Hanover Insurance Group has been granted a patent for a cluster analysis method using a server to classify documents into clusters based on content similarity. The method involves extracting sets, calculating similarities, classifying clusters, and generating association information. The patent aims to improve document organization and analysis efficiency. 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 May 2024 was 30%. Grant share is based on the ratio of number of grants to total number of patents.
Cluster analysis method for classifying documents based on content
A recently granted patent (Publication Number: US11989222B2) discloses a cluster analysis method that involves a computer classifying a plurality of documents into clusters based on their content. The method includes extracting sets of documents under different conditions, calculating inter-document similarity within each set, classifying documents into clusters based on this similarity, and generating association information linking relevant clusters across different sets. The display data generation step visualizes the clusters and their inter-cluster similarities by connecting relevant clusters with lines, representing clusters as circles with varying sizes based on the number of documents, and indicating inter-cluster similarity through the thickness of the connecting lines.
Moreover, the patent also includes provisions for linking time information to the documents, where conditions for extracting sets can be based on time information. The cluster associating step links clusters with inter-cluster similarity above a predetermined threshold, and the display data generation step further visualizes relationships between clusters across different sets. Additionally, the display data can arrange clusters from different sets in a time-series order, providing a comprehensive visualization of the clusters and their inter-relationships for efficient analysis and understanding of document content. This innovative cluster analysis method aims to enhance document organization and analysis processes by effectively clustering documents based on their content and visualizing their relationships in a user-friendly manner.
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