LLM-based Extraction of Contradictions from Patents
Stefan Trapp Joachim Warschat
Already since the 1950s TRIZ shows that patents and the technical
contradictions they solve are an important source of inspiration for the
development of innovative products. However, TRIZ is a heuristic based on a
historic patent analysis and does not make use of the ever-increasing number of
latest technological solutions in current patents. Because of the huge number
of patents, their length, and, last but not least, their complexity there is a
need for modern patent retrieval and patent analysis to go beyond
keyword-oriented methods. Recent advances in patent retrieval and analysis
mainly focus on dense vectors based on neural AI Transformer language models
like Google BERT. They are, for example, used for dense retrieval, question
answering or summarization and key concept extraction. A research focus within
the methods for patent summarization and key concept extraction are generic
inventive concepts respectively TRIZ concepts like problems, solutions,
advantage of invention, parameters, and contradictions. Succeeding rule-based
approaches, finetuned BERT-like language models for sentence-wise
classification represent the state-of-the-art of inventive concept extraction.
While they work comparatively well for basic concepts like problems or
solutions, contradictions - as a more complex abstraction - remain a challenge
for these models. This paper goes one step further, as it presents a method to
extract TRIZ contradictions from patent texts based on Prompt Engineering using
a generative Large Language Model (LLM), namely OpenAI's GPT-4. Contradiction
detection, sentence extraction, contradiction summarization, parameter
extraction and assignment to the 39 abstract TRIZ engineering parameters are
all performed in a single prompt using the LangChain framework. Our results
show that "off-the-shelf" GPT-4 is a serious alternative to existing
approaches.