Presenting Terrorizer: an algorithm for consolidating company names in
  patent assignees
Grazia Sveva Ascione Valerio Sterzi
The problem of disambiguation of company names poses a significant challenge
in extracting useful information from patents. This issue biases research
outcomes as it mostly underestimates the number of patents attributed to
companies, particularly multinational corporations which file patents under a
plethora of names, including alternate spellings of the same entity and,
eventually, companies' subsidiaries. To date, addressing these challenges has
relied on labor-intensive dictionary based or string matching approaches,
leaving the problem of patents' assignee harmonization on large datasets mostly
unresolved. To bridge this gap, this paper describes the Terrorizer algorithm,
a text-based algorithm that leverages natural language processing (NLP),
network theory, and rule-based techniques to harmonize the variants of company
names recorded as patent assignees. In particular, the algorithm follows the
tripartite structure of its antecedents, namely parsing, matching and filtering
stage, adding an original "knowledge augmentation" phase which is used to
enrich the information available on each assignee name. We use Terrorizer on a
set of 325'917 companies' names who are assignees of patents granted by the
USPTO from 2005 to 2022. The performance of Terrorizer is evaluated on four
gold standard datasets. This validation step shows us two main things: the
first is that the performance of Terrorizer is similar over different kind of
datasets, proving that our algorithm generalizes well. Second, when comparing
its performance with the one of the algorithm currently used in PatentsView for
the same task (Monath et al., 2021), it achieves a higher F1 score. Finally, we
use the Tree-structured Parzen Estimator (TPE) optimization algorithm for the
hyperparameters' tuning. Our final result is a reduction in the initial set of
names of over 42%.