White Paper Text Clustering on Patents

Overview Amongst various analyses performed on patents, the area where specialized software helps immensely is text‐mining and two of the most popular text mining techniques used over patent data are: ƒ Text segmentation / Tokenization ƒ Text Clustering / Topic identification Text segmentation is a process of analyzing the patent text and identifying smaller meaningful segments from the text. These segments are also called as Tokens or keywords. Various analyses can be performed using these tokens; however for large patent sets the number of unique tokens can be very high thereby making it unsuitable for certain types of analyses. Text clustering helps identify important topics or concepts (clusters) from a set of documents. Clustering of key patent data documents (such as Title, Abstract and Claims) has been used in various Patent Analysis tools and can help bring out the otherwise hidden insights within patents. Analyzing relationships between generated clusters or analyzing relationships between patent classifications and clusters are popular mechanisms used by researchers especially those in a competitive intelligence role. Clustering Algorithms Classical algorithms used for clustering are TF‐IDF, K‐Means or Bayesian Naïve. Their output is a set of topics (single level or hierarchical with multiple levels), each of which contain a group of documents cluster under the topic. The label (or name) of a topic is derived from the text of the patent and can be a combination of multiple words. Usually advanced algorithms understand parts‐of‐speech and interpret names accordingly. For instance, " tip of the probe… " , " using a probe tip… " , " with a probe whose tip is used for… " , and " probing the substrate with the tip… " will be represented as " probe tip ". Algorithms can differ on their capability to cluster patent under single or multiple topics. For patents it is desirable to have algorithms that place a patent under more than one topic since an attempt make a best‐match for the patent under a single topic may lead to errors in user interpretation. One of the key challenges of clustering algorithms has been to make the overall process transparent for IP Professionals who usually find it hard to accept results of " black‐box " clustering engines. Newer clustering algorithms provide the analyst control over most aspects of the clustering process thereby allowing then to fine tune the clustering output and train the algorithm to deliver more relevant …