Build Links Between Problems and Solutions in the Patent

Abstract

Inventive Design Method mostly relies on the presence of exploitable knowledge. It has been elaborated to formalize some aspects of TRIZ being expert-dependent. Patents are appropriate candidates since they contain problems and their corresponding partial solutions. When associated with patents of different fields, problems and partial solutions constitute a potential inventive solution scheme for a target problem. Nevertheless, our study found that links between these two major components are worth studying further. We postulate that problem-solution effectively matching contains a hidden value to automate the solution retrieval and uncover inventive details in patents in order to facilitate R&D activities. In this paper, we assimilate this challenge to the field of the Question Answering system instead of the traditional syntactic analysis approaches and proposed a model called IDM-Matching. Technically, a state-of-the-art neural network model named XLNet in the Natural Language Processing field is combined into our IDM-Matching to capture the corresponding partial solution for the given query that we masked using the related problem. Then we construct links between these problems and solutions. The final experimental results on the real-world U.S. patent dataset illustrates our model’s ability to effectively match IDM-related knowledge with each other. A detailed case study is demonstrated to prove the usage and latent perspective of our proposal in the TRIZ field.

BibTeX

@inproceedings{ni:hal-04810189,
  TITLE = {{Build Links Between Problems and Solutions in the Patent}},
  AUTHOR = {Ni, Xin and Samet, Ahmed and Cavallucci, Denis},
  URL = {https://hal.science/hal-04810189},
  BOOKTITLE = {{Systematic Complex Problem Solving in the Age of Digitalization and Open Innovation. 20th International TRIZ Future Conference, TFC 2020, Cluj-Napoca, Romania, October 14--16, 2020, Proceedings}},
  ADDRESS = {Cluj-Napoca, Romania},
  PUBLISHER = {{Springer Verlag (Germany)}},
  SERIES = {IFIP Advances in Information and Communication Technology},
  VOLUME = {597},
  PAGES = {64-76},
  YEAR = {2020},
  MONTH = Oct,
  DOI = {10.1007/978-3-030-61295-5\_6},
  KEYWORDS = {XLNet ; Neural networks ; Question answering system ; Patent analysis ; TRIZ},
  HAL_ID = {hal-04810189},
  HAL_VERSION = {v1},
}