  Web-scale repositories of products, patents and scientific papers offer an
opportunity for creating automated systems that scour millions of ideas and
assist users in discovering inspirations and solutions. Yet the common
representation of ideas is in the form of raw textual descriptions, lacking
important structure that is required for supporting creative innovation. Prior
work has pointed to the importance of functional structure -- capturing the
mechanisms and purposes of inventions -- for allowing users to discover
structural connections across ideas and creatively adapt existing technologies.
However, the use of functional representations was either coarse and limited in
expressivity, or dependent on curated knowledge bases with poor coverage and
significant manual effort from users.
  To help bridge this gap and unlock the potential of large-scale idea mining,
we propose a novel computational representation that automatically breaks up
products into fine-grained functional facets. We train a model to extract these
facets from a challenging real-world corpus of invention descriptions, and
represent each product as a set of facet embeddings. We design similarity
metrics that support granular matching between functional facets across ideas,
and use them to build a novel functional search capability that enables
expressive queries for mechanisms and purposes. We construct a graph capturing
hierarchical relations between purposes and mechanisms across an entire corpus
of products, and use the graph to help problem-solvers explore the design space
around a focal problem and view related problem perspectives. In empirical user
studies, our approach leads to a significant boost in search accuracy and in
the quality of creative inspirations, outperforming strong baselines and
state-of-art representations of product texts by 50-60%.
