Hanover Insurance Group. has been granted a patent for a cluster analysis method that classifies documents into clusters based on content. The method involves extracting sets of documents, calculating inter-document and inter-cluster similarities, and generating visual display data to represent the relationships between clusters. GlobalData’s report on Hanover Insurance Group gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Hanover Insurance Group, was a key innovation area identified from patents. Hanover Insurance Group's grant share as of June 2024 was 38%. Grant share is based on the ratio of number of grants to total number of patents.
Cluster analysis method for document classification and association
The granted patent US11989222B2 outlines a sophisticated cluster analysis method designed for the classification of documents based on their content. The method involves a multi-step process where documents are first extracted into two distinct sets under different conditions. Following this, inter-document similarity is calculated within each set, allowing for the classification of documents into clusters based on their similarities. The method further includes an inter-cluster similarity calculation step, which assesses the relationships between clusters formed from the two sets. This is complemented by a cluster associating step that generates association information linking relevant clusters across the sets, ultimately leading to the generation of display data that visually represents these relationships.
Additionally, the patent specifies that the method can incorporate time information as part of the conditions for document extraction. Clusters can be linked based on a predetermined threshold of inter-cluster similarity, enhancing the relevance of the associations made. The display data generated not only indicates connections between clusters but also visually represents the size of each cluster based on the number of documents it contains, and the strength of the inter-cluster similarity through line thickness. This comprehensive approach to cluster analysis aims to provide a clearer understanding of document relationships, potentially benefiting various applications in data analysis and information retrieval.
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