Presenting Terrorizer: an algorithm for consolidating company names in patent assignees
This addresses a domain-specific problem for researchers and analysts working with patent data, offering an incremental improvement over prior methods.
The paper tackles the problem of disambiguating company names in patent assignees, which biases research outcomes by underestimating patent counts for multinational corporations. It introduces the Terrorizer algorithm, which reduces the initial set of 325,917 company names by over 42% and achieves a higher F1 score than the existing PatentsView algorithm.
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%.