Machine Learning in Patent Analytics - Part 3: Spatial Concept Maps for Exploring Large Domains

The visualizations associated with this task go by many names including thematic, or concept maps, and involve determining the similarity of documents, and representing it as a relative distance. Since the characteristic being visualized is relative distance, maps are often used as a visual device since, as a species, humans are accustomed to comparing distances using them. Beyond organizing, or clustering the documents, calculating relative distance adds the additional benefit of determining which clusters are related to one another. This places distinct, but related sub-categories, or methods closer to one another, while placing different approaches or methods in another location. While spatial concept maps are used frequently, and have been for over a decade, by patent information professionals, analysts still get many questions on how they are generated, and how they should be interpreted. There is also a desire to be able to influence the key attributes that are represented in the corresponding visualization to ensure that the immediate impact of the labeling and organization of the map is meaningful to their clients. By understanding the process involved in creating documents vectors, and recognizing ways that it can be adjusted analysts can produce maps that are directed to the attributes they want to highlight. Labels can also be changed in order to provide immediate relevance to the end-users of the analysis.

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