As more and more people are increasingly turning to nature for design inspiration, tools and methodologies are developed to support the systematic bioideation process. State-of-the-art approaches struggle with expanding their knowledge bases because of interactive work required by humans per biological strategy. As an answer to this persistent challenge, a scalable search for systematic biologically inspired design (SEABIRD) system is proposed. This system leverages experience from the product aspects in design by analogy tool that identifies candidate products for between-domain design by analogy. SEABIRD is based on two conceptual representations, product and organism aspects, extracted from, respectively, a patent and a biological database, that enable leveraging the ever growing body of natural-language biological texts in the systematic bioinspired design process by eliminating interactive work by humans during corpus expansion. SEABIRD's search is illustrated and validated with three well-known biologically inspired design cases.

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Systematically developing innovative products demands effective idea generation methods. The effectiveness can be verified based on a set of metrics, of which the variety metric is one. It is demonstrated that this metric exhibits several shortcomings, such as the lack of level-based measurements, and arbitrarily defined level weights. The currently applied variety metrics, furthermore, do not measure the degree of uniformness of the distribution of ideas over nodes on an abstraction level. A level-based, correctly normalized variety metric which accounts for the degree of uniformness of the distribution of concepts over nodes, is proposed, and is shown to resolve the above issues.

TRIZ trends describe the evolutionary status of a system by identifying the trend phases, and assist in predicting improvements by identifying evolutionary potential. This process encompasses analyzing and categorizing patents in known trend phases, relying on intrinsic skills of a TRIZ expert, and depicting the results on an evolutionary potential radar plot. To structure this approach, an algorithm is proposed that, through patent analysis and identification of word categories, extracts information concerning the product properties, which relate to trend phases. Allowing controlled and repeatable measurements of trends, this algorithm can support the problem specification and requirements gathering phases.

This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.