Semi-automatic extraction of technological causality from patents

Abstract

The goal of this study is to suggest a method to extract technological causalities from patents, which are formal documents that include a large amount and large variety of information about technology. The core of patents is composed of both inventive principles to solve problems, and purposes that the invention achieves by solving them. The principles and purposes can be understood as a concept of technological causality which is reusable knowledge as technological analogy. Because reading and understanding patent documents that generally consist of dozens of pages and have difficult and profound statement of technologies is hard even for technology experts, a method to extract technological causalities is needed. As a solution, this paper proposed a method to extract technological data from patents, to identify technological causes and effect relation from the extracted data and to calculate the representativeness of technological causes and effect. Based on this study, technology experts can be given a list of ranked alternatives for technological causes and effect. This study helps to analyze patent, and it finally contributes to new product development and technology opportunity discovery. To achieve the objectives, the proposed method included the characteristics of patents that are structured documents consisting of various particular fields that have each different contents and importance. And natural language processing technology is adopted to automatically extract meaningful data and to perform linguistic processing. The implementation and case study of the proposed method demonstrated how a prototype system can be developed and utilized.