_history.htm|date=June 12, 2010}} which emerged in the late 1980s. Data mining involves statistics, artificial intelligence, and machine learning.{{cite web|url=http://www.exforsys.com/tutorials/data-mining/how-data-mining-is-evolving.html|title=How Data Mining is Evolving|publisher=}} Patent data mining extracts information from the structured data of the patent document.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. These structured data are bibliographic fields such as location, date or status.

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.{{cn|date=July 2023}}

Text mining is used to search through unstructured text documents.[http://comminfo.rutgers.edu/~msharp/text_mining.htm] {{webarchive|url=https://web.archive.org/web/20101017010444/http://comminfo.rutgers.edu/~msharp/text_mining.htm|date=October 17, 2010}}{{cite journal| doi=10.1016/j.wpi.2009.05.008 | volume=32 | title=Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics | year=2010 | journal=World Patent Information | pages=30–38 | last1 = Bonino | first1 = Dario | last2 = Ciaramella | first2 = Alberto | last3 = Corno | first3 = Fulvio}} This technique is widely used on the Internet, it has had success in bioinformatics and now in the intellectual property environment.Sholom Weiss and al, Text Mining : Predictive Methods for Analyzing Unstructured Information, 1er ed. (Springer 2004).

Text mining is based on a statistical analysis of word recurrence in a corpus.Antoine Blanchard "La cartographie des brevets" La Recherche n°.398 (2006) : 82-83 An algorithm extracts words and expressions from title, summary and claims and gathers them by declension. "And" and "if" are labeled as non-information bearing words and are stored in the stopword 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:Gerard Salton et Christopher Buckley, "Term-weighting approaches in automatic text retrieval," Information Processing & Management 24, n°. 5 (1988): 513-523.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.

$Weight=\backslash frac\{Term\backslash \; Frequency\}\{Document\backslash \; Frequency\}=\backslash frac\{Frequency\backslash \; of\backslash \; the\backslash \; word\backslash \; or\backslash \; expression\backslash \; in\backslash \; the\backslash \; Text\backslash \; Sea\}\{Number\backslash \; of\backslash \; documents\backslash \; containing\backslash \; the\backslash \; expression\backslash \; or\backslash \; word\}$

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.

Four text parts can be processed with text-mining :

• Title
• Abstract
• Claim
• Patent Full-Text

Software offer different combinations but title, abstract and claim are generally the most used, providing a good balance between interferences and relevancy.

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.{{cn|date=July 2023}}

{{Further|Patent map}}

Allying patent analysis 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.

Mapping visualisations can be used for both text-mining and data-mining results.

What patent visualisation can highlight:Miyake, M., Mune, Y. and Himeno, K. "Strategic Intellectual Property Portfolio Management: Technology Appraisal by Using the 'Technology Heat Map'", Nomura Research Institute (NRI) Papers, n°. 83, (December 2004).Charles Boulakia [http://sciencecareers.sciencemag.org/career_development/previous_issues/articles/1190/patent_mapping "Patent mapping"] {{Webarchive|url=https://web.archive.org/web/20110313122156/http://sciencecareers.sciencemag.org/career_development/previous_issues/articles/1190/patent_mapping |date=2011-03-13 }}

• Competitors
• Partners
• New innovations
• Technologic environment descriptionRichard Seymour, "Platinum Group Metals Patent Analysis and Mapping," Platinum Metals Review 52, n°. 4 (10, 2008): 231-240.
• Networks

Field application:Susan E Cullen, "Introduction, From acorns to oak trees : how patent audits help innovations reach their full potential" IP Value 2010 - An International Guide for the Boardroom : 26--30

• R&D strategy management
• Competitive intelligence
• Licensing
• Strategy

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