Gendered Words and Grant Rates: A Textual Analysis of Disparate Outcomes in the Patent System

TL;DR

This study uses machine learning and NLP to analyze patent texts, revealing gendered linguistic patterns, predicting patent grants with over 60% accuracy, and showing women often invent in higher rejection fields, raising concerns about gender bias.

Contribution

It demonstrates that gender can be inferred from patent text, predicts patent outcomes based on textual features, and uncovers gender disparities across technological areas.

Findings

Gender can be identified from patent text without names.

Textual features influence patent grant predictions more than gender.

Women tend to invent in fields with higher rejection rates.

Abstract

Text is a vehicle to convey information that reflects the writer's linguistic style and communicative patterns. By studying these attributes, we can discover latent insights about the author and their underlying message. This article uses such an approach to better understand patent applications and their inventors. While prior research focuses on patent metadata, we employ machine learning and natural language processing to extract hidden information from the words in patent applications. Through these methods, we find that inventor gender can often be identified from textual attributes - even without knowing the inventor's name. This ability to discern gender through text suggests that anonymized patent examination - often proposed as a solution to mitigate disparities in patent grant rates - may not fully address gendered outcomes in securing a patent. Our study also investigates whether objective features of a patent application can predict if it will be granted. Using a classifier algorithm, we correctly predicted whether a patent was granted over 60% of the time. Further analysis emphasized that writing style - like vocabulary and sentence complexity - disproportionately influenced grant predictions relative to other attributes such as inventor gender and subject matter keywords. Lastly, we examine whether women disproportionately invent in technological areas with higher rejection rates. Using a clustering algorithm, applications were allocated into groups with related subject matter. We found that 85% of female-dominated clusters have abnormally high rejection rates, compared to only 45% for male-dominated groupings. These findings highlight complex interactions between textual choices, gender, and success in securing a patent. They also raise questions about whether current proposals will be sufficient to achieve gender equity and efficiency in the patent system.