Innovation and creativity are at the core of technological advancement, particularly in fields like engineering. However, the patents which embody these inventions often remain underutilised resources for designers. The complexity of patent language and legal jargon make it difficult for engineers to extract meaningful insights for early design stages. This challenge is at the core of the recent work by myself and my colleagues (Jiang et al. 2023) on the extraction and linking of motivation, specification, and structure of inventions, which presents a novel methodology to streamline this process for designers.
At the foundation of our research is the acknowledgement that – while patents are rich with technical knowledge – they have been historically difficult to analyse without specialist expertise. “The rich technical content within a patent can be hidden within the legal terminologies used, thus requiring considerable effort and expertise to extract” (Jiang et al, 2023). To address this, we propose a method for systematically extracting the key components of patents: Motivation, Specification, and Structure.
- Motivation reveals the “why” behind an invention – what problem the inventor aimed to solve.
- Specification outlines the “what” in terms of the invention’s functions and requirements
- Structure presents the “how” through the technical components or systems described.
By leveraging Natural Language Processing (NLP) and other AI-driven methods, this approach automates much of the patent analysis, simplifying it into actionable insights that align with a designer’s way of thinking.
This structured method allows cross-patent analysis, creating opportunities for early-stage innovation where designers can quickly explore patent-driven solutions and ideas. The end result is a prototype knowledge network that helps designers identify technological trends, find design inspiration, and even assess legal risks during the conceptual phase.
Read the full article to discover how this method is reshaping patent analysis for design innovation: https://doi.org/10.1080/09544828.2023.2227934).
