Patent Analysis for Systematic Innovation: Automatic Function In-terpretation and Automatic Classification of Level of Invention using Natural Language Processing and Artificial Neural Networks


  • Zhen Li Texas Tech University
  • Derrick Tate Texas Tech University



With advances in computing power and the processes of globalization, the analytical and engineering science skills that contribute to innovation are becoming a commodity, and the activities of research and development—and innovation—are being outsourced. These trends leave the creative and systems integrative skills of engineering design as the value-added part of innovation. This paper presents a framework to address this challenge, termed mass innovation, which can be defined as expanding and diffusing innovation activities to the general population through connecting inventors and entrepreneurs with the engineering tools and services needed to assess and realize their novel design concepts. As part of mass innovation, this paper presents the development of an approach for automatic function interpretation, and an example is given, in the context of sustainable design, of the application of automatic function interpretation and automatic classification of level of invention. The method for automatic function interpretation is based on text extraction, natural language processing using a parser, and semantic definition of functional requirements and design parameters. The classification of level of invention is based on an artificial neural network model using inputs based on patent citation measures.

Author Biographies

Zhen Li, Texas Tech University

Zhen Li received his Bachelor degree in mechanical engineering from the Beijing Jiaotong University, Beijing, China, in the year of 2005. He is currently pursuing the Ph.D. degree at the Texas Tech University, Lubbock, Texas. And his current work involves applying data-mining, machine learning and natural language processing techniques to online patents in order to understand design purpose and to estimate patent creativeness.

Derrick Tate, Texas Tech University

Dr. Derrick Tate, Assistant Professor in the Mechanical Engineering Department at Texas Tech University will be a Co-PI for this work. Dr. Tate aims to impact society through bringing design thinking to areas of strategic importance: developing sustainable approaches for building systems, transportation, and manufacturing; facilitating mass innovation; and enabling innovation in enterprises. His current and recent projects include development of sustainable wall systems funded by West Texas entrepreneurs, and a US-Tanzania Workshop: Advancing the Structural Use of Earth-based Bricks, funded by NSF.